QMEAN: A comprehensive scoring function for model quality assessment

诱饵 计算机科学 成对比较 蛋白质结构预测 数据挖掘 生物系统 机器学习 算法 人工智能 蛋白质结构 化学 生物 生物化学 受体
作者
Pascal Benkert,Silvio C. E. Tosatto,Dietmar Schomburg
出处
期刊:Proteins [Wiley]
卷期号:71 (1): 261-277 被引量:996
标识
DOI:10.1002/prot.21715
摘要

Abstract In protein structure prediction, a considerable number of alternative models are usually produced from which subsequently the final model has to be selected. Thus, a scoring function for the identification of the best model within an ensemble of alternative models is a key component of most protein structure prediction pipelines. QMEAN, which stands for Qualitative Model Energy ANalysis, is a composite scoring function describing the major geometrical aspects of protein structures. Five different structural descriptors are used. The local geometry is analyzed by a new kind of torsion angle potential over three consecutive amino acids. A secondary structure‐specific distance‐dependent pairwise residue‐level potential is used to assess long‐range interactions. A solvation potential describes the burial status of the residues. Two simple terms describing the agreement of predicted and calculated secondary structure and solvent accessibility, respectively, are also included. A variety of different implementations are investigated and several approaches to combine and optimize them are discussed. QMEAN was tested on several standard decoy sets including a molecular dynamics simulation decoy set as well as on a comprehensive data set of totally 22,420 models from server predictions for the 95 targets of CASP7. In a comparison to five well‐established model quality assessment programs, QMEAN shows a statistically significant improvement over nearly all quality measures describing the ability of the scoring function to identify the native structure and to discriminate good from bad models. The three‐residue torsion angle potential turned out to be very effective in recognizing the native fold. Proteins 2008. © 2007 Wiley‐Liss, Inc.
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