FASPR: an open-source tool for fast and accurate protein side-chain packing

被引:51
|
作者
Huang, Xiaoqiang [1 ]
Pearce, Robin [1 ]
Zhang, Yang [1 ,2 ]
机构
[1] Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Dept Biol Chem, Ann Arbor, MI 48109 USA
基金
美国国家科学基金会;
关键词
DEAD-END ELIMINATION; ROTAMER LIBRARY; ALGORITHM; PREDICTION; DESIGN; SPACE; MODEL;
D O I
10.1093/bioinformatics/btaa234
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: Protein structure and function are essentially determined by how the side-chain atoms interact with each other. Thus, accurate protein side-chain packing (PSCP) is a critical step toward protein structure prediction and protein design. Despite the importance of the problem, however, the accuracy and speed of current PSCP programs are still not satisfactory. Results: We present FASPR for fast and accurate PSCP by using an optimized scoring function in combination with a deterministic searching algorithm. The performance of FASPR was compared with four state-of-the-art PSCP methods (CISRR, RASP, SCATD and SCWRL4) on both native and non-native protein backbones. For the assessment on native backbones, FASPR achieved a good performance by correctly predicting 69.1% of all the side-chain dihedral angles using a stringent tolerance criterion of 20, compared favorably with SCWRL4, CISRR, RASP and SCATD which successfully predicted 68.8%, 68.6%, 67.8% and 61.7%, respectively. Additionally, FASPR achieved the highest speed for packing the 379 test protein structures in only 34.3s, which was significantly faster than the control methods. For the assessment on non-native backbones, FASPR showed an equivalent or better performance on ITASSER predicted backbones and the backbones perturbed from experimental structures. Detailed analyses showed that the major advantage of FASPR lies in the optimal combination of the dead-end elimination and tree decomposition with a well optimized scoring function, which makes FASPR of practical use for both protein structure modeling and protein design studies.
引用
收藏
页码:3758 / 3765
页数:8
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