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Protein structure prediction, pleiotropic patterns, and more: the PLOS Comp Biol October Issue

Here are our highlights from October’s PLOS Computational Biology.

Proteins execute many functions in the cell, and these biological functions are strongly linked to their three-dimensional structure. By constructing a probabilistic model for sequence variability, Erik Aurell and colleagues present an advance in how to predict protein structure in three-dimensional space. In the paper “Improving Contact Prediction along Three Dimensions”, the authors highlight that it is possible to improve along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have so far been the focus of the field.

Rendering of complete dendritic morphologies for 2770 generated granule cells from the center 20 µm of the model dentate gyrus. Image credit: Calvin James Schneider
Rendering of complete dendritic morphologies for 2770 generated granule cells from the center 20 µm of the model dentate gyrus. Image credit: Calvin James Schneider

Pleiotropy refers to a phenomenon in which a single genetic locus affects two or more phenotypic traits. The study of pleiotropy is useful in gene function discovery and in the study of the evolution of a gene. In “Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery”, Jose Seoane and colleagues present a new methodology, based on Canonical Correlation Analysis, for multiple association testing with high dimensional datasets. In applying the methodology to a genotype dataset and a set of cardiovascular related phenotypes, the authors discovered a new association between gene NRG1 and phenotypes related with left ventricular hypertrophy. The methodology can also be used to find pleiotropic patterns or multiple associations in other omics datasets.

Genome-scale metabolic models provide a powerful means of harnessing information from genomes to deepen biological insights. However, manually constructing accurate metabolic networks is a difficult task, and computational algorithms that rely on network topology-based approaches can result in solutions that are inconsistent with existing genomic data. Nathan Price and colleagues have developed an algorithm that directly incorporates genomic evidence into the decision-making process for gap-filling reactions. This algorithm both maximizes the consistency of gap-filled reactions with available genomic data and identifies candidate genes for gap-filled reactions.

Many protein-protein interactions (PPIs) are compelling targets for drug discovery. Disrupting the interaction between two large proteins requires forming a high affinity binding site that can bind both peptides and non-peptide drug-like compounds. Through a comparison of ligand-free and ligand-bound structures, Sandor Vajda and colleagues examine the mechanism of binding site formation in the interface region of proteins that are PPI disruption targets. The measures used for structure comparison are based on binding hot spots, regions that are major contributors to the binding free energy. The results provide insight on the origin of sites that can bind small molecules in protein-protein interfaces.

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