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Maria Kurnikova, PhD

Adjunct Associate Professor


M.S. (Applied Physics and Mathematics), Moscow Physical and Technical Institute, Russia, 1998
Ph.D. (Theoretical Chemistry), University of Pittsburgh, 1998 

My research is in the area of computational chemistry and biophysics.

Theory, Computational Chemistry, Biophysical, Molecular Modeling, Continuum Electrostatics, Drift-Diffusion Models, Ion Channels, Membrane Receptors, Signal Transduction, Membrane Protein Structure-Function Relations, Flexibility and Rigidity in Protein Dynamics.

Membrane Proteins and Ion Channels

I am interested in understanding the work of membrane proteins, such as receptors, signal transduction proteins, toxins and ion channels. 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. The systems I am interested specifically include Glutamate Receptors (AMPA and NMDA types), alpha-Hemolysin, Diphteria Toxin t-domain, Gramicidin A, PDZ-domain — ligand interaction of the NHERF1 protein.

Protein Flexibility/Rigidity

In many protein systems and complexes protein flexibility and rigidity play important role in inducing functionally important movements and conformational transitions. In other cases small fluctuations of protein atoms, due to thermal noise, create a conducive environment for initiation of functionally significant rearrangements of atoms. We apply a range of methods and models of computational chemistry and biology to characterize dynamics of proteins in a wide spatial and temporal scale


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. The name of the game in this field is Statistical Mechanics, which is the corner-stone theory for understanding behavior of large molecular ensembles. Huge computational resources 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 a right balance between the complexity of the model and an effectiveness of it in the simulation — is a significant and yet unsolved intellectual challenge for many biologically important systems and processes. The educational background and interests needed to succeed in this field is physical chemistry, soft condensed matter physics and biophysics.



Journal Articles

Yonkunas M and M Kurnikova.  Characterizing the energetic states of the GluR2 ligand binding domain core-dimer. Biophys J 100:L5-L7, 2011.
Simakov N and M Kurnikova.  Soft wall ion channel in continuum representation with application to modeling ion currents in a-hemolysin.  J Phys Chem B 114:15180-15190, 2010.
Mamonova TB, AV Glyakina, MG Kurnikova and OG Galzitskaya.  Flexibility and mobility in mesophilic and thermophilic homologous proteins from molecular dynamics and fold unfold method.  J Bioinform and Comp Biol 8:377-394, 2010.
Speranskiy K and M Kurnikova.  Modeling of peptides connecting the ligand-binding and transmembrane domains in the GluR2 glutamate receptor.  Prot Struct Func Bioinf 76:271-280, 2009.
Mamonova T, M Yonkunas and M Kurnikova.  Energetics of the cleft closing transition and glutamate binding in the glutamate receptor ligand binding domain.  Biochemistry 47:11077-11085, 2008.
Speranskiy K, M Cascio and M Kurnikova.  Homology modeling and molecular dynamics simulations of the glycine receptor ligand binding domain.  Prot Struct Func Bioinf 67:950-960, 2007.