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Modular Organization of Residue-Level Contacts Shapes the Selection Pressure on Individual Amino Acid Sites of Ribosomal Proteins.

ABSTRACT: Understanding the molecular evolution of macromolecular complexes in the light of their structure, assembly, and stability is of central importance. Here, we address how the modular organization of native molecular contacts shapes the selection pressure on individual residue sites of ribosomal complexes. The bacterial ribosomal complex is represented as a residue contact network where nodes represent amino acid/nucleotide residues and edges represent their van der Waals interactions. We find statistically overrepresented native amino acid-nucleotide contacts (OaantC, one amino acid contacts one or multiple nucleotides, internucleotide contacts are disregarded). Contact number is defined as the number of nucleotides contacted. Involvement of individual amino acids in OaantCs with smaller contact numbers is more random, whereas only a few amino acids significantly contribute to OaantCs with higher contact numbers. An investigation of structure, stability, and assembly of bacterial ribosome depicts the involvement of these OaantCs in diverse biophysical interactions stabilizing the complex, including high-affinity protein-RNA contacts, interprotein cooperativity, intersubunit bridge, packing of multiple ribosomal RNA domains, etc. Amino acid-nucleotide constituents of OaantCs with higher contact numbers are generally associated with significantly slower substitution rates compared with that of OaantCs with smaller contact numbers. This evolutionary rate heterogeneity emerges from the strong purifying selection pressure that conserves the respective amino acid physicochemical properties relevant to the stabilizing interaction with OaantC nucleotides. An analysis of relative molecular orientations of OaantC residues and their interaction energetics provides the biophysical ground of purifying selection conserving OaantC amino acid physicochemical properties.

SUBMITTER: Mallik S

PROVIDER: S-EPMC5388290 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Modular Organization of Residue-Level Contacts Shapes the Selection Pressure on Individual Amino Acid Sites of Ribosomal Proteins.

Mallik Saurav S Kundu Sudip S

Genome biology and evolution 20170401 4

Understanding the molecular evolution of macromolecular complexes in the light of their structure, assembly, and stability is of central importance. Here, we address how the modular organization of native molecular contacts shapes the selection pressure on individual residue sites of ribosomal complexes. The bacterial ribosomal complex is represented as a residue contact network where nodes represent amino acid/nucleotide residues and edges represent their van der Waals interactions. We find sta ...[more]

PMID: 28338825

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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

Modular Organization of Residue-Level Contacts Shapes the Selection Pressure on Individual Amino Acid Sites of Ribosomal Proteins.

Publications

Modular Organization of Residue-Level Contacts Shapes the Selection Pressure on Individual Amino Acid Sites of Ribosomal Proteins.

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