Proteins, Vol. 42, 422-431 (2001)
Learning effective amino acid interactions through iterative
stochastic techniques
Cristian Micheletti, Flavio Seno, Jayanth R. Banavar and Amos Maritan
Link to online article.
The prediction of the three-dimensional structures of the native state
of proteins from the sequences of their amino acids is one of the most
important challenges in molecular biology. An essential ingredient to
solve this problem within coarse-grained models is the task of
deducing effective interaction potentials between the amino
acids. Over the years several techniques have been developed to
extract potentials that are able to discriminate satisfactorily
between the native and non-native folds of a pre-assigned protein
sequence. In general, when these potentials are used in actual
dynamical folding simulations, they lead to a drift of the native
structure outside the quasi-native basin. In this study, we present
and validate an approach to overcome this difficulty. By exploiting
several numerical and analytical tools we set up a rigorous iterative
scheme to extract potentials satisfying a pre-requisite of any viable
potential: the stabilization of proteins within their native basin
(less than 3-4 \AA$\ $ cRMS). The scheme is flexible and is
demonstrated to be applicable to a variety of parametrizations of the
energy function and provides, in each case, the optimal potentials.