Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on three datasets of 579 proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively. Further, our contact-assisted models have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction. Finally, in recent blind CAMEO benchmark our method successfully folded 5 test proteins with a novel fold

Bayesian statistical approach for protein residue-residue contact prediction

Despite continuous efforts in automating experimental structure determination and systematic target selection in structural genomics projects, the gap between the number of known amino acid sequences and solved 3D structures for proteins is constantly widening. While DNA sequencing technologies are advancing at an extraordinary pace, thereby constantly increasing throughput while at the same time reducing costs, protein structure determination is still labour intensive, time-consuming and expensive. This trend illustrates the essential importance of complementary computational approaches in order to bridge the so-called sequence-structure gap. About half of the protein families lack structural annotation and therefore are not amenable to techniques that infer protein structure from homologs. These protein families can be addressed by de novo structure prediction approaches that in practice are often limited by the immense computational costs required to search the conformational space for the lowest-energy conformation. Improved predictions of contacts between amino acid residues have been demonstrated to sufficiently constrain the overall protein fold and thereby extend the applicability of de novo methods to larger proteins. Residue-residue contact prediction is based on the idea that selection pressure on protein structure and function can lead to compensatory mutations between spatially close residues. This leaves an echo of correlation signatures that can be traced down from the evolutionary record. Despite the success of contact prediction methods, there are several challenges. The most evident limitation lies in the requirement of deep alignments, which excludes the majority of protein families without associated structural information that are the focus for contact guided de novo structure prediction. The heuristics applied by current contact prediction methods pose another challenge, since they omit available coevolutionary information. This work presents two different approaches for addressing the limitations of contact prediction methods. Instead of inferring evolutionary couplings by maximizing the pseudo-likelihood, I maximize the full likelihood of the statistical model for protein sequence families. This approach performed with comparable precision up to minor improvements over the pseudo-likelihood methods for protein families with few homologous sequences. A Bayesian statistical approach has been developed that provides posterior probability estimates for residue-residue contacts and eradicates the use of heuristics. The full information of coevolutionary signatures is exploited by explicitly modelling the distribution of statistical couplings that reflects the nature of residue-residue interactions. Surprisingly, the posterior probabilities do not directly translate into more precise predictions than obtained by pseudo-likelihood methods combined with prior knowledge. However, the Bayesian framework offers a statistically clean and theoretically solid treatment for the contact prediction problem. This flexible and transparent framework provides a convenient starting point for further developments, such as integrating more complex prior knowledge. The model can also easily be extended towards the Derivation of probability estimates for residue-residue distances to enhance the precision of predicted structures

DISCRETIZED GEOMETRIC APPROACHES TO THE ANALYSIS OF PROTEIN STRUCTURES

Proteins play crucial roles in a variety of biological processes. While we know that their amino acid sequence determines their structure, which in turn determines their function, we do not know why particular sequences fold into particular structures. My work focuses on discretized geometric descriptions of protein structure—conceptualizing native structure space as composed of mostly discrete, geometrically defined fragments—to better understand the patterns underlying why particular sequence elements correspond to particular structure elements. This discretized geometric approach is applied to multiple levels of protein structure, from conceptualizing contacts between residues as interactions between discrete structural elements to treating protein structures as an assembly of discrete fragments. My earlier work focused on better understanding inter-residue contacts and estimating their energies statistically. By scoring structures with energies derived from a stricter notion of contact, I show that native protein structures can be identified out of a set of decoy structures more often than when using energies derived from traditional definitions of contact and how this has implications for the evaluation of predictions that rely on structurally defined contacts for validation. Demonstrating how useful simple geometric descriptors of structure can be, I then show that these energies identify native structures on par with well-validated, detailed, atomistic energy functions. Moving to a higher level of structure, in my later work I demonstrate that discretized, geometrically defined structural fragments make good objects for the interactive assembly of protein backbones and present a software application which lets users do so. Finally, I use these fragments to generate structure-conditioned statistical energies, generalizing the classic idea of contact energies by incorporating specific structural context, enabling these energies to reflect the interaction geometries they come from. These structure-conditioned energies contain more information about native sequence preferences, correlate more highly with experimentally determined energies, and show that pairwise sequence preferences are tightly coupled to their structural context. Considered jointly, these projects highlight the degree to which protein structures and the interactions they comprise can be understood as geometric elements coming together in finely tuned ways

Information Theory in Molecular Evolution: From Models to Structures and Dynamics

This Special Issue collects novel contributions from scientists in the interdisciplinary field of biomolecular evolution. Works listed here use information theoretical concepts as a core but are tightly integrated with the study of molecular processes. Applications include the analysis of phylogenetic signals to elucidate biomolecular structure and function, the study and quantification of structural dynamics and allostery, as well as models of molecular interaction specificity inspired by evolutionary cues

Application of coevolution-based methods and deep learning for structure prediction of protein complexes

The three-dimensional structures of proteins play a critical role in determining their biological functions and interactions. Experimental determination of protein and protein complex structures can be expensive and difficult. Computational prediction of protein and protein complex structures has therefore been an open challenge for decades. Recent advances in computational structure prediction techniques have resulted in increasingly accurate protein structure predictions. These techniques include methods that leverage information about coevolving residues to predict residue interactions and that apply deep learning techniques to enable better prediction of residue contacts and protein structures. Prior to the work outlined in this thesis, coevolution-based methods and deep learning had been shown to improve the prediction of single protein domains or single protein chains. Most proteins in living organisms do not function on their own but interact with other proteins either through transient interactions or by forming stable protein complexes. Knowledge of protein complex structures can be useful for biological and disease research, drug discovery and protein engineering. Unfortunately, a large number of protein complexes do not have experimental structures or close homolog structures that can be used as templates. In this thesis, methods previously developed and applied to the de novo prediction of single protein domains or protein monomer chains were modified and leveraged for the prediction of protein heterodimer and homodimer complexes. A number of coevolution-based tools and deep learning methods are explored for the purpose of predicting inter-chain and intra-chain residue contacts in protein dimers. These contacts are combined with existing protein docking methods to explore the prediction of homodimers and heterodimers. Overall, the work in this thesis demonstrates the promise of leveraging coevolution and deep-learning for the prediction of protein complexes, shows improvements in protein complex prediction tasks achieved using coevolution based methods and deep learning methods, and demonstrates remaining challenges in protein complex prediction

A new ensemble coevolution system for detecting HIV-1 protein coevolution

Background: A key challenge in the field of HIV-1 protein evolution is the identification of coevolving amino acids at the molecular level. In the past decades, many sequence-based methods have been designed to detect position-specific coevolution within and between different proteins. However, an ensemble coevolution system that integrates different methods to improve the detection of HIV-1 protein coevolution has not been developed. Results: We integrated 27 sequence-based prediction methods published between 2004 and 2013 into an ensemble coevolution system. This system allowed combinations of different sequence-based methods for coevolution predictions. Using HIV-1 protein structures and experimental data, we evaluated the performance of individual and combined sequence-based methods in the prediction of HIV-1 intra- and inter-protein coevolution. We showed that sequence-based methods clustered according to their methodology, and a combination of four methods outperformed any of the 27 individual methods. This four-method combination estimated that HIV-1 intra-protein coevolving positions were mainly located in functional domains and physically contacted with each other in the protein tertiary structures. In the analysis of HIV-1 inter-protein coevolving positions between Gag and protease, protease drug resistance positions near the active site mostly coevolved with Gag cleavage positions (V128, S373-T375, A431, F448-P453) and Gag C-terminal positions (S489-Q500) under selective pressure of protease inhibitors. Conclusions: This study presents a new ensemble coevolution system which detects position-specific coevolution using combinations of 27 different sequence-based methods. Our findings highlight key coevolving residues within HIV-1 structural proteins and between Gag and protease, shedding light on HIV-1 intra- and inter-protein coevolution. Reviewers: This article was reviewed by Dr. Zoltán Gáspári. © Li et al