Principal component analysis in protein tertiary structure prediction
Principal component analysis in protein tertiary structure prediction
复制标题
DOI：
10.1142/s0219720018500051
复制
发表时间：
2018-04-01
影响因子：
1
通讯作者：
Kloczkowski, Andrzej
中科院分区：
文献类型：
--
作者：
Alvarez, Oscar;Luis Fernandez-Martinez, Juan;Kloczkowski, Andrzej
讨论了主成分分析(PCA)在从氨基酸序列预测蛋白质三级结构中的适用性。本文提出的算法属于蛋白质精化模型的范畴，涉及建立一个低维空间，在该空间中通过粒子群优化算法(PSO)进行采样(和优化)。通过对先前使用不同优化技术找到的一组低能量蛋白质模型执行主成分分析来找到减少的空间。通过将最佳诱饵投影到PCA基集并计算残差模型，在该展开中增加了高频项。本术语旨在提供能量优化中的高频细节。本研究的目的是分析降维对粒子群算法预测能力的影响。为此，对来自蛋白质结构预测技术关键评估实验的不同蛋白质进行了建模。在所有情况下，最佳诱饵的能量和到原始结构的距离都降低了。我们的分析还显示了预测的本征构象和替代低能态的主干结构相对于主元分析维度的变化。一般来说，用10个主成分和高频项就可以成功地实现重构。我们还对由降维的主成分重构结构逆问题的蛋白质能量分布进行了计算分析，表明降维缓解了这一高维能量优化问题的不适定性。本文解释的过程非常快，并且允许测试不同的主元分析展开。结果表明，当考虑到足够多的主成分分析项时，粒子群优化算法提高了主成分分析中最佳诱饵的能量。
We discuss applicability of principal component analysis (PCA) for protein tertiary structure prediction from amino acid sequence. The algorithm presented in this paper belongs to the category of protein refinement models and involves establishing a low-dimensional space where the sampling (and optimization) is carried out via particle swarm optimizer (PSO). The reduced space is found via PCA performed for a set of low-energy protein models previously found using different optimization techniques. A high frequency term is added into this expansion by projecting the best decoy into the PCA basis set and calculating the residual model. This term is aimed at providing high frequency details in the energy optimization. The goal of this research is to analyze how the dimensionality reduction affects the prediction capability of the PSO procedure. For that purpose, different proteins from the Critical Assessment of Techniques for Protein Structure Prediction experiments were modeled. In all the cases, both the energy of the best decoy and the distance to the native structure have decreased. Our analysis also shows how the predicted backbone structure of native conformation and of alternative low energy states varies with respect to the PCA dimensionality. Generally speaking, the reconstruction can be successfully achieved with 10 principal components and the high frequency term. We also provide a computational analysis of protein energy landscape for the inverse problem of reconstructing structure from the reduced number of principal components, showing that the dimensionality reduction alleviates the ill-posed character of this high-dimensional energy optimization problem. The procedure explained in this paper is very fast and allows testing different PCA expansions. Our results show that PSO improves the energy of the best decoy used in the PCA when the adequate number of PCA terms is considered.