Our group works at the interface between the physics and biology, exploring the structure and function of biological
macromolecules and their complexes. We develop minimalistic physical models and computational approaches and use them for modelling and predictions of structural and functional characteristics of biomolecules, which are verified in collaboration with our experimental colleagues. The current areas of our research include, but not limited to: (i) evolution and design of the protein function; (ii) allostery in proteins and molecular machines; (iii) biophysics of chromatin and epigenetic regulation; (iv) molecular mechanisms of adaptation to extreme environments.
Our long-standing interest to the emergence and evolution of the protein function culminated this year in the design of
“Simple yet functional phosphate-loop proteins”. In this collaborative effort we de novo computationaly designed β-α repeat proteins and obtained soluble, stable molecules that bind ATP in a magnesium-independent manner, polynucleotides, RNA, and single-strand DNA (Figure 1A).
The basic evolutionary prototypes of the phosphate-binding loop (P-loop, Figure 1B) used in the design procedure were
derived on the basis of our concept of elementary functional loops (EFLs) – basic building blocks of the protein function. A collection of EFLs that are involved into interactions with 24 nucleotide-containing ligands and biologically relevant coenzymes/cofactors - NBDB database - served as a source of the P-loop EFL in the “Ploop protein” work, and we plan to use prototypes of other EFLs in future design efforts.
Continuing our work on the mechanisms of allostery we developed a new approach on the basis of structure-based
statistical mechanical model of allostery (SBSMMA) that allows to exhaustively describe allosteric communication in the protein, to tune already existing signalling, and to design new elements of regulation in order to take protein activity under allosteric control. Figure 2 shows a scheme of the comprehensive allosteric control of the protein activity, illustrating possible models of allosteric regulation originated by different types of perturbations, such as ligand binding, stabilizing and destabilizing mutations, and their combinations. To generically characterize the allosteric effect of mutations regardless of the native residue in a selected position, a range of allosteric modulations is obtained as a difference between the modulations obtained upon stabilizing (UP, substitution into bulky and strongly interacting with the environment residue) and destabilizing (DOWN, substitution into Ala/Gly-like residue) mutations.
The Allosteric Signaling Map (ASM) are introduced as a source of an exhaustive data on the allosteric signaling that can be originated by potentially every residue of the protein. Figure 2 shows the ASM for Insulin-Degrading Enzyme, which is an object of in-depth computational studies and experimental verification performed in the group. The ASM data can be directly used for the design and tuning of allosteric site, prediction of latent regulatory exosites and allosteric effects of mutations, using the reverse perturbation approach. The AlloMAPS database provides ASMs for 1987 proteins and protein chains, including (i) 46 proteins with comprehensively annotated functional and allosteric sites; (ii) 1908 protein chains from PDBselect set of chains with low (less than 25%) sequence identity; (iii) 33 proteins with more than 50 known pathological SNPs in each molecule. In addition to energetics of allosteric signaling between known functional and regulatory sites, allosteric modulation caused by the binding to these sites, by SNPs, and by mutations designated by the user can be also explored.
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.
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.