Fast Computation of the Fitness Function for Protein Folding Prediction in a 2D Hydrophobic-Hydrophilic Model

Protein Folding Prediction (PFP) is essentially an energy minimization problem formalised by the definition of a fitness function. Several PFP models have been proposed including the Hydrophobic-Hydrophilic (HP) model, which is widely used as a test-bed for evaluating new algorithms. The calculation of the fitness is the major computational task in determining the native conformation of a protein in the HP model and this paper presents a new efficient search algorithm (ESA) for deriving the fitness value requiring only O(n) complexity in contrast to the full search approach, which takes O(n2). The improved efficiency of ESA is achieved by exploiting some intrinsic properties of the HP model, with a resulting reduction of more than 50% in the overall time complexity when compared with the previously reported Caching Approach, with the added benefit that the additional space complexity is linear instead of quadratic

Computers and Liquid State Statistical Mechanics

The advent of electronic computers has revolutionised the application of statistical mechanics to the liquid state. Computers have permitted, for example, the calculation of the phase diagram of water and ice and the folding of proteins. The behaviour of alkanes adsorbed in zeolites, the formation of liquid crystal phases and the process of nucleation. Computer simulations provide, on one hand, new insights into the physical processes in action, and on the other, quantitative results of greater and greater precision. Insights into physical processes facilitate the reductionist agenda of physics, whilst large scale simulations bring out emergent features that are inherent (although far from obvious) in complex systems consisting of many bodies. It is safe to say that computer simulations are now an indispensable tool for both the theorist and the experimentalist, and in the future their usefulness will only increase. This chapter presents a selective review of some of the incredible advances in condensed matter physics that could only have been achieved with the use of computers.Comment: 22 pages, 2 figures. Chapter for a boo

Progress Towards Petascale Applications in Biology: Status in 2006

Petascale computing is currently a common topic of discussion in the high performance computing community. Biological applications, particularly protein folding, are often given as examples of the need for petascale computing. There are at present biological applications that scale to execution rates of approximately 55 teraflops on a special-purpose supercomputer and 2.2 teraflops on a general-purpose supercomputer. In comparison, Qbox, a molecular dynamics code used to model metals, has an achieved performance of 207.3 teraflops. It may be useful to increase the extent to which operation rates and total calculations are reported in discussion of biological applications, and use total operations (integer and floating point combined) rather than (or in addition to) floating point operations as the unit of measure. Increased reporting of such metrics will enable better tracking of progress as the research community strives for the insights that will be enabled by petascale computing.This research was supported in part by the Indiana Genomics Initiative and the Indiana Metabolomics and Cytomics Initiative. The Indiana Genomics Initiative of Indiana University and the Indiana Metabolomics and Cytomics Initiative of Indiana University are supported in part by Lilly Endowment, Inc. The authors also wish to thank IBM, Inc. for support via Shared University Research Grants and partnerships via IU’s relationship as an IBM Life Sciences Institute of Innovation. Indiana University also thanks the TeraGrid partners; IU’s participation in the TeraGrid is funded by National Science Foundation grant numbers 0338618, 0504075, and 0451237. The early development of this paper was supported by a Fulbright Senior Scholars award from the Council for International Exchange of Scholars (CIES) and the United States Department of State to Dr. Craig A. Stewart; Matthias Mueller and the Technische Universität Dresden were hosts. Many reviewers contributed to the improvement of the ideas expressed in this paper and are gratefully appreciated; Thom Dunning, Robert Germain, Chris Mueller, Jim Phillips, Richard Repasky, Ralph Roskies, and Allan Snavely are thanked particularly for their insights

Twists and turns in protein folding

AbstractWith the burgeoning number of protein sequences now appearing as a result of all the genome efforts, the race is on to develop computer models of how proteins take shape. Michael Gross reports

Biomolecular simulations at the exascale: From drug design to organelles and beyond.

The rapid advancement in computational power available for research offers to bring not only quantitative improvements, but also qualitative changes in the field of biomolecular simulation. Here, we review the state of biomolecular dynamics simulations at the threshold to exascale resources becoming available. Both developments in parallel and distributed computing will be discussed, providing a perspective on the state of the art of both. A main focus will be on obtaining binding and conformational free energies, with an outlook to macromolecular complexes and (sub)cellular assemblies

Validating DOE's Office of Science "capability" computing needs.

A study was undertaken to validate the 'capability' computing needs of DOE's Office of Science. More than seventy members of the community provided information about algorithmic scaling laws, so that the impact of having access to Petascale capability computers could be assessed. We have concluded that the Office of Science community has described credible needs for Petascale capability computing

A Multiobjective Approach Applied to the Protein Structure Prediction Problem

Interest in discovering a methodology for solving the Protein Structure Prediction problem extends into many fields of study including biochemistry, medicine, biology, and numerous engineering and science disciplines. Experimental approaches, such as, x-ray crystallographic studies or solution Nuclear Magnetic Resonance Spectroscopy, to mathematical modeling, such as minimum energy models are used to solve this problem. Recently, Evolutionary Algorithm studies at the Air Force Institute of Technology include the following: Simple Genetic Algorithm (GA), messy GA, fast messy GA, and Linkage Learning GA, as approaches for potential protein energy minimization. Prepackaged software like GENOCOP, GENESIS, and mGA are in use to facilitate experimentation of these techniques. In addition to this software, a parallelized version of the fmGA, the so-called parallel fast messy GA, is found to be good at finding semi-optimal answers in reasonable wall clock time. The aim of this work is to apply a Multiobjective approach to solving this problem using a modified fast messy GA. By dividing the CHARMm energy model into separate objectives, it should be possible to find structural configurations of a protein that yield lower energy values and ultimately more correct conformations