Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

Abstract

Accurate and large-scale prediction of protein-protein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of 'hub' nodes that distribute and integrate signals to and from up to tens of different interaction partners.

Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis

“…A random matching between SK and RR inside species would make, on average, one correct prediction per species; that is, only a fraction of 712/8,998 = 7.9% of all matched SK/RR pairs would be correct. Earlier approaches to match SK and RR have used Bayesian residue networks (23) or aligned protein similarity networks (31); although they improve substantially over random matchings, their accuracy remains inferior to the algorithms presented here.…”

“…Coevolution-based procedures to analyze PPI have extensively used colocalization (22)(23)(24)(25). A natural question is, what fraction of the known bacterial interactome comes from colocalized genes?…”

“…Colocalization is used extensively in the construction of joint MSA for covariance analysis (22)(23)(24)(25). However, the case of multiple paralogs with noncolocalized genes has remained out of reach for coevolutionary analysis, despite its enormous relevance: Out of the 4,499 Pfam-29 (26) protein families with more than 500 sequences, 3,221 have, on average, more than two paralogs per species, and 1,378 families even more than five paralogs.…”

Toward rationally redesigning bacterial two-component signaling systems using coevolutionary information

“…TCS signaling partners are often adjacent to one another on the genome, e.g., cognate pairs from the same operon, a number of studies (11,(13)(14)(15)(16)(17)(18)(19)(20) have applied statistical methods to collections of cognate pairs to identify the evolutionarily conserved interactions between HK and RR signaling partners from their multiplesequence alignments (MSA). These studies extend upon early work using statistical methods to infer protein-protein interactions (21,22) from coevolutionary data.…”

Determinants of specificity in two-component signal transduction

“…(1) Developing methods to predict interaction partners. Some algorithms have been developed [47,48], but making reliable and accurate predictions, particularly for orphan signaling proteins, remains a significant challenge. (2) Adapting the ability to rewire twocomponent pathways for synthetic biology efforts.…”