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Deep Analysis of Residue Constraints (DARC): identifying determinants of protein functional specificity.

ABSTRACT: Protein functional constraints are manifest as superfamily and functional-subgroup conserved residues, and as pairwise correlations. Deep Analysis of Residue Constraints (DARC) aids the visualization of these constraints, characterizes how they correlate with each other and with structure, and estimates statistical significance. This can identify determinants of protein functional specificity, as we illustrate for bacterial DNA clamp loader ATPases. These load ring-shaped sliding clamps onto DNA to keep polymerase attached during replication and contain one ?, three ?, and one ?' AAA+ subunits semi-circularly arranged in the order ?-?1-?2-?3-?'. Only ? is active, though both ? and ?' functionally influence an adjacent ? subunit. DARC identifies, as functionally-congruent features linking allosterically the ATP, DNA, and clamp binding sites: residues distinctive of ? and of ?/?' that mutually interact in trans, centered on the catalytic base; several ?/?'-residues and six ?/?'-covariant residue pairs within the DNA binding N-termini of helices ?2 and ?3; and ?/?'-residues associated with the ?2 C-terminus and the clamp-binding loop. Most notable is a trans-acting ?/?' hydroxyl group that 99% of other AAA+ proteins lack. Mutation of this hydroxyl to a methyl group impedes clamp binding and opening, DNA binding, and ATP hydrolysis-implying a remarkably clamp-loader-specific function.

SUBMITTER: Tondnevis F

PROVIDER: S-EPMC6997377 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Deep Analysis of Residue Constraints (DARC): identifying determinants of protein functional specificity.

Tondnevis Farzaneh F Dudenhausen Elizabeth E EE Miller Andrew M AM McKenna Robert R Altschul Stephen F SF Bloom Linda B LB Neuwald Andrew F AF

Scientific reports 20200203 1

Protein functional constraints are manifest as superfamily and functional-subgroup conserved residues, and as pairwise correlations. Deep Analysis of Residue Constraints (DARC) aids the visualization of these constraints, characterizes how they correlate with each other and with structure, and estimates statistical significance. This can identify determinants of protein functional specificity, as we illustrate for bacterial DNA clamp loader ATPases. These load ring-shaped sliding clamps onto DNA ...[more]

PMID: 32015389

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Project description:BackgroundThe ability to predict which pairs of amino acid residues in a protein are in contact with each other offers many advantages for various areas of research that focus on proteins. For example, contact prediction can be used to reduce the computational complexity of predicting the structure of proteins and even to help identify functionally important regions of proteins. These predictions are becoming especially important given the relatively low number of experimentally determined protein structures compared to the amount of available protein sequence data.ResultsHere we have developed and benchmarked a set of machine learning methods for performing residue-residue contact prediction, including random forests, direct-coupling analysis, support vector machines, and deep networks (stacked denoising autoencoders). These methods are able to predict contacting residue pairs given only the amino acid sequence of a protein. According to our own evaluations performed at a resolution of +/- two residues, the predictors we trained with the random forest algorithm were our top performing methods with average top 10 prediction accuracy scores of 85.13% (short range), 74.49% (medium range), and 54.49% (long range). Our ensemble models (stacked denoising autoencoders combined with support vector machines) were our best performing deep network predictors and achieved top 10 prediction accuracy scores of 75.51% (short range), 60.26% (medium range), and 43.85% (long range) using the same evaluation. These tests were blindly performed on targets from the CASP11 dataset; and the results suggested that our models achieved comparable performance to contact predictors developed by groups that participated in CASP11.ConclusionsDue to the challenging nature of contact prediction, it is beneficial to develop and benchmark a variety of different prediction methods. Our work has produced useful tools with a simple interface that can provide contact predictions to users without requiring a lengthy installation process. In addition to this, we have released our C++ implementation of the direct-coupling analysis method as a standalone software package. Both this tool and our RFcon web server are freely available to the public at http://dna.cs.miami.edu/RFcon /.

Dataset Information

Deep Analysis of Residue Constraints (DARC): identifying determinants of protein functional specificity.

Publications

Deep Analysis of Residue Constraints (DARC): identifying determinants of protein functional specificity.

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