To see the other types of publications on this topic, follow the link: Protein Model Discrimination.
Journal articles on the topic 'Protein Model Discrimination'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Protein Model Discrimination.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Adkar, Bharat V., Arti Tripathi, Anusmita Sahoo, Kanika Bajaj, Devrishi Goswami, Purbani Chakrabarti, Mohit K. Swarnkar, Rajesh S. Gokhale, and Raghavan Varadarajan. "Protein Model Discrimination Using Mutational Sensitivity Derived from Deep Sequencing." Structure 20, no. 2 (February 2012): 371–81. http://dx.doi.org/10.1016/j.str.2011.11.021.
Full text
2
Khare, Shruti, Kritika Gupta, and Arti Tripathi. "Mutant Phenotype Prediction and Protein Model Discrimination using Deep Sequencing Data." Biophysical Journal 114, no. 3 (February 2018): 199a. http://dx.doi.org/10.1016/j.bpj.2017.11.1116.
Full text
3
Khare, Shruti, Munmun Bhasin, Anusmita Sahoo, and Raghavan Varadarajan. "Protein model discrimination attempts using mutational sensitivity, predicted secondary structure, and model quality information." Proteins: Structure, Function, and Bioinformatics 87, no. 4 (January 15, 2019): 326–36. http://dx.doi.org/10.1002/prot.25654.
Full text
4
Bernacki, Joseph P., and Regina M. Murphy. "Model Discrimination and Mechanistic Interpretation of Kinetic Data in Protein Aggregation Studies." Biophysical Journal 96, no. 7 (April 2009): 2871–87. http://dx.doi.org/10.1016/j.bpj.2008.12.3903.
Full text
5
Kirven, Sydney, Heather Farmer, and Amy Thierry. "Perceived Everyday Discrimination and C- Reactive Protein Influence on Cognition of Older Black Adults." Innovation in Aging 5, Supplement_1 (December 1, 2021): 1040. http://dx.doi.org/10.1093/geroni/igab046.3718.
Full textAbstract:
Abstract Black adults and women are more likely to experience serious cognitive decline in older age than their white and male counterparts. Evidence suggests perceived discrimination is associated with poor cognition in older adults, though the mechanisms remain unclear. Perceived discrimination has been linked to elevated inflammatory markers, such as C-reactive protein (CRP), which increases risk for worse cognitive functioning. Yet, little research has investigated whether CRP is implicated in the association between discrimination and cognition among Black older adults or if this relationship differs by gender. Using 2006-2016 data from Black adults ≥65 years old(N=1343) in the nationally representative Health and Retirement Study, random effects linear regression models (1) tested the association between discrimination and cognitive functioning; (2) explored whether this relationship differed for women and men; and (3) assessed whether elevated CRP mediated the association between discrimination and cognitive functioning. More frequent discrimination was associated with worse cognitive functioning (b= -0.24, SE=0.11, p<0.05), though gender did not moderate this relationship. Elevated CRP was significantly associated with worse cognitive functioning (b= 0.40, SE=0.18, p<0.05). Discrimination remained statistically significant in this model, indicating no mediation by CRP. Of note, inclusion of depressive symptoms and cardiometabolic conditions accounted for the association between both discrimination and CRP with cognitive functioning. These findings demonstrate the need for more within-group research on older Black adults documenting the complex relationship between discrimination, inflammation, and cognitive health. This approach will provide greater understanding of the biopsychosocial mechanisms underlying disparities in cognitive functioning in Black adults.
6
Safo, Sandra, Lillian Haine, Jason Baker, Cavan Reilly, Daniel Duprez, Jim Neaton, Jiuzhou Wang, et al. "89976 ASSESSING PROTEIN BIOMARKERS ROLE IN CVD RISK PREDICTION IN PERSONS LIVING WITH HIV (PWH)." Journal of Clinical and Translational Science 5, s1 (March 2021): 47–48. http://dx.doi.org/10.1017/cts.2021.526.
Full textAbstract:
ABSTRACT IMPACT: Our findings could potentially identify CVD at-risk persons living with HIV who might benefit from aggressive risk-reduction. OBJECTIVES/GOALS: PWH have higher rates of CVD than the general population yet CVD risk prediction models rely on traditional risk factors and fail to capture the heterogeneity of CVD risk in PWH. Here we identify protein biomarkers that are able to discriminate between CVD cases and controls in PWH, and we assess their added benefit beyond traditional risk factors. METHODS/STUDY POPULATION: We analyzed 459 baseline protein expression levels from five OLINK panels in a matched CVD (MI, coronary revascularization, stroke, CVD death) case-control study with 390 PWH from INSIGHT trials (131 cases, 259 controls). We formed 200 datasets via bootstrap. For each bootstrap set, a two-component partial least squares discriminant model (PLSDA) was fit. The importance of each variable in the discrimination of cases and controls in the PLSDA projection was assessed by the variable importance in projection (VIP) score. Proteins with average VIP scores > 1 were used in penalized logistic regression models with elastic net penalty, and proteins were ranked based on the number of times the protein had a nonzero coefficient. Proteins in the top 25th percentile were considered to have high discrimination. RESULTS/ANTICIPATED RESULTS: Participants had mean age 47 years, 13% were females, 4.9% had CVD at baseline and 69% were on ART at baseline. Eight proteins including the hepatocyte growth factor and interleukin-6 were identified as able to distinguish between CVD cases and controls within PWH. A protein score (PS) of the top-ranked proteins was developed using the bootstrap (for weights) and the entire data. The PS was found to be predictive of CVD independent of established CVD and HIV factors (Odds ratio: 2.17 CI: 1.58-2.99). A model with the PS and traditional risk factors had a 5.9% improvement in AUC over the baseline model (AUC=0.731 vs 0.69), which is an increase in model predictive power of 18%. Individuals with a PS above the median score were 3.1 (CI: 1.83- 5.41) times more likely to develop CVD than those with a protein score below the median score. DISCUSSION/SIGNIFICANCE OF FINDINGS: A protein score developed improved discrimination of PWH with CVD and those without, and helped identify PWH with high risk for developing CVD. If validated, this score and/or the individual proteins could be used in addition with established factors to identify CVD at-risk individuals who might benefit from aggressive risk-reduction.
7
Yanagisawa, Kiyoshi, Shuta Tomida, Keitaro Matsuo, Chinatsu Arima, Miyoko Kusumegi, Yukihiro Yokoyama, Shigeru B. H. Ko, et al. "Seven-Signal Proteomic Signature for Detection of Operable Pancreatic Ductal Adenocarcinoma and Their Discrimination from Autoimmune Pancreatitis." International Journal of Proteomics 2012 (May 14, 2012): 1–11. http://dx.doi.org/10.1155/2012/510397.
Full textAbstract:
There is urgent need for biomarkers that provide early detection of pancreatic ductal adenocarcinoma (PDAC) as well as discrimination of autoimmune pancreatitis, as current clinical approaches are not suitably accurate for precise diagnosis. We used mass spectrometry to analyze protein profiles of more than 300 plasma specimens obtained from PDAC, noncancerous pancreatic diseases including autoimmune pancreatitis patients and healthy subjects. We obtained 1063 proteomic signals from 160 plasma samples in the training cohort. A proteomic signature consisting of 7 mass spectrometry signals was used for construction of a proteomic model for detection of PDAC patients. Using the test cohort, we confirmed that this proteomic model had discrimination power equal to that observed with the training cohort. The overall sensitivity and specificity for detection of cancer patients were 82.6% and 90.9%, respectively. Notably, 62.5% of the stage I and II cases were detected by our proteomic model. We also found that 100% of autoimmune pancreatitis patients were correctly assigned as noncancerous individuals. In the present paper, we developed a proteomic model that was shown able to detect early-stage PDAC patients. In addition, our model appeared capable of discriminating patients with autoimmune pancreatitis from those with PDAC.
8
Wang, Cong, Yabing Hai, Xiaoqing Liu, Nanfang Liu, Yuhua Yao, Pingan He, and Qi Dai. "Prediction of High-Risk Types of Human Papillomaviruses Using Statistical Model of Protein “Sequence Space”." Computational and Mathematical Methods in Medicine 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/756345.
Full textAbstract:
Discrimination of high-risk types of human papillomaviruses plays an important role in the diagnosis and remedy of cervical cancer. Recently, several computational methods have been proposed based on protein sequence-based and structure-based information, but the information of their related proteins has not been used until now. In this paper, we proposed using protein “sequence space” to explore this information and used it to predict high-risk types of HPVs. The proposed method was tested on 68 samples with known HPV types and 4 samples without HPV types and further compared with the available approaches. The results show that the proposed method achieved the best performance among all the evaluated methods with accuracy 95.59% andF1-score 90.91%, which indicates that protein “sequence space” could potentially be used to improve prediction of high-risk types of HPVs.
9
Lowry, Troy W., Aubrey E. Kusi-Appiah, Debra Ann Fadool, and Steven Lenhert. "Odor Discrimination by Lipid Membranes." Membranes 13, no. 2 (January 24, 2023): 151. http://dx.doi.org/10.3390/membranes13020151.
Full textAbstract:
Odor detection and discrimination in mammals is known to be initiated by membrane-bound G-protein-coupled receptors (GPCRs). The role that the lipid membrane may play in odor discrimination, however, is less well understood. Here, we used model membrane systems to test the hypothesis that phospholipid bilayer membranes may be capable of odor discrimination. The effect of S-carvone, R-carvone, and racemic lilial on the model membrane systems was investigated. The odorants were found to affect the fluidity of supported lipid bilayers as measured by fluorescence recovery after photobleaching (FRAP). The effect of odorants on surface-supported lipid multilayer microarrays of different dimensions was also investigated. The lipid multilayer micro- and nanostructure was highly sensitive to exposure to these odorants. Fluorescently-labeled lipid multilayer droplets of 5-micron diameter were more responsive to these odorants than ethanol controls. Arrays of lipid multilayer diffraction gratings distinguished S-carvone from R-carvone in an artificial nose assay. Our results suggest that lipid bilayer membranes may play a role in odorant discrimination and molecular recognition in general.
10
Tang, Hua, Ren-Zhi Cao, Wen Wang, Tie-Shan Liu, Li-Ming Wang, and Chun-Mei He. "A two-step discriminated method to identify thermophilic proteins." International Journal of Biomathematics 10, no. 04 (March 28, 2017): 1750050. http://dx.doi.org/10.1142/s1793524517500504.
Full textAbstract:
Improving thermostability of an enzyme can accelerate the relevant chemical reaction. Thus, the analysis and prediction of thermophilic proteins are conducive to protein engineering and enzyme design. In this study, a novel method based on two-step discrimination was proposed to distinguish between thermophilic and non-thermophilic proteins. The model was rigorously benchmarked on an objective dataset including 915 thermophilic proteins and 793 non-thermophilic proteins. Results showed that the overall accuracy of our method is 94.44% in 5-fold cross-validation, which is higher than those of other published methods. We believe that the two-step discriminated strategy will become a promising method in the relevant field of protein bioinformatics.
11
Lin, Xin, Xin Hai, Ning Wang, Xu-Wei Chen, and Jian-Hua Wang. "Dual-signal model array sensor based on GQDs/AuNPs system for sensitive protein discrimination." Analytica Chimica Acta 992 (November 2017): 105–11. http://dx.doi.org/10.1016/j.aca.2017.09.006.
Full text
12
Urban, Stephan, and Philippe Gripon. "Inhibition of Duck Hepatitis B Virus Infection by a Myristoylated Pre-S Peptide of the Large Viral Surface Protein." Journal of Virology 76, no. 4 (February 15, 2002): 1986–90. http://dx.doi.org/10.1128/jvi.76.4.1986-1990.2002.
Full textAbstract:
ABSTRACT We have used the duck hepatitis B virus (DHBV) model to study the interference with infection by a myristoylated peptide representing an N-terminal pre-S subdomain of the large viral envelope protein. Although lacking the essential part of the carboxypeptidase D (formerly called gp180) receptor binding site, the peptide binds hepatocytes and subsequently blocks DHBV infection. Since its activity requires an amino acid sequence involved in host discrimination between DHBV and the related heron HBV (T. Ishikawa and D. Ganem, Proc. Natl. Acad. Sci. USA 92:6259-6263, 1995), we suggest that it is related to the postulated host-discriminating cofactor of infection.
13
Raychaudhuri, Subhadip, and Philippos Tsourkas. "Monte Carlo model elucidates mechanisms of B cell affinity discrimination and its modulation by B cell immune synapse formation (34.1)." Journal of Immunology 182, no. 1_Supplement (April 1, 2009): 34.1. http://dx.doi.org/10.4049/jimmunol.182.supp.34.1.
Full textAbstract:
Abstract B cells are known to use their signaling mechanism to recognize antigens over a wide range of affinities (10 6 - 10 10 M -1) and generate a graded response that depends on the affinity of antigen binding, a phenomenon known as affinity discrimination. However, the molecular mechanism behind such affinity discrimination is not clear. We have developed a computational model of B cell signaling that reveals that a minimum threshold time of BCR-antigen binding is needed for such affinity discrimination to occur. For a given antigenic affinity, our numerical experiments give rise to stochastically varying levels of activated signaling molecules, such as phosphorylated BCR ITAMs and Syk molecules, as is the case in single cell experiments. Recent experiments have revealed that B cell signaling is coupled with the immunological synapse pattern - an ordered protein segregation structure consisting of BCR/Antigen and LFA-1/ICAM-1 molecules that forms at the cell-cell contact area. We used our Monte Carlo computer model to show that synapse formation modulates the affinity discrimination in B cells.
14
Barber-Zucker, Shiran, Jenny Hall, Afonso Froes, Sofiya Kolusheva, Fraser MacMillan, and Raz Zarivach. "The cation diffusion facilitator protein MamM's cytoplasmic domain exhibits metal-type dependent binding modes and discriminates against Mn2+." Journal of Biological Chemistry 295, no. 49 (September 23, 2020): 16614–29. http://dx.doi.org/10.1074/jbc.ra120.014145.
Full textAbstract:
Cation diffusion facilitator (CDF) proteins are a conserved family of divalent transition metal cation transporters. CDF proteins are usually composed of two domains: the transmembrane domain, in which the metal cations are transported through, and a regulatory cytoplasmic C-terminal domain (CTD). Each CDF protein transports either one specific metal or multiple metals from the cytoplasm, and it is not known whether the CTD takes an active regulatory role in metal recognition and discrimination during cation transport. Here, the model CDF protein MamM, an iron transporter from magnetotactic bacteria, was used to probe the role of the CTD in metal recognition and selectivity. Using a combination of biophysical and structural approaches, the binding of different metals to MamM CTD was characterized. Results reveal that different metals bind distinctively to MamM CTD in terms of their binding sites, thermodynamics, and binding-dependent conformations, both in crystal form and in solution, which suggests a varying level of functional discrimination between CDF domains. Furthermore, these results provide the first direct evidence that CDF CTDs play a role in metal selectivity. We demonstrate that MamM's CTD can discriminate against Mn2+, supporting its postulated role in preventing magnetite formation poisoning in magnetotactic bacteria via Mn2+ incorporation.
15
Fredrick, Kurt. "Another Look at Mutations in Ribosomal Protein S4 Lends Strong Support to the Domain Closure Model." Journal of Bacteriology 197, no. 6 (December 29, 2014): 1014–16. http://dx.doi.org/10.1128/jb.02579-14.
Full textAbstract:
Ribosomes employ a “kinetic discrimination” mechanism, in which correct substrates are incorporated more rapidly than incorrect ones. The structural basis of this mechanism may involve 30S domain closure, a global conformational change that coincides with codon recognition. In a direct screen for fidelity-altering mutations,Agarwal and coworkers(D. Agarwal, D. Kamath, S. T. Gregory, and M. O'Connor, J Bacteriol 197:1017–1025, 2015, doi:10.1128/JB.02485-14) isolated mutations that progressively truncate the C terminus of S4. All of these promote miscoding and undoubtedly destabilize the S4-S5 interface, consistent with the domain closure model.
16
Wright, Leah, Timothy J. Barnes, Paul Joyce, and Clive A. Prestidge. "Optimisation of a High-Throughput Model for Mucus Permeation and Nanoparticle Discrimination Using Biosimilar Mucus." Pharmaceutics 14, no. 12 (November 30, 2022): 2659. http://dx.doi.org/10.3390/pharmaceutics14122659.
Full textAbstract:
High-throughput permeation models are essential in drug development for timely screening of new drug and formulation candidates. Nevertheless, many current permeability assays fail to account for the presence of the gastrointestinal mucus layer. In this study, an optimised high-throughput mucus permeation model was developed employing a highly biorelevant mucus mimic. While mucus permeation is primarily conducted in a simple mucin solution, the complex chemistry, nanostructure and rheology of mucus is more accurately modelled by a synthetic biosimilar mucus (BSM) employing additional protein, lipid and rheology-modifying polymer components. Utilising BSM, equivalent permeation of various molecular weight fluorescein isothiocyanate-dextrans were observed, compared with native porcine jejunal mucus, confirming replication of the natural mucus permeation barrier. Furthermore, utilising synthetic BSM facilitated the analysis of free protein permeation which could not be quantified in native mucus due to concurrent proteolytic degradation. Additionally, BSM could differentiate between the permeation of poly (lactic-co-glycolic) acid nanoparticles (PLGA-NP) with varying surface chemistries (cationic, anionic and PEGylated), PEG coating density and size, which could not be achieved by a 5% mucin solution. This work confirms the importance of utilising highly biorelevant mucus mimics in permeation studies, and further development will provide an optimal method for high-throughput mucus permeation analysis.
17
Feng, Shichao, Hong-Long Ji, Huan Wang, Bailu Zhang, Ryan Sterzenbach, Chongle Pan, and Xuan Guo. "MetaLP: An integrative linear programming method for protein inference in metaproteomics." PLOS Computational Biology 18, no. 10 (October 21, 2022): e1010603. http://dx.doi.org/10.1371/journal.pcbi.1010603.
Full textAbstract:
Metaproteomics based on high-throughput tandem mass spectrometry (MS/MS) plays a crucial role in characterizing microbiome functions. The acquired MS/MS data is searched against a protein sequence database to identify peptides, which are then used to infer a list of proteins present in a metaproteome sample. While the problem of protein inference has been well-studied for proteomics of single organisms, it remains a major challenge for metaproteomics of complex microbial communities because of the large number of degenerate peptides shared among homologous proteins in different organisms. This challenge calls for improved discrimination of true protein identifications from false protein identifications given a set of unique and degenerate peptides identified in metaproteomics. MetaLP was developed here for protein inference in metaproteomics using an integrative linear programming method. Taxonomic abundance information extracted from metagenomics shotgun sequencing or 16s rRNA gene amplicon sequencing, was incorporated as prior information in MetaLP. Benchmarking with mock, human gut, soil, and marine microbial communities demonstrated significantly higher numbers of protein identifications by MetaLP than ProteinLP, PeptideProphet, DeepPep, PIPQ, and Sipros Ensemble. In conclusion, MetaLP could substantially improve protein inference for complex metaproteomes by incorporating taxonomic abundance information in a linear programming model.
18
Wüstner, Daniel. "Dynamic Mode Decomposition of Fluorescence Loss in Photobleaching Microscopy Data for Model-Free Analysis of Protein Transport and Aggregation in Living Cells." Sensors 22, no. 13 (June 23, 2022): 4731. http://dx.doi.org/10.3390/s22134731.
Full textAbstract:
The phase separation and aggregation of proteins are hallmarks of many neurodegenerative diseases. These processes can be studied in living cells using fluorescent protein constructs and quantitative live-cell imaging techniques, such as fluorescence recovery after photobleaching (FRAP) or the related fluorescence loss in photobleaching (FLIP). While the acquisition of FLIP images is straightforward on most commercial confocal microscope systems, the analysis and computational modeling of such data is challenging. Here, a novel model-free method is presented, which resolves complex spatiotemporal fluorescence-loss kinetics based on dynamic-mode decomposition (DMD) of FLIP live-cell image sequences. It is shown that the DMD of synthetic and experimental FLIP image series (DMD-FLIP) allows for the unequivocal discrimination of subcellular compartments, such as nuclei, cytoplasm, and protein condensates based on their differing transport and therefore fluorescence loss kinetics. By decomposing fluorescence-loss kinetics into distinct dynamic modes, DMD-FLIP will enable researchers to study protein dynamics at each time scale individually. Furthermore, it is shown that DMD-FLIP is very efficient in denoising confocal time series data. Thus, DMD-FLIP is an easy-to-use method for the model-free detection of barriers to protein diffusion, of phase-separated protein assemblies, and of insoluble protein aggregates. It should, therefore, find wide application in the analysis of protein transport and aggregation, in particular in relation to neurodegenerative diseases and the formation of protein condensates in living cells.
19
Stojanov, Spase, Tina Vida Plavec, Julijana Kristl, Špela Zupančič, and Aleš Berlec. "Engineering of Vaginal Lactobacilli to Express Fluorescent Proteins Enables the Analysis of Their Mixture in Nanofibers." International Journal of Molecular Sciences 22, no. 24 (December 20, 2021): 13631. http://dx.doi.org/10.3390/ijms222413631.
Full textAbstract:
Lactobacilli are a promising natural tool against vaginal dysbiosis and infections. However, new local delivery systems and additional knowledge about their distribution and mechanism of action would contribute to the development of effective medicine. This will be facilitated by the introduction of the techniques for effective, inexpensive, and real-time tracking of these probiotics following their release. Here, we engineered three model vaginal lactobacilli (Lactobacillus crispatus ATCC 33820, Lactobacillus gasseri ATCC 33323, and Lactobacillus jensenii ATCC 25258) and a control Lactobacillus plantarum ATCC 8014 to express fluorescent proteins with different spectral properties, including infrared fluorescent protein (IRFP), green fluorescent protein (GFP), red fluorescent protein (mCherry), and blue fluorescent protein (mTagBFP2). The expression of these fluorescent proteins differed between the Lactobacillus species and enabled quantification and discrimination between lactobacilli, with the longer wavelength fluorescent proteins showing superior resolving power. Each Lactobacillus strain was labeled with an individual fluorescent protein and incorporated into poly (ethylene oxide) nanofibers using electrospinning, as confirmed by fluorescence and scanning electron microscopy. The lactobacilli retained their fluorescence in nanofibers, as well as after nanofiber dissolution. To summarize, vaginal lactobacilli were incorporated into electrospun nanofibers to provide a potential solid vaginal delivery system, and the fluorescent proteins were introduced to distinguish between them and allow their tracking in the future probiotic-delivery studies.
20
Adiyaman and McGuffin. "Methods for the Refinement of Protein Structure 3D Models." International Journal of Molecular Sciences 20, no. 9 (May 9, 2019): 2301. http://dx.doi.org/10.3390/ijms20092301.
Full textAbstract:
The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.
21
Palstrøm, Nicolai Bjødstrup, Aleksandra M. Rojek, Hanne E. H. Møller, Charlotte Toftmann Hansen, Rune Matthiesen, Lars Melholt Rasmussen, Niels Abildgaard, and Hans Christian Beck. "Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics." International Journal of Molecular Sciences 23, no. 1 (December 28, 2021): 319. http://dx.doi.org/10.3390/ijms23010319.
Full textAbstract:
Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically throughout the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fibril-forming protein, and its accurate identification is essential to the choice of treatment. Mass spectrometry-based proteomics has become the method of choice for the identification of the amyloidogenic protein. Regrettably, this identification relies on manual and subjective interpretation of mass spectrometry data by an expert, which is undesirable and may bias diagnosis. To circumvent this, we developed a statistical model-assisted method for the unbiased identification of amyloid-containing biopsies and amyloidosis subtyping. Based on data from mass spectrometric analysis of amyloid-containing biopsies and corresponding controls. A Boruta method applied on a random forest classifier was applied to proteomics data obtained from the mass spectrometric analysis of 75 laser dissected Congo Red positive amyloid-containing biopsies and 78 Congo Red negative biopsies to identify novel “amyloid signature” proteins that included clusterin, fibulin-1, vitronectin complement component C9 and also three collagen proteins, as well as the well-known amyloid signature proteins apolipoprotein E, apolipoprotein A4, and serum amyloid P. A SVM learning algorithm were trained on the mass spectrometry data from the analysis of the 75 amyloid-containing biopsies and 78 amyloid-negative control biopsies. The trained algorithm performed superior in the discrimination of amyloid-containing biopsies from controls, with an accuracy of 1.0 when applied to a blinded mass spectrometry validation data set of 103 prospectively collected amyloid-containing biopsies. Moreover, our method successfully classified amyloidosis patients according to the subtype in 102 out of 103 blinded cases. Collectively, our model-assisted approach identified novel amyloid-associated proteins and demonstrated the use of mass spectrometry-based data in clinical diagnostics of disease by the unbiased and reliable model-assisted classification of amyloid deposits and of the specific amyloid subtype.
22
Awada, Chawki, Mohammed Mahfoudh BA BA Abdullah, Hassan Traboulsi, Chahinez Dab, and Adil Alshoaibi. "SARS-CoV-2 Receptor Binding Domain as a Stable-Potential Target for SARS-CoV-2 Detection by Surface—Enhanced Raman Spectroscopy." Sensors 21, no. 13 (July 5, 2021): 4617. http://dx.doi.org/10.3390/s21134617.
Full textAbstract:
In this work, we report a new approach for detecting SARS-CoV-2 RBD protein (RBD) using the surface-enhanced Raman spectroscopy (SERS) technique. The optical enhancement was obtained thanks to the preparation of nanostructured Ag/Au substrates. Fabricated Au/Ag nanostructures were used in the SERS experiment for RBD protein detection. SERS substrates show higher capabilities and sensitivity to detect RBD protein in a short time (3 s) and with very low power. We were able to push the detection limit of proteins to a single protein detection level of 1 pM. The latter is equivalent to 1 fM as a detection limit of viruses. Additionally, we have shown that the SERS technique was useful to figure out the presence of RBD protein on antibody functionalized substrates. In this case, the SERS detection was based on protein-antibody recognition, which led to shifts in the Raman peaks and allowed signal discrimination between RBD and other targets such as Bovine serum albumin (BSA) protein. A perfect agreement between a 3D simulated model based on finite element method and experiment was reported confirming the SERS frequency shift potential for trace proteins detection. Our results could open the way to develop a new prototype based on SERS sensitivity and selectivity for rapid detection at a very low concentration of virus and even at a single protein level.
23
Seliger, Barbara, Sandra Leisz, Kristin Schulz, Susanne Erb, Ena Wang, Francesco Marincola, and Franziska Stehle. "Discrimination between Von Hippel-Lindau gene and hypoxia-regulated alterations in the metabolism and protein expression in renal cell carcinoma using ome-based strategies." Journal of Clinical Oncology 32, no. 4_suppl (February 1, 2014): 447. http://dx.doi.org/10.1200/jco.2014.32.4_suppl.447.
Full textAbstract:
447 Background: The objective of this project is to dissect the von Hippel-Lindau- and hypoxia-regulated alterations in human renal cell carcinoma to define both novel prognostic markers as well as therapeutic targets. Methods: For this, the VHL-negative RCC cell line 786-O next to recently established gain of function VHL transfectants thereof were used as a model system and subsequently cultured under normoxic or hypoxic conditions. Whereas direct effects on the altered energy metabolism were determined by lactate, pyruvate and pH measurements. the resulting effects at the gene/protein expression profiles were determined by comparative cDNA micro array analysis/ 2DE-based proteome analyses, respectively. Some of the differentially expressed genes/proteins were verified by qPCR and/or Western blot analysis in a panel of RCC cell lines. Results: By employing the VHL-/VHL+RCC model system VHL-dependent, HIF-dependent as well as VHL/HIF-independent alterations in the gene and protein expression pattern were found. Some of the differentially expressed genes/proteins were either associated with the loss of VHL function, the hypoxic conditions or both. The genes/proteins defined as differentially expressed under these conditions are mainly involved in the cellular metabolism and predominantly localized in the cytoplasm or mitochondrion. The verification of the differentially expressed genes/proteins by qPCR and Western blot analysis revealed a heterogeneous expression in RCC cells and lesions. One such gene/protein is represented by the glutamine gamma glutamyl transferase (TGM2). Its expression was associated with the loss of VHL and induction of hypoxia. Furthermore, TGM2 might be an important marker for both VHL-independent and/or hypoxia-induced responses. Conclusions: The data demonstrate that VHL functionality and/or hypoxia induce distinct effects on the metabolic switch of RCC.
24
Seok, Seung-Hyeon, Hookang Im, Hyung-Sik Won, Min-Duk Seo, Yoo-Sup Lee, Hye-Jin Yoon, Min-Jeong Cha, Jin-Young Park, and Bong-Jin Lee. "Structures of inactive CRP species reveal the atomic details of the allosteric transition that discriminates cyclic nucleotide second messengers." Acta Crystallographica Section D Biological Crystallography 70, no. 6 (May 30, 2014): 1726–42. http://dx.doi.org/10.1107/s139900471400724x.
Full textAbstract:
The prokaryotic global transcription factor CRP has been considered to be an ideal model for in-depth study of both the allostery of the protein and the differential utilization of the homologous cyclic nucleotide second messengers cAMP and cGMP. Here, atomic details from the crystal structures of two inactive CRP species, an apo form and a cGMP-bound form, in comparison with a known active conformation, the cAMP–CRP complex, provide macroscopic and microscopic insights into CRP allostery, which is coupled to specific discrimination between the two effectors. The cAMP-induced conformational transition, including dynamic fluctuations, can be driven by the fundamental folding forces that cause water-soluble globular proteins to construct an optimized hydrophobic core, including secondary-structure formation. The observed conformational asymmetries underlie a negative cooperativity in the sequential binding of cyclic nucleotides and a stepwise manner of binding with discrimination between the effector molecules. Additionally, the finding that cGMP, which is specifically recognized in asynconformation, induces an inhibitory conformational change, rather than a null effect, on CRP supports the intriguing possibility that cGMP signalling could be widely utilized in prokaryotes, including in aggressive inhibition of CRP-like proteins.
25
Zhang, Min, Nuo Lei, Xian-Long Zhang, Yanmin Xu, Hui-Fen Chen, Li-Zhe Fu, Fang Tang, Xusheng Liu, and Yifan Wu. "Developing and validating a prognostic prediction model for patients with chronic kidney disease stages 3–5 based on disease conditions and intervention methods: a retrospective cohort study in China." BMJ Open 12, no. 5 (May 2022): e054989. http://dx.doi.org/10.1136/bmjopen-2021-054989.
Full textAbstract:
ObjectivesTo develop and validate a nomogram model to predict chronic kidney disease (CKD) stages 3–5 prognosis.DesignA retrospective cohort study. We used univariate and multivariate Cox regression analysis to select the relevant predictors. To select the best model, we evaluated the prediction models’ accuracy by concordance index (C-index), calibration curve, net reclassification index (NRI) and integrated discrimination improvement (IDI). We evaluated the clinical utility by decision curve analysis.SettingChronic Disease Management (CDM) Clinic in the Nephrology Department at the Guangdong Provincial Hospital of Chinese Medicine.ParticipantsPatients with CKD stages 3–5 in the derivation and validation cohorts were 459 and 326, respectively.Primary outcome measureRenal replacement therapy (haemodialysis, peritoneal dialysis, renal transplantation) or death.ResultsWe built four models. Age, estimated glomerular filtration rate and urine protein constituted the most basic model A. Haemoglobin, serum uric acid, cardiovascular disease, primary disease, CDM adherence and predictors in model A constituted model B. Oral medications and predictors in model A constituted model C. All the predictors constituted model D. Model B performed well in both discrimination and calibration (C-index: derivation cohort: 0.881, validation cohort: 0.886). Compared with model A, model B showed significant improvement in the net reclassification and integrated discrimination (model A vs model B: NRI: 1 year: 0.339 (−0.011 to 0.672) and 2 years: 0.314 (0.079 to 0.574); IDI: 1 year: 0.066 (0.010 to 0.127), p<0.001 and 2 years: 0.063 (0.008 to 0.106), p<0.001). There was no significant improvement between NRI and IDI among models B, C and D. Therefore, we selected model B as the optimal model.ConclusionsWe constructed a prediction model to predict the prognosis of patients with CKD stages 3–5 in the first and second year. Applying this model to clinical practice may guide clinical decision-making. Also, this model needs to be externally validated in the future.Trial registration numberChiCTR1900024633 (http://www.chictr.org.cn).
26
Huang, Dageng, Yangyang Wang, Jing Lv, Yuzhu Yan, Ya Hu, Cuicui Liu, Feng Zhang, Jihan Wang, and Dingjun Hao. "Proteomic profiling analysis of postmenopausal osteoporosis and osteopenia identifies potential proteins associated with low bone mineral density." PeerJ 8 (April 14, 2020): e9009. http://dx.doi.org/10.7717/peerj.9009.
Full textAbstract:
Postmenopausal osteoporosis (PMOP) is a major global public health concern and older women are more susceptible to experiencing fragility fractures. Our study investigated the associations between circulating proteins with bone mineral density (BMD) in postmenopausal women with or without low BMD (osteoporosis and osteopenia) using a tandem mass tag (TMT) labeling proteomic experiment and parallel reaction monitoring testing. Across all plasma samples, we quantitatively measured 1,092 proteins, and the OP and normal control (NC) samples were differentiated by principal component analysis and a partial least squares-discrimination analysis model based on the protein profiling data. The differentially abundant proteins between the low BMD and NC samples mostly exhibited binding, molecular function regulator, transporter and molecular transducer activity, and were involved in metabolic and cellular processes, stimulus response, biological regulation, immune system processes and so forth. TMT analysis and RRM validation indicated that the expression of protein Lysozyme C (P61626) was negatively related to BMD, while the expression of proteins Glucosidase (A0A024R592) and Protein disulfideisomerase A5 (Q14554) was positively related to BMD values. Collectively, our results suggest that postmenopausal women with low BMD have a different proteomic profile or signature. Protein alterations may play an important role in the pathogenesis of PMOP, and they may act as novel biomarkers and targets of therapeutic agents for this disease.
27
Takagi, Kimiaki, Hiroshi Takahashi, Tomomi Miura, Kasumi Yamagiwa, Kota Kawase, Yuka Muramatsu-Maekawa, Takuya Koie, and Masashi Mizuno. "Prognostic Value of the Controlling Nutritional Status (CONUT) Score in Patients at Dialysis Initiation." Nutrients 14, no. 11 (May 31, 2022): 2317. http://dx.doi.org/10.3390/nu14112317.
Full textAbstract:
Protein-energy wasting (PEW) is common in patients with chronic kidney disease (CKD), and affects their prognosis. The Controlling Nutritional Status (CONUT) score is a nutritional screening tool calculated using only blood test data. This study aimed to investigate the prognostic value of CONUT score in patients just initiating dialysis. A total of 311 CKD patients who stably initiated dialysis were enrolled. Only 27 (8.7%) patients were classified as having normal nutritional status. The CONUT score was also independently correlated with elevated C-reactive protein levels (β = 0.485, p < 0.0001). During the median follow-up of 37 months, 100 patients (32.2%) died. The CONUT score was an independent predictor of all-cause mortality (adjusted hazard ratio 1.13, 95% confidence interval 1.04–1.22, p < 0.0024). As model discrimination, the addition of the CONUT score to a prediction model based on established risk factors significantly improved net reclassification improvement (0.285, p = 0.028) and integrated discrimination improvement (0.025, p = 0.023). The CONUT score might be a simplified surrogate marker of the PEW with clinical utility and could predict all-cause mortality, in addition to improving the predictability in CKD patients just initiating dialysis. The CONUT score also could predict infectious-disease mortality.
28
Jiang, Shimin, Jinying Fang, Tianyu Yu, Lin Liu, Guming Zou, Hongmei Gao, Li Zhuo, and Wenge Li. "Novel Model Predicts Diabetic Nephropathy in Type 2 Diabetes." American Journal of Nephrology 51, no. 2 (December 19, 2019): 130–38. http://dx.doi.org/10.1159/000505145.
Full textAbstract:
Background: Clinical indicators for accurately distinguishing diabetic nephropathy (DN) from non-diabetic renal disease in type 2 diabetes (T2D) are lacking. This study aimed to develop and validate a nomogram for predicting DN in T2D patients with kidney disease. Methods: A total of 302 consecutive patients with T2D who underwent renal biopsy at China-Japan Friendship Hospital between January 2014 and June 2019 were included in the study. The data were randomly split into a training set containing 70% of the patients (n = 214) and a validation set containing the remaining 30% of patients (n = 88). Multivariable logistic regression analyses were applied to develop a prediction nomogram incorporating the candidates selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using a concordance index (C-index), calibration plot, and decision curve analysis. Both internal and external validations were assessed. Results: A multivariable model that included gender, diabetes duration, diabetic retinopathy, hematuria, glycated hemoglobin A1c, anemia, blood pressure, urinary protein excretion, and estimated glomerular filtration rate was represented as the nomogram. The model demonstrated very good discrimination with a C-index of 0.934 (95% CI 0.904–0.964). The calibration plot diagram of predicted probabilities against observed DN rates indicated excellent concordance. The C-index value was 0.91 for internal validation and 0.875 for external validation. Decision curve analysis demonstrated that the novel nomogram was clinically useful. Conclusion: The novel model was very useful for predicting DN in patients with T2D and kidney disease, and thereby could be used by clinicians either in triage or as a replacement for biopsy.
29
Volkova, Oxana A., Yury V. Kondrakhin, Timur A. Kashapov, and Ruslan N. Sharipov. "Comparative analysis of protein-coding and long non-coding transcripts based on RNA sequence features." Journal of Bioinformatics and Computational Biology 16, no. 02 (April 2018): 1840013. http://dx.doi.org/10.1142/s0219720018400139.
Full textAbstract:
RNA plays an important role in the intracellular cell life and in the organism in general. Besides the well-established protein coding RNAs (messenger RNAs, mRNAs), long non-coding RNAs (lncRNAs) have gained the attention of recent researchers. Although lncRNAs have been classified as non-coding, some authors reported the presence of corresponding sequences in ribosome profiling data (Ribo-seq). Ribo-seq technology is a powerful experimental tool utilized to characterize RNA translation in cell with focus on initiation (harringtonine, lactimidomycin) and elongation (cycloheximide). By exploiting translation starts obtained from the Ribo-seq experiment, we developed a novel position weight matrix model for the prediction of translation starts. This model allowed us to achieve 96% accuracy of discrimination between human mRNAs and lncRNAs. When the same model was used for the prediction of putative ORFs in RNAs, we discovered that the majority of lncRNAs contained only small ORFs ([Formula: see text][Formula: see text]nt) in contrast to mRNAs.
30
Akbar, Shahid, Maqsood Hayat, Muhammad Kabir, and Muhammad Iqbal. "iAFP-gap-SMOTE: An Efficient Feature Extraction Scheme Gapped Dipeptide Composition is Coupled with an Oversampling Technique for Identification of Antifreeze Proteins." Letters in Organic Chemistry 16, no. 4 (March 20, 2019): 294–302. http://dx.doi.org/10.2174/1570178615666180816101653.
Full textAbstract:
Antifreeze proteins (AFPs) perform distinguishable roles in maintaining homeostatic conditions of living organisms and protect their cell and body from freezing in extremely cold conditions. Owing to high diversity in protein sequences and structures, the discrimination of AFPs from non- AFPs through experimental approaches is expensive and lengthy. It is, therefore, vastly desirable to propose a computational intelligent and high throughput model that truly reflects AFPs quickly and accurately. In a sequel, a new predictor called “iAFP-gap-SMOTE” is proposed for the identification of AFPs. Protein sequences are expressed by adopting three numerical feature extraction schemes namely; Split Amino Acid Composition, G-gap di-peptide Composition and Reduce Amino Acid alphabet composition. Usually, classification hypothesis biased towards majority class in case of the imbalanced dataset. Oversampling technique Synthetic Minority Over-sampling Technique is employed in order to increase the instances of the lower class and control the biasness. 10-fold cross-validation test is applied to appraise the success rates of “iAFP-gap-SMOTE” model. After the empirical investigation, “iAFP-gap-SMOTE” model obtained 95.02% accuracy. The comparison suggested that the accuracy of” iAFP-gap-SMOTE” model is higher than that of the present techniques in the literature so far. It is greatly recommended that our proposed model “iAFP-gap-SMOTE” might be helpful for the research community and academia.
31
Sheng, Yajun, Xingye Qiu, Chen Zhang, Jun Xu, Yanping Zhang, Wei Zheng, and Ke Chen. "Quad-PRE: A Hybrid Method to Predict Protein Quaternary Structure Attributes." Computational and Mathematical Methods in Medicine 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/715494.
Full textAbstract:
The protein quaternary structure is very important to the biological process. Predicting their attributes is an essential task in computational biology for the advancement of the proteomics. However, the existing methods did not consider sufficient properties of amino acid. To end this, we proposed a hybrid method Quad-PRE to predict protein quaternary structure attributes using the properties of amino acid, predicted secondary structure, predicted relative solvent accessibility, and position-specific scoring matrix profiles and motifs. Empirical evaluation on independent dataset shows that Quad-PRE achieved higher overall accuracy 81.7%, especially higher accuracy 92.8%, 93.3%, and 90.6% on discrimination for trimer, hexamer, and octamer, respectively. Our model also reveals that six features sets are all important to the prediction, and a hybrid method is an optimal strategy by now. The results indicate that the proposed method can classify protein quaternary structure attributes effectively.
32
Ross, Melissa H., Ricardo Henao, Thomas W. Burke, Micah T. McClain, Geoffrey S. Ginsburg, Chris W. Woods, Ephraim L. Tsalik, and Ephraim L. Tsalik. "1330. Evaluation of Multiple Host Response-Based Strategies to Classify Acute Respiratory Illness." Open Forum Infectious Diseases 6, Supplement_2 (October 2019): S481. http://dx.doi.org/10.1093/ofid/ofz360.1194.
Full textAbstract:
Abstract Background Host response-based diagnostics are an alternative to pathogen-based tests. Host response strategies include proteomic and transcriptomic approaches. Here, we compare three host response strategies for ARI diagnosis: Procalcitonin (PCT), a 3-protein panel, and an mRNA panel. Methods PCT, a 3-protein panel (CRP, IP-10, TRAIL), and a host gene expression mRNA panel were measured in a cohort of 286 participants presenting to one of the four Emergency Departments with ARI due to bacterial (n = 47), viral (n = 162), or noninfectious (n = 77) etiologies. Multinomial logistic regression and leave-one-out cross-validation were used to train and evaluate the protein and mRNA panels. Performance characteristics were calculated for each method, and their combination, for the ability to discriminate bacterial vs. non-bacterial infection and viral vs. nonviral infection. PCT was not evaluated for viral vs. nonviral discrimination since it does not discriminate viral and noninfectious etiologies. McNemar’s test was used to compare overall accuracy of mRNA and protein panels. Results For discriminating bacterial vs. non-bacterial etiologies, the mRNA panel had an AUC of 0.93 vs. 0.83 for both the protein panel and PCT. A model utilizing all three strategies was the same as mRNA alone. Using previously established cutoffs, overall accuracy was similar between mRNA and protein panels, but the protein panel had widely discordant sensitivity (43%) and specificity (92%). When selecting an optimal cutoff for the protein panel that balanced the two (82% and 73%, respectively), the mRNA panel had a significantly greater overall accuracy (P < 0.001). Similar results were found when discriminating viral vs. non-viral subjects: the mRNA panel (AUC = 0.93) outperformed the protein panel (AUC = 0.84). Combining the mRNA and protein panels was equivalent to the mRNA panel alone. Conclusion A host-based gene expression signature is the most effective platform for classifying subjects with bacterial, viral, or noninfectious ARI. A gene expression approach, when translated to a clinically available platform, may facilitate diagnosis and clinical management of acute infectious diseases, mitigating antibiotic overuse. Disclosures Ephraim L. Tsalik, MD, MHS, PhD, Immunexpress: Consultant; Predigen, Inc.: Officer or Board Member, Research Grant.
33
Mauchline, Tim H., Rachel Knox, Sharad Mohan, Stephen J. Powers, Brian R. Kerry, Keith G. Davies, and Penny R. Hirsch. "Identification of New Single Nucleotide Polymorphism-Based Markers for Inter- and Intraspecies Discrimination of Obligate Bacterial Parasites (Pasteuria spp.) of Invertebrates." Applied and Environmental Microbiology 77, no. 18 (July 29, 2011): 6388–94. http://dx.doi.org/10.1128/aem.05185-11.
Full textAbstract:
ABSTRACTProtein-encoding and 16S rRNA genes ofPasteuria penetranspopulations from a wide range of geographic locations were examined. Most interpopulation single nucleotide polymorphisms (SNPs) were detected in the 16S rRNA gene. However, in order to fully resolve all populations, these were supplemented with SNPs from protein-encoding genes in a multilocus SNP typing approach. Examination of individual 16S rRNA gene sequences revealed the occurrence of “cryptic” SNPs which were not present in the consensus sequences of anyP. penetranspopulation. Additionally, hierarchical cluster analysis separatedP. penetrans16S rRNA gene clones into four groups, and one of which contained sequences from the most highly passaged population, demonstrating that it is possible to manipulate the population structure of this fastidious bacterium. The other groups were made from representatives of the other populations in various proportions. Comparison of sequences among threePasteuriaspecies, namely,P. penetrans,P. hartismeri, andP. ramosa, showed that the protein-encoding genes provided greater discrimination than the 16S rRNA gene. From these findings, we have developed a toolbox for the discrimination ofPasteuriaat both the inter- and intraspecies levels. We also provide a model to monitor genetic variation in other obligate hyperparasites and difficult-to-culture microorganisms.
34
Klein, Daniel, René Breuch, Jessica Reinmüller, Carsten Engelhard, and Peter Kaul. "Investigation and Rapid Discrimination of Food-Related Bacteria under Stress Treatments Using IR Microspectroscopy." Foods 10, no. 8 (August 11, 2021): 1850. http://dx.doi.org/10.3390/foods10081850.
Full textAbstract:
Because the robust and rapid determination of spoilage microorganisms is becoming increasingly important in industry, the use of IR microspectroscopy, and the establishment of robust and versatile chemometric models for data processing and classification, is gaining importance. To further improve the chemometric models, bacterial stress responses were induced, to study the effect on the IR spectra and to improve the chemometric model. Thus, in this work, nine important food-relevant microorganisms were subjected to eight stress conditions, besides the regular culturing as a reference. Spectral changes compared to normal growth conditions without stressors were found in the spectral regions of 900–1500 cm−1 and 1500–1700 cm−1. These differences might stem from changes in the protein secondary structure, exopolymer production, and concentration of nucleic acids, lipids, and polysaccharides. As a result, a model for the discrimination of the studied microorganisms at the genus, species and strain level was established, with an accuracy of 96.6%. This was achieved despite the inclusion of various stress conditions and times after incubation of the bacteria. In addition, a model was developed for each individual microorganism, to separate each stress condition or regular treatment with 100% accuracy.
35
Tabita, F. Robert, Thomas E. Hanson, Sriram Satagopan, Brian H. Witte, and Nathan E. Kreel. "Phylogenetic and evolutionary relationships of RubisCO and the RubisCO-like proteins and the functional lessons provided by diverse molecular forms." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1504 (May 16, 2008): 2629–40. http://dx.doi.org/10.1098/rstb.2008.0023.
Full textAbstract:
Ribulose 1,5-bisphosphate (RuBP) carboxylase/oxygenase (RubisCO) catalyses the key reaction by which inorganic carbon may be assimilated into organic carbon. Phylogenetic analyses indicate that there are three classes of bona fide RubisCO proteins, forms I, II and III, which all catalyse the same reactions. In addition, there exists another form of RubisCO, form IV, which does not catalyse RuBP carboxylation or oxygenation. Form IV is actually a homologue of RubisCO and is called the RubisCO-like protein (RLP). Both RubisCO and RLP appear to have evolved from an ancestor protein in a methanogenic archaeon, and comprehensive analyses indicate that the different forms (I, II, III and IV) contain various subgroups, with individual sequences derived from representatives of all three kingdoms of life. The diversity of RubisCO molecules, many of which function in distinct milieus, has provided convenient model systems to study the ways in which the active site of this protein has evolved to accommodate necessary molecular adaptations. Such studies have proven useful to help provide a framework for understanding the molecular basis for many important aspects of RubisCO catalysis, including the elucidation of factors or functional groups that impinge on RubisCO carbon dioxide/oxygen substrate discrimination.
36
Ioerger, Thomas R. "Automated detection of disulfide bridges in electron density maps using linear discriminant analysis." Journal of Applied Crystallography 38, no. 1 (January 19, 2005): 121–25. http://dx.doi.org/10.1107/s0021889804030250.
Full textAbstract:
The ability to recognize disulfide bridges automatically in electron density maps would be useful to both protein crystallographers and automated model-building programs. A computational method is described for recognizing disulfide bridges in uninterpreted maps based on linear discriminant analysis. For each localized spherical region in a map, a vector of rotation-invariant numeric features is calculated that captures various aspects of the local pattern of density. These features values are then input into a linear equation, with coefficients computed to optimize discrimination of a set of training examples (disulfides and non-disulfides), and compared with a decision threshold. The method is shown to be highly accurate at distinguishing disulfides from non-disulfides in both the original training data and in real (experimental) electron density maps of other proteins.
37
Noce, Antonin Della, Stergios Christodoulidis, Antonio Di Meglio, Julie Havas, Alicia Tran-Dien, Fabrice André, Ines Vaz-Luis, Paul-Henry Cournède, and Stefan Michiels. "Abstract P4-07-17: Association between plasma-based sequential windowed acquisition mass spectrometry (SWATH-MS) and invasive disease free survival (iDFS) in HR+/HER2- early breast cancer in the CANTO cohort." Cancer Research 82, no. 4_Supplement (February 15, 2022): P4–07–17—P4–07–17. http://dx.doi.org/10.1158/1538-7445.sabcs21-p4-07-17.
Full textAbstract:
Abstract Background: The definition of breast cancer (BC) prognosis has historically relied on clinico-pathological factors. Novel omics markers including proteomic analyses could improve our understanding of the biological host drivers of breast cancer recurrence and survival. We aimed at identifying patients (pts) at high risk of recurrence based on proteomic markers in plasma.Methods: CANTO is a multicenter, prospective cohort study of stage I-III BCS (NCT01993498). Plasma samples were collected on HR+/HER2- pts at diagnosis (dx) and analyzed by SWATH-MS, implemented by Biognosys AG (Schlieren, Switzerland), resulting in a relative quantification of the abundance of 500 proteins in the plasma. A Cox model was fitted to estimate to associate proteomic and clinical variables with the primary endpoint IDFS Clinical covariates consisted of age, stage and grade. An adaptive Lasso method was used to perform model selection. The discrimination performances of the model were assessed on 100 random train-test partitions of the cohort. Results: There were 457 pts with analyzed plasma samples. The median age at dx was 59.3 years, and the repartition of cancer stage was 52% for stage I, 37% for stage II and 11% for stage III. The mean duration of follow-up was 5.4 years, and 53 (11.5%) IDFS events (non local recurrences, second primary cancers and deaths) were reported. In total, 7 proteins were selected by the adaptive Lasso process; associated with the age, the stage and the grade at dx, 3 proteins were retained as having a significant impact on the IDFS: GTP-binding nuclear protein Ran (RAN), involved in cell division and GTP metabolic process, C4b-binding protein alpha-chain (C4BPA), involved in complement activation, and prothrombin (THRB), involved in acute-phase response and blood activation. Concordance indices were computed on 100 random test subsets of the cohort for the model with clinical variables only (0.67+/- 0.08), for the model with selected protein features only (0.74 +/- 0.07) and for the model with both proteomic and clinical covariates (0.75 +/-0.06). Conclusion: The discrimination performances of the estimated model suggest that proteomics provide relevant markers associated with BC prognosis. Validation on an independent validation set is required. Host related plasma proteins represent an avenue worth exploring to improve our understanding of BC relapse risk Table 1.Estimated hazard ratios of the linear Cox model.FeaturesHR* (95% CI)p-valuesRAN (for 1 SD increase)0.66 (0.51-0.85)<0.005THRB (for 1 SD increase)1.43 (0.99-2.06)0.05C4BPA (for 1 SD increase)1.44 (1.02-2.02)0.04stage--II vs I1.68 (0.82-3.46)0.16III vs I4.29 (1.88-9.75)<0.005HR = hazard ratio CI = confidence interval * adjusted by age and grade Citation Format: Antonin Della Noce, Stergios Christodoulidis, Antonio Di Meglio, Julie Havas, Alicia Tran-Dien, Fabrice André, Ines Vaz-Luis, Paul-Henry Cournède, Stefan Michiels. Association between plasma-based sequential windowed acquisition mass spectrometry (SWATH-MS) and invasive disease free survival (iDFS) in HR+/HER2- early breast cancer in the CANTO cohort [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-07-17.
38
Zhou, Bing-Mei, Zhao-Lei Qiu, Kai-Xuan Niu, Yin-E. Wang, and Fu-Chen Jie. "Construction of a Nomogram Model for Predicting Pleural Effusion Secondary to Severe Acute Pancreatitis." Emergency Medicine International 2022 (March 19, 2022): 1–5. http://dx.doi.org/10.1155/2022/4199209.
Full textAbstract:
Background. This study aims to investigate the risk factors of pleural effusion (PE) secondary to severe acute pancreatitis (SAP) and to build a nomogram model. Methods. The clinical parameters of SAP patients admitted to the emergency department of the First Affiliated Hospital of Bengbu Medical College from January 2019 to August 2021 were retrospectively collected. The independence risk factors of PE secondary to SAP were analyzed by univariate analysis and multivariate logistic regression analysis. A nomogram risk prediction model was established and validated through the area under the ROC curve. Result. Two hundred twenty-two SAP patients were included for analysis, of which 65 patients experienced secondary PE. The incidence of PE secondary to SAP was 29.28% (65/222). Logistic regression analysis showed that serum albumin (ALB) (OR = 0.830, 95% CI: 0.736∼0.936), fibrinogen (FIB) (OR = 4.573, 95% CI: 1.795∼11.648), C-reactive protein (CRP) (OR = 1.046, 95% CI: 1.009∼1.083), acute physiology, chronic health score system (APACHE-II) score (OR = 1.484, 95% CI: 1.106∼1.990), and sequential organ failure score (SOFA) (OR = 43.038, 95% CI: 2.030∼4.548) were independent risk factors for PE secondary to SAP ( P < 0.05 ) and entered into the nomogram. The nomogram showed robust discrimination with an index of concordance of 0.755 and an area under the receiver operating characteristic curve of 0.837 (95% CI: 0.779∼0.894). Conclusion. We developed a nomogram model for PE secondary to SAP with ALB, FIB, CRP, APACHE-II scores, and SOFA scores. The nomogram model showed good discrimination and consistency, and it can better predict the risk of PE secondary to SAP.
39
Preisner, Ornella, Raquel Guiomar, Jorge Machado, Jos� Cardoso Menezes, and Jo�o Almeida Lopes. "Application of Fourier Transform Infrared Spectroscopy and Chemometrics for Differentiation of Salmonella enterica Serovar Enteritidis Phage Types." Applied and Environmental Microbiology 76, no. 11 (April 2, 2010): 3538–44. http://dx.doi.org/10.1128/aem.01589-09.
Full textAbstract:
ABSTRACT Fourier transform infrared (FT-IR) spectroscopy and chemometric techniques were used to discriminate five closely related Salmonella enterica serotype Enteritidis phage types, phage type 1 (PT1), PT1b, PT4b, PT6, and PT6a. Intact cells and outer membrane protein (OMP) extracts from bacterial cell membranes were subjected to FT-IR analysis in transmittance mode. Spectra were collected over a wavenumber range from 4,000 to 600 cm−1. Partial least-squares discriminant analysis (PLS-DA) was used to develop calibration models based on preprocessed FT-IR spectra. The analysis based on OMP extracts provided greater separation between the Salmonella Enteritidis PT1-PT1b, PT4b, and PT6-PT6a groups than the intact cell analysis. When these three phage type groups were considered, the method based on OMP extract FT-IR spectra was 100% accurate. Moreover, complementary local models that considered only the PT1-PT1b and PT6-PT6a groups were developed, and the level of discrimination increased. PT1 and PT1b isolates were differentiated successfully with the local model using the entire OMP extract spectrum (98.3% correct predictions), whereas the accuracy of discrimination between PT6 and PT6a isolates was 86.0%. Isolates belonging to different phage types (PT19, PT20, and PT21) were used with the model to test its robustness. For the first time it was demonstrated that FT-IR analysis of OMP extracts can be used for construction of robust models that allow fast and accurate discrimination of different Salmonella Enteritidis phage types.
40
Wang, Luqing, Li Feng, Jiasi Wang, Jie Li, Hongbin Li, Fanxin Zeng, and Liangli Sun. "A Variable-Clustering-Based Feature Selection to Improve Positive and Negative Discrimination of P53 Protein in Colorectal Cancer Patients." Computational and Mathematical Methods in Medicine 2022 (November 17, 2022): 1–7. http://dx.doi.org/10.1155/2022/9261713.
Full textAbstract:
P53 protein tumor suppressor gene plays a guiding role in the treatment and prognosis of colorectal cancer (CRC). This paper aimed at proposing a feature selection method based on variable clustering to improve positive and negative discrimination of P53 protein in CRC patients. In this approach, we cluster the preprocessed dataset with variables, and then find the features with the largest information value (IV) for each cluster to form a feature subset. We call this method as IV_Cluster. In the actual medical data test, compared with the information value feature selection method, the accuracy of the 10-fold cross-validation logistic regression model increased by 4.4%, 2.0%, and 5.8%, and Kappa values increased by 21.8%, 8.6%, and 22.4%, respectively, under 5, 10, and 15 feature sets.
41
Smith, Alexander E. F., Farzin Farzaneh, and Kevin G. Ford. "Single zinc-finger extension: enhancing transcriptional activity and specificity of three-zinc-finger proteins." Biological Chemistry 386, no. 2 (February 1, 2005): 95–99. http://dx.doi.org/10.1515/bc.2005.012.
Full textAbstract:
AbstractIn order to demonstrate that an existing zinc-finger protein can be simply modified to enhance DNA binding and sequence discrimination in both episomal and chromatin contexts using existing zinc-finger DNA recognition code data, and without recourse to phage display and selection strategies, we have examined the consequences of a single zinc-finger extension to a synthetic three-zinc-finger VP16 fusion protein, on transcriptional activation from model target promoters harbouring the zinc-finger binding sequences. We report a nearly 10-fold enhanced transcriptional activation by the four-zinc-finger VP16 fusion protein relative to the progenitor three-finger VP16 protein in transient assays and a greater than five-fold enhancement in stable reporter-gene expression assays. A marked decrease in transcriptional activation was evident for the four-zinc-finger derivative from mutated regulatory regions compared to the progenitor protein, as a result of recognition site-size extension. This discriminatory effect was shown to be protein concentration-dependent. These observations suggest that four-zinc-finger proteins are stable functional motifs that can be a significant improvement over the progenitor three-zinc-finger protein, both in terms of specificity and the ability to target transcriptional function to promoters, and that single zinc-finger extension can therefore have a significant impact on DNA zinc-finger protein interactions. This is a simple route for modifying or enhancing the binding properties of existing synthetic zinc-finger-based transcription factors and may be particularly suited for the modification of endogenous zinc-finger transcription factors for promoter biasing applications.
42
LIU, C., W. LIU, X. LU, W. CHEN, F. CHEN, J. YANG, and L. ZHENG. "Non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants using multispectral imaging and chemometric methods." Journal of Agricultural Science 154, no. 1 (November 10, 2014): 1–12. http://dx.doi.org/10.1017/s0021859614001142.
Full textAbstract:
SUMMARYSoybean is an important oil- and protein-producing crop and over the last few decades soybean genetic transformation has made rapid strides. The probability of occurrence of transgene flow should be assessed, although the discrimination of conventional and transgenic soybean seeds and their hybrid descendants is difficult in fields. The feasibility of non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants was examined by a multispectral imaging system combined with chemometric methods. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM) and back propagation neural network (BPNN) methods were applied to classify soybean seeds. The current results demonstrated that clear differences among conventional and glyphosate-resistant soybean seeds and their hybrid descendants could be easily visualized and an excellent classification (98% with BPNN model) could be achieved. It was concluded that multispectral imaging together with chemometric methods would be a promising technique to identify transgenic soybean seeds with high efficiency.
43
Wang, Xue, Yu Yang, Jian Zhang, and Shuang Zang. "Development and validation of a prediction model for the prolonged length of stay in Chinese patients with lower extremity atherosclerotic disease: a retrospective study." BMJ Open 13, no. 2 (February 2023): e069437. http://dx.doi.org/10.1136/bmjopen-2022-069437.
Full textAbstract:
ObjectivesThis study aims to develop and internally validate a prediction model, which takes account of multivariable and comprehensive factors to predict the prolonged length of stay (LOS) in patients with lower extremity atherosclerotic disease (LEAD).DesignThis is a retrospective study.SettingChina.Participants, primary and secondary outcomesData of 1694 patients with LEAD from a retrospective cohort study between January 2014 and November 2021 were analysed. We selected nine variables and created the prediction model using the least absolute shrinkage and selection operator (LASSO) regression model after dividing the dataset into training and test sets in a 7:3 ratio. Prediction model performance was evaluated by calibration, discrimination and Hosmer-Lemeshow test. The effectiveness of clinical utility was estimated using decision curve analysis.ResultsLASSO regression analysis identified age, gender, systolic blood pressure, Fontaine classification, lesion site, surgery, C reactive protein, prothrombin time international normalised ratio and fibrinogen as significant predictors for predicting prolonged LOS in patients with LEAD. In the training set, the prediction model showed good discrimination using a 500-bootstrap analysis and good calibration with an area under the receiver operating characteristic of 0.750. The Hosmer-Lemeshow goodness of fit test for the training set had a p value of 0.354. The decision curve analysis showed that using the prediction model both in training and tests contributes to clinical value.ConclusionOur prediction model is a valuable tool using easily and routinely obtained clinical variables that could be used to predict prolonged LOS in patients with LEAD and help to better manage these patients in routine clinical practice.
44
Zhou, Fengyan, Guozheng Cao, Songjun Dai, Guo Li, Hao Li, Zhu Ding, Shouqing Hou, et al. "Chelicerata sDscam isoforms combine homophilic specificities to define unique cell recognition." Proceedings of the National Academy of Sciences 117, no. 40 (September 22, 2020): 24813–24. http://dx.doi.org/10.1073/pnas.1921983117.
Full textAbstract:
Thousands of Down syndrome cell adhesion molecule (Dscam1) isoforms and ∼60 clustered protocadhrein (cPcdh) proteins are required for establishing neural circuits in insects and vertebrates, respectively. The strict homophilic specificity exhibited by these proteins has been extensively studied and is thought to be critical for their function in neuronal self-avoidance. In contrast, significantly less is known about the Dscam1-related family of ∼100 shortened Dscam (sDscam) proteins in Chelicerata. We report that Chelicerata sDscamα and some sDscamβ protein trans interactions are strictly homophilic, and that the trans interaction is meditated via the first Ig domain through an antiparallel interface. Additionally, different sDscam isoforms interact promiscuously in cis via membrane proximate fibronectin-type III domains. We report that cell–cell interactions depend on the combined identity of all sDscam isoforms expressed. A single mismatched sDscam isoform can interfere with the interactions of cells that otherwise express an identical set of isoforms. Thus, our data support a model by which sDscam association in cis and trans generates a vast repertoire of combinatorial homophilic recognition specificities. We propose that in Chelicerata, sDscam combinatorial specificity is sufficient to provide each neuron with a unique identity for self–nonself discrimination. Surprisingly, while sDscams are related to Drosophila Dscam1, our results mirror the findings reported for the structurally unrelated vertebrate cPcdh. Thus, our findings suggest a remarkable example of convergent evolution for the process of neuronal self-avoidance and provide insight into the basic principles and evolution of metazoan self-avoidance and self–nonself discrimination.
45
Helgason, Hannes, Thjodbjorg Eiriksdottir, Magnus O. Ulfarsson, Abhishek Choudhary, Sigrun H. Lund, Erna V. Ivarsdottir, Grimur Hjorleifsson Eldjarn, et al. "Evaluation of Large-Scale Proteomics for Prediction of Cardiovascular Events." JAMA 330, no. 8 (August 22, 2023): 725. http://dx.doi.org/10.1001/jama.2023.13258.
Full textAbstract:
ImportanceWhether protein risk scores derived from a single plasma sample could be useful for risk assessment for atherosclerotic cardiovascular disease (ASCVD), in conjunction with clinical risk factors and polygenic risk scores, is uncertain.ObjectiveTo develop protein risk scores for ASCVD risk prediction and compare them to clinical risk factors and polygenic risk scores in primary and secondary event populations.Design, Setting, and ParticipantsThe primary analysis was a retrospective study of primary events among 13 540 individuals in Iceland (aged 40-75 years) with proteomics data and no history of major ASCVD events at recruitment (study duration, August 23, 2000 until October 26, 2006; follow-up through 2018). We also analyzed a secondary event population from a randomized, double-blind lipid-lowering clinical trial (2013-2016), consisting of individuals with stable ASCVD receiving statin therapy and for whom proteomic data were available for 6791 individuals.ExposuresProtein risk scores (based on 4963 plasma protein levels and developed in a training set in the primary event population); polygenic risk scores for coronary artery disease and stroke; and clinical risk factors that included age, sex, statin use, hypertension treatment, type 2 diabetes, body mass index, and smoking status at the time of plasma sampling.Main Outcomes and MeasuresOutcomes were composites of myocardial infarction, stroke, and coronary heart disease death or cardiovascular death. Performance was evaluated using Cox survival models and measures of discrimination and reclassification that accounted for the competing risk of non-ASCVD death.ResultsIn the primary event population test set (4018 individuals [59.0% women]; 465 events; median follow-up, 15.8 years), the protein risk score had a hazard ratio (HR) of 1.93 per SD (95% CI, 1.75 to 2.13). Addition of protein risk score and polygenic risk scores significantly increased the C index when added to a clinical risk factor model (C index change, 0.022 [95% CI, 0.007 to 0.038]). Addition of the protein risk score alone to a clinical risk factor model also led to a significantly increased C index (difference, 0.014 [95% CI, 0.002 to 0.028]). Among White individuals in the secondary event population (6307 participants; 432 events; median follow-up, 2.2 years), the protein risk score had an HR of 1.62 per SD (95% CI, 1.48 to 1.79) and significantly increased C index when added to a clinical risk factor model (C index change, 0.026 [95% CI, 0.011 to 0.042]). The protein risk score was significantly associated with major adverse cardiovascular events among individuals of African and Asian ancestries in the secondary event population.Conclusions and RelevanceA protein risk score was significantly associated with ASCVD events in primary and secondary event populations. When added to clinical risk factors, the protein risk score and polygenic risk score both provided statistically significant but modest improvement in discrimination.
46
Widyastuti, Yunita, Djayanti Sari, Juni Kurniawaty, Untung Widodo, Calcarina Fitriani R.W, Akhmad Yun Jufan, Ketut Sutaendy, Purnama Jaya, and Dinda Ulfa. "A simple diagnostic scoring system for COVID-19 screening." Anaesthesia, Pain & Intensive Care 26, no. 6 (December 7, 2022): 785–93. http://dx.doi.org/10.35975/apic.v26i6.2076.
Full textAbstract:
Background: The COVID‐19 pandemic has prompted the world to make various efforts to control its spread by finding ways to diagnose COVID‐19 quickly and accurately. Early identification of COVID‐19 infection is essential, especially in hospitals with limited resources. We aimed to generate two scores based upon clinical and laboratory findings in patients screen for COVID-19 infection.
Methodology: This study used a retrospective cohort design that involved 705 adults (≥ 18 y old) admitted in Dr. Sardjito Hospital and Dr. S. Hardjolukito Hospital. The patients' data collected included demographic characteristics, anamnesis on signs and symptoms, history of contact with COVID-19 patients, history of staying or visiting an endemic area, comorbidities, and laboratory and radiologic indicators. All variables with a P < 0.25 on the bivariate test were included in a univariable logistic regression. If the P < 0.05, the variable was included in the multivariable logistic regression with a P < 0.05 considered significant. Receiver Operating Characteristic (ROC) producing an area under the curve (AUC) with 95% confidence intervals (CIs) was used to assess discrimination power.
Results: Two scores were generated; score in Model 1 consisted of clinical signs, basic laboratory indicators, and chest X-ray, and in Model 2 consisted of clinical signs, chest X-ray, basic and advanced laboratory indicators, including C-reactive protein (CRP), lactate dehydrogenase (LDH), albumin, and D-dimer. The ROC score of Model 1 was 0.801 (0.764−0. 838), which is considered good discrimination, and of Model 2 had excellent discrimination with a ROC of 0.858 (0.826−0. 891); the differences in the ROC of the two models was statistically significant (P = 0.03). The score of Model 1 more than 5 had 85% sensitivity and 61% specificity of positive COVID-19. A score of Model 2 more than 4 had 83% sensitivity and 72% specificity for diagnosing positive COVID-19.
Conclusions: A simple score consisting of clinical symptoms and signs, and simple laboratory indicators can be used to screen for COVID-19 infection.
Abbreviations: ARDS: Acute respiratory distress syndrome; CRP: C-reactive protein; MLR: monocyte-to-lymphocyte ratio; NLR: Neutrophil-to-lymphocyte ratio; RT-PCR: Reverse Transcription-Polymerase Chain Reaction;
Key words: COVID-19; Screening System; Clinical Symptoms; Laboratory Indicators
Citation: Widyastuti Y, Sari D, Kurniawaty J, Widodo U, Fitriani RW C, Jufan AY, Sutaendy K, Jaya P, Ulfa D. A simple diagnostic scoring system for COVID-19 screening. Anaesth. pain intensive care 2022;26(6):784-791.
DOI: 10.35975/apic.v26i6.2076
47
Fassio, Larissa O., Marcelo R. Malta, Gladyston R. Carvalho, Antônio A. Pereira, Ackson D. Silva, Gilberto R. Liska, Adriene W. Pedrosa, Vany P. Ferraz, and Rosemary G. F. A. Pereira. "Discrimination of Genealogical Groups of Arabica Coffee by the Chemical Composition of the Beans." Journal of Agricultural Science 11, no. 16 (September 30, 2019): 141. http://dx.doi.org/10.5539/jas.v11n16p141.
Full textAbstract:
This work aimed to characterize and discriminate genealogical groups of coffee as to the chemical composition of the grains through the model created by PLS-DA method. 22 accessions of Coffea arabica, from the Active Germplasm Bank of Minas Gerais, were divided into groups according to the genealogical origin. Samples of ripe fruits were harvested selectively and processed by the wet method, to obtain pulped coffee beans, with 11% (b.u.) of water content. The raw beans were assessed as to the content of polyphenols, total sugars, total lipids, protein, caffeine, sucrose, and fatty acids. The data were submitted the chemometric analysis, PCA and PLS-DA. The results of PLS-DA identified the variables which most influence the classification of genealogical groups and possible chemical markers to accessions processed by the pulped method. The sucrose content was an important marker for the Exotic accession group. However, the content of polyphenols has been identified as a marker for the group Tymor Hybrid, and the caffeine for the bourbon group. The different fatty acids have been identified as markers for all genealogical groups, at different levels. The model PLS-DA is effective in discriminating genealogical groups from the chemical composition of the beans.
48
Stefan, C. J., and K. J. Blumer. "The third cytoplasmic loop of a yeast G-protein-coupled receptor controls pathway activation, ligand discrimination, and receptor internalization." Molecular and Cellular Biology 14, no. 5 (May 1994): 3339–49. http://dx.doi.org/10.1128/mcb.14.5.3339-3349.1994.
Full textAbstract:
To identify functional domains of G-protein-coupled receptors that control pathway activation, ligand discrimination, and receptor regulation, we have used as a model the alpha-factor receptor (STE2 gene product) of the yeast Saccharomyces cerevisiae. From a collection of random mutations introduced in the region coding for the third cytoplasmic loop of Ste2p, six ste2sst alleles were identified by genetic screening methods that increased alpha-factor sensitivity 2.5- to 15-fold. The phenotypic effects of ste2sst and sst2 mutations were not additive, consistent with models in which the third cytoplasmic loop of the alpha-factor receptor and the regulatory protein Sst2p control related aspects of pheromone response and/or desensitization. Four ste2sst mutations did not dramatically alter cell surface expression or agonist binding affinity of the receptor; however, they did permit detectable responses to an alpha-factor antagonist. One ste2sst allele increased receptor binding affinity for alpha-factor and elicited stronger responses to antagonist. Results of competition binding experiments indicated that wild-type and representative mutant receptors bound antagonist with similar affinities. The antagonist-responsive phenotypes caused by ste2sst alleles were therefore due to defects in the ability of receptors to discriminate between agonist and antagonist peptides. One ste2sst mutation caused rapid, ligand-independent internalization of the receptor. These results demonstrate that the third cytoplasmic loop of the alpha-factor receptor is a multifunctional regulatory domain that controls pathway activation and/or desensitization and influences the processes of receptor activation, ligand discrimination, and internalization.
49
Stefan, C. J., and K. J. Blumer. "The third cytoplasmic loop of a yeast G-protein-coupled receptor controls pathway activation, ligand discrimination, and receptor internalization." Molecular and Cellular Biology 14, no. 5 (May 1994): 3339–49. http://dx.doi.org/10.1128/mcb.14.5.3339.
Full textAbstract:
To identify functional domains of G-protein-coupled receptors that control pathway activation, ligand discrimination, and receptor regulation, we have used as a model the alpha-factor receptor (STE2 gene product) of the yeast Saccharomyces cerevisiae. From a collection of random mutations introduced in the region coding for the third cytoplasmic loop of Ste2p, six ste2sst alleles were identified by genetic screening methods that increased alpha-factor sensitivity 2.5- to 15-fold. The phenotypic effects of ste2sst and sst2 mutations were not additive, consistent with models in which the third cytoplasmic loop of the alpha-factor receptor and the regulatory protein Sst2p control related aspects of pheromone response and/or desensitization. Four ste2sst mutations did not dramatically alter cell surface expression or agonist binding affinity of the receptor; however, they did permit detectable responses to an alpha-factor antagonist. One ste2sst allele increased receptor binding affinity for alpha-factor and elicited stronger responses to antagonist. Results of competition binding experiments indicated that wild-type and representative mutant receptors bound antagonist with similar affinities. The antagonist-responsive phenotypes caused by ste2sst alleles were therefore due to defects in the ability of receptors to discriminate between agonist and antagonist peptides. One ste2sst mutation caused rapid, ligand-independent internalization of the receptor. These results demonstrate that the third cytoplasmic loop of the alpha-factor receptor is a multifunctional regulatory domain that controls pathway activation and/or desensitization and influences the processes of receptor activation, ligand discrimination, and internalization.
50
McBride, John M., Jean Pierre Eckmann, and Tsvi Tlusty. "General theory of specific binding: insights from a genetic-mechano-chemical protein model." Molecular Biology and Evolution, October 8, 2022. http://dx.doi.org/10.1093/molbev/msac217.
Full textAbstract:
Abstract Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. But despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics and genetics, and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination - by varying degrees of flexibility and shape/chemistry complementarity - but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine-tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is enabled by increasing the protein size, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. The model makes testable, specific predictions about the role of flexibility and shape mismatch in discrimination, and how evolution can independently tune affinity and specificity. Thus, the proposed theory of specific binding addresses the natural question of “why are proteins so big?”. A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.