Finite sample sizes and phylogeny do not ACCOUNT FOR THE MUTUAL INFORMATION OBSERVED FOR MOST SITE-PAIRS IN MULTIPLE SEQUENCE ALIGNMENT

Mutual information (MI) is a measure frequently used to find co-evolving sites in protein families. However, factors unrelated to protein structure and function, in particular sampling variance in amino acid counts and complex evolutionary relationships among sequences, contribute to ML Understanding the contribution of these components is essential for isolating the MI associated with structural or functional co-evolution. To date, the contributions of these factors to mutual information have not been fully elucidated. We find that stochastic variations in amino acid counts and shared phylogeny each contribute substantially to measured MI. Nonetheless, the mutual information observed in real-world protein families is consistently higher than the expected contribution of these two factors. In contrast, when using synthetic data with realistic substitution rates and phylogenies, but without structural or functional constraints, the observed levels of MI match those expected due to stochastic and phylogenetic background. Our results suggest that either low levels of co-evolution are ubiquitous across positions in protein families, or some unknown factor exists beyond the currently hypothesized components of intra-protein mutual information: sampling variance, phylogenetics and structural/functional co-evolution

Accurate contact predictions using covariation techniques and machine learning.

Here we present the results of residue-residue contact predictions achieved in CASP11 by the CONSIP2 server, which is based around our MetaPSICOV contact prediction method. On a set of 40 target domains with a median family size of around 40 effective sequences, our server achieved an average top-L/5 long-range contact precision of 27%. MetaPSICOV method bases on a combination of classical contact prediction features, enhanced with three distinct covariation methods embedded in a two-stage neural network predictor. Some unique features of our approach are (1) the tuning between the classical and covariation features depending on the depth of the input alignment and (2) a hybrid approach to generate deepest possible multiple-sequence alignments by combining jackHMMer and HHblits. We discuss the CONSIP2 pipeline, our results and show that where the method underperformed, the major factor was relying on a fixed set of parameters for the initial sequence alignments and not attempting to perform domain splitting as a preprocessing step. Proteins 2015. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc

Correlation Analysis for Protein Evolutionary Family Based on Amino Acid Position Mutations and Application in PDZ Domain

BACKGROUND: It has been widely recognized that the mutations at specific directions are caused by the functional constraints in protein family and the directional mutations at certain positions control the evolutionary direction of the protein family. The mutations at different positions, even distantly separated, are mutually coupled and form an evolutionary network. Finding the controlling mutative positions and the mutative network among residues are firstly important for protein rational design and enzyme engineering. METHODOLOGY: A computational approach, namely amino acid position conservation-mutation correlation analysis (CMCA), is developed to predict mutually mutative positions and find the evolutionary network in protein family. The amino acid position mutative function, which is the foundational equation of CMCA measuring the mutation of a residue at a position, is derived from the MSA (multiple structure alignment) database of protein evolutionary family. Then the position conservation correlation matrix and position mutation correlation matrix is constructed from the amino acid position mutative equation. Unlike traditional SCA (statistical coupling analysis) approach, which is based on the statistical analysis of position conservations, the CMCA focuses on the correlation analysis of position mutations. CONCLUSIONS: As an example the CMCA approach is used to study the PDZ domain of protein family, and the results well illustrate the distantly allosteric mechanism in PDZ protein family, and find the functional mutative network among residues. We expect that the CMCA approach may find applications in protein engineering study, and suggest new strategy to improve bioactivities and physicochemical properties of enzymes