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High performance computing in biology: multimillion atom simulations of nanoscale systems.

ABSTRACT: Computational methods have been used in biology for sequence analysis (bioinformatics), all-atom simulation (molecular dynamics and quantum calculations), and more recently for modeling biological networks (systems biology). Of these three techniques, all-atom simulation is currently the most computationally demanding, in terms of compute load, communication speed, and memory load. Breakthroughs in electrostatic force calculation and dynamic load balancing have enabled molecular dynamics simulations of large biomolecular complexes. Here, we report simulation results for the ribosome, using approximately 2.64 million atoms, the largest all-atom biomolecular simulation published to date. Several other nano-scale systems with different numbers of atoms were studied to measure the performance of the NAMD molecular dynamics simulation program on the Los Alamos National Laboratory Q Machine. We demonstrate that multimillion atom systems represent a 'sweet spot' for the NAMD code on large supercomputers. NAMD displays an unprecedented 85% parallel scaling efficiency for the ribosome system on 1024 CPUs. We also review recent targeted molecular dynamics simulations of the ribosome that prove useful for studying conformational changes of this large biomolecular complex in atomic detail.

SUBMITTER: Sanbonmatsu KY

PROVIDER: S-EPMC1868470 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature

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High performance computing in biology: multimillion atom simulations of nanoscale systems.

Sanbonmatsu K Y KY Tung C-S CS

Journal of structural biology 20061110 3

Computational methods have been used in biology for sequence analysis (bioinformatics), all-atom simulation (molecular dynamics and quantum calculations), and more recently for modeling biological networks (systems biology). Of these three techniques, all-atom simulation is currently the most computationally demanding, in terms of compute load, communication speed, and memory load. Breakthroughs in electrostatic force calculation and dynamic load balancing have enabled molecular dynamics simulat ...[more]

PMID: 17187988

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Project description:A part of current research has intensively been focused on the proliferation and metabolic processes governing biological systems. Since the advent of high throughput methodologies like microarrays, the load of genomic data has increased geometrically and along with that the need for computational methods which will interpret these data. In the present work we study in vitro the common proliferation and metabolic processes, which are combined to the common oncogenic pathways, as far as gene expression is concerned, between the T-cell acute lymphoblastic leukemia (CCRF-CEM) and the rhabdomyosarcoma (TE-671) cell lines. We present a computational approach, using cDNA microarrays, in order to identify commonalities between diverse biological systems. Our analysis predicted that JAK1, STAT1, PIAS2 and CDK4 are the driving forces in the two cell lines. This type of analysis can lead to the understanding of the common mechanisms that transform physiological cells to malignant, as well as it reveals a new holistic way to understandthe the dynamics of tumor onset as well as the mechanistics of oncogenic drivers. The present work is concerned with the common expressional profile of two cell lines: the T-cell acute lymphoblastic leukemia (CCRF-CEM) and the rhabdomyosarcoma (TE-671) cell lines. Our investigation was focused on the identification of genes that share a common expression profile between the two cell lines. Both cell lines are characterized by the fact that their differentiation has stopped at an early stage, before they mature to their final cell type. Normally, these cells would have matured and progressed into differentiated cells, constituting blood and muscle cells, respectively. At some unknown point, normal differentiation ceased for these cells and they became malignant. From that point on, to the first manifestation of symptoms of malignancy, there is a lack of knowledge regarding the mechanisms underlying oncogenesis. From these observations, the question whether two distinct cell types destined to fulfill different functions, manifest similar mechanisms of growth and progression due to their malignant character, arises. The present study was focused on the identification of the differential expression profiles underlying the two cell lines. A previous report studied the expression profile of seven ARMS cell lines possessing the PAX3-FKHR fusion gene, along with other cell lines of different tumor types (22). To our knowledge, this is the first time that a comparison between two totally different types of neoplasia, such as the CCRF-CEM and TE-671 cell lines, is attempted. These mechanisms are examined with purpose to identify common drivers that lead to the progression of tumor cells. We hereby propose a new computational approach for the investigation of common oncogenic drivers.

Project description:BACKGROUND:Protein-DNA docking is a very challenging problem in structural bioinformatics and has important implications in a number of applications, such as structure-based prediction of transcription factor binding sites and rational drug design. Protein-DNA docking is very computational demanding due to the high cost of energy calculation and the statistical nature of conformational sampling algorithms. More importantly, experiments show that the docking quality depends on the coverage of the conformational sampling space. It is therefore desirable to accelerate the computation of the docking algorithm, not only to reduce computing time, but also to improve docking quality. METHODS:In an attempt to accelerate the sampling process and to improve the docking performance, we developed a graphics processing unit (GPU)-based protein-DNA docking algorithm. The algorithm employs a potential-based energy function to describe the binding affinity of a protein-DNA pair, and integrates Monte-Carlo simulation and a simulated annealing method to search through the conformational space. Algorithmic techniques were developed to improve the computation efficiency and scalability on GPU-based high performance computing systems. RESULTS:The effectiveness of our approach is tested on a non-redundant set of 75 TF-DNA complexes and a newly developed TF-DNA docking benchmark. We demonstrated that the GPU-based docking algorithm can significantly accelerate the simulation process and thereby improving the chance of finding near-native TF-DNA complex structures. This study also suggests that further improvement in protein-DNA docking research would require efforts from two integral aspects: improvement in computation efficiency and energy function design. CONCLUSIONS:We present a high performance computing approach for improving the prediction accuracy of protein-DNA docking. The GPU-based docking algorithm accelerates the search of the conformational space and thus increases the chance of finding more near-native structures. To the best of our knowledge, this is the first ad hoc effort of applying GPU or GPU clusters to the protein-DNA docking problem.

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High performance computing in biology: multimillion atom simulations of nanoscale systems.

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High performance computing in biology: multimillion atom simulations of nanoscale systems.

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