Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles
Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles
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DOI：
10.1016/j.bpj.2014.02.009
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发表时间：
2014-03-18
影响因子：
3.4
通讯作者：
Das, Rhiju
中科院分区：
文献类型：
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作者：
Beauchamp, Kyle A.;Pande, Vijay S.;Das, Rhiju
预测生物结构对于具有无数构象的无序蛋白质等系统仍然具有挑战性。混合模拟/实验策略已被破坏的计算模型的不准确性和数据的不确定性的误差评估的困难。最大熵理论和非平衡态热力学的最新建议的基础上，我们解决这些问题，通过贝叶斯能量景观倾斜（BELT）计划计算贝叶斯hyperensembles构象合奏。BELT使用马尔可夫链蒙特卡罗直接采样最大熵构象系综与一组输入的实验观测值一致。为了测试这个框架，我们应用BELT模型三丙氨酸，从不同意模拟力场ff 96，ff 99，ff 99 sbnmr-ildn，CHARMM 27，和OPLS-AA。有限化学位移的BELT掺入和（3）J测量给出了所有情况下肽的α、β和PPII构象群的收敛值。作为预测能力的测试，所有五个BELT超集成都可以恢复拟合中未使用的预留测量值，并报告准确的误差，即使是从高度不准确的模拟开始。因此，BELT的原则性框架能够从不一致的模拟和稀疏的数据中对复杂的生物分子系统进行实际预测。
Predicting biological structure has remained challenging for systems such as disordered proteins that take on myriad conformations. Hybrid simulation/experiment strategies have been undermined by difficulties in evaluating errors from computational model inaccuracies and data uncertainties. Building on recent proposals from maximum entropy theory and nonequilibrium thermodynamics, we address these issues through a Bayesian energy landscape tilting (BELT) scheme for computing Bayesian hyperensembles over conformational ensembles. BELT uses Markov chain Monte Carlo to directly sample maximum-entropy conformational ensembles consistent with a set of input experimental observables. To test this framework, we apply BELT to model trialanine, starting from disagreeing simulations with the force fields ff96, ff99, ff99sbnmr-ildn, CHARMM27, and OPLS-AA. BELT incorporation of limited chemical shift and (3)J measurements gives convergent values of the peptide's alpha, beta, and PPII, conformational populations in all cases. As a test of predictive power, all five BELT hyperensembles recover set-aside measurements not used in the fitting and report accurate errors, even when starting from highly inaccurate simulations. BELT's principled framework thus enables practical predictions for complex biomolecular systems from discordant simulations and sparse data.