Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era

133

145

A challenge in molecular biology is to distinguish the key subset of residues that allow two-component signaling (TCS) proteins to recognize their correct signaling partner such that they can transiently bind and transfer signal, i.e., phosphoryl group. Detailed knowledge of this information would allow one to search sequence space for mutations that can be used to systematically tune the signal transmission between TCS partners as well as potentially encode a TCS protein to preferentially transfer signals to a nonpartner. Motivated by the notion that this detailed information is found in sequence data, we explore the sequence coevolution between signaling partners to better understand how mutations can positively or negatively alter their ability to transfer signal. Using direct coupling analysis for determining evolutionarily conserved protein-protein interactions, we apply a metric called the direct information score to quantify mutational changes in the interaction between TCS proteins and demonstrate that it accurately correlates with experimental mutagenesis studies probing the mutational change in measured in vitro phosphotransfer. Furthermore, by subtracting from our metric an appropriate null model corresponding to generic, conserved features in TCS signaling pairs, we can isolate the determinants that give rise to interaction specificity and recognition, which are variable among different TCS partners. Our methodology forms a potential framework for the rational design of TCS systems by allowing one to quickly search sequence space for mutations or even entirely new sequences that can increase or decrease our metric, as a proxy for increasing or decreasing phosphotransfer ability between TCS proteins. statistical inference | signal transduction | information theory | covariation | protein recognition

Section: Significancementioning

confidence: 99%

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

Cheng

Morcos

Levine

et al. 2014

133

145

“…contacts between bacterial signal transduction proteins, to help to assemble protein complexes (15,16) and shed light on interaction specificity (17,18). The applicability of DCA and related coevolutionary approaches (19,20) to protein−protein interactions far beyond the signaling system has been recently established (21,22).…”

mentioning

confidence: 99%

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

Gueudré

Baldassi

Zamparo

et al. 2016

127

173

Understanding protein−protein interactions is central to our understanding of almost all complex biological processes. Computational tools exploiting rapidly growing genomic databases to characterize protein−protein interactions are urgently needed. Such methods should connect multiple scales from evolutionary conserved interactions between families of homologous proteins, over the identification of specifically interacting proteins in the case of multiple paralogs inside a species, down to the prediction of residues being in physical contact across interaction interfaces. Statistical inference methods detecting residue−residue coevolution have recently triggered considerable progress in using sequence data for quaternary protein structure prediction; they require, however, large joint alignments of homologous protein pairs known to interact. The generation of such alignments is a complex computational task on its own; application of coevolutionary modeling has, in turn, been restricted to proteins without paralogs, or to bacterial systems with the corresponding coding genes being colocalized in operons. Here we show that the direct coupling analysis of residue coevolution can be extended to connect the different scales, and simultaneously to match interacting paralogs, to identify interprotein residue−residue contacts and to discriminate interacting from noninteracting families in a multiprotein system. Our results extend the potential applications of coevolutionary analysis far beyond cases treatable so far.A lmost all biological processes depend on interacting proteins.Understanding protein−protein interactions is therefore key to our understanding of complex biological systems. In this context, at least two questions are of interest: First, the question "who with whom," i.e., which proteins interact; this concerns the networks connecting specific proteins inside one organism, but alsoin the context of this article-the evolutionary perspective of protein−protein interactions, which are conserved across different species. Their coevolution is at the basis of many modern computational techniques for characterizing protein−protein interactions. The second question is the question "how" proteins interact with each other, in particular, which residues are involved in the interaction interfaces, and which residues are in contact across the interfaces. Such knowledge may provide important mechanistic insight into questions related to interaction specificity or competitive interaction with partially shared interfaces.The experimental identification of protein−protein interactions is an arduous task (for reviews, cf. refs. 1 and 2): High-throughput techniques that aim to identify protein−protein interactions in vivo or in vitro are well documented and include large-scale yeast two-hybrid assays and protein affinity mass spectrometry assays. Such large-scale efforts have revealed useful information but are hampered by high false positive and false negative error rates. Structural approaches based on protein cocrystalli...

“…This capability is especially consequential for multisegment TERMs, because it means that segments distant in sequence can be predicted to be adjacent in space-a major challenge in structure prediction. Significant strides toward addressing this challenge have recently emerged from prediction of contacts based on evolutionary covariation (70,71), but, importantly, such prediction is only applicable to native proteins, and especially those with available deep MSAs. On the other hand, TERM-based mining appears to be quite applicable to de novo proteins and requires no homology (Fig.…”

Section: Discussionmentioning

confidence: 99%

Tertiary alphabet for the observable protein structural universe

Mackenzie

Zhou

Grigoryan

2016

Here, we systematically decompose the known protein structural universe into its basic elements, which we dub tertiary structural motifs (TERMs). A TERM is a compact backbone fragment that captures the secondary, tertiary, and quaternary environments around a given residue, comprising one or more disjoint segments (three on average). We seek the set of universal TERMs that capture all structure in the Protein Data Bank (PDB), finding remarkable degeneracy. Only ∼600 TERMs are sufficient to describe 50% of the PDB at sub-Angstrom resolution. However, more rare geometries also exist, and the overall structural coverage grows logarithmically with the number of TERMs. We go on to show that universal TERMs provide an effective mapping between sequence and structure. We demonstrate that TERM-based statistics alone are sufficient to recapitulate close-to-native sequences given either NMR or X-ray backbones. Furthermore, sequence variability predicted from TERM data agrees closely with evolutionary variation. Finally, locations of TERMs in protein chains can be predicted from sequence alone based on sequence signatures emergent from TERM instances in the PDB. For multisegment motifs, this method identifies spatially adjacent fragments that are not contiguous in sequence-a major bottleneck in structure prediction. Although all TERMs recur in diverse proteins, some appear specialized for certain functions, such as interface formation, metal coordination, or even water binding. Structural biology has benefited greatly from previously observed degeneracies in structure. The decomposition of the known structural universe into a finite set of compact TERMs offers exciting opportunities toward better understanding, design, and prediction of protein structure.tertiary motif | structural degeneracy | protein structural universe | sequence-structure relationships | structural modularity I n this work, we aim to decompose the protein structure space into its basic elements as a way of understanding its design principles and describing its limits. Reductionist representations of protein structure have been of long-standing interest (1), with many studies having shown degeneracy at various structural levels (2-5). Features ranging from backbone or side-chain dihedral angles (6, 7) to domains and folds (8, 9) have been classified, and structural basins of attraction have been found in select motifs (10-17), offering a glimpse of a modular space with frequently repeating elements. The reason behind this modularity appears to be a combination of evolutionary history and the fundamental physics of structure. In particular, degeneracy at the level of domains, folds, and functional modules is likely strongly influenced by evolution, whereby such elements recur in different proteins often because of a common ancestor (18,19). On the other hand, statistics of more detailed structural features are better explained from the thermodynamic perspective. For example, observed Ramachandran backbone dihedral angle preferences are largely determined...