Probing protein orientation near charged surfaces: a BEM-based implicit-solvent model that compares well with all-atom simulation
Tuesday, March 4, 2014
3:30PM – 5PM
Boundary element methods (BEM) can be used to solve elliptic partial differential equations via an integral equation, and a boundary mesh. In biophysics, they are used for the Poisson-Boltzmann system of protein electrostatics, as an alternative to finite-difference or finite-element methods. We recently developed a BEM solver using Python and GPUs that also uses fast algorithms—we call it PyGBe. It has the feature of being able to deal with multiple surfaces; this is useful to treat proteins that have pockets of water and to deal with a near-surface layer devoid of salts (Stern layer). To show that it works, we did a methodical comparison with a well-loved finite-difference solver called APBS. PyGBe is competitive, especially as you move to higher accuracies. Now, we are extending PyGBe to study interactions between proteins and surfaces of fixed charge, which is a situation relevant to biosensors. With a fresh, new analytical solution using spherical geometries, we were able to do verification and convergence analyses. Our latest effort shows that it's possible to use BEM to find preferred orientations of proteins near a charged surface. And did we say it's open source?
Lorena A. Barba is Associate Professor of Mechanical and Aerospace Engineering at the George Washington University, in Washington DC. She has MSc and PhD degrees in Aeronautics from the California Institute of Technology and BSc and PEng degrees in Mechanical Engineering from Universidad Técnica Federico Santa María in Chile. Previous to joining GW, she was Assistant Professor of Mechanical Engineering at Boston University (2008–2013) and Lecturer/Senior Lecturer of Applied Mathematics at University of Bristol, UK (2004–2008). Barba is an Amelia Earhart Fellow of the Zonta Foundation (1999), an awardee of the Engineering and Physical Sciences Research Council (EPSRC) First Grant scheme (UK, 2007), an NVIDIA Academic Partner award recipient (2011), and a recipient of the National Science Foundation Early CAREER award (2012).
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