Relevant bibliographies by topics / Ramachandran map- Tertiary structure of protein

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Academic literature on the topic 'Ramachandran map- Tertiary structure of protein'

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Published: 6 September 2023

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Journal articles on the topic "Ramachandran map- Tertiary structure of protein"

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Walther, Dirk, and Fred E. Cohen. "Conformational attractors on the Ramachandran map." Acta Crystallographica Section D Biological Crystallography 55, no. 2 (February 1, 1999): 506–17. http://dx.doi.org/10.1107/s0907444998013353.

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Frequency distributions of protein backbone dihedral angles φ and ψ have been analyzed systematically for their apparent correlation with various crystallographic parameters, including the resolution at which the protein structures had been determined, the R factor and the free R factor, and the results have been displayed in novel differential Ramachandran maps. With improved sensitivity compared with conventionally derived heuristic Ramachandran maps, such differential maps automatically reveal conformational `attractors' to which φ/ψ distributions converge as the crystallographic resolution improves, as well as conformations tied specifically to low-resolution structures. In particular, backbone angular combinations associated with residues in α-helical conformation show a pronounced consolidation with substantially narrowed φ/ψ distributions at higher (better) resolution. Convergence to distinct conformational attractors was also observed for all other secondary-structural types and random-coil conformations. Similar resolution-dependent φ/ψ evolutions were obtained for different crystallographic refinement packages, documenting the absence of any significant artificial biases in the refinement programs investigated here. A comparison of differential Ramachandran maps derived for the R factor and the free R factor as independent parameters proved the better suitability of the free R factor for structure-quality assessment. The resolution-based differential Ramachandran map is available as a reference for comparison with actual protein structural data under WebMol, a Java-based structure viewing and analysis program (http://www.cmpharm.ucsf.edu/cgi-bin/webmol.pl).
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SZABADKA, ZOLTÁN, RAFAEL ÖRDÖG, and VINCE GROLMUSZ. "THE RAMACHANDRAN MAP OF MORE THAN 6,500 PERFECT POLYPEPTIDE CHAINS." Biophysical Reviews and Letters 02, no. 03n04 (October 2007): 267–71. http://dx.doi.org/10.1142/s1793048007000519.

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The Protein Data Bank (PDB) is the most important depository of protein structural information, containing more than 45,000 deposited entries today. Because of its inhomogeneous structure, its fully automated processing is almost impossible. In a previous work, we cleaned and re-structured the entries in the Protein Data Bank, and from the result we have built the RS-PDB database. Using the RS-PDB database, we draw a Ramachandran-plot from 6,593 "perfect" polypeptide chains found in the PDB, containing 1,192,689 residues. This is a more than tenfold increase in the size of data analyzed before this work. The density of the data points makes it possible to draw a logarithmic heat map enhanced Ramachandran map, showing the fine inner structure of the right-handed α-helix region.
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Zaman, Ahmed Bin, and Amarda Shehu. "Building maps of protein structure spaces in template-free protein structure prediction." Journal of Bioinformatics and Computational Biology 17, no. 06 (December 2019): 1940013. http://dx.doi.org/10.1142/s0219720019400134.

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An important goal in template-free protein structure prediction is how to control the quality of computed tertiary structures of a target amino-acid sequence. Despite great advances in algorithmic research, given the size, dimensionality, and inherent characteristics of the protein structure space, this task remains exceptionally challenging. It is current practice to aim to generate as many structures as can be afforded so as to increase the likelihood that some of them will reside near the sought but unknown biologically-active/native structure. When operating within a given computational budget, this is impractical and uninformed by any metrics of interest. In this paper, we propose instead to equip algorithms that generate tertiary structures, also known as decoy generation algorithms, with memory of the protein structure space that they explore. Specifically, we propose an evolving, granularity-controllable map of the protein structure space that makes use of low-dimensional representations of protein structures. Evaluations on diverse target sequences that include recent hard CASP targets show that drastic reductions in storage can be made without sacrificing decoy quality. The presented results make the case that integrating a map of the protein structure space is a promising mechanism to enhance decoy generation algorithms in template-free protein structure prediction.
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Javed, Ambreen, Gulshan Ara Trali, Hassan Burair Abbas, and Alia Sadiq. "IN SILICO CHARACTERIZATION OF HUMAN INTERFERON ALPHA/BETA RECEPTOR 2 (ISOFORM A, B AND C) PROTEIN." PAFMJ 71, no. 6 (December 31, 2021): 2091–94. http://dx.doi.org/10.51253/pafmj.v71i6.6571.

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Objective: To predict the tertiary structure of human interferon alpha/beta receptor 2 protein. Study Design: Structure prediction by using bio informatics tools. Place and Duration of Study: Department of Biochemistry, Swat Medical College (STMC), Saidu Shareef, Swat, Pakistan, from Aug 2019 to Dec 2019. Methodology: All protein sequences of human interferon alpha/beta receptor 2 (isoforma, b and c) (IFNAR-2) were retrieved through the BLAST search (The Basic Local Alignment Search Tool) from available databases ‘NCBI’ (National Centre for Biotechnology Information) and ‘Uni Prot KB’ (The Universal Protein Resource). Sequence alignment was conducted by using Clustal Omega, to get the consensus sequence for IFNAR-2 protein. Consensus protein sequence of human IFNAR-2 was used for the prediction of the three-dimensional structure by employing Swiss-Model Server. Moreover, subcellular localization analysis was also performed by using CELLO2GO program. Results: Structural model of human IFNAR-2 protein was predicted and evaluated by Ramachandran dimension. Cellular localization of tertiary topological domains of the predicted models were revealed probability of localization of IFNAR-2 protein (isoform a, b & c) is highest in the plasma membrane due to the presence of the transmembrane alpha helical regions. Conclusion: This study predicted the tertiary structural dimensions of human IFNAR-2 protein, including the specific topological domains that contribute towards the subcellular compartmentalization and functional characteristics.
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Malagón Bernal, Rafael Eduardo, Manuel Alejandro Fernández Navas, and Orlando Emilio Acevedo Sarmiento. "Modelo molecular teórico del receptor serotoninérgico 5HT2A acoplado a proteína G." Universitas Scientiarum 17, no. 2 (June 1, 2012): 119. http://dx.doi.org/10.11144/javeriana.sc17-2.tmmo.

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<strong>Objective</strong> Build a theoretical molecular model of the tertiary structure of the Homo sapiens 5HT2A receptor from experimentally obtained structures as templates. <strong>Materials</strong> <strong>and methods</strong> In the construction of the theoretical model we considered the protocol established by Ballesteros and Weinstein for the construction of the G-protein coupled receptor, by the alignment of the amino acid sequence, hydrophobicity profiles, refinement of loops by spatial restrictions and energy minimization with the force field OPLS_2005. <strong>Results</strong> The resulting model was validated by the Ramachandran plot with 91.7% of amino acids within the limits set for angles phi and psi and a RMSD of 0.95 Å with respect to bovine rhodopsin. <strong>Conclusions</strong> We obtained a validated theoretical model useful in studies of ligand-receptor docking.<br /><strong>Key words</strong>: G protein receptor, hydrophobicity profile, Ramachandran plot, orthosteric site, molecular modelling.
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Aslam, Shakira, Hafiz Muzzammel Rehman, Muhammad Zeeshan Sarwar, Ajaz Ahmad, Nadeem Ahmed, Muhammad Imran Amirzada, Hafiz Muhammad Rehman, Humaira Yasmin, Tariq Nadeem, and Hamid Bashir. "Computational Modeling, High-Level Soluble Expression and In Vitro Cytotoxicity Assessment of Recombinant Pseudomonas aeruginosa Azurin: A Promising Anti-Cancer Therapeutic Candidate." Pharmaceutics 15, no. 7 (June 26, 2023): 1825. http://dx.doi.org/10.3390/pharmaceutics15071825.

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Azurin is a natural protein produced by Pseudomonas aeruginosa that exhibits potential anti-tumor, anti-HIV, and anti-parasitic properties. The current study aimed to investigate the potential of azurin protein against breast cancer using both in silico and in vitro analyses. The amino acid sequence of Azurin was used to predict its secondary and tertiary structures, along with its physicochemical properties, using online software. The resulting structure was validated and confirmed using Ramachandran plots and ERRAT2. The mature azurin protein comprises 128 amino acids, and the top-ranked structure obtained from I-TASSER was shown to have a molecular weight of 14 kDa and a quality factor of 100% by ERRAT2, with 87.4% of residues in the favored region of the Ramachandran plot. Docking and simulation studies of azurin protein were conducted using HDOCK and Desmond servers, respectively. The resulting analysis revealed that Azurin docked against p53 and EphB2 receptors demonstrated maximum binding affinity, indicating its potential to cause apoptosis. The recombinant azurin gene was successfully cloned and expressed in a BL21 (DE3) strain using a pET20b expression vector under the control of the pelB ladder, followed by IPTG induction. The azurin protein was purified to high levels using affinity chromatography, yielding 70 mg/L. In vitro cytotoxicity assay was performed using MCF-7 cells, revealing the significant cytotoxicity of the azurin protein to be 105 µg/mL. These findings highlight the potential of azurin protein as an anticancer drug candidate.
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CHEON, MOOKYUNG, MUYOUNG HEO, IKSOO CHANG, and CHOONGRAK KIM. "CLASSIFICATIONS OF AMINO ACIDS IN PROTEINS BY THE SELF-ORGANIZING MAP." International Journal of Modern Physics C 16, no. 10 (October 2005): 1609–16. http://dx.doi.org/10.1142/s0129183105008175.

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We present the clustering properties of amino acids, which are building blocks of proteins, according to their physico-chemical characters. To classify the 20 kinds of amino acids, we employ a Self-Organizing Map (SOM) analysis for the Miyazawa-Jernigan (MJ) pairwise-contact matrix, the Environment-dependent One-body energy Parameters (EOP) and the one-body energy parameters incorporating the Ramachandran angle information (EOPR) over the EOP in proteins. We provide the new result of the SOM clustering for amino acids based on the EOPR and compare that with those from the MJ and the EOP matrix. All three kinds of energy parameters capture the leading role played by the hydrophobicity and the hydrophilicity of amino acids in protein folding. Our SOM analysis generally illustrates that both the EOP and the EOPR can provide the collective clustering of amino acids by the side chain characteristics and the secondary structure information. However, EOP is better at classifying amino acids according to their side chain characteristics whereas EOPR is better with secondary structure. We show that the EOP and the EOPR matrix manifests more detailed physico-chemical classification of amino acids than those from the MJ matrix, which does not contain a local environmental information of amino acids in the protein structures.
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Adegoke, Afeez Babatunde. "Molecular Dynamic (MD) Simulation and Modeling the Bio-molecular Structure of Human UDP glucose -6-dehydrogenase Isoform 1 (hUGDH) Related to Prostate Cancer." BASRA JOURNAL OF SCIENCE 38, no. 3 (August 1, 2020): 448–66. http://dx.doi.org/10.29072/basjs.202036.

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Computational methods were used to investigate both the physical and chemical properties of UDP-glucose 6-dehydrogenase (hUGDH). Secondary structure analysis of the query model was done using the Self-Optimized Prediction method With Alignment (SOPMA), the secondary structure predictions comprise of 40.69% Alpha helixes (Hh), 17.61% Extended strand (Ee), 7.69% Beta turn (Tt) and 34.01% of Random coil (Cc) with aliphatic index of 90.00 and instability index of 33.26 which classify the protein model to be thermally stable irrespective of it environment. Comparative modeling was used to predict a reliable tertiary structure for hUGDH and the obtained 3-dimensional model was validated using DOPE score profile, Ramachandran plot, and the QMEAN Z-score. The DOPE score profile shows a high similarity between the model and the template as little or no disparity was found in the profile patterns. Ramachandran plot of the model also shows that 92.5% of the amino acid residues were found at the most favored regions which make it stereo-chemically stable. The QMEAN z-score of UDP-glucose 6-dehydrogenase was predicted to be -0.15. The superimposed structure of the model and the template also gave RMSD of 0.125. All this shows that the predicted model is of good quality. An RMSD and Rg run via molecular dynamics (MD) simulation equally shows that the protein model attained stability at around 10ns. Protein – Protein interaction (PPI) network was also generated for the model with a high confidence score from UDP-glucuronic acid decarboxylase 1 (UXS1) when interacted with the other twenty proteins. In addition, the docking studies of the model and 3PRJ receptors with two prostate cancer drugs i.e. Apalutamide and Darolutamide gave similar binding affinity ranging between 6.0kcal/mol – 8.0kcal/mol for the most favored binding of the two drugs. Hence, the model can serve as a molecular target for designing new inhibitors for prostate cancer
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Saikat, Abu Saim Mohammad, Rabiul Islam, Shahriar Mahmud, Md Abu Sayeed Imran, Mohammad Shah Alam, Mahmudul Hasan Masud, and Md Ekhlas Uddin. "Structural and Functional Annotation of Uncharacterized Protein NCGM946K2_146 of Mycobacterium Tuberculosis: An In-Silico Approach." Proceedings 66, no. 1 (December 30, 2020): 13. http://dx.doi.org/10.3390/proceedings2020066013.

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The human pathogen Mycobacterium tuberculosis (MTB) is indeed one of the renowned, important, longtime infectious diseases, tuberculosis (TB). Interestingly, MTB infection has become one of the world’s leading causes of human death. In trehalose synthase, the protein NCGM 946K2 146 found in MTB has an important role. For carbohydrate transport and metabolism, trehalose synthase is required. The protein is not clarified yet, though. In this research, an in silico approach was, therefore, formulated for functional and structural documentation of the uncharacterized protein NCGM946K2_146.Three distinct servers, including Modeller, Phyre2, and Swiss Model, were used to evaluate the predicted tertiary structure. The top materials are selected using structural evaluations conducted with the analysis of Ramachandran Plot, Swiss-Model Interactive Workplace, ProSA-web, Verify 3D, and Z scores. This analysis aimed to uncover the value of the NCGM946K2_146 protein of MTB. This research will, therefore, improve our pathogenesis awareness and give us a chance to target the protein compound.
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Fahim, Ammad, Zaira Rehman, Muhammad Faraz Bhatti, Amjad Ali, Nasar Virk, Amir Rashid, and Rehan Zafar Paracha. "Structural insights and characterization of human Npas4 protein." PeerJ 6 (June 14, 2018): e4978. http://dx.doi.org/10.7717/peerj.4978.

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Npas4 is an activity dependent transcription factor which is responsible for gearing the expression of target genes involved in neuro-transmission. Despite the importance of Npas4 in many neuronal diseases, the tertiary structure of Npas4 protein along with its physico-chemical properties is limited. In the current study, first we perfomed the phylogenetic analysis of Npas4 and determined the content of hydrophobic, flexible and order-disorder promoting amino acids. The protein binding regions, post-translational modifications and crystallization propensity of Npas4 were predicted through different in-silico methods. The three dimensional model of Npas4 was predicted through LOMET, SPARSKS-X, I-Tasser, RaptorX, MUSTER and Pyhre and the best model was selected on the basis of Ramachandran plot, PROSA, and Qmean scores. The best model was then subjected to further refinement though MODREFINER. Finally the interacting partners of Npas4 were identified through STRING database. The phylogenetic analysis showed the human Npas4 gene to be closely related to other primates such as chimpanzees, monkey, gibbon. The physiochemical properties of Npas4 showed that it is an intrinsically disordered protein with N-terminal ordered region. The post-translational modification analyses indicated absence of acetylation and mannosylation sites. Three potential phosphorylation sites (S108, T130 and T136) were found in PAS A domain whilst a single phosphorylation site (S273) was present in PAS B domain. The predicted tertiary structure of Npas4 showed that bHLH domain and PAS domain possess tertiary structures while the rest of the protein exhibited disorder property. Protein-protein interaction analysis revealed NPas4 interaction with various proteins which are mainly involved in nuclear trafficking of proteins to cytoplasm, activity regulated gene transcription and neurodevelopmental disorders. Moreover the analysis also highlighted the direct relation to proteins involved in promoting neuronal survival, plasticity and cAMP responsive element binding protein proteins. The current study helps in understanding the physicochemical properties and reveals the neuro-modulatory role of Npas4 in crucial pathways involved in neuronal survival and neural signalling hemostasis.
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Dissertations / Theses on the topic "Ramachandran map- Tertiary structure of protein"

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Gibbs, Nicholas. "Ab initio protein tertiary structure prediction using restricted ramachandran geometries and physio-chemical potentials." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340353.

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DasGupta, Debarati. "Compuatational studies on tertiary structure prediction of small protiens and energetics of folding." Thesis, 2018. http://localhost:8080/iit/handle/2074/7627.

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Book chapters on the topic "Ramachandran map- Tertiary structure of protein"

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Adebiyi, Marion Olubunmi, and Ibidun Christiana Obagbuwa. "Homology Modeling and Binding Site Analysis of SARS-CoV-2 (COVID-19) Main Protease 3D Structure." In Advanced Bioinspiration Methods for Healthcare Standards, Policies, and Reform, 79–96. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5656-9.ch004.

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The severe acute respiratory syndrome coronavirus 2 (SAR-Cov-2) caused the coronavirus (COVID-19) pandemic. The global concern is the discovery of a new target drug for the total cure. Recently, some research showed that a few COVID-19 enzymes may have been contemplated to be potential drug targets, but not much is known about its structural biology. This research investigates the 3-D structure of protease SAR-CoV-2. The tertiary structure was determined by homology modeling. The Swiss-Model workspace and the basic local alignment search tool (BLAST) were employed for modeling, and the resulted model was validated with programs that include PROCHECK, Verify3D, and QMEAN to ascertain reliability. To establish the structures that fitted, HHBlits software was employed. To verify the structure quality, a Ramachandran plot was exploited. The binding site was determined using CASTp and DeepSite algorithms, which showed that this protein can be exploited as a prospective pharmaceutical target. Albeit further experimentation is required as a COVID-19 virus vaccine-design/target-drug.
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