Motivation: The mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal solutions in order to identify the states close to the native. We propose a novel multimodal optimization protocol to improve the conformation sampling efficiency and modeling accuracy of de novo protein structure folding simulations. Results: A distance-assisted multimodal optimization sampling algorithm, MMpred, is proposed for de novo protein structure prediction. The protocol consists of three stages: The first is a modal exploration stage, in which a structural similarity evaluation model DMscore is designed to control the diversity of conformations, generating a population of diverse structures in different low-energy basins. The second is a modal maintaining stage, where an adaptive clustering algorithm MNDcluster is proposed to divide the populations and merge the modal by adjusting the annealing temperature to locate the promising basins. In the last stage of modal exploitation, a greedy search strategy is used to accelerate the convergence of the modal. Distance constraint information is used to construct the conformation scoring model to guide sampling. MMpred is tested on a large set of 320 non-redundant proteins, where MMpred obtains models with TM-score≥0.5 on 268 cases, which is 20.3% higher than that of Rosetta guided with the same set of distance constraints. The results showed that MMpred can help significantly improve the model accuracy of protein assembly simulations through the sampling of multiple promising energy basins with enhanced structural diversity.

中文翻译：

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MMpred：用于从头蛋白质结构预测的距离辅助多峰构象采样

动机：由于能量力场的不完善，计算蛋白质折叠模拟中的数学上最优解并不总是与天然结构相对应。因此，有必要寻找更多样化的次优解决方案，以识别出接近本机的状态。我们提出了一种新颖的多峰优化协议，以提高构象采样效率和从头蛋白质结构折叠模拟的建模精度。结果：提出了一种距离辅助的多峰优化采样算法MMpred，用于从头进行蛋白质结构预测。该协议包括三个阶段：第一个阶段是模式探索阶段，其中设计了结构相似性评估模型DMscore来控制构象的多样性，在不同的低能流域中产生各种结构的种群。第二个是模态维持阶段，在此阶段，提出了一种自适应聚类算法MNDcluster来划分种群并通过调节退火温度来定位有希望的盆地来合并模态。在模式开发的最后阶段，使用贪婪搜索策略来加速模式的收敛。距离约束信息用于构造构象评分模型以指导采样。在大量320种非冗余蛋白上测试了MMpred，其中MMpred在268个案例中获得了TM-score≥0.5的模型，这比在相同距离约束条件下指导的Rosetta的模型高20.3％。