HOME- R&D
- Bryn Mawr Conference
- Hyderabad Conference
- Workshops & Training
- Program
- Drug Discovery Innovation
- Proteins
- Abstracts
- Speakers
- Buldyrev, S
- Daura, X
- Ding, F
- Dixon, R
- Dokholyan, N
- Duan, Y
- Fasnacht, M
- Kiefhaber, T
- Miranker, A
- Pande, V
- Pitera, J
- Shea, J
- Teplow, D
- Tiana, G
- van Gunsteren, W
- Vendruscola, M
- Wenzel, W
- Winn, P
- Zhou, R
- Quantum Biochemistry
- Screening
- Web Services
- Pharmacophores
- Graph Mining
- Nanotech
- Membranes & Ion Channels
- Exhibition
- Registration
- Jobs
- Contact
- Schedule
|
|
|
|
|
|
|
| Nikolay V. Dokholyan, University of North Carolina |
|
Nikolay V. Dokholyan was born in Tbilisi (Republic of Georgia, Former USSR) in 1971. He received his MS in Physics from the Moscow Institute of Physics and Technology in 1994 and a PhD in Physics in 1999 from Boston University. In 2002, after three years of postdoctoral research at Harvard University in the Department of Chemistry and Chemical Biology, he joined the University of North Carolina at Chapel Hill Department of Biochemistry and Biophysics as an Assistant Professor. Dr. Dokholyan is also affiliated with the Carolina Center for Genome Sciences, the Molecular and Cellular Biophysics Program, and the Bioinformatics Program.
Dr. Dokholyan focuses primarily on understanding protein dynamics and on how induced changes in protein folding lead to disease. One prominent example of this is the hypothesized misfolding of superoxide dismutase associated with the neurodegenerative disease Amyotrophic Lateral Sclerosis (ALS). Mutations in the dimeric enzyme superoxide dismutase (SOD1) have been linked to familial cases of ALS. Formation of toxic SOD1 aggregates is associated with both sporadic and familial ALS. Dr. Dokholyan aims to uncover the origin of mutant SOD1 toxicity at the molecular level by using a combination of computational and experimental approaches. Dr. Dokholyan also plans to identify the structure of SOD1 aggregates using rapid Discrete Molecular Dynamics. Determining the structure of SOD1 aggregates is critical for designing small molecules that can prevent or reverse the formation of these toxic aggregates.
Dr . Dokholyan is also working on solving the protein folding problem. Solving this problem is critical for making accurate protein structure/function predictions. Dr. Dokholyan is developing a hierarchy of interaction models, from simplified coarse-grained models to more detailed ones, and determining their interaction parameters. These interaction models are then used to perform simulations of protein models using a range of molecular dynamics simulations methodologies designed to accommodate the interaction models.
|
|
Simple yet Predictive Protein Models
Nikolay V. Dokholyan, University of North Carolina at Chapel Hill, School of Medicine, Campus Box 7260, Chapel Hill, NC 27599, USA
The traditional approach to computational biophysics studies of molecular systems is brute force molecular dynamics simulations under conditions of interest. The disadvantages of this traditional approach are that the time and length scales accessible to computer simulations often do not reach biologically-relevant scales. An alternative approach, which we call intuitive modeling, is hypothesis-driven and is based on tailoring simplified protein models to the systems of interest. Using intuitive modeling, the length and time scales that are achievable using simplified protein models by far exceed those of the traditional molecular dynamics simulations. We will describe several recent studies that signify the predictive power of simplified protein models within the intuitive modeling approach.
|
Reconstructing evasive protein states using NMR and molecular modeling: the Focal Adhesion Kinase story
Nikolay V. Dokholyan, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, School of Medicine, Campus Box 7260, Chapel Hill, NC 27599
It has been recognized that the dynamic properties of proteins often determine the function of proteins and, therefore, are of great importance. However these states are often inaccessible to experimental approaches. For example, the life span of protein conformations of interest may lie in measurement dead time - such states are "invisible" to experiments. In such a case, a set of computational approximations, led by experimental data in a kinetically accessible regime, may be developed to reconstruct these "invisible" states. We developed a computational approach to utilize amide hydrogen-exchange protection factors - a measurement of exposure of amino acids to solvent - to bias discrete molecular dynamics simulations, to unveil the ensemble of protein conformations consistent with observed protection factors. We have applied our methodology to cell adhesion protein focal adhesion kinase. We were able to identify weakly populated intermediates in these proteins and provided novel insights into how the conformational dynamics of these proteins may modulate ligand binding and dimerization.
|
|
|
|
|
|
|
|
|
