Correlated mutations at gp120 positions 322 and 440: Implications for gp120 structure

2008 ◽  
Vol 71 (3) ◽  
pp. 1066-1070 ◽  
Author(s):  
Osnat Rosen ◽  
Avraham O. Samson ◽  
Jacob Anglister
Keyword(s):  
Correlated Mutations ◽  
Gp120 Structure

Protein Inter-Residue Contacts Prediction: Methods, Performances and Applications

2019 ◽  
Vol 14 (3) ◽  
pp. 178-189 ◽  
Author(s):  
Xiaoyang Jing ◽  
Qimin Dong ◽  
Ruqian Lu ◽  
Qiwen Dong
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Tertiary Structure ◽  
Prediction Methods ◽  
Learning Methods ◽  
Typical Application ◽  
Machine Learning Methods ◽  
Residue Contacts ◽  
Fusion Methods ◽  
Correlated Mutations

Background:Protein inter-residue contacts prediction play an important role in the field of protein structure and function research. As a low-dimensional representation of protein tertiary structure, protein inter-residue contacts could greatly help de novo protein structure prediction methods to reduce the conformational search space. Over the past two decades, various methods have been developed for protein inter-residue contacts prediction.Objective:We provide a comprehensive and systematic review of protein inter-residue contacts prediction methods.Results:Protein inter-residue contacts prediction methods are roughly classified into five categories: correlated mutations methods, machine-learning methods, fusion methods, templatebased methods and 3D model-based methods. In this paper, firstly we describe the common definition of protein inter-residue contacts and show the typical application of protein inter-residue contacts. Then, we present a comprehensive review of the three main categories for protein interresidue contacts prediction: correlated mutations methods, machine-learning methods and fusion methods. Besides, we analyze the constraints for each category. Furthermore, we compare several representative methods on the CASP11 dataset and discuss performances of these methods in detail.Conclusion:Correlated mutations methods achieve better performances for long-range contacts, while the machine-learning method performs well for short-range contacts. Fusion methods could take advantage of the machine-learning and correlated mutations methods. Employing more effective fusion strategy could be helpful to further improve the performances of fusion methods.

scholarly journals Prediction of contact maps with neural networks and correlated mutations

2001 ◽  
Vol 14 (11) ◽  
pp. 835-843 ◽  
Author(s):  
Piero Fariselli ◽  
Osvaldo Olmea ◽  
Alfonso Valencia ◽  
Rita Casadio
Keyword(s):  
Neural Networks ◽  
Contact Maps ◽  
Correlated Mutations

The visualCMAT: A web-server to select and interpret correlated mutations/co-evolving residues in protein families

2018 ◽  
Vol 16 (02) ◽  
pp. 1840005 ◽  
Author(s):  
Dmitry Suplatov ◽  
Yana Sharapova ◽  
Daria Timonina ◽  
Kirill Kopylov ◽  
Vytas Švedas
Keyword(s):  
Rational Design ◽  
Visual Analysis ◽  
Structural Information ◽  
Protein Structures ◽  
Web Server ◽  
Physical Contact ◽  
Protein Families ◽  
Sequence Alignments ◽  
Homologous Proteins ◽  
Correlated Mutations

The visualCMAT web-server was designed to assist experimental research in the fields of protein/enzyme biochemistry, protein engineering, and drug discovery by providing an intuitive and easy-to-use interface to the analysis of correlated mutations/co-evolving residues. Sequence and structural information describing homologous proteins are used to predict correlated substitutions by the Mutual information-based CMAT approach, classify them into spatially close co-evolving pairs, which either form a direct physical contact or interact with the same ligand (e.g. a substrate or a crystallographic water molecule), and long-range correlations, annotate and rank binding sites on the protein surface by the presence of statistically significant co-evolving positions. The results of the visualCMAT are organized for a convenient visual analysis and can be downloaded to a local computer as a content-rich all-in-one PyMol session file with multiple layers of annotation corresponding to bioinformatic, statistical and structural analyses of the predicted co-evolution, or further studied online using the built-in interactive analysis tools. The online interactivity is implemented in HTML5 and therefore neither plugins nor Java are required. The visualCMAT web-server is integrated with the Mustguseal web-server capable of constructing large structure-guided sequence alignments of protein families and superfamilies using all available information about their structures and sequences in public databases. The visualCMAT web-server can be used to understand the relationship between structure and function in proteins, implemented at selecting hotspots and compensatory mutations for rational design and directed evolution experiments to produce novel enzymes with improved properties, and employed at studying the mechanism of selective ligand’s binding and allosteric communication between topologically independent sites in protein structures. The web-server is freely available at https://biokinet.belozersky.msu.ru/visualcmat and there are no login requirements.

scholarly journals I-COMS: Interprotein-COrrelated Mutations Server

2015 ◽  
Vol 43 (W1) ◽  
pp. W320-W325 ◽  
Author(s):  
Javier Iserte ◽  
Franco L. Simonetti ◽  
Diego J. Zea ◽  
Elin Teppa ◽  
Cristina Marino-Buslje
Keyword(s):  
Correlated Mutations

scholarly journals Correlated Mutations: A Hallmark of Phenotypic Amino Acid Substitutions

2010 ◽  
Vol 6 (9) ◽  
pp. e1000923 ◽  
Author(s):  
Andreas Kowarsch ◽  
Angelika Fuchs ◽  
Dmitrij Frishman ◽  
Philipp Pagel
Keyword(s):  
Amino Acid ◽  
Amino Acid Substitutions ◽  
Correlated Mutations
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