Computational Structural Biology II
MSCBIO/CMPBIO 2035

Format: Lecture
Instructor: Hagai Meirovitch
   Phone: (412)648-3338
   Email: hagaim@pitt.edu
   Office: BST3 3059

Credits: 3 Credits
Grading Basis: Letter Grade or Satisfactory/No Credit (Audit)
Prerequisites: MSCBIO/CMPBIO 2030, Intro to Computational Structural Biology
Day/Time/Location: TuTh/12:00 noon-1:30 pm/BST3 3073

The aim of computational structural biology is to understand the function of biological macromolecules such as proteins and nucleic acids in terms of fundamental physical forces.  An important goal, for example, is to predict the 3D structure of proteins based on sequence data, to predict the structural organization of protein-protein and protein-dna clusters, and to study the role of dynamics in molecular function (e.g., in enzymatic reactions).  These studies are important in rational drug design.  A great deal of progress has been made in the last 30 years, where sophisticated models and techniques have been developed consisting of quantum and statistical mechanics, simulation theory, electrostatics, etc.  However, the challenges are still enormous.  The objective of this course is to provide the student with a deeper understanding of the above disciplines as related to biological systems, which will enable him/her to participate in a state-of-the-art research.  The course will cover some topics in irreversible thermodynamics (e.g., the fluctuation-dissipation theorem, Onsager relations), complementary material in statistical mechanics (phase transitions), simulation theory as applied to polymer chains and proteins (Rosenbluth, dimerization, scanning, multicanonical and its derivatives), free energy and potential of mean force (e.g., Jarzynki), continuum electrostatics (Laplace, Poisson, and Poisson-Boltzmann equations, Born formula, generalized Born treatments), kinetics, and quantum mechanics.  Most of the material will be presented in lectures by the course instructor, with the balance being presented by expert guest lecturers.

Course Materials (password protected)