Rabbit Anti-Human CASP5 monoclonal antibody, clone TE314-3

A set of software tools designed to study protein structure and kinetics has been developed. The core of these tools is a program called Folding Machine (FM) which is able to generate low resolution folding pathways using modest computational resources. The FM is based on a coarse-grained kinetic ab initio Monte-Carlo sampler that can optionally use information extracted from secondary structure prediction servers or from fragment libraries of local structure. The model underpinning this algorithm contains two novel elements: (a) the conformational space is discretized using the Ramachandran basins defined in the local phi-psi energy maps; and (b) the solvent is treated implicitly by rescaling the pairwise terms of the non-bonded energy function according to the local solvent environments. The purpose of this hybrid ab initio/knowledge-based approach is threefold: to cover the long time 'scales of folding, to generate useful 3-dimensional models of protein structures, and, to gain insight on the protein folding kinetics. Even though the algorithm is not yet fully developed, it has been used in a recent blind test of protein structure prediction (CASP5). The FM generated models within 6 Angstrom backbone rmsd for fragments of about 60-70 residues of alpha-helical proteins. For a CASP5 target that turned out to be natively unfolded, the trajectory obtained for this sequence uniquely failed to converge. Also, a new measure to evaluate structure predictions is presented and used along the standard CASP assessment methods. Finally, recent improvements in the prediction of beta-sheet structures are briefly described.

The accurate modeling of loop segments in proteins is an important component of the overall protein folding problem. The challenge of the protein folding problem is to understand and predict the formation of the native three-dimensional structure of a protein given its primary amino acid sequence. In this paper, two methods are introduced to determine the structure of loop segments within the context of ASTRO-FOLD, an overall approach for the structure prediction of proteins. These approaches address a more difficult problem than that of traditional loop prediction in the sense that the separation distances between the loop stem regions are not assumed to be known a priori. When considering these additional degrees of freedom, the proposed methods perform extremely well, which is a result of both new modeling and algorithmic developments. In particular, the methods are validated on a testbed of benchmark protein systems, as well as a number of blind predictions from the recent CASP5 experiment. (c) 2004 Elsevier Ltd. All rights reserved.