De novo prediction of polypeptide conformations using dihedral probability grid Monte Carlo methodology.
De novo prediction of polypeptide conformations using dihedral probability grid Monte Carlo methodology.
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使用二面概率网格蒙特卡罗方法从头预测多肽构象。
DOI：
10.1002/pro.5560040618
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
Goddard3rd,WA
中科院分区：
文献类型：
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作者：
Evans,JS;Mathiowetz,AM;Chan,SI;Goddard3rd,WA
我们测试了二面体概率网格蒙特卡罗（DPG‐MC）方法来确定多肽的最佳构象，并将其应用于预测两种已知溶液核磁共振结构的肽的低能系集：整合素受体肽（YGRGDSP, Type II β‐turn）和S3 α‐螺旋肽（YMSEDELKAAEAAFKRHGPT）。DPG‐MC涉及重要性抽样，当前局部最小值附近的局部随机步进，以及接受或拒绝新结构的Metropolis抽样标准。内部坐座值基于侧链特定的二面角概率分布（来自高分辨率蛋白质晶体结构的分析）。DPG‐MC的重要特征是：(1)每个DPG‐MC步骤从一个离散网格中选择扭转角（ϕ， ψ, χ），然后直接应用于结构。扭转角增量可以取S = 60、30、15、10或5°，具体取决于应用。(2) DPG‐MC利用与温度相关的概率函数(P)结合Metropolis采样来接受或拒绝新的结构。对于每个肽，我们发现与位于DPG‐MC的低能构象系系的已知结构密切一致。这表明DPG‐MC将有助于预测其他多肽的构象。
We tested the dihedral probability grid Monte Carlo (DPG‐MC) methodology to determine optimal conformations of polypeptides by applying it to predict the low energy ensemble for two peptides whose solution NMR structures are known: integrin receptor peptide (YGRGDSP, Type II β‐turn) and S3 α‐helical peptide (YMSEDELKAAEAAFKRHGPT).DPG‐MC involves importance sampling, local random stepping in the vicinity of a current local minima, and Metropolis sampling criteria for acceptance or rejection of new structures. Internal coordinate values are based on side‐chain‐specific dihedral angle probability distributions (from analysis of high‐resolution protein crystal structures). Important features of DPG‐MC are: (1) Each DPG‐MC step selects the torsion angles (ϕ, ψ, χ) from a discrete grid that are then applied directly to the structure. The torsion angle increments can be taken as S = 60, 30, 15, 10, or 5°, depending on the application. (2) DPG‐MC utilizes a temperature‐dependent probability function (P) in conjunction with Metropolis sampling to accept or reject new structures.For each peptide, we found close agreement with the known structure for the low energy conformational ensemble located with DPG‐MC. This suggests that DPG‐MC will be useful for predicting conformations of other polypeptides.