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Artykuły w czasopismach na temat „Protein binding – Mathematical models”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 12 lutego 2022

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1

Palacio-Castañeda, Valentina, Simon Dumas, Philipp Albrecht, Thijmen J. Wijgers, Stéphanie Descroix, and Wouter P. R. Verdurmen. "A Hybrid In Silico and Tumor-on-a-Chip Approach to Model Targeted Protein Behavior in 3D Microenvironments." Cancers 13, no. 10 (May 18, 2021): 2461. http://dx.doi.org/10.3390/cancers13102461.

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To rationally improve targeted drug delivery to tumor cells, new methods combining in silico and physiologically relevant in vitro models are needed. This study combines mathematical modeling with 3D in vitro co-culture models to study the delivery of engineered proteins, called designed ankyrin repeat proteins (DARPins), in biomimetic tumor microenvironments containing fibroblasts and tumor cells overexpressing epithelial cell adhesion molecule (EpCAM) or human epithelial growth factor receptor (HER2). In multicellular tumor spheroids, we observed strong binding-site barriers in combination w

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2

Middendorf, Thomas R., and Richard W. Aldrich. "Structural identifiability of equilibrium ligand-binding parameters." Journal of General Physiology 149, no. 1 (December 19, 2016): 105–19. http://dx.doi.org/10.1085/jgp.201611702.

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Understanding the interactions of proteins with their ligands requires knowledge of molecular properties, such as binding site affinities and the effects that binding at one site exerts on binding at other sites (cooperativity). These properties cannot be measured directly and are usually estimated by fitting binding data with models that contain these quantities as parameters. In this study, we present a general method for answering the critical question of whether these parameters are identifiable (i.e., whether their estimates are accurate and unique). In cases in which parameter estimates

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3

Premarathna, Galkande Iresha, and Leif Ellingson. "A mathematical representation of protein binding sites using structural dispersion of atoms from principal axes for classification of binding ligands." PLOS ONE 16, no. 4 (April 8, 2021): e0244905. http://dx.doi.org/10.1371/journal.pone.0244905.

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Many researchers have studied the relationship between the biological functions of proteins and the structures of both their overall backbones of amino acids and their binding sites. A large amount of the work has focused on summarizing structural features of binding sites as scalar quantities, which can result in a great deal of information loss since the structures are three-dimensional. Additionally, a common way of comparing binding sites is via aligning their atoms, which is a computationally intensive procedure that substantially limits the types of analysis and modeling that can be done

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4

Ruan, Shuxiang, and Gary D. Stormo. "Inherent limitations of probabilistic models for protein-DNA binding specificity." PLOS Computational Biology 13, no. 7 (July 7, 2017): e1005638. http://dx.doi.org/10.1371/journal.pcbi.1005638.

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Sedaghat, Ahmad R., Arthur Sherman, and Michael J. Quon. "A mathematical model of metabolic insulin signaling pathways." American Journal of Physiology-Endocrinology and Metabolism 283, no. 5 (November 1, 2002): E1084—E1101. http://dx.doi.org/10.1152/ajpendo.00571.2001.

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We develop a mathematical model that explicitly represents many of the known signaling components mediating translocation of the insulin-responsive glucose transporter GLUT4 to gain insight into the complexities of metabolic insulin signaling pathways. A novel mechanistic model of postreceptor events including phosphorylation of insulin receptor substrate-1, activation of phosphatidylinositol 3-kinase, and subsequent activation of downstream kinases Akt and protein kinase C-ζ is coupled with previously validated subsystem models of insulin receptor binding, receptor recycling, and GLUT4 transl

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6

Kimchi, Ofer, Carl P. Goodrich, Alexis Courbet, Agnese I. Curatolo, Nicholas B. Woodall, David Baker, and Michael P. Brenner. "Self-assembly–based posttranslational protein oscillators." Science Advances 6, no. 51 (December 2020): eabc1939. http://dx.doi.org/10.1126/sciadv.abc1939.

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Recent advances in synthetic posttranslational protein circuits are substantially impacting the landscape of cellular engineering and offer several advantages compared to traditional gene circuits. However, engineering dynamic phenomena such as oscillations in protein-level circuits remains an outstanding challenge. Few examples of biological posttranslational oscillators are known, necessitating theoretical progress to determine realizable oscillators. We construct mathematical models for two posttranslational oscillators, using few components that interact only through reversible binding and

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7

Wang, Debby D., Haoran Xie, and Hong Yan. "Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity." Bioinformatics 37, no. 17 (February 27, 2021): 2570–79. http://dx.doi.org/10.1093/bioinformatics/btab132.

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Abstract Motivation Reliable predictive models of protein–ligand binding affinity are required in many areas of biomedical research. Accurate prediction based on current descriptors or molecular fingerprints (FPs) remains a challenge. We develop novel interaction FPs (IFPs) to encode protein–ligand interactions and use them to improve the prediction. Results Proteo-chemometrics IFPs (PrtCmm IFPs) formed by combining extended connectivity fingerprints (ECFPs) with the proteo-chemometrics concept. Combining PrtCmm IFPs with machine-learning models led to efficient scoring models, which were vali

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8

Conradi Smith, Gregory Douglas. "Allostery in oligomeric receptor models." Mathematical Medicine and Biology: A Journal of the IMA 37, no. 3 (December 10, 2019): 313–33. http://dx.doi.org/10.1093/imammb/dqz016.

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Abstract We show how equilibrium binding curves of receptor homodimers can be expressed as rational polynomial functions of the equilibrium binding curves of the constituent monomers, without approximation and without assuming independence of receptor monomers. Using a distinguished spanning tree construction for reduced graph powers, the method properly accounts for thermodynamic constraints and allosteric interactions between receptor monomers (i.e. conformational coupling). The method is completely general; it begins with an arbitrary undirected graph representing the topology of a monomer

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9

Jiang, Yao, Hui-Fang Liu, and Rong Liu. "Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions." PLOS Computational Biology 17, no. 4 (April 19, 2021): e1008951. http://dx.doi.org/10.1371/journal.pcbi.1008951.

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The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then develope

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10

Sohrabi-Jahromi, Salma, and Johannes Söding. "Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins." Bioinformatics 37, Supplement_1 (July 1, 2021): i308—i316. http://dx.doi.org/10.1093/bioinformatics/btab300.

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Abstract Motivation Understanding how proteins recognize their RNA targets is essential to elucidate regulatory processes in the cell. Many RNA-binding proteins (RBPs) form complexes or have multiple domains that allow them to bind to RNA in a multivalent, cooperative manner. They can thereby achieve higher specificity and affinity than proteins with a single RNA-binding domain. However, current approaches to de novo discovery of RNA binding motifs do not take multivalent binding into account. Results We present Bipartite Motif Finder (BMF), which is based on a thermodynamic model of RBPs with

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11

Freedman, Simon L., Cristian Suarez, Jonathan D. Winkelman, David R. Kovar, Gregory A. Voth, Aaron R. Dinner, and Glen M. Hocky. "Mechanical and kinetic factors drive sorting of F-actin cross-linkers on bundles." Proceedings of the National Academy of Sciences 116, no. 33 (July 25, 2019): 16192–97. http://dx.doi.org/10.1073/pnas.1820814116.

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In cells, actin-binding proteins (ABPs) sort to different regions to establish F-actin networks with diverse functions, including filopodia used for cell migration and contractile rings required for cell division. Recent experimental work uncovered a competition-based mechanism that may facilitate spatial localization of ABPs: binding of a short cross-linker protein to 2 actin filaments promotes the binding of other short cross-linkers and inhibits the binding of longer cross-linkers (and vice versa). We hypothesize this sorting arises because F-actin is semiflexible and cannot bend over short

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12

Cortes, Eliceo, José Mora, and Edgar Márquez. "Modelling the Anti-Methicillin-Resistant Staphylococcus Aureus (MRSA) Activity of Cannabinoids: A QSAR and Docking Study." Crystals 10, no. 8 (August 11, 2020): 692. http://dx.doi.org/10.3390/cryst10080692.

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Twenty-four cannabinoids active against MRSA SA1199B and XU212 were optimized at WB97XD/6-31G(d,p), and several molecular descriptors were obtained. Using a multiple linear regression method, several mathematical models with statistical significance were obtained. The robustness of the models was validated, employing the leave-one-out cross-validation and Y-scrambling methods. The entire data set was docked against penicillin-binding protein, iso-tyrosyl tRNA synthetase, and DNA gyrase. The most active cannabinoids had high affinity to penicillin-binding protein (PBP), whereas the least active

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13

Déchaud, H., H. Lejeune, M. Garoscio-Cholet, R. Mallein, and M. Pugeat. "Radioimmunoassay of testosterone not bound to sex-steroid-binding protein in plasma." Clinical Chemistry 35, no. 8 (August 1, 1989): 1609–14. http://dx.doi.org/10.1093/clinchem/35.8.1609.

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Abstract To measure the concentration of testosterone (T) that is not bound to sex-steroid-binding protein (SBP) in plasma, we quantified by radioimmunoassay the T in the supernates of plasma samples after precipitation with 50%-saturated ammonium sulfate. The concentrations of non-SBP-bound T. directly measured with this assay, correlated significantly (P less than 0.001) with those deduced from measurement of the percentage of non-SBP-bound T determined with [3H]T as tracer or from mathematical models according to the law of mass action. It also correlated significantly with the ratio of T t

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14

Yamada, Naomi, William K. M. Lai, Nina Farrell, B. Franklin Pugh, and Shaun Mahony. "Characterizing protein–DNA binding event subtypes in ChIP-exo data." Bioinformatics 35, no. 6 (August 28, 2018): 903–13. http://dx.doi.org/10.1093/bioinformatics/bty703.

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Abstract Motivation Regulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein–protein interactions with other regulators. Each recruitment mechanism may be associated with distinct motifs and may also result in distinct characteristic patterns in high-resolution protein–DNA binding assays. For example, the ChIP-exo protocol precisely characterizes protein–DNA crosslinking patterns by combining chromatin immunoprecipitation (ChIP) with 5′ → 3′ exonuclease digestion. Since different regulatory complexes will result in different protein–DNA crossli

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15

Ahmed, Asad, Bhavika Mam, and Ramanathan Sowdhamini. "DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity." Bioinformatics and Biology Insights 15 (January 2021): 117793222110303. http://dx.doi.org/10.1177/11779322211030364.

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Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the abili

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16

Michelson, Seth. "Multidrug Resistance and Its Reversal: Mathenatical Models." Journal of Theoretical Medicine 1, no. 2 (1997): 103–15. http://dx.doi.org/10.1080/10273669708833011.

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Classic multidrug resistance (MDR) is a phenomenon by which cells nonspecifically extrude noxious agents from the cutoplasm before lethal concentrations buils up. Some chemotherapeutically treated tumors exhibit these same dynamics. In tumor systems, the most common mechanism of facilitating MDR is the upregulation of the P-glycoprotein pump. This protein forms a transmembrance channel, and agter binding the chemotherapeutic agent and 2ATP molecules, forces the noxius agent through the channel. Hydrolysis of ATP to ADP provides the energy component of this reaction. General mathematical models

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17

Erban, Radek. "From molecular dynamics to Brownian dynamics." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 470, no. 2167 (July 8, 2014): 20140036. http://dx.doi.org/10.1098/rspa.2014.0036.

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Three coarse-grained molecular dynamics (MD) models are investigated with the aim of developing and analysing multi-scale methods which use MD simulations in parts of the computational domain and (less detailed) Brownian dynamics (BD) simulations in the remainder of the domain. The first MD model is formulated in one spatial dimension. It is based on elastic collisions of heavy molecules (e.g. proteins) with light point particles (e.g. water molecules). Two three-dimensional MD models are then investigated. The obtained results are applied to a simplified model of protein binding to receptors

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18

Garcea, Robert L. "Biologic Constraints on Modelling Virus Assembly." Computational and Mathematical Methods in Medicine 9, no. 3-4 (2008): 257–64. http://dx.doi.org/10.1080/17486700802168007.

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The mathematic modelling of icosahedral virus assembly has drawn increasing interest because of the symmetric geometry of the outer shell structures. Many models involve equilibrium expressions of subunit binding, with reversible subunit additions forming various intermediate structures. The underlying assumption is that a final lowest energy state drives the equilibrium toward assembly. In their simplest forms, these models have explained why high subunit protein concentrations and strong subunit association constants can result in kinetic traps forming off pathway partial and aberrant struct

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19

Zhang, Linda Yu, Emilio Gallicchio, Richard A. Friesner, and Ronald M. Levy. "Solvent models for protein-ligand binding: Comparison of implicit solvent poisson and surface generalized born models with explicit solvent simulations." Journal of Computational Chemistry 22, no. 6 (2001): 591–607. http://dx.doi.org/10.1002/jcc.1031.

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Rasmusson, R. L., J. W. Clark, W. R. Giles, E. F. Shibata, and D. L. Campbell. "A mathematical model of a bullfrog cardiac pacemaker cell." American Journal of Physiology-Heart and Circulatory Physiology 259, no. 2 (August 1, 1990): H352—H369. http://dx.doi.org/10.1152/ajpheart.1990.259.2.h352.

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Previous models of cardiac cellular electrophysiology have been based largely on voltage-clamp measurements obtained from multicellular preparations and often combined data from different regions of the heart and a variety of species. We have developed a model of cardiac pacemaking based on a comprehensive set of voltage-clamp measurements obtained from single cells isolated from one specific tissue type, the bullfrog sinus venosus (SV). Consequently, sarcolemmal current densities and kinetics are not influenced by secondary phenomena associated with multicellular preparations, allowing us to

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21

Cholewa-Waclaw, Justyna, Ruth Shah, Shaun Webb, Kashyap Chhatbar, Bernard Ramsahoye, Oliver Pusch, Miao Yu, Philip Greulich, Bartlomiej Waclaw, and Adrian P. Bird. "Quantitative modelling predicts the impact of DNA methylation on RNA polymerase II traffic." Proceedings of the National Academy of Sciences 116, no. 30 (July 9, 2019): 14995–5000. http://dx.doi.org/10.1073/pnas.1903549116.

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Patterns of gene expression are primarily determined by proteins that locally enhance or repress transcription. While many transcription factors target a restricted number of genes, others appear to modulate transcription levels globally. An example is MeCP2, an abundant methylated-DNA binding protein that is mutated in the neurological disorder Rett syndrome. Despite much research, the molecular mechanism by which MeCP2 regulates gene expression is not fully resolved. Here, we integrate quantitative, multidimensional experimental analysis and mathematical modeling to indicate that MeCP2 is a

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22

Lai, Hien T. T., Do Minh Ha, Duc Manh Nguyen, and Toan T. Nguyen. "Homology modeling of mouse NLRP3 NACHT protein domain and molecular dynamics simulation of its ATP binding properties." International Journal of Modern Physics C 31, no. 03 (January 8, 2020): 2050036. http://dx.doi.org/10.1142/s0129183120500369.

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Gout is an extremely painful form of inflammatory arthritis, caused by the formation of monosodium urate (MSU) crystals in the joints. MSU crystals are one of the triggers for the activation of nucleotide-binding domain (NOD)-like receptor protein 3 (NLRP3) inflammasome (NACHT, LRR and PYD domains-containing protein), which in turn induces caspase-1 activation and a nonspecific immune responses that cause inflammation. Further structural studies and ligand designs are needed to block the interaction of NLRP3 with MSU or allow the interaction without activating caspase-1. This would facilitate

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Zhang, Fuhao, Wenbo Shi, Jian Zhang, Min Zeng, Min Li, and Lukasz Kurgan. "PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection." Bioinformatics 36, Supplement_2 (December 2020): i735—i744. http://dx.doi.org/10.1093/bioinformatics/btaa806.

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Abstract Motivation Knowledge of protein-binding residues (PBRs) improves our understanding of protein−protein interactions, contributes to the prediction of protein functions and facilitates protein−protein docking calculations. While many sequence-based predictors of PBRs were published, they offer modest levels of predictive performance and most of them cross-predict residues that interact with other partners. One unexplored option to improve the predictive quality is to design consensus predictors that combine results produced by multiple methods. Results We empirically investigate predict

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Trott, L., M. Hafezparast, and A. Madzvamuse. "A mathematical understanding of how cytoplasmic dynein walks on microtubules." Royal Society Open Science 5, no. 8 (August 2018): 171568. http://dx.doi.org/10.1098/rsos.171568.

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Cytoplasmic dynein 1 (hereafter referred to simply as dynein) is a dimeric motor protein that walks and transports intracellular cargos towards the minus end of microtubules. In this article, we formulate, based on physical principles, a mechanical model to describe the stepping behaviour of cytoplasmic dynein walking on microtubules from the cell membrane towards the nucleus. Unlike previous studies on physical models of this nature, we base our formulation on the whole structure of dynein to include the temporal dynamics of the individual subunits such as the cargo (for example, an endosome,

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Romero-Durana, Miguel, Brian Jiménez-García, and Juan Fernández-Recio. "pyDockEneRes: per-residue decomposition of protein–protein docking energy." Bioinformatics 36, no. 7 (December 6, 2019): 2284–85. http://dx.doi.org/10.1093/bioinformatics/btz884.

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Abstract Motivation Protein–protein interactions are key to understand biological processes at the molecular level. As a complement to experimental characterization of protein interactions, computational docking methods have become useful tools for the structural and energetics modeling of protein–protein complexes. A key aspect of such algorithms is the use of scoring functions to evaluate the generated docking poses and try to identify the best models. When the scoring functions are based on energetic considerations, they can help not only to provide a reliable structural model for the compl

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Shi, Wentao, Jeffrey M. Lemoine, Abd-El-Monsif A. Shawky, Manali Singha, Limeng Pu, Shuangyan Yang, J. Ramanujam, and Michal Brylinski. "BionoiNet: ligand-binding site classification with off-the-shelf deep neural network." Bioinformatics 36, no. 10 (February 13, 2020): 3077–83. http://dx.doi.org/10.1093/bioinformatics/btaa094.

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Abstract Motivation Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with min

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Yan, Zichao, William L. Hamilton, and Mathieu Blanchette. "Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions." Bioinformatics 36, Supplement_1 (July 1, 2020): i276—i284. http://dx.doi.org/10.1093/bioinformatics/btaa456.

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Abstract Motivation RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting the sequence and structural traits of RNA that impact the binding specificity for different proteins. Yet our ability to predict these interactions in silico remains relatively poor. Results In this study, we introduce RPI-Net, a graph neural network approach for RNA-protein interaction prediction. RPI-Net learns and exploits a graph repre

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Zhang, Jian, Sina Ghadermarzi, and Lukasz Kurgan. "Prediction of protein-binding residues: dichotomy of sequence-based methods developed using structured complexes versus disordered proteins." Bioinformatics 36, no. 18 (June 17, 2020): 4729–38. http://dx.doi.org/10.1093/bioinformatics/btaa573.

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Abstract Motivation There are over 30 sequence-based predictors of the protein-binding residues (PBRs). They use either structure-annotated or disorder-annotated training datasets, potentially creating a dichotomy where the structure-/disorder-specific models may not be able to cross-over to accurately predict the other type. Moreover, the structure-trained predictors were shown to substantially cross-predict PBRs among residues that interact with non-protein partners (nucleic acids and small ligands). We address these issues by performing first-of-its-kind comparative study of a representativ

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SACHSE, F. B., K. G. GLÄNZEL, and G. SEEMANN. "MODELING OF PROTEIN INTERACTIONS INVOLVED IN CARDIAC TENSION DEVELOPMENT." International Journal of Bifurcation and Chaos 13, no. 12 (December 2003): 3561–78. http://dx.doi.org/10.1142/s0218127403008855.

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Modeling of protein interactions responsible for cardiac tension development can enhance the understanding of physiological and pathophysiological phenomena of the heart. Principal components of muscular tension development are the proteins actin, myosin, troponin and tropomyosin. The tension is produced by cross-bridge cycling of actin and myosin using adenosine triphosphate as energy source. The cross-bridge cycling is initiated by binding of intracellular calcium to troponin, resulting in configuration changes of tropomyosin. In this work a hybrid model of protein interactions in cardiac te

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Lu, Wei, Carlos Bueno, Nicholas P. Schafer, Joshua Moller, Shikai Jin, Xun Chen, Mingchen Chen, et al. "OpenAWSEM with Open3SPN2: A fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations." PLOS Computational Biology 17, no. 2 (February 12, 2021): e1008308. http://dx.doi.org/10.1371/journal.pcbi.1008308.

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We present OpenAWSEM and Open3SPN2, new cross-compatible implementations of coarse-grained models for protein (AWSEM) and DNA (3SPN2) molecular dynamics simulations within the OpenMM framework. These new implementations retain the chemical accuracy and intrinsic efficiency of the original models while adding GPU acceleration and the ease of forcefield modification provided by OpenMM’s Custom Forces software framework. By utilizing GPUs, we achieve around a 30-fold speedup in protein and protein-DNA simulations over the existing LAMMPS-based implementations running on a single CPU core. We show

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Santana, Charles A., Sabrina de A. Silveira, João P. A. Moraes, Sandro C. Izidoro, Raquel C. de Melo-Minardi, António J. M. Ribeiro, Jonathan D. Tyzack, Neera Borkakoti, and Janet M. Thornton. "GRaSP: a graph-based residue neighborhood strategy to predict binding sites." Bioinformatics 36, Supplement_2 (December 2020): i726—i734. http://dx.doi.org/10.1093/bioinformatics/btaa805.

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Abstract Motivation The discovery of protein–ligand-binding sites is a major step for elucidating protein function and for investigating new functional roles. Detecting protein–ligand-binding sites experimentally is time-consuming and expensive. Thus, a variety of in silico methods to detect and predict binding sites was proposed as they can be scalable, fast and present low cost. Results We proposed Graph-based Residue neighborhood Strategy to Predict binding sites (GRaSP), a novel residue centric and scalable method to predict ligand-binding site residues. It is based on a supervised learnin

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Rubinstein, Boris Y., Henry H. Mattingly, Alexander M. Berezhkovskii, and Stanislav Y. Shvartsman. "Long-term dynamics of multisite phosphorylation." Molecular Biology of the Cell 27, no. 14 (July 15, 2016): 2331–40. http://dx.doi.org/10.1091/mbc.e16-03-0137.

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Multisite phosphorylation cycles are ubiquitous in cell regulation systems and are studied at multiple levels of complexity, from molecules to organisms, with the ultimate goal of establishing predictive understanding of the effects of genetic and pharmacological perturbations of protein phosphorylation in vivo. Achieving this goal is essentially impossible without mathematical models, which provide a systematic framework for exploring dynamic interactions of multiple network components. Most of the models studied to date do not discriminate between the distinct partially phosphorylated forms

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Norris, Noele, Naomi M. Levine, Vicente I. Fernandez, and Roman Stocker. "Mechanistic model of nutrient uptake explains dichotomy between marine oligotrophic and copiotrophic bacteria." PLOS Computational Biology 17, no. 5 (May 19, 2021): e1009023. http://dx.doi.org/10.1371/journal.pcbi.1009023.

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Marine bacterial diversity is immense and believed to be driven in part by trade-offs in metabolic strategies. Here we consider heterotrophs that rely on organic carbon as an energy source and present a molecular-level model of cell metabolism that explains the dichotomy between copiotrophs—which dominate in carbon-rich environments—and oligotrophs—which dominate in carbon-poor environments—as the consequence of trade-offs between nutrient transport systems. While prototypical copiotrophs, like Vibrios, possess numerous phosphotransferase systems (PTS), prototypical oligotrophs, such as SAR11,

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Young, David J., Jun O. Liu, and Donald Small. "Combinatorial Approaches to Overcome Plasma Protein Inhibition of FLT3 Tyrosine Kinase Inhibitors." Blood 132, Supplement 1 (November 29, 2018): 1362. http://dx.doi.org/10.1182/blood-2018-99-118820.

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Abstract Background: The FMS-like tyrosine kinase 3 (FLT3) is the most frequently mutated gene in acute myeloid leukemia (AML) and also results in poor prognosis for adult and pediatric patients, and thus represents an attractive target for tyrosine kinase inhibitors (TKI). The activity of FLT3-targeted TKI is inhibited to varying extents by plasma protein binding. Staurosporine (STS)-derived TKI are inhibited almost exclusively by the plasma protein alpha-1 acid glycoprotein (AGP), an acute-phase reactant. We studied the impact of AGP binding on the other STS-derivatives and report the develo

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Mullins, R. Dyche, Walter F. Stafford, and Thomas D. Pollard. "Structure, Subunit Topology, and Actin-binding Activity of the Arp2/3 Complex from Acanthamoeba." Journal of Cell Biology 136, no. 2 (January 27, 1997): 331–43. http://dx.doi.org/10.1083/jcb.136.2.331.

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The Arp2/3 complex, first isolated from Acanthamoeba castellani by affinity chromatography on profilin, consists of seven polypeptides; two actinrelated proteins, Arp2 and Arp3; and five apparently novel proteins, p40, p35, p19, p18, and p14 (Machesky et al., 1994). The complex is homogeneous by hydrodynamic criteria with a Stokes' radius of 5.3 nm by gel filtration, sedimentation coefficient of 8.7 S, and molecular mass of 197 kD by analytical ultracentrifugation. The stoichiometry of the subunits is 1:1:1:1:1:1:1, indicating the purified complex contains one copy each of seven polypeptides.

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Tesei, Giulio, João M. Martins, Micha B. A. Kunze, Yong Wang, Ramon Crehuet, and Kresten Lindorff-Larsen. "DEER-PREdict: Software for efficient calculation of spin-labeling EPR and NMR data from conformational ensembles." PLOS Computational Biology 17, no. 1 (January 22, 2021): e1008551. http://dx.doi.org/10.1371/journal.pcbi.1008551.

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Owing to their plasticity, intrinsically disordered and multidomain proteins require descriptions based on multiple conformations, thus calling for techniques and analysis tools that are capable of dealing with conformational ensembles rather than a single protein structure. Here, we introduce DEER-PREdict, a software program to predict Double Electron-Electron Resonance distance distributions as well as Paramagnetic Relaxation Enhancement rates from ensembles of protein conformations. DEER-PREdict uses an established rotamer library approach to describe the paramagnetic probes which are bound

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González, Janneth, Angela Gálvez, Ludis Morales, George E. Barreto, Francisco Capani, Omar Sierra, and Yolima Torres. "Integrative Approach for Computationally Inferring Interactions between the Alpha and Beta Subunits of the Calcium-Activated Potassium Channel (BK): A Docking Study." Bioinformatics and Biology Insights 7 (January 2013): BBI.S10077. http://dx.doi.org/10.4137/bbi.s10077.

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Three-dimensional models of the alpha- and beta-1 subunits of the calcium-activated potassium channel (BK) were predicted by threading modeling. A recursive approach comprising of sequence alignment and model building based on three templates was used to build these models, with the refinement of non-conserved regions carried out using threading techniques. The complex formed by the subunits was studied by means of docking techniques, using 3D models of the two subunits, and an approach based on rigid-body structures. Structural effects of the complex were analyzed with respect to hydrogen-bon

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Vijayakrishnan, Swetha, Philip Callow, Margaret A. Nutley, Donna P. McGow, David Gilbert, Peter Kropholler, Alan Cooper, Olwyn Byron, and J. Gordon Lindsay. "Variation in the organization and subunit composition of the mammalian pyruvate dehydrogenase complex E2/E3BP core assembly." Biochemical Journal 437, no. 3 (July 13, 2011): 565–74. http://dx.doi.org/10.1042/bj20101784.

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Crucial to glucose homoeostasis in humans, the hPDC (human pyruvate dehydrogenase complex) is a massive molecular machine comprising multiple copies of three distinct enzymes (E1–E3) and an accessory subunit, E3BP (E3-binding protein). Its icosahedral E2/E3BP 60-meric ‘core’ provides the central structural and mechanistic framework ensuring favourable E1 and E3 positioning and enzyme co-operativity. Current core models indicate either a 48E2+12E3BP or a 40E2+20E3BP subunit composition. In the present study, we demonstrate clear differences in subunit content and organization between the recomb

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Bicknell, Brendan A., and Geoffrey J. Goodhill. "Emergence of ion channel modal gating from independent subunit kinetics." Proceedings of the National Academy of Sciences 113, no. 36 (August 22, 2016): E5288—E5297. http://dx.doi.org/10.1073/pnas.1604090113.

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Many ion channels exhibit a slow stochastic switching between distinct modes of gating activity. This feature of channel behavior has pronounced implications for the dynamics of ionic currents and the signaling pathways that they regulate. A canonical example is the inositol 1,4,5-trisphosphate receptor (IP3R) channel, whose regulation of intracellular Ca2+ concentration is essential for numerous cellular processes. However, the underlying biophysical mechanisms that give rise to modal gating in this and most other channels remain unknown. Although ion channels are composed of protein subunits

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Bernard, Samuel, Branka Čajavec, Laurent Pujo-Menjouet, Michael C. Mackey, and Hanspeter Herzel. "Modelling transcriptional feedback loops: the role of Gro/TLE1 in Hes1 oscillations." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 364, no. 1842 (March 21, 2006): 1155–70. http://dx.doi.org/10.1098/rsta.2006.1761.

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The transcriptional repressor Hes1, a basic helix-loop-helix family protein, periodically changes its expression in the presomitic mesoderm. Its periodic pattern of expression is retained in a number of cultured murine cell lines. In this paper, we introduce an extended mathematical model for Hes1 oscillatory expression that includes regulation of Hes1 transcription by Drosophila Groucho (Gro) or its vertebrate counterpart, the transducine-like enhancer of split/Groucho-related gene product 1 (TLE1). Gro/TLE1 is a necessary corepressor required by a number of DNA-binding transcriptional repres

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Jha, Amrita, and Neeru Adlakha. "Two-dimensional finite element model to study unsteady state Ca2+ diffusion in neuron involving ER LEAK and SERCA." International Journal of Biomathematics 08, no. 01 (January 2015): 1550002. http://dx.doi.org/10.1142/s1793524515500023.

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In this paper, finite element approach using two-dimensional unsteady state problem has been developed to study radial and angular calcium diffusion problem in neurons. Calcium is responsible messenger for transmitting information in communication process between neurons. The most important Ca 2+ binding proteins for the dynamics of Ca 2+ is itself buffer and other physiological parameters are located in Ca 2+ stores. In this study, the model incorporates the physiological parameters like diffusion coefficient, receptors, exogenous buffers etc. Appropriate boundary conditions have been framed

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Kapla, Jon, Ismael Rodríguez-Espigares, Flavio Ballante, Jana Selent, and Jens Carlsson. "Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?" PLOS Computational Biology 17, no. 5 (May 13, 2021): e1008936. http://dx.doi.org/10.1371/journal.pcbi.1008936.

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The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a c

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Asif, Maor, and Yaron Orenstein. "DeepSELEX: inferring DNA-binding preferences from HT-SELEX data using multi-class CNNs." Bioinformatics 36, Supplement_2 (December 2020): i634—i642. http://dx.doi.org/10.1093/bioinformatics/btaa789.

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Abstract Motivation Transcription factor (TF) DNA-binding is a central mechanism in gene regulation. Biologists would like to know where and when these factors bind DNA. Hence, they require accurate DNA-binding models to enable binding prediction to any DNA sequence. Recent technological advancements measure the binding of a single TF to thousands of DNA sequences. One of the prevailing techniques, high-throughput SELEX, measures protein–DNA binding by high-throughput sequencing over several cycles of enrichment. Unfortunately, current computational methods to infer the binding preferences fro

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Igashov, Ilia, Kliment Olechnovič, Maria Kadukova, Česlovas Venclovas, and Sergei Grudinin. "VoroCNN: deep convolutional neural network built on 3D Voronoi tessellation of protein structures." Bioinformatics 37, no. 16 (February 23, 2021): 2332–39. http://dx.doi.org/10.1093/bioinformatics/btab118.

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Abstract Motivation Effective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance. Results For the first time, we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D mol

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Brown, Aidan I., and Elena F. Koslover. "Design principles for the glycoprotein quality control pathway." PLOS Computational Biology 17, no. 2 (February 1, 2021): e1008654. http://dx.doi.org/10.1371/journal.pcbi.1008654.

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Newly-translated glycoproteins in the endoplasmic reticulum (ER) often undergo cycles of chaperone binding and release in order to assist in folding. Quality control is required to distinguish between proteins that have completed native folding, those that have yet to fold, and those that have misfolded. Using quantitative modeling, we explore how the design of the quality-control pathway modulates its efficiency. Our results show that an energy-consuming cyclic quality-control process, similar to the observed physiological system, outperforms alternative designs. The kinetic parameters that o

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Chen, Peng, Tong Shen, Youzhi Zhang, and Bing Wang. "A Sequence-segment Neighbor Encoding Schema for Protein Hotspot Residue Prediction." Current Bioinformatics 15, no. 5 (October 14, 2020): 445–54. http://dx.doi.org/10.2174/1574893615666200106115421.

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Background: Hotspots are those residues that contribute major free energy of binding in protein-protein interactions. Protein functions are frequently dependent on hotspot residues. At present, hotspot residues are always identified by Alanine scanning mutagenesis technology, which is costly, time-consuming and laborious. Objective: Therefore, more accurate and efficient methods have to be developed to identify protein hotspot residues. Methods: This paper proposed a novel encoding schema of sequence-segment neighbors and constructed a random forest-based model to identify hotspots in protein

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Liu, Yang, Xia-hui Ouyang, Zhi-Xiong Xiao, Le Zhang, and Yang Cao. "A Review on the Methods of Peptide-MHC Binding Prediction." Current Bioinformatics 15, no. 8 (January 1, 2021): 878–88. http://dx.doi.org/10.2174/1574893615999200429122801.

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Background: T lymphocyte achieves an immune response by recognizing antigen peptides (also known as T cell epitopes) through major histocompatibility complex (MHC) molecules. The immunogenicity of T cell epitopes depends on their source and stability in combination with MHC molecules. The binding of the peptide to MHC is the most selective step, so predicting the binding affinity of the peptide to MHC is the principal step in predicting T cell epitopes. The identification of epitopes is of great significance in the research of vaccine design and T cell immune response. Objective: The tradition

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Jiang, Hanlun, Fu Kit Sheong, Lizhe Zhu, Xin Gao, Julie Bernauer, and Xuhui Huang. "Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement." PLOS Computational Biology 11, no. 7 (July 16, 2015): e1004404. http://dx.doi.org/10.1371/journal.pcbi.1004404.

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Koide, Hiroki, Noriyuki Kodera, Shveta Bisht, Shoji Takada, and Tsuyoshi Terakawa. "Modeling of DNA binding to the condensin hinge domain using molecular dynamics simulations guided by atomic force microscopy." PLOS Computational Biology 17, no. 7 (July 30, 2021): e1009265. http://dx.doi.org/10.1371/journal.pcbi.1009265.

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The condensin protein complex compacts chromatin during mitosis using its DNA-loop extrusion activity. Previous studies proposed scrunching and loop-capture models as molecular mechanisms for the loop extrusion process, both of which assume the binding of double-strand (ds) DNA to the hinge domain formed at the interface of the condensin subunits Smc2 and Smc4. However, how the hinge domain contacts dsDNA has remained unknown. Here, we conducted atomic force microscopy imaging of the budding yeast condensin holo-complex and used this data as basis for coarse-grained molecular dynamics simulati

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WANG, ZHI-XIANG, and YONG DUAN. "DIRECT INTERACTION ENERGY: A COMPUTATIONAL QUANTITY FOR PARAMETERIZATION OF CONDENSED-PHASE FORCE FIELDS AND ITS APPLICATION TO HYDROGEN BONDING." Journal of Theoretical and Computational Chemistry 04, spec01 (January 2005): 689–705. http://dx.doi.org/10.1142/s0219633605001726.

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Using N-Methylacetamide (NMA) dimer and NMA–water as model complexes, the solvent effect on the protein inter- N – H ⋯ O =C and intra- N – H ⋯ OH 2, and C = O ⋯ H 2 O hydrogen bonding have been studied by the polarizable continuum model (PCM) ab initio calculations in the four media (vacuum, ether, nitromethane and water). In contrast to the empirical approaches, we suggested using the direction interaction energies (DE) to consider the solvent polarization, which can be derived from PCM ab initio calculations. The DEs of the model compounds in solvents are larger than their in vacuo binding e

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