Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques
Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques
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DOI：
10.3390/molecules25112467
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发表时间：
2020-06-01
期刊：
影响因子：
4.6
通讯作者：
Kloczkowski, Andrzej
中科院分区：
文献类型：
--
作者：
Alvarez-Machancoses, Oscar;Luis Fernandez-Martinez, Juan;Kloczkowski, Andrzej
我们讨论了使用正则化线性判别分析（LDA）作为一种模型降阶技术结合粒子群优化（PSO）在蛋白质三级结构预测，其次是基于奇异值分解（SVD）和PSO的结构细化。本文提出的算法属于基于模板建模的范畴。该算法在通过正则化LDA构造低维子空间之前进行蛋白质模板的预选。在减少空间的蛋白质坐标采样使用高度探索性的优化算法，回归-回归PSO（RR-PSO）。然后将得到的结构通过奇异值分解投影到一个约简空间上，并通过RR-PSO进一步优化以进行结构细化。最终的结构与最好的结构预测工具，如Rossetta和Zhang服务器预测的结构相似。我们的方法的主要优点是，阐明了不适定性的蛋白质结构预测问题的高维优化。它也能够采样范围广泛的构象空间，由于应用正则化线性判别分析，这使我们能够扩大减少基集的差异。
We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presented in this paper corresponds to the category of template-based modeling. The algorithm performs a preselection of protein templates before constructing a lower dimensional subspace via a regularized LDA. The protein coordinates in the reduced spaced are sampled using a highly explorative optimization algorithm, regressive-regressive PSO (RR-PSO). The obtained structure is then projected onto a reduced space via singular value decomposition and further optimized via RR-PSO to carry out a structure refinement. The final structures are similar to those predicted by best structure prediction tools, such as Rossetta and Zhang servers. The main advantage of our methodology is that alleviates the ill-posed character of protein structure prediction problems related to high dimensional optimization. It is also capable of sampling a wide range of conformational space due to the application of a regularized linear discriminant analysis, which allows us to expand the differences over a reduced basis set.