Predicting peptide structures using NMR data and deterministic global optimization

The ability to analyze large molecular structures by NMR techniques require s efficient methods for structure calculation. Currently, there are several widely available methods for tackling these problems, which, in general, r ely on the optimization of penalty-type target functions to satisfy the con formational restraints. Typically, these methods combine simulated annealin g protocols with molecular dynamics and local minimization, either in dista nce or torsional angle space. In this work, both a novel formulation and al gorithmic procedure for the solution of the NMR structure prediction proble m is outlined. First, the unconstrained, penalty-type structure prediction problem is reformulated using nonlinear constraints, which can be individua lly enumerated for all, or subsets, of the distance restraints. In this way , the violation can be controlled as a constraint, in contrast to the usual penalty-type restraints. In addition, the customary simplified objective f unction is replaced by a full atom force field in the torsional angle space . This guarantees a better description of atomic interactions, which dictat e the native structure of the molecule along with the distance restraints. The second novel portion of this work involves the solution method. Rather than pursue the typical simulated annealing procedure, this work relies on a deterministic method, which theoretically guarantees that the global solu tion can be located. This branch and bound technique, based on the alpha BB algorithm, has already been successfully applied to the identification of global minimum energy structures of peptides modeled by full atom force fie lds. Finally, the approach is applied to the Compstatin structure predictio n, and it is found to possess some important merits when compared to existi ng techniques. (C) 1999 John Wiley & Sons, Inc.