A reliable prediction of 3D protein structures from sequence data remains a big challenge due to both theoretical and computational difficulties. We have previously shown that our kinetostatic compliance method (KCM) implemented into the Protofold package can overcome some of the key difficulties faced by other de novo structure prediction methods, such as the very small time steps required by the molecular dynamics (MD) approaches or the very large number of samples needed by the Monte Carlo (MC) sampling techniques. In this article, we improve the free energy formulation used in Protofold by including the typically underrated entropic effects, imparted due to differences in hydrophobicity of the chemical groups, which dominate the folding of most water-soluble proteins. In addition to the model enhancement, we revisit the numerical implementation by redesigning the algorithms and introducing efficient data structures that reduce the expected complexity from quadratic to linear. Moreover, we develop and optimize parallel implementations of the algorithms on both central and graphics processing units (CPU GPU) achieving speed-ups up to two orders of magnitude on the GPU. Our simulations are consistent with the general behavior observed in the folding process in aqueous solvent, confirming the effectiveness of model improvements. We report on the folding process at multiple levels; namely, the formation of secondary structural elements and tertiary interactions between secondary elements or across larger domains. We also observe significant enhancements in running times that make the folding simulation tractable for large molecules.
On the other hand, methods that utilize models formulated empirically or obtained from physical principles are less reliable and more time-consuming, but apply to a wider range of folding simulations @cite_11 . These methods range from de novo @cite_68 @cite_52 to ab initio @cite_85 @cite_14 prediction techniques. Here we briefly review some of the common ab initio approaches, namely sampling methods and MD simulations @cite_11 .
@cite_14 @cite_85 @cite_52 @cite_68 @cite_11
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■ Abstract Considerable recent progress has been made in the field of ab initio protein structure prediction, as witnessed by the third Critical Assessment of Structure Prediction (CASP3). In spite of this progress, much work remains, for the field has yet to produce consistently reliable ab initio structure prediction protocols. In this work, we review the features of current ab initio protocols in an attempt to highlight the foundations of recent progress in the field and suggest promising directions for future work. Abstract Ab initio protein folding methods have been developing rapidly over the past few years and, at the last Critical assessment of methods of protein structure prediction (CASP) meeting, it was shown that important progress has been made in generating structure from sequence. Both methods based on statistical potentials and methods using physics-based potentials have shown improvements. Most current methods use statistics-based potentials and the development of these is ongoing. Additionally, the inclusion of multiple sequence data in the algorithms in order to aid in finding the native structure is a common theme. The use of physics-based potentials is less developed, which means that less progress has been made in understanding why a sequence forms a structure. The prediction of the structures and interactions of biological macromolecules at the atomic level and the design of new structures and interactions are critical tests of our understanding of the interatomic interactions that underlie molecular biology. Equally important, the capability to accurately predict and design macromolecular structures and interactions would streamline the interpretation of genome sequence information and allow the creation of macromolecules with new and useful functions. This review summarizes recent progress in modeling that suggests that we are entering an era in which high-resolution prediction and design will make increasingly important contributions to biology and medicine. The prediction of protein structure from amino acid sequence is a grand challenge of computational molecular biology. By using a combination of improved low- and high-resolution conformational sampling methods, improved atomically detailed potential functions that capture the jigsaw puzzle–like packing of protein cores, and high-performance computing, high-resolution structure prediction ( The prediction of the three-dimensional native structure of proteins from the knowledge of their amino acid sequence, known as the protein folding problem, is one of the most important yet unsolved issues of modern science. Since the conformational behaviour of flexible molecules is nothing more than a complex physical problem, increasingly more physicists are moving into the study of protein systems, bringing with them powerful mathematical and computational tools, as well as the sharp intuition and deep images inherent to the physics discipline. This work attempts to facilitate the first steps of such a transition. In order to achieve this goal, we provide an exhaustive account of the reasons of enormous relevance underlying the protein folding problem and summarize the present-day status of the methods aimed at solving it. We also provide an introduction to the particular structure of these biological heteropolymers, and we physically define the problem stating the assumptions behind this (commonly imp...