Maria Kurnikova
Professor, Chemistry
- Mellon Institute 503
- 412-268-9772
Bio
Other appointments:
- Co-Director, The Molecular Biophysics & Structural Biology (MBSB) Graduate Program at the University of Pittsburgh and 一本道无码
- Affiliated faculty, Joint Carnegie Mellon-University of Pittsburgh Graduate Program in Computational Biology (CPCB)
Education
M.Sc, Moscow Institute of Physics and Technology, 1984–1990
Ph.D., University of Pittsburgh, 1998
Postdoctoral Fellow, University of Tel Aviv, 1998–1999
Postdoctoral Fellow, University of Pittsburgh/NIST, 1999–2001
Research
Keywords: Theory, physical chemistry, computational chemistry, biophysical, molecular modeling
My research is in the areas of computational chemistry, molecular modeling, and theoretical biophysics. My research aims at understanding the structure-function relationships in proteins. My group uses high-performance computing for molecular modeling and computational chemistry to predict biological macromolecules' realistic behavior and energetics. Our goal is to connect our understanding of the structure and dynamics at the atomistic level with the experimental findings, which are often on longer time scales and are averaged out over ensembles of multiple molecules.
ResearcherID: H-4510-2017
Projects
MEMBRANE PROTEIN RECEPTORS AND ION CHANNELS
Membrane-associated proteins and protein complexes remain relatively poorly understood despite being crucially important for medicine, pharmaceutical science, environmental sciences, and bioengineering. One focus of my current research program is on developing models to understand how the structure of the membrane proteins and complexes dictates their function in signaling and ion transport. In part, the difficulty in modeling membrane biomolecular systems stems from the relatively large size of their functional elements (such as proteins and protein complexes) and their intrinsic inhomogeneity in space and time. A model that starts with the atomistic description of such a system and can predict experimentally measurable observables has to span multiple time- and length-scale. At the same time, a good model should help one understand a real system, expose its main features, and thus provide insight. Therefore, if a model is correct but is too convoluted, it is not likely to be useful. To avoid excessive complexity, a good theoretical model of a molecular and sub-cellular biological system must be structured hierarchically to couple multiple length scales from atomistic to “material”-like and time scales from pico-seconds to seconds and beyond. The goal is to model and predict structure-function relationships in these proteins associated with ligand binding, gating of channels, and mechanisms of selectivity and mobility in the confined environment of the channel.
STRUCTURE-BASED IN SILICO DRUG DESIGN
In collaboration with O. Isayev's group, we are developing high-throughput approaches to computing the free energy of ligand binding for predicting and optimizing hit molecules. Our methodology, which combined Active Learning and other Machine Learning methods, proved highly successful in recent competitions for in silico drug design.
APPROACHES
The approach my research group is taking includes a combination of physics-based computational methodologies, such as molecular dynamics simulations, continuum electrostatics and quantum chemistry as well as machine learning methods (ML). The game's name in this field is Statistical Mechanics, which is the cornerstone theory for understanding the behavior of large molecular ensembles. Large computational resources and high-performance computing tools are needed to obtain correct statistics in biomolecular modeling. Thus, we are active users of the national super-computer facilities sponsored by NSF and NIH, such as for example Pittsburgh Super Computer Center. Another challenge in this field is to develop models of intermolecular interactions that account for the properties of the system on a quantitative level yet are simple enough computationally to be evaluated effectively. Finding the right balance between the complexity of the model and its effectiveness in the simulation — is a significant yet unsolved intellectual challenge for many biologically important systems and processes. The educational background and interests needed to succeed in this field are physical chemistry, soft condensed matter physics, and biophysics.
Publications
Gangwar SP, Yelshanskaya MV, Nadezhdin KD, Yen LY, Newton TP, Aktolun M, Kurnikova MG, Sobolevsky AI. Nature. 2024 630(8017):762-768
Gutkin E, Gusev F, Gentile F, Ban F, Koby S, Narangoda C, Isayev O, Cherkasov A, Kurnikova MG. Chemical Science 2024,15, 8800-8812
Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions
Illarionov A, Sakipov S, Pereyaslavets L, Kurnikov IV, Kamath G, Butin O, Voronina E, Ivahnenko I, Leontyev I, Nawrocki G, Darkhovskiy M, Olevanov M, Cherniavskyi YK, Lock C, Greenslade S, Sankaranarayanan SK, Kurnikova MG, Potoff J, Kornberg RD, Levitt M, Fain B. J Am Chem Soc. 2023 Nov 1;145(43):23620-2362
Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling
Gusev F, Gutkin E, Kurnikova MG, Isayev O. J Chem Inf Model. 2023 63(2):583-594
Schmidt E, Narangoda C, Nörenberg W, Egawa M, Rössig A, Leonhardt M, Schaefer M, Zierler S, Kurnikova MG, Gudermann T, Chubanov V. Cell Mol Life Sci. 2022 79(5):225
Opening of glutamate receptor channel to subconductance levels
Yelshanskaya MV, Patel DS, Kottke CM, Kurnikova MG, Sobolevsky AI. Nature 2022 605(7908):172-178
Appointments
| Years | Position |
|---|---|
| 2022–present | Professor of Chemistry, 一本道无码 |
| 2011 | Visiting Fellow, Princeton University |
| 2009–2022 | Associate Professor of Chemistry, 一本道无码 |
| 2005 | Visiting Researcher, Arizona State University |
| 2003–2009 | Assistant Professor of Chemistry, 一本道无码 |
| 2002 | Visiting Researcher, Michigan State University |
| 2001–2003 | Assistant Professor, Marquette University |
| 1999–2001 | Research Associate, University of Pittsburgh |
| 1999–2000 | Guest Researcher, National Institute of Standards and Technology (NIST) |
| 1998–1999 | Postdoctoral Fellow, University of Tel-Aviv, Israel |
Awards and Distinctions
| Years | Award |
|---|---|
| 2024 | |
| 2002 | Research Corporation Innovation Award |