Laboratory of Computer and Mathematical Modeling of Biological Systems

Phystech-BIO building, office 315
Moscow Institute of Physics and Technology,
9 Institusky per,
Dolgoprudny, Moscow Area,
141701, Russia

Laboratory of Computer and Mathematical Modeling of Biological Systems was founded in Moscow Institute of Physics and Technology in 2016. It is lead by Artem Zhmurov, Philipp Orekhov and Valeri Barsegov, a visiting professor from University of Massachusetts at Lowell. Laboratory employs 9 MIPT students, including 3 undergraduate, 5 graduate and 1 PhD student. The key research objectives of the laboratory is (1) to develop novel GPU-based computational approaches for biomolecular modeling and (2) to use widely avalible and in-house software to investigate biochemical, biomechanical and biological properties of various biomolecules. We use wide variety of molecular modeling techniques, including explicit solvent Molecular Dynamics (in NAMD, Gromacs, ACEMD), implicit solvent Langevin Dynamics simulations (using our own software), coarse-grained molecular modeling (SOP-GPU software package, MARTINI force-field in Gromacs and our own codes), Monte-Carlo simulations. We work with various biological molecules, including fibrinogen, fibrin oligomers, protofibrils, fibrin fibers and fibrin gel; viral capsids, both containing DNA and not; α-helical coiled coils and β-helical structures; bacterial receptors and biomembranes.

People

Artem Zhmurov

Head of the Laboratory,
Teaching Associate, Division of Computational Mathematics

EMail: zhmurov@gmail.com
Phone: +7 926 793 0460
Web Page: http://hpc.mipt.ru/zhmurov/

Profiles: Google Scholar, Researcher ID, Scopus

Philipp Orekhov

Deputy Head of the Laboratory,
Senior Researcher

EMail: ps.orekhov@gmail.com
Phone: +7 968 463 3964

Profiles: Google Scholar

Valeri Barsegov

Visiting Scientist

EMail: vbarsegov@gmail.com
Web Page: http://faculty.uml.edu/vbarsegov

Profiles: Google Scholar, Researcher ID

Olga Kononova

Visiting Scientist

EMail: 0lgagkononova@gmail.com

Profiles: Google Scholar, Researcher ID

Nikolay Shuvalov

PhD Student

Kirill Minin

PhD Student

Eugeny Klyuchnikov

Graduate Student

Ilya Rozhok

Graduate Student

Ilya Kirillov

Graduate Student

Nikita Berezyuk

Graduate Student

Ksenia Yagafarova

Graduate Student

Anna Goncharova

Graduate Student

Tatyana Selezneva

Graduate Student

Research

Structure and nanomechanics of fibrin polymers

Fibrinogen, a branched polymer that provides the scaffold for a thrombus in vertebrates, is 45 nm in length and 4.5 nm in diameter (M = 340 kDa). It consists of two Aα-chains (610 residues), two Bβ-chains (461 residues), and two γ-chains (411 residues), linked by 29 disulfide bonds. The D and E regions are connected by two 17 nm long coiled-coils. The D region contains the βC- and γC-terminal domains (β- and γ-nodules). Formation of fibrin polymers, the final stage of blood clotting, occurs through proteolysis of fibrinogen by thrombin, resulting in the exposure of the knobs ‘A’ and ‘B’ that bind to the holes ‘a’ and ‘b’. This is followed by the self-assembly of fibrin monomers into fibrin protofibrils, and their lateral aggregation to fibrin fibers (0.6–0.8 μm in length and 80–120 nm in diameter), which form a three-dimensional network, called a blood clot.

To study the mechanisms of fibrin formation, we developed a "computational crystallomics" approach to molecular modeling and employed 27 crystal structures of human fibrinogen and its fragments resolved to date to reconstruct, for the first time, the double-stranded half-staggered fibrin oligomers up to a 19-mer protofobril. We incorporated missing flexible portions of fibrin and performed stepwise elongation beginning with end-to-end single-stranded constructs reinforced by covalent γ-γ crosslinking, which were then connected laterally via non-covalent A-a and B-b knob-hole bonds. The full-atomic structures of fibrin protofibrils with the αC regions, which were validated by quantitative comparison with experimental ultramicroscopic images of fibrin oligomers and protofibrils, reveal intermolecular interactions that stabilize a twisted arrangement.

We also study biomechanics of fibrin in silico on various spatial scales. Using SOP-GPU software package, we showed what micromolecular transitions lead to unique fibrinogen force-extension profile. On atomic resolution, we characterized an α-to-β phase transition in fibrinogen coiled coils. We also work on the forced-unfolding of fibrin oligomers and protofibrils. We develop a coarse-grained model that allow us to connect the micromechanics of individual fibrin monomers with the force-elongation profile of fibrin gel.

Publications:

  1. R.I. Litvinov, O. Kononova, A. Zhmurov, K.A. Marx, D. Thirumalai, V. Barsegov, and J.W. Weisel, "A regulatory element in fibrin triggers tension-activated transition from catch to slip bonds", Proc. Natl. Acad. Sci. USA, 115: 8575-8580 (2018).
  2. A. Zhmurov, A.D. Protopopova, R.I. Litvinov, P. Zhukov, J.W. Weisel, and V. Barsegov, "Atomic structural models of fibrin oligomers", Structure, 26: 857-868 (2018).
  3. Y.F. Zuev, R.I. Litvinov, A.E. Sitnitsky, B.Z. Idiyatullin, D.R. Bakirova, D.K. Galanakis, A. Zhmurov, V. Barsegov, and J.W. Weisel, "Conformational flexibility and self-association of fibrinogen in concentrated solutions,", J. Phys. Chem. B, 121: 7833-7843 (2017).
  4. A. Zhmurov, A.D. Protopopova, R.I. Litvinov, P. Zhukov, A.R. Mukhitov, J.W. Weisel, and V. Barsegov, "Structural basis of interfacial flexibility in fibrin oligomers", Structure, 24: 1907-1917 (2016).
  5. O. Kononova, R.I. Litvinov, A. Zhmurov, A. Alekseenko, C.H. Cheng, S. Agarwal, K.A. Marx, J.W. Weisel, and V. Barsegov, "Molecular mechanisms, thermodynamics, and dissociation kinetics of knob­-hole interactions in fibrin", J. Biol. Chem., 288: 22681­-22692 (2013).
  6. A. Zhmurov, O. Kononova, Y. Kholodov, and V. Barsegov, "Force-induced phase transition from α-­helices to β-sheets in fibrous proteins", (in Russian) Computational Research and Modelling, 5: 705­725 (2013).
  7. A. Zhmurov, O. Kononova, R.I. Litvinov, R.I. Dima, V. Barsegov, and J.W. Weisel, "Mechanical transition from α-­helical coiled-coils to β­-sheets in fibrin(ogen)", J. Am. Chem. Soc., 134: 20396-­20402 (2012).
  8. A. Zhmurov, A.E.X. Brown, R.I. Litvinov, R.I. Dima, J.W. Weisel, and V. Barsegov, "Mechanism of fibrin(ogen) forced unfolding", Structure, 19: 1615-1624 (2011).


Nanomechanics of viral capsids


Viral capsids must meet the conflicting goals of being stable enough to protect their genomic material, yet being sufficiently unstable that they release their genome into the host cells during the process of infection. Virus-like bacteriophage nanocompartments, too, should possess a broad range of evolved biomechanical characteristics to be able to regulate the influx of substrate and efflux of product and to efficiently facilitate the enzymatic catalysis in their interior. These examples makes the exploration of the physico-chemical and biomechanical properties of these important biological particles into a highly significant research objective. Virus and encapsulin capsids possess modular structure (capsomers) aranged symmetrically to form a protein shell. The physical properties of these hierarchical supramolecular assemblies could, in principle, be extracted from the AFM-based dynamic force measurements in vitro, whereby the forced compression of the particles wall deforms their structure and ruptures the non-covalent protein-protein bonds. However, in practice, the molecular-level interpretations of the experimental force-indentation spectra is difficult.

These limitations call for the development of alternative research strategies. To overcome these problems we employ our unique methodology of in silico nanoindentation, which proved to be invaluable at providing a structure-based understanding of the mechanism of the deformation and collapse. In this method, mechanical loading of a biological particle is carried out in silico. Importantly, the nanoindentation measurements are performed under experimental conditions of force application. That is, in our simulations we use the experimentally relevant force-loading rates, so that the results of experiment and simulations can be directly compared. Structural transitions can be resolved by examining the coordinates of amino acids, and thermodynamic quantities and mechanical characteristics can be gathered through detailed analysis of the energy output from simulations.

Publications:

  1. O. Kononova, A. Zhmurov, K.A. Marx, and V. Barsegov, "Mechanics of viruses", in Series of Computational Biophysics: Coarse-grained Modeling of Biomolecules. Editors: N. Dokholyan and G. Papoian, Taylor & Francis Publishers (2017).
  2. O. Kononova, K.A. Marx, and V. Barsegov, "Nanoindentation in silico of biological particles", in Applied Nanoindentation in Advanced Materials. Editor: Atul Tiwari, John Wiley & Sons (2017).
  3. J. Snijder, O. Kononova, I.M. Barbu, C. Uetrecht, W. F. Rurup, R.J. Burnley, M.S.T. Koay, J.J.L.M. Cornelissen, W.H. Roos, V. Barsegov, G.J.L. Wuite, and A.J.R. Heck, "Assembly and mechanical properties of the cargo-free and cargo-loaded bacterial nanocompartment encapsulin", Biomacromolecules, 17: 2522-2529 (2016).
  4. O. Kononova, J. Snijder, Y. Kholodov, K.A. Marx, G.J.L. Wuite, W.H. Roos, and V. Barsegov, "Fluctuating nonlinear spring model of mechanical deformation of biological particles", PLoS Comput. Biol., 12: e1004729 (2016).
  5. A. Zhmurov, K. Rybnikov, Y. Kholodov, and V. Barsegov, "Generation of random numbers on graphics processors: Forced indentation in silico of the bacteriophage HK97", J. Phys. Chem. B, 115: 5278-­5288 (2011).


Nanomechanics of supersecondary structures

α-helical coiled coils is a common protein structure motif, frequently seen in protein whose function is purely mechanical. For instance, α-helical coiled-coil tail domain of myosin II passes the mechanical tension upon muscle contraction. Fibrinogen, a precursor of a blood clot, has a tri-nodular structure with three globular nodules connected by two three-stranded coiled-coils. Protein NDC-80 connects microtubules to kinetochores via elongated coiled-coil domain. Intermediate filaments, a major part of cell cytoskeleton, also formed by α-helical coiled coils. Recent improvements in de novo design of coiled-coils open a big potential in the design of new biological materials, including these with specified mechanical properties.

The coiled-coils are very common subject for the experimental studies using Atomic Force Microscopy (AFM) and laser tweezers. In these experiments, the coiled-coils show a remarkable mechanical footprint. At lower strain, the reaction to external tension is purely elastic. When certain force threshold is reached, the reaction force stays constant while strain increases. After the coiled coils fully elongate, the response becomes non-linear when the external force works against covalent bonds.

Unique force-extension profile of the coiled-coils was connected to α-to-β transition that occurs in these systems under tension. This fact was confirmed both experimentally and using molecular simulations. The transition from elastic to plastic regime is not unique to the coiled-coils, and was shown experimentally for collagen triple-helix, single α-helical polypeptide and for the β-helices. In our lab, we use molecular modeling to characterize nanomechanical properties of α-helical coiled-coils and β-helical domains from various proteins.

Publications:

  1. K.A. Minin, A. Zhmurov, K.A. Marx, P.K. Purohit, and V. Barsegov, "Dynamic transition from α-helices to β-sheets in polypeptide coiled-coil motifs", J. Am. Chem. Soc., 139: 16168-16177 (2017).
  2. A. Zhmurov, O. Kononova, Y. Kholodov, and V. Barsegov, "Force-induced phase transition from α-­helices to β-sheets in fibrous proteins", (in Russian) Computational Research and Modelling, 5: 705­725 (2013).
  3. A. Zhmurov, O. Kononova, R.I. Litvinov, R.I. Dima, V. Barsegov, and J.W. Weisel, "Mechanical transition from α-­helical coiled-coils to β-sheets in fibrin(ogen)", J. Am. Chem. Soc., 134: 20396-­20402 (2012).


Molecular dynamics simulations of the bacterial/eukaryotic cell walls and cell membranes and their interactions with antibacterial drugs and drug-like molecules

MCell membranes form a semipermeable barrier, which controls the transport of various molecules outside an inside the cell, plays an essential role in the cell energetics, adhesion and signaling and many more. Most importantly, they protect cells from their surroundings. Additional structures, which are situated outside the cell membrane of the most of Bacteria, Fungi and Plants and are in general termed the cell wall, can contribute into the latter function. The cell walls and cell membranes play a crucial role in pharmacodynamics and often represent a serious barrier on the way of antibiotics hampering their ability to reach their molecular targets. On the other hand, the elements of bacterial cell wall can serve as such targets themselves. We use atomistic and coarse-grained molecular dynamics simulations in order to track the interactions of various drug-like molecules with the bacterial and eukaryotic membranes and cell walls of different composition. Particularly, we study the antimicrobials with the photodynamic activity and the carbon nanoparticles (see the figure). The latter appear as effective devices for various biomedical applications including the vectoral delivery of drugs, and at the same moment they conceivably have certain therapeutical effects themselves.

Publications:

  1. M.E. Bozdaganyan, P.S. Orekhov, A.K. Shaytan, and K.V. Shaitan, "Comparative computational study of interaction of C 60-fullerene and Tris-malonyl-C 60-fullerene isomers with lipid bilayer: relation to their antioxidant effect", PloS ONE, 9: e102487 (2014).


Molecular mechanisms of signaling and adaptation in prokaryotic receptors

Motile microorganisms navigate through their environment using special molecular machinery (see the figure) in order to sense gradients of various signals: chemotaxis (reactions to chemical compounds) and phototaxis (to light) sensory cascades. Transmembrane receptors play a central role in these cascades as they receive input signals and transmit them inside the cell, where they modulate activity of the kinases CheA, which are tightly bound to their cytoplasmic domains. CheA further phosphorylates the response regulator protein CheY, which regulates the flagella. At the same time, CheA phosphorylates and, by means of this, activates another response regulator, CheB, which, along with the constantly active CheR protein, catalyzes two opposite reactions: methylation and demethylation of the specific glutamic acid residues located at the cytoplasmic domain of the receptors. The latter reactions establish the adaptation mechanism, which allows microbes to sense in a very broad range of the input signal intensities. Many functional, structural and dynamical aspects of the signal propagation through the prokaryotic receptors as well as a mechanism of the signal amplification remain still unclear. We use various techniques of computational biophysics, chiefly molecular dynamics (MD) simulations, in order to approach these problems.

Publications:

  1. P. Orekhov, A. Bothe, H.‐J. Steinhoff, K.V. Shaitan, S. Raunser, D. Fotiadis, R. Schlesinger, J.P. Klare, and M. Engelhard, "Sensory rhodopsin I and sensory rhodopsin II form trimers of dimers in complex with their cognate transducers,", Photochem. Photobiol., 93: 796-804 (2017).
  2. P.S. Orekhov, D. Klose, A.Y. Mulkidjanian, K.V. Shaitan, M. Engelhard, J.P. Klare, H.-J. Steinhoff, "Signaling and adaptation modulate the dynamics of the photosensoric complex of natronomonas pharaonis", PLoS Comput. Biol., 11: e1004561 (2015).


Software for coarse-grained and all-atom simulations on GPU

Molecular Dynamics simulations have become a powerful tool to investigate the biochemical and biophysical properties of biological molecules. Compared to X-Ray diffraction, which provides only a static snapshot, Molecular Dynamics allows one to see molecules in motion, providing coordinates for every atom of the system at any given time. Powerful it might be, there are certain limitations to full-atom Molecular Dynamics simulations. Firstly, the system containing an average protein surrounded by water may be as large as 10,000–100,000 atoms. Secondly, fast motion of small atoms lead to numerical instability if the integration timestep is larger than 1–5 fs. Hence, observing an average protein on the timescale of 1–10 μs using full-atom Molecular Dynamics requires significant computational resources. Reaching biologically relevant timescales of milliseconds remains elusive even for small proteins.

Coarse-grained models allows one to overcome these barriers by reducing the number of degrees of freedom in the system by removing fast motions of single atoms, hence increasing stability limits for the integration timestep. When used in conjunction with computational power of Graphics Processing Units, these models look even more attractive. In our lab, we develop novel GPU-based implementations of coarse-grained models. We also work on improving parametrization of these models against short full-atomic simulation trajectories and experimental data.

GitHub links:

  1. SOP-GPU package for coarse-grained simulations of proteins.
  2. MDis software package for molecular dynamics simulations in SASA implicit solvent on GPU.
  3. ASAP MD package for coarse-grained simulations of systems containing both protein and DNA molecules.

Publications:

  1. A. Alekseenko, O. Kononova, Y. Kholodov, K.A. Marx, and V. Barsegov, "SOP‐GPU: influence of solvent‐induced hydrodynamic interactions on dynamic structural transitions in protein assemblies", J. Comput. Chem., 37: 1537-1551 (2016).
  2. A. Zhmurov, K. Rybnikov, Y. Kholodov, and V. Barsegov, "Generation of random numbers on graphics processors: Forced indentation in silico of the bacteriophage HK97", J. Phys. Chem. B, 115: 5278-­5288 (2011).
  3. A.A. Zhmurov, I.I. Morozov, Y.A. Kholodov, V.A. Barsegov, and A.S. Kholodov, "Efficient generators of pseudorandom numbers for molecular modeling on graphics processors", (in Russian) Computational Research and Modelling, 3: 296-­311 (2011).
  4. A.A. Zhmurov, V.A. Barsegov, S.V. Trifonov, Y.A. Kholodov, and A.S. Kholodov, "Modeling micromechanics on graphics processors using Langevin dynamics", (in Russian) Mathematical Modeling, 23: 133-­156 (2011).
  5. A. Zhmurov, R.I. Dima, Y. Kholodov, and V. Barsegov, "SOP­-GPU: Accelerating biomolecular simulations in the centisecond timescale on graphics processors", Proteins, 78: 2984-2999 (2010).


Agent-based modeling of biological systems

Agent based modeling is an in silico method, in which the system is represented as a set of interacting centers (called agents). Instances of various sizes and shapes may act as agents: from single atom to an entire organism. Agent-based modeling has recently become even more popular due to its highly parallel nature: each agent may be assigned to a separate computational thread and many threads can handle many agents simultaneously. In our laboratory, we use agent-based modeling in biological reaction-diffusion problem. These include protein diffusion in crowded environment, phase transitions and phase separations, fibrin polymerization. We also use agent-base approach to investigate the process of the development of the cancer cell population and how it can be controlled with treatment.

Publications

2019

  1. P.S. Orekhov, M.Y. Bozdaganyan, N. Voskoboynikova, A.Y. Mulkidjanian, H.-J. Steinhoff, and K.V. Shaitan, "Styrene-maleic acid copolymers form SMALPs by pulling lipid patches out of the lipid bilayer", Langmuir, doi:10.1021/acs.langmuir.8b03978 (2019). Supplementary files (download).

2018

  1. R.I. Litvinov, O. Kononova, A. Zhmurov, K.A. Marx, D. Thirumalai, V. Barsegov, and J.W. Weisel, "A regulatory element in fibrin triggers tension-activated transition from catch to slip bonds", Proc. Natl. Acad. Sci. USA, 115: 8575-8580 (2018).
  2. A. Zhmurov, A.D. Protopopova, R.I. Litvinov, P. Zhukov, J.W. Weisel, and V. Barsegov, "Atomic structural models of fibrin oligomers", Structure, 26: 857-868 (2018).
  3. P.S. Orekhov, E.G. Kholina, M.E. Bozdaganyan, A.M. Nesterenko, I.B. Kovalenko, and M.G. Strakhovskaya, "Molecular mechanism of uptake of cationic photoantimicrobial phthalocyanine across bacterial membranes revealed by molecular dynamics simulations", J. Phys. Chem. B, 122: 3711-3722 (2018).
  4. O. Kononova, F. Maksudov, K.A. Marx and V. Barsegov, "TensorCalculator: Exploring the evolution of mechanical stress in the CCMV capsid", J. Phys.: Condens. Matter, 30: 044006 (2018).
  5. M.D. West, I. Labat, H. Sternberg, D. Larocca, I. Nasonkin, K.B. Chapman, R. Singh, E. Makarev, A. Aliper, A. Kazennov, A. Alekseenko, N. Shuvalov, E. Cheskidova, A. Alekseev, A. Artemov, E. Putin, P. Mamoshina, N. Pryanichnikov, J. Larocca, K. Copeland, E. Izumchenko, M. Korzinkin and A. Zhavoronkov, "Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of COX7A1 in embryonic and cancer cells", Oncotarget, 9: 7796-7811 (2018).

2017

  1. P.S. Orekhov, M.E. Bozdaganyan, E. Peshkova, and C. Nicolini, "Stability and radiation damage of protein crystals as studied by means of molecular dynamics and Monte Carlo simulation", Nanoworld J., 3: S9-S14 (2017).
  2. K.A. Minin, A. Zhmurov, K.A. Marx, P.K. Purohit, and V. Barsegov, "Dynamic transition from α-helices to β-sheets in polypeptide coiled-coil motifs", J. Am. Chem. Soc., 139: 16168-16177 (2017).
  3. V. Barsegov, J. Ross, and R. Dima, "Dynamics of microtubules: Highlights of recent computational and experimental investigations,", J. Phys.: Condens. Matter, 29: 433003 (2017).
  4. Y.F. Zuev, R.I. Litvinov, A.E. Sitnitsky, B.Z. Idiyatullin, D.R. Bakirova, D.K. Galanakis, A. Zhmurov, V. Barsegov, and J.W. Weisel, "Conformational flexibility and self-association of fibrinogen in concentrated solutions,", J. Phys. Chem. B, 121: 7833-7843 (2017).
  5. P. Orekhov, A. Bothe, H.‐J. Steinhoff, K.V. Shaitan, S. Raunser, D. Fotiadis, R. Schlesinger, J.P. Klare, and M. Engelhard, "Sensory rhodopsin I and sensory rhodopsin II form trimers of dimers in complex with their cognate transducers,", Photochem. Photobiol., 93: 796-804 (2017).
  6. O. Kononova, R.I. Litvinov, D.S. Blokhin, V.V. Klochkov, J.W. Weisel, J.S. Bennett, and V. Barsegov, "Mechanistic basis for the binding of RGD-and AGDV-peptides to the platelet integrin αIIbβ3", Biochemistry, 56: 1932-1942 (2017).
  7. O. Kononova, K.A. Marx, and V. Barsegov, "Nanoindentation in silico of biological particles", in Applied Nanoindentation in Advanced Materials. Editor: Atul Tiwari, John Wiley & Sons (2017).
  8. O. Kononova, A. Zhmurov, K.A. Marx, and V. Barsegov, "Mechanics of viruses", in Series of Computational Biophysics: Coarse-grained Modeling of Biomolecules. Editors: N. Dokholyan and G. Papoian, Taylor & Francis Publishers (2017).
  9. N.I. Akberova, A.A. Zhmurov, T.A. Nevzorova, and R.I. Litvinov, "An anti-DNA antibody prefers damaged dsDNA over native", J. Biomol. Struct. Dyn., 35: 219-232 (2017).

2016

  1. A. Zhmurov, A.D. Protopopova, R.I. Litvinov, P. Zhukov, A.R. Mukhitov, J.W. Weisel, and V. Barsegov, "Structural basis of interfacial flexibility in fibrin oligomers", Structure, 24: 1907-1917 (2016).
  2. J. Snijder, O. Kononova, I.M. Barbu, C. Uetrecht, W. F. Rurup, R.J. Burnley, M.S.T. Koay, J.J.L.M. Cornelissen, W.H. Roos, V. Barsegov, G.J.L. Wuite, and A.J.R. Heck, "Assembly and mechanical properties of the cargo-free and cargo-loaded bacterial nanocompartment encapsulin", Biomacromolecules, 17: 2522-2529 (2016).
  3. A. Alekseenko, O. Kononova, Y. Kholodov, K.A. Marx, and V. Barsegov, "SOP‐GPU: influence of solvent‐induced hydrodynamic interactions on dynamic structural transitions in protein assemblies", J. Comput. Chem., 37: 1537-1551 (2016).
  4. O. Kononova, J. Snijder, Y. Kholodov, K.A. Marx, G.J.L. Wuite, W.H. Roos, and V. Barsegov, "Fluctuating nonlinear spring model of mechanical deformation of biological particles", PLoS Comput. Biol., 12: e1004729 (2016).
  5. N.I. Akberova, A.A. Zhmurov, T.A. Nevzorova, and R.I. Litvinov, "Essential dynamics of DNA-antibody complexes", BioNanoScience, 6: 543-549 (2016).
  6. N.I. Akberova, A.A. Zhmurov, T.A. Nevzorova, and R.I. Litvinov, "Molecular dynamics of immune complex of photoadduct-containing DNA with Fab-Anti-DNA antibody fragment", Mol. Biol., 50: 442-451 (2016).

Teaching

  1. "Modeling of biological molecules using Langevin dynamics on GPU", undegraduate course for students from Department of Aerophysics and Space Research (Artem Zhmurov).
  2. "Computational mathematics", undergraduate course (Artem Zhmurov, Nikolay Schuvalov).
  3. More info (in Russian)...

Collaborations

John Weisel and Rustem Litvinov;
Perelman School of Medicine, University of Pennsylvania, USA.
Valeri Barsegov;
University of Massachusetts at Lowell, USA.
Ilya Kovalenko (Dept. of Biophysics, School of Biology), Konstantin Shaitan (Dept. of Bioengineering, School of Biology), Fazli Ataullakhanov and Nikita Gudimchuk (School of Physics);
Moscow State University, Russia.
Heinz-Jürgen Steinhoff (Experimental Physics/Macromolecular Structure group);
University of Osnabrück, Germany.
Natalia Akberova;
Kazan Federal University, Russia.

Funding

Russian Foundation for Basic Research:
Grant #17-00-00479: "A multi-scale molecular modeling study of tubulin and its complex with Ndc80".
Grant #15-37-21027: "A molecular modeling study of the protein fibers nanomechanics".
Grant #15-01-06721: "Multi-scale molecular modeling study of fibrin oligomers, protofibrils, fibrin fibers and fibrin gel".
Russian Science Foundation:
Grant #17-71-10202: "Describing intra- and inter-protein interactions using co-evolution analisys".
5-100 Competative Growth program, Moscow Institute of Physics and Technology:
Laboratory startup grant.