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. We highlight here two major areas of research pursued in the group: (i) protein allostery; (ii) biophysics of chromatin and epigenetic regulation. Other research topics in the group include, but are not limited to, evolution of protein function, evolutionary-inspired design of protein function, and molecular mechanisms of adaptation.
Continuing our work on allostery , we explored allosteric mechanisms mediated by structural disorder  using our structure-based statistical mechanical model of allostery (SBSMMA, [3,4]). The first goal here was to learn how back and forth disorder-order transitions can work as perturbations causing allosteric signalling in different phenomenologies of the allosteric regulation, such as formation of the dimer interface in BirA, preorganization of the binding pocket in SrtA, blocking the active conformation of Rac1 by engineered extrinsic disorder. Then, considering a set of esterase homologues with different thermostability, we addressed a question of a generic strategy for disorder/order-based allosteric tuning of protein activity. In collaboration with Professor Ganesh Anand (DBS/NUS, currently Pennsylvania State University), we studied allosteric signal propagation in cooperative, dual-liganded protein kinase PDK1 . Analyzing combinatorial coupling effects of a cooperative ligand pair binding with the help of amide hydrogen-deuterium exchange mass spectrometry (HDXMS), we obtained a quantitative description (using SBSMMA) of the integration of allosteric signals at Lys111. We also observed and investigated bidirectional and synergistic allosteric propagation, which offers a specific strategy for combinatorial drug development.
BIOPHYSICS OF CHROMATIN AND EPIGENETIC REGULATION
We developed a comprehensive method for reconstructing the wholegenome chromatin ensemble from the Hi-C data . The procedure starts from the Markov state modeling (MSM), delineating the structural hierarchy of chromatin organization with partitioning and effective interactions archetypal for corresponding levels of hierarchy. Then, an original stochastic embedding (SEP) developed in the group provides the 3D ensemble reconstruction, using effective interactions obtained by the MSM as the input. The structural ensemble of a genome obtained in the procedure allows one to model the functional and the cell-type variability in chromatin. The whole-genome reconstructions are exemplified here by the human B lymphoblastoid (GM12878) and lung fibroblast (IMR90) Hi-C data, which show distinctions in their morphologies and in the spatial arrangement of intermingling chromosomal territories. The stochastic embedding procedure developed for this reconstruction allows to obtain an ensemble of the whole-genome chromatin conformations, thus, paving the way to studies of chromatin dynamics, developmental changes, and conformational transitions taking place in normal cells, cell differentiation, and during potential pathological developments.
Figure 4. Reconstructions of the human B lymphoblastoid (GM12878) and lung fibroblast (IMR90) 3D whole-genome chromatin structures.
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 researcher 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.
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