|Simbios Talk by Alexey Onufriev, Virginia Tech, March 21, 2007
Title: Bio-molecular electrostatics for everyone: novel fast algorithms and web-based applications
Abstract:The ability to compute the electrostatic properties of molecules is often essential in understanding the mechanism behind their function; examples include catalytic activity, ligand binding, complex formation and charge transport. Traditionally, the availability of these types of calculations have often been limited, mainly due to conceptual complexity of the underlying algorithms and their high computational costs. The situation is beginning to change, for at least two reasons. Web-based applications are beginning to emerge that automate many of the complex steps involved. Concurrently, conceptually simpler approaches are being proposed that make bio-molecular electrostatics calculations significantly more facile and fast.
In this talk, I will describe two applications, H++ and GEM, currently being developed in my group that exemplify the above point.
1) H++ [ http://biophysics.cs.vt.edu/H++ ] is an automated web system that computes pK values of ionizable groups in macromolecules and adds missing hydrogen atoms according to the specified pH of the environment. Given a (PDB) structure file on input, H++ outputs the completed structure and provides a set of tools useful for analysis of electrostatic-related molecular properties.
2) GEM is a package designed to compute, visualize and analyze electrostatic potential generated by bio-molecules. Unlike the mainstream approaches, GEM is based on an analytical solution to the (linearized) Poisson-Boltzmann equation and is suitable for realistic biomolecules of virtually any size. Compared with the traditional numerical methods, the GEM approach is considerably less expensive computationally, yet accurate enough to be considered as an alternative. The usefulness of the approach is demonstrated by computing and analyzing the electrostatic potential generated by full capsid of the tobacco ring spot virus (half a million atoms) at atomic resolution. The details of the potential distribution on the molecular surface sheds light on the mechanism behind the high selectivity of the capsid to the viral RNA. These results are generated with the modest computational power of a desktop.