Javascript must be enabled to continue!
GPU-I-TASSER: a GPU accelerated I-TASSER protein structure prediction tool
View through CrossRef
Abstract
Motivation
Accurate and efficient predictions of protein structures play an important role in understanding their functions. Iterative Threading Assembly Refinement (I-TASSER) is one of the most successful and widely used protein structure prediction methods in the recent community-wide CASP experiments. Yet, the computational efficiency of I-TASSER is one of the limiting factors that prevent its application for large-scale structure modeling.
Results
We present I-TASSER for Graphics Processing Units (GPU-I-TASSER), a GPU accelerated I-TASSER protein structure prediction tool for fast and accurate protein structure prediction. Our implementation is based on OpenACC parallelization of the replica-exchange Monte Carlo simulations to enhance the speed of I-TASSER by extending its capabilities to the GPU architecture. On a benchmark dataset of 71 protein structures, GPU-I-TASSER achieves on average a 10× speedup with comparable structure prediction accuracy compared to the CPU version of the I-TASSER.
Availability and implementation
The complete source code for GPU-I-TASSER can be downloaded and used without restriction from https://zhanggroup.org/GPU-I-TASSER/.
Supplementary information
Supplementary data are available at Bioinformatics online.
Oxford University Press (OUP)
Title: GPU-I-TASSER: a GPU accelerated I-TASSER protein structure prediction tool
Description:
Abstract
Motivation
Accurate and efficient predictions of protein structures play an important role in understanding their functions.
Iterative Threading Assembly Refinement (I-TASSER) is one of the most successful and widely used protein structure prediction methods in the recent community-wide CASP experiments.
Yet, the computational efficiency of I-TASSER is one of the limiting factors that prevent its application for large-scale structure modeling.
Results
We present I-TASSER for Graphics Processing Units (GPU-I-TASSER), a GPU accelerated I-TASSER protein structure prediction tool for fast and accurate protein structure prediction.
Our implementation is based on OpenACC parallelization of the replica-exchange Monte Carlo simulations to enhance the speed of I-TASSER by extending its capabilities to the GPU architecture.
On a benchmark dataset of 71 protein structures, GPU-I-TASSER achieves on average a 10× speedup with comparable structure prediction accuracy compared to the CPU version of the I-TASSER.
Availability and implementation
The complete source code for GPU-I-TASSER can be downloaded and used without restriction from https://zhanggroup.
org/GPU-I-TASSER/.
Supplementary information
Supplementary data are available at Bioinformatics online.
Related Results
Optimising tool wear and workpiece condition monitoring via cyber-physical systems for smart manufacturing
Optimising tool wear and workpiece condition monitoring via cyber-physical systems for smart manufacturing
Smart manufacturing has been developed since the introduction of Industry 4.0. It consists of resource sharing and networking, predictive engineering, and material and data analyti...
Endothelial Protein C Receptor
Endothelial Protein C Receptor
IntroductionThe protein C anticoagulant pathway plays a critical role in the negative regulation of the blood clotting response. The pathway is triggered by thrombin, which allows ...
Vina-GPU 2.1: towards further optimizing docking speed and precision of AutoDock Vina and its derivatives
Vina-GPU 2.1: towards further optimizing docking speed and precision of AutoDock Vina and its derivatives
AbstractAutoDock Vina and its derivatives have established themselves as a prevailing pipeline for virtual screening in contemporary drug discovery. Our Vina-GPU method leverages t...
Protein contact distance and structure prediction driven by deep learning
Protein contact distance and structure prediction driven by deep learning
Proteins, fundamental building blocks of living organisms, play a crucial role in various biological processes. Understanding protein structure is essential for unraveling their fu...
Vina-GPU 2.0:further accelerating AutoDock Vina and its derivatives with GPUs
Vina-GPU 2.0:further accelerating AutoDock Vina and its derivatives with GPUs
Modern drug discovery typically faces large virtual screens from huge compound databases where multiple docking tools are involved for meeting various real scenes or improving the ...
Robot tool use: A survey
Robot tool use: A survey
Using human tools can significantly benefit robots in many application domains. Such ability would allow robots to solve problems that they were unable to without tools. However, r...
Accelerated hydrologic modeling: ParFlow GPU implementation
Accelerated hydrologic modeling: ParFlow GPU implementation
<p>&#160; ParFlow is known as a numerical model that simulates the hydrologic cycle from the bedrock to the top of the plant canopy. The original codebase pro...
IdentPMP: identification of moonlighting proteins in plants using sequence-based learning models
IdentPMP: identification of moonlighting proteins in plants using sequence-based learning models
Background
A moonlighting protein refers to a protein that can perform two or more functions. Since the current moonlighting protein prediction tools mainly focus on...


