Accommodating protein flexibility for structure based drug design
Data can also be gained with Brownian dynamics and discrete molecular dynamics (9) or more specialized approaches, e.g.to define hinges between domains, as Stone Hinge (12), Hinge Prot (13) and t CONCOORD (14), which predict conformational flexibility based on geometrical considerations.The method is implemented in Predy Flexy web server.Results are similar to those obtained with the most recent, cutting-edge methods based on direct learning of flexibility data conducted with sophisticated algorithms. X-ray experiments have been valuable tools to understand the intimate relation between protein structures and biological functions.The most popular approaches consist in atomistic molecular dynamics simulations which are available through different packages, such as Gromacs (5), Amber (6), NAMD (7) or Charmm (8).Principal component analyses of the resulting data allow identifying regions involved in the different type of motions and provide relevant information about the visited conformational space.The prediction from the sequence is done with support vector machines (SVMs) (26,27).We take advantage of the method we previously elaborated to predict local protein structures.
rigid or flexible, using simple statistical analyses of B-factor values (15,16).
So, we have proposed to look at protein flexibility using novel methods: (i) using a structural alphabet and (ii) combining classical X-ray B-factor data and molecular dynamics simulations.
First, we established a library composed of structural prototypes (LSPs) to describe protein structure by a limited set of recurring local structures.
B-factors available with X-ray structures were first used as the main criteria to define protein rigidity and flexibility.
Nowadays, the distinction between flexible and rigid regions takes advantage of dedicated approaches for exploring dynamics.