Shingate Prashant, Manoharan Malini, Sukhwal Anshul, Sowdhamini Ramanathan
National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore 560065, India.
BMC Bioinformatics. 2014 Sep 16;15(1):303. doi: 10.1186/1471-2105-15-303.
Various methods have been developed to computationally predict hotspot residues at novel protein-protein interfaces. However, there are various challenges in obtaining accurate prediction. We have developed a novel method which uses different aspects of protein structure and sequence space at residue level to highlight interface residues crucial for the protein-protein complex formation.
ECMIS (Energetic Conservation Mass Index and Spatial Clustering) algorithm was able to outperform existing hotspot identification methods. It was able to achieve around 80% accuracy with incredible increase in sensitivity and outperforms other existing methods. This method is even sensitive towards the hotspot residues contributing only small-scale hydrophobic interactions.
Combination of diverse features of the protein viz. energy contribution, extent of conservation, location and surrounding environment, along with optimized weightage for each feature, was the key for the success of the algorithm. The academic version of the algorithm is available at http://caps.ncbs.res.in/download/ECMIS/ECMIS.zip.
已经开发出各种方法来通过计算预测新型蛋白质-蛋白质界面处的热点残基。然而,在获得准确预测方面存在各种挑战。我们开发了一种新方法,该方法在残基水平上利用蛋白质结构和序列空间的不同方面来突出对蛋白质-蛋白质复合物形成至关重要的界面残基。
ECMIS(能量守恒质量指数和空间聚类)算法能够优于现有的热点识别方法。它能够达到约80%的准确率,同时灵敏度有惊人的提高,并且优于其他现有方法。该方法甚至对仅贡献小规模疏水相互作用的热点残基也很敏感。
蛋白质的多种特征(即能量贡献、保守程度、位置和周围环境)的组合,以及对每个特征的优化权重,是该算法成功的关键。该算法的学术版本可在http://caps.ncbs.res.in/download/ECMIS/ECMIS.zip获取。