University of Southern California
2013 Nobel laureate in Chemistry
Talk Title: Simulating the action of Complex Biological Systems
Despite the enormous advances in structural studies of biological systems we are frequently left without a clear structure function correlation and cannot fully describe how different systems actually work. This introduces a major challenge for computer modeling approaches that are aimed at a realistic simulation of biological functions. The unresolved questions range from the elucidation of the basis for enzyme action to the understanding of the directional motion of complex molecular motors. Here we review the progress in simulating biological functions, starting with the early stages of the field and the development of QM/MM approaches for simulations of enzymatic reactions (1). We provide overwhelming support to the idea that enzyme catalysis is due to electrostatic preorganization and then move to the renormalization approaches aimed at modeling long time processes, demonstrating that dynamical effects cannot change the rate of the chemical steps in enzymes (2). Next we describe the use our electrostatic augmented coarse grained (CG) model (2) and the renormalization method to simulate the action of different challenging complex systems. It is shown that our CG model produces, for the first time, realistic landscapes for vectorrial process such as the actions of F1 ATPase (3,4), F0 ATPase (5) and myosinV (6). It is also shown that such machines are working by exploiting free energy gradients and cannot just use Brownian motions as the vectorial driving force. Significantly, at present, to the best of our knowledge, theses studies are the only studies that reproduced consistently (rather than assumed) a structure based vectorial action of molecular motors. We also describe a breakthrough in CG modeling of voltage activated ion channels (7). We also outline a recent simulation of the tag of war between staled elongated peptide in the ribosome and the translocon as an illustration of the power of our CG approach (8). The emerging finding from all of our simulations is that electrostatic effects are the key to generating functional free energy landscapes. Finally we present some thought on the future of the field, taking drug resistance as an example (9)1. Electrostatic Basis for Enzyme Catalysis, A. Warshel, P. K. Sharma, M. Kato, Y. Xiang, H. Liu and M. H. M. Olsson, Chem. Rev., 106, 3210 (2006).
2. Coarse-Grained (Multiscale) Simulations in Studies of Biophysical and Chemical Systems, S. C. L.Kamerlin, S. Vicatos, A. Dryga and A. Warshel, , Ann. Rev. Phys. Chem. 62,41 (2011).
3. Electrostatic Origin of The Mechanochemical Rotary Mechanism And The Catalytic Dwell of F1-ATPase, S. Mukherjee and A.Warshel, Proc. Natl. Acad. Sci. USA ,108, 20550 (2011).
4. Torque, chemistry and efficiency in molecular motors: a study of the rotary–chemical coupling in F1-ATPase, S. Mukherjee, R. B.Prasad and A. Warshel, QRB, Discovery, 48, 395–403 (2015).
5. Realistic simulations of the coupling between the protomotive force and the mechanical rotation of the F0-ATPase, Proc. Natl. Acad. Sci. USA, 109,14876 (2012).
6. Electrostatic origin of the unidirectionality of walking myosin V motors, S. Mukherjee and A. Warshel , Proc. Natl. Acad. Sci. USA, ,110 , 17326-17331 ( 2013).
7. Converting Structural Information Into an Allosteric-Energy-Based Picture for Elongation Factor Tu Activation by The Ribosome, A. J. Adamczyk and A. Warshel, Proc. Natl. Acad. Sci. USA ,108 ,9827 (2011).
8. Simulating the pulling of stalled elongated peptide from the ribosome by the translocon, A. Rychkova, S. Mukherjee, R. P. Bora, and A. Warshel, Proc. Natl. Acad. Sci. USA ,110, 10195-10200 (2013) .
9. Prediction of Drug Resistance muation of HIV Protease, H. Ishikita and A. Warshel, Angew. Chem. Int. Ed., 47,697-700 (2008).
The Technion - Israel Institute of Technology
2004 Nobel laureate in Chemistry
Talk Title: The Personalized Medicine Revolution: Are We Going to Cure all Diseases and at What Price?
Many important drugs such as penicillin were discovered by serendipity. Other major drugs like the cholesterol-reducing statins were discovered using more advanced technologies, such as screening of large chemical libraries. In all these cases, the mechanism of action of the drug were largely unknown at the time of their discovery and was unraveled later. With the realization that patients with apparently similar diseases – breast or prostate cancer, for example - respond differently to similar treatments, we have begun to understand that the molecular bases of what we thought is the same disease entity, are different. Thus, breast or prostate cancers appear to be sub-divided to smaller distinct classes according to their molecular characteristics. As a result, we are exiting the era where the treatment of many diseases is “one size fits all”, and enter a new era of “personalized medicine” where the treatment is tailored according to the patient’s molecular/mutational profile. Here, the understanding of the mechanism will drive the development of new drugs. This era will be characterized initially by the development of technologies to sequence individual genomes, transcriptomes, proteomes and metabolomes, followed by identification and characterization of new disease-specific molecular markers and drug targets, and by design of novel, mechanism-based drugs to these targets. This era will be also accompanied by complex bioethical problems, where genetic information of large populations will become available, and protection of privacy will become an important issue.