Working at the interface between the physics and biology, we develop physical models and computational approaches for modelling structures of biomolecules and for predicting their functional characteristics. We verify our models and predictions in collaboration with experimentalists. Below we highlight two areas of research pursued in the group: evolution-inspired design of protein function and protein allostery. Other research topic in the group include, and are not limited to, biophysics of chromatin and epigenetic regulation, and molecular mechanisms of adaptation.
On the basis of our studies of the emergence and evolution of contemporary proteins and their functions [1-4], we draw parallels between events of the natural protein evolution and approaches in protein engineering and design illustrated in Figure 1. We argue that while ancestral reconstruction can provide a correct starting point for directed evolution-based efforts in protein engineering, de novo design should be based on the understanding of events that took place during emergence and early evolution of protein folds and functions . As a result, we suggested the fragment-based design of protein function  on the basis of evolution-inspired elementary functional loops (EFLs, [1,4]), descendants of the first ring-like functional peptides that originated modern proteins. We propose to use comprehensive “descriptors of EFLs”  that can be instrumental in de novo design efforts, complementing obtained scaffolds with functional signatures and flexibility necessary for their functions.
Recent advances in the allostery studies
Our group makes constant progress in the field of protein allostery, where we are among world leaders (see recent review  in Structure that contains report from CECAM Workshop “Computational approaches to investigating allostery” including INB’s invited talk “Towards allosteric control of protein activity”). A recent journal cover illustration (Figure 2) featured our review “On the perturbation nature of allostery: sites, mutations, and signal modulation” , where we explain why the perturbation nature of allosteric signalling serves as a foundation for experimental and theoretical studies of allosteric mechanisms with implications to the design of allosteric drugs and to the analysis of allosteric effects of mutations [10, 12-14].
Recent developments of the Structure-Based Statistical Mechanical Model of Allostery (SBSMMA, ), including Allosteric Signalling Maps (ASMs, [10,13]), allowed us to tackle the problem of allosteric effects of mutations [13,14]. To this end, we started from two questions: (i) Is there an allosteric component in the action of disease-causing nsSNPs? and (ii) Can some protein residues work as “latent allosteric triggers”, which may affect widely-defined function of the protein by allosteric modulation on corresponding sites (binding, catalytic, post-translational modification etc.)? Based on the observation that in addition to known pathological nsSNPs many other residues in the protein can originate similar allosteric modulation upon mutations, we proposed a notion of allosteric polymorphism, according to which mutations at a number of protein positions may allosterically modulate its function (the notion is illustrated in Figure 3). By the analogy with the function of so-called latent drivers in cancerogenesis, allosteric polymorphism hints at the potential presence of “latent allosteric triggers” that might impair protein function upon certain mutations.
We proposed a new computational method for exploring chromatin structural organization based on the Markov State Modelling of Hi-C data. In this approach, we interpret the Hi-C frequencies of chromatin interactions in terms of pairwise contact energies, obtaining a corresponding energy landscape that represents the structure and interactions in chromatin. The ruggedness of this landscape is explored by the random walk of a travelling probe, which is formalized in the framework of a Markov State Model. The multilevel energy landscape induces metastability in the Markov process, revealing the hierarchy of chromatin structural organization. Structural partitions determined by the basins in the energy landscape can be naturally obtained at different levels of hierarchy without any preliminary assumptions. Figure 3 shows an example of the analysis of human chromosome 17. Effective interactions between partitions are evaluated, providing a blueprint of individual chromosomes’ and the whole-genome’s organization and functional interactions, which can be further substantiated by mapping information on gene expression regulators and different epigenetic factors.
Work in Progress and Future Research Directions
On the basis of the recently described concept of the descriptor of the elementary function , we are currently developing a computational framework for deriving the descriptor and for using it in the engineering and design of the protein function [1-4]. The allostery project [6-14] is developing in several directions, including role of the disorder in allostery, derivation of the sequence-dependent potential, allostery in GPCRs, conservatism and diversity of signalling in protein folds. We are working on a model of 3D whole-genome reconstruction on the basis of the structural partitions observed in the Markov State Analysis of the Hi-C data . We have also started a project on molecular mechanisms of halophilic adaptation.
Igor Berezovsky studied physics at the Moscow Engineering Physics Institute (MSc, 1993) and obtained PhD in physics and mathematics from the Moscow Institute of Physics and Technology (1997). He started his scientific career at the Engelhardt Institute of Molecular Biology (Moscow) where he conducted his MSc and PhD research, then worked as a research fellow (until 1998). After postdoctoral research at the Weizmann Institute of Science (1999-2002) and the Harvard University (2003-2006), Igor was a senior scientist/group leader at the Bergen Center for Computational Science, University of Bergen (Norway) before joining the Bioinformatics Institute in January 2014. Since 2014, he is also an Adjunct Associate Professor at the Department of Biological Sciences, National University of Singapore.
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