Saeed Izadi izadi


Department of Early Stage Pharmaceutical Development, Genentech Inc., South San Francisco, 2016-present


Ph.D., Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, 2016
M.Sc., Department of Aerospace Engineering, Amirkabir University of Technogoly, Tehran, Iran, 2011
B.Sc., Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran, 2008


Dr. Alexey V. Onufriev, Departments of Computer Science and Physics


Optimal Point Charge (OPC) explicit water model
Simplified classical water models are currently an indispensable component in practical atomistic simulations. Yet, despite several decades of intense research, these models are still far from perfect. We have developed a new approach to constructing widely used point charge water models, which is completely different from the mainstream water modeling parametrization techniques. In contrast to the conventional approach, we do not impose any geometry constraints on the model other than the symmetry. Instead, we optimize the distribution of point charges to best describe the “electrostatics” of the water molecule. We used the new approach to develop 4-point OPC and 3-point OPC3 rigid water models, that are shown to reproduce a comprehensive set of bulk properties significantly more accurately than commonly used rigid models. The OPC water model is available in the solvent library of Amber 2015 and Amber 2016 molecular dynamics (MD) software packages (see DOWNLOADS for information about using OPC in GROMACS).

Speeding up the calculation of electrostatic interactions
Appropriate treatment of electrostatic interactions are of fundamental importance in MD simulations of biological systems. Due to computational costs, however, calculation of these pairwise electrostatic interactions can become intractable in realistic biomolecular systems containing large numbers of atoms (e. g. hundred throusands to million atoms). We have developed an algorithm (hierarchical charge partitioning (HCP) approximation) that computes the long-range component of intermolecular interactions in an approximate manner to speed up the computations. I have developed computational and mathematical methods that can approximate large charge distributions with only a smaller set of point charges that optimally represent the original electrostatic potential. We use these approximate point charges for calculating distant electrostatic interactions while using the full set of charges for nearby interactions. The generalized Born model developed based on this method (the GB_HCP tool now available in Amber 2016) speeds up molecular dynamics simulations by two orders of magnitude, depending on the structure size, compared to the original generalized Born model without approximation.

Improving the accuracy of implicit solvent generalized Born model
An accurate description of the solvent environment is essential to estimate free energy of solute-solvent interactions in structural and chemical processes, such as folding or conformational transitions of proteins, DNA, RNA, and polysaccharides, or transport of drugs across biological membranes. The explicit solvent framework, within which the movements of individual solvent molecules are explicitly calculated, suffers from considerable computational costs due to the need to account for the very large number of solvent degree of freedom. An alternative is the implicit solvent framework that is based on representing solvent as a continuous medium instead of individual explicit molecules. In this project, I contributed to the development of the GBNSR6 tool, now available in Amber 2015, a surface-based GB model that is shown to deliver significant accuracy in electrostatic binding free energy calculations.



Google Scholar Profile

  1. Saeed Izadi, Ramu Anandakrishnan, and Alexey V. Onufriev. Implicit solvent model for million-atom atomistic simulations: insights into the organization of 30-nm chromatin fiber. Journal of Chemical Theory and Computation , 2016

  2. Saeed Izadi, and Alexey V. Onufriev. Accuracy limit of rigid 3-point water models. J. Chem. Phys.,145, 074501, 2016

  3. Saeed Izadi, Boris Aguilar, and Alexey V. Onufriev. Protein–Ligand Electrostatic Binding Free Energies from Explicit and Implicit Solvation. Journal of Chemical Theory and Computation,11 (9), pp 4450–4459, 2015

  4. D.A. Case, J.T. Berryman, R.M. Betz, D.S. Cerutti, T.E. Cheatham, III, T.A. Darden, R.E. Duke, T.J. Giese, H. Gohlke, A.W. Goetz, N. Homeyer, S. Izadi, P. Janowski, J. Kaus, A. Kovalenko, T.S. Lee, S. LeGrand, P. Li, T. Luchko, R. Luo, B. Madej, K.M. Merz, G. Monard, P. Needham, H. Nguyen, H.T. Nguyen, I. Omelyan, A. Onufriev, D.R. Roe, A. Roitberg, R. Salomon-Ferrer, C.L. Simmerling, W. Smith, J. Swails, R.C. Walker, J. Wang, R.M. Wolf, X. Wu, D.M. York and P.A. Kollman (2015), AMBER 2015, University of California, San Francisco.

  5. Saeed Izadi, Ramu Anandakrishnan, and Alexey V. Onufriev. Building Water Models: A Different Approach. The Journal of Physicl Chemistry Letters, 5(21), pp 3863-3871, 2014

  6. *Ramu Anandakrishnan, *Charles Baker, *Saeed Izadi and Alexey V. Onufriev. Point Charges Optimally Placed to Represent the Multipole Expansion of Charge Distributions. PLoS ONE , 8(7), e67715, 2013
    *These authors contributed equally to this work.

  7. Saeed Izadi, Shibabrat Naik, and Rafael V. Davalos. Role of Magnus effect in cell separation using dielectrophoresis, 2013

  8. Karimian, S. M. H. and Izadi, S., Bin Size Determination for the Measurement of Mean Flow Velocity in Molecular Dynamics Simulations. International Journal for Numerical Methods in Fluids , 71: 930-938, 2013

  9. Karimian, S. M. H., Izadi, S. and Farimani, A. B., A Study on the Measurement of Mean Velocity and Its Convergence in Molecular Dynamics Simulations. International Journal for Numerical Methods in Fluids , 67:2130-2140, 2011