Correlated mutations and residue contacts in proteins

  title={Correlated mutations and residue contacts in proteins},
  author={Ulrike G{\"o}bel and Chris Sander and Reinhard Schneider and Alfonso Valencia},
  journal={Proteins: Structure},
The maintenance of protein function and structure constrains the evolution of amino acid sequences. This fact can be exploited to interpret correlated mutations observed in a sequence family as an indication of probable physical contact in three dimensions. Here we present a simple and general method to analyze correlations in mutational behavior between different positions in a multiple sequence alignment. We then use these correlations to predict contact maps for each of 11 protein families… 

Prediction of protein residue contacts with a PDB-derived likelihood matrix.

Using empirical methods, derived from known protein structures, would provide useful predictive power for correlated mutation analysis and the new CMA method could supply restraints for predicting still undetermined structures.

Correlated mutations contain information about protein-protein interaction.

This work applies a method for detecting correlated changes in multiple sequence alignments to a set of interacting protein domains and shows that positions where changes occur in a correlated fashion in the two interacting molecules tend to be close to the protein-protein interfaces.

On the Upper Bound of the Prediction Accuracy of Residue Contacts in Proteins with Correlated Mutations: The Case Study of the Similarity Matrices

It is obtained that the upper limit to the accuracy achievable in the prediction of the protein residue contacts is independent of the optimized similarity matrix, suggesting that poor scoring may be due to the choice of the linear correlation function in evaluating correlated mutations.

Improvements in structural contact prediction: opportunities in prediction diculty and pairing preference of amino acids

It is demonstrated that precise structural contact prediction can be further improved by a combination of machine learning algorithms and amino acid characteristics.

Prediction of Structures and Interactions from Genome Information.

  • S. Miyazawa
  • Biology
    Advances in experimental medicine and biology
  • 2018
This work reviews statistical methods for extracting causative correlations and various approaches to describe protein structure, complex, and flexibility based on predicted contacts to improve contact prediction.

An introduction to protein contact prediction.

This chapter shows how evolutionary information contained in protein sequences and multiple sequence alignments can be used to predict protein structure, and the state-of-the-art predictors and their methodologies are reviewed.

Prediction of distant residue contacts with the use of evolutionary information

A novel correlated mutations analysis method that is significantly more accurate than previously reported CMA methods and based on physicochemical properties of residues (predictors) and not on substitution matrices, which results in reliable prediction of pairs of residues that are distant in protein sequence but proximal in its three dimensional tertiary structure.

Direct coupling analysis for protein contact prediction.

Direct Coupling Analysis has been shown to produce highly accurate estimates of amino-acid pairs that have direct reciprocal constraints in evolution and instructions and protocols on how to use the algorithmic implementations of DCA starting from data extraction to predicted-contact visualization in contact maps or representative protein structures are provided.

Coevolutionary Signals and Structure-Based Models for the Prediction of Protein Native Conformations.

This chapter introduces a general and efficient methodology to perform coevolutionary analysis on protein sequences and to use this information in combination with computational physical models to predict the native 3D conformation of functional polypeptides.



Can three-dimensional contacts in protein structures be predicted by analysis of correlated mutations?

A new experimental approach to protein structure determination is suggested in which selection of functional mutants after random mutagenesis and analysis of correlated mutations provide sufficient proximity constraints for calculation of the protein fold.

Accurate prediction of the stability and activity effects of site-directed mutagenesis on a protein core

T theoretical calculation of stabilization energies for 78 triple-site sequence variants of λ repressor characterized experimentally by Lim and Sauer are reported, correctly identifying two of the mutants to be more stable than the wild type.

Coordinated amino acid changes in homologous protein families.

By relaxing the criteria for residue selection, the method was adapted to cover a broader range of protein families and to study regions of the proteins having weaker structural constraints.

A geometrical constraint approach for reproducing the native backbone conformation of a protein

The basic assumption and effectiveness of the present method were compared with those of previous studies employing the geometrical constraint approach, and it has become clear that the specific, one‐dimensional information is more effective than nonspecific, two‐dimensional constraints, such as average interresidue distances between particular types of amino acids.

Protein structure and neutral theory of evolution.

Physical requirements for a functional globular protein are formulated and it is shown that many of these requirement do not involve strategical selection of amino acid sequences during biological evolution but are inherent also for typical random sequences.

Volume changes in protein evolution.

Additivity of mutant effects assessed by binomial mutagenesis.

  • L. GregoretR. Sauer
  • Biology
    Proceedings of the National Academy of Sciences of the United States of America
  • 1993
The effects of multiple substitutions are largely additive and are able to predict the activity class of the binomial mutants with 90% accuracy by using a model that simply sums penalty scores derived from the alanine substitution frequencies.