Development of novel statistical potentials for protein fold recognition
Development of novel statistical potentials for protein fold recognition
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DOI：
10.1016/j.sbi.2004.03.002
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发表时间：
2004-04-01
影响因子：
6.8
通讯作者：
Thirumalai, D
中科院分区：
文献类型：
--
作者：
Buchete, NV;Straub, JE;Thirumalai, D
对蛋白质折叠识别、结构预测和蛋白质相互作用进行大规模研究的需要导致了蛋白质残基水平最小模型的新发展。有用的蛋白质力场的最低要求是它们能够成功识别天然构象。在描述蛋白质内特定相互作用的细节水平和使用最小蛋白质模型获得的准确性之间的平衡是当前许多蛋白质研究的焦点。最近的研究结果表明，在一个粗粒度的，残留水平的模型中引入明确的方向依赖性，提高了残基间电位识别的原生状态的能力。新的统计和优化计算算法可以用来获得准确的残基依赖的潜力，用于蛋白质折叠识别，更重要的是，结构预测。
The need to perform large-scale studies of protein fold recognition, structure prediction and protein-protein interactions has led to novel developments of residue-level minimal models of proteins. A minimum requirement for useful protein force-fields is that they be successful in the recognition of native conformations. The balance between the level of detail in describing the specific interactions within proteins and the accuracy obtained using minimal protein models is the focus of many current protein studies. Recent results suggest that the introduction of explicit orientation dependence in a coarse-grained, residue-level model improves the ability of inter-residue potentials to recognize the native state. New statistical and optimization computational algorithms can be used to obtain accurate residue-dependent potentials for use in protein fold recognition and, more importantly, structure prediction.