Ghosh Soma, Vishveshwara Saraswathi
Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India ; I.I.Sc. Mathematics Initiative, Indian Institute of Science, Bangalore, 560012, India.
I.I.Sc. Mathematics Initiative, Indian Institute of Science, Bangalore, 560012, India.
F1000Res. 2014 Jan 21;3:17. doi: 10.12688/f1000research.3-17.v1. eCollection 2014.
Determining the correct structure of a protein given its sequence still remains an arduous task with many researchers working towards this goal. Most structure prediction methodologies result in the generation of a large number of probable candidates with the final challenge being to select the best amongst these. In this work, we have used Protein Structure Networks of native and modeled proteins in combination with Support Vector Machines to estimate the quality of a protein structure model and finally to provide ranks for these models. Model ranking is performed using regression analysis and helps in model selection from a group of many similar and good quality structures. Our results show that structures with a rank greater than 16 exhibit native protein-like properties while those below 10 are non-native like. The tool is also made available as a web-server ( http://vishgraph.mbu.iisc.ernet.in/GraProStr/native_non_native_ranking.html), where, 5 modelled structures can be evaluated at a given time.
根据蛋白质序列确定其正确结构仍然是一项艰巨的任务，许多研究人员都在朝着这个目标努力。大多数结构预测方法会生成大量可能的候选结构，最终的挑战是从这些候选结构中选出最佳的。在这项工作中，我们将天然蛋白质和模型蛋白质的蛋白质结构网络与支持向量机相结合，以评估蛋白质结构模型的质量，并最终为这些模型提供排名。模型排名使用回归分析进行，有助于从一组许多相似且质量良好的结构中选择模型。我们的结果表明，排名大于16的结构具有类似天然蛋白质的特性，而排名低于10的结构则不具有类似天然蛋白质的特性。该工具还作为一个网络服务器提供（http://vishgraph.mbu.iisc.ernet.in/GraProStr/native_non_native_ranking.html），在该服务器上，给定时间可以评估5个模型结构。
