Structure-based algorithms for protein-protein interaction prediction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 109-124).Protein-protein interactions (PPIs) play a central role in all biological processes. Akin to the complete sequencing of genomes, complete descriptions of interactomes is a fundamental step towards a deeper understanding of biological processes, and has a vast potential to impact systems biology, genomics, molecular biology and therapeutics. PPIs are critical in maintenance of cellular integrity, metabolism, transcription/ translation, and cell-cell communication. This thesis develops new methods that significantly advance our efforts at structure- based approaches to predict PPIs and boost confidence in emerging high-throughput (HTP) data. The aims of this thesis are, 1) to utilize physicochemical properties of protein interfaces to better predict the putative interacting regions and increase coverage of PPI prediction, 2) increase confidence in HTP datasets by identifying likely experimental errors, and 3) provide residue-level information that gives us insights into structure-function relationships in PPIs. Taken together, these methods will vastly expand our understanding of macromolecular networks. In this thesis, I introduce two computational approaches for structure-based proteinprotein interaction prediction: iWRAP and Coev2Net. iWRAP is an interface threading approach that utilizes biophysical properties specific to protein interfaces to improve PPI prediction. Unlike previous structure-based approaches that use single structures to make predictions, iWRAP first builds profiles that characterize the hydrophobic, electrostatic and structural properties specific to protein interfaces from multiple interface alignments. Compatibility with these profiles is used to predict the putative interface region between the two proteins. In addition to improved interface prediction, iWRAP provides better accuracy and close to 50% increase in coverage on genome-scale PPI prediction tasks. As an application, we effectively combine iWRAP with genomic data to identify novel cancer related genes involved in chromatin remodeling, nucleosome organization and ribonuclear complex assembly - processes known to be critical in cancer. Coev2Net addresses some of the limitations of iWRAP, and provides techniques to increase coverage and accuracy even further. Unlike earlier sequence and structure profiles, Coev2Net explicitly models long-distance correlations at protein interfaces. By formulating interface co-evolution as a high-dimensional sampling problem, we enrich sequence/structure profiles with artificial interacting homologus sequences for families which do not have known multiple interacting homologs. We build a spanning-tree based graphical model induced by the simulated sequences as our interface profile. Cross-validation results indicate that this approach is as good as previous methods at PPI prediction. We show that Coev2Net's predictions correlate with experimental observations and experimentally validate some of the high-confidence predictions. Furthermore, we demonstrate how analysis of the predicted interfaces together with human genomic variation data can help us understand the role of these mutations in disease and normal cells.by Raghavendra Hosur.Ph.D

iWRAP: An Interface Threading Approach with Application to Prediction of Cancer-Related Protein–Protein Interactions

Current homology modeling methods for predicting protein–protein interactions (PPIs) have difficulty in the “twilight zone” (< 40%) of sequence identities. Threading methods extend coverage further into the twilight zone by aligning primary sequences for a pair of proteins to a best-fit template complex to predict an entire three-dimensional structure. We introduce a threading approach, iWRAP, which focuses only on the protein interface. Our approach combines a novel linear programming formulation for interface alignment with a boosting classifier for interaction prediction. We demonstrate its efficacy on SCOPPI, a classification of PPIs in the Protein Databank, and on the entire yeast genome. iWRAP provides significantly improved prediction of PPIs and their interfaces in stringent cross-validation on SCOPPI. Furthermore, by combining our predictions with a full-complex threader, we achieve a coverage of 13% for the yeast PPIs, which is close to a 50% increase over previous methods at a higher sensitivity. As an application, we effectively combine iWRAP with genomic data to identify novel cancer-related genes involved in chromatin remodeling, nucleosome organization, and ribonuclear complex assembly. iWRAP is available at http://iwrap.csail.mit.edu.National Institutes of Health (U.S.) (Grant 1R01GM081871

Cx3cr1-deficient microglia exhibit a premature aging transcriptome.

CX3CR1, one of the highest expressed genes in microglia in mice and humans, is implicated in numerous microglial functions. However, the molecular mechanisms underlying Cx3cr1 signaling are not well understood. Here, we analyzed transcriptomes of Cx3cr1-deficient microglia under varying conditions by RNA-sequencing (RNA-seq). In 2-mo-old mice, Cx3cr1 deletion resulted in the down-regulation of a subset of immune-related genes, without substantial epigenetic changes in markers of active chromatin. Surprisingly, Cx3cr1-deficient microglia from young mice exhibited a transcriptome consistent with that of aged Cx3cr1-sufficient animals, suggesting a premature aging transcriptomic signature. Immunohistochemical analysis of microglia in young and aged mice revealed that loss of Cx3cr1 modulates microglial morphology in a comparable fashion. Our results suggest that CX3CR1 may regulate microglial function in part by modulating the expression levels of a subset of inflammatory genes during chronological aging, making Cx3cr1-deficient mice useful for studying aged microglia

Structure-based prediction reveals capping motifs that inhibit β-helix aggregation

The parallel beta-helix is a geometrically regular fold commonly found in the proteomes of bacteria, viruses, fungi, archaea, and some vertebrates. beta-helix structure has been observed in monomeric units of some aggregated amyloid fibers. In contrast, soluble beta-helices, both right- and left-handed, are usually “capped” on each end by one or more secondary structures. Here, an in-depth classification of the diverse range of beta-helix cap structures reveals subtle commonalities in structural components and in interactions with the beta-helix core. Based on these uncovered commonalities, a toolkit of automated predictors was developed for the two distinct types of cap structures. In vitro deletion of the toolkit-predicted C-terminal cap from the pertactin beta-helix resulted in increased aggregation and the formation of soluble oligomeric species. These results suggest that beta-helix cap motifs can prevent specific, beta-sheet-mediated oligomeric interactions, similar to those observed in amyloid formation.National Institutes of Health (U.S.) (NIH grant U54-LM008748)National Institutes of Health (U.S.) (NIH grant R01-GM25874)National Institutes of Health (U.S.) (NIH grant R01GM081871

Coev2Net: a computational framework for boosting confidence in high-throughput protein-protein interaction datasets

Improving the quality and coverage of the protein interactome is of tantamount importance for biomedical research, particularly given the various sources of uncertainty in high-throughput techniques. We introduce a structure-based framework, Coev2Net, for computing a single confidence score that addresses both false-positive and false-negative rates. Coev2Net is easily applied to thousands of binary protein interactions and has superior predictive performance over existing methods. We experimentally validate selected high-confidence predictions in the human MAPK network and show that predicted interfaces are enriched for cancer-related or damaging SNPs.National Institutes of Health (U.S.) (Grant R01GM081871

CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

BackgroundThe Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.ResultsPerformance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.ConclusionsResults show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead