Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward

Protein Folding with Coarse-Grained Off-Lattice Models of the Polypeptide Chain

A hierarchical approach, together with the United Residue (UNRES) model of the polypeptide chain, is used to study protein structure prediction. First, an efficient method has been developed as an extension of the hierarchical approach for packing alpha-helices in proteins. The results for 42 proteins show that the approach reproduces native-like folds of alpha-helical proteins as low-energy local minima. Moreover, this technique successfully predicted the structure of the largest protein obtained so far with the UNRES force field in the sixth Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Next, two popular methods of global optimization are coupled, and the performance of the resulting method is compared with that of its components and with other global optimization techniques. The Replica-Exchange Method together with Monte Carlo-Minimization (REMCM) was applied to search the conformational space of coarse-grained protein systems described by the UNRES force field. In summary, REMCM located global minima for four proteins faster and more consistently than two of three other global optimization methods, while being comparable to the third method used for comparison. Finally, efficient methods for calculating thermodynamic averages were implemented with the UNRES force field, namely a Replica Exchange method (REM), a Replica Exchange Multicanonical method (REMUCA), and Replica Exchange Multicanonical with Replica Exchange (REMUCAREM), in both Monte Carlo (MC) and Molecular Dynamics (MD) versions. The algorithms were applied to one peptide and two small proteins (with alpha-helical and alpha+beta topologies). To compare the different methods, thermodynamic averages are calculated, and it is found that REM MD has the best performance. Consequently, free energy maps are computed with REM MD, to evaluate the folding behavior for all test systems.This work was supported by National Science Foundation (NSF) and National Institutes of Health (NIH). Support was also received from the National Foundation for Cancer Research. This research was carried out by using the resources of our 392-processor Beowulf cluster at the Baker Laboratory of Chemistry and Chemical Biology, Cornell University, the National Science Foundation Terascale Computing System at the Pittsburgh Supercomputer Center, and the National Center for Supercomputing Applications System at the University of Illinois at Urbana-Champaign

Packing helices in proteins by global optimization of a potential energy function

An efficient method has been developed for packing α-helices in proteins. It treats α-helices as rigid bodies and uses a simplified Lennard–Jones potential with Miyazawa–Jernigan contact-energy parameters to describe the interactions between the α-helical elements in this coarse-grained system. Global conformational searches to generate packing arrangements rapidly are carried out with a Monte Carlo-with-minimization type of approach. The results for 42 proteins show that the approach reproduces native-like folds of α-helical proteins as low-energy local minima of this highly simplified potential function

Free-energy-driven folding and thermodynamics of the 67-residue protein GS-alpha3W - A large-scale Monte Carlo study

Utilizing the computational power of a few thousand processors on a BlueGene/P, we have explored the folding mechanism of the 67-residue protein GS-alpha(3)W. Results from our large-scale simulation indicate a diffusion-collision mechanism for folding. However, the lower-than-expected frequency of native-like configurations at physiological temperatures indicates shortcomings of our energy function. Our results suggest that computational studies of large proteins call for redevelopment and reparametrization of force fields that in turn require extensive simulations only possible with the newly available supercomputers with computing powers reaching the petaflop range