Department of Computational Medicine and Bioinformatics.
Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
Bioinformatics. 2020 Jun 1;36(12):3758-3765. doi: 10.1093/bioinformatics/btaa234.
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.
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 34.3 s, which was significantly faster than the control methods. For the assessment on non-native backbones, FASPR showed an equivalent or better performance on I-TASSER 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.
The web server, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/FASPR and https://github.com/tommyhuangthu/FASPR.
Supplementary data are available at Bioinformatics online.
蛋白质的结构和功能主要取决于侧链原子之间的相互作用。因此,准确的蛋白质侧链堆积(PSCP)是蛋白质结构预测和蛋白质设计的关键步骤。然而,尽管这个问题很重要,但是当前 PSCP 程序的准确性和速度仍然不能令人满意。
我们提出了 FASPR,通过使用优化的评分函数结合确定性搜索算法来实现快速准确的 PSCP。我们将 FASPR 的性能与四种最先进的 PSCP 方法(CISRR、RASP、SCATD 和 SCWRL4)在天然和非天然蛋白质骨架上进行了比较。在天然骨架的评估中,FASPR 采用严格的 20°容限标准,成功预测了所有侧链二面角的 69.1%,表现良好,优于 SCWRL4、CISRR、RASP 和 SCATD,它们分别成功预测了 68.8%、68.6%、67.8%和 61.7%。此外,FASPR 仅用 34.3s 即可完成 379 个测试蛋白质结构的堆积,速度明显快于对照方法。在非天然骨架的评估中,FASPR 在 I-TASSER 预测的骨架和从实验结构中扰动的骨架上表现出等效或更好的性能。详细分析表明,FASPR 的主要优势在于与经过优化的评分函数相结合的死端消除和树分解的最佳组合,这使得 FASPR 在蛋白质结构建模和蛋白质设计研究中具有实际用途。
网络服务器、源代码和数据集可在 https://zhanglab.ccmb.med.umich.edu/FASPR 和 https://github.com/tommyhuangthu/FASPR 上免费获得。
补充数据可在 Bioinformatics 在线获得。