基于曼哈顿距离构建非参数Q矩阵修正方法

将被试得分、理想反应距离和被试得分异常原理相结合,并加入属性计分下的被试得分特性,开发了用于多级评分情境下属性计分曼哈顿距离法(SA-MD),在不同条件下验证了SA-MD的稳定性和适宜性.通过模拟研究和实证研究表明:(i)从逻辑推导出SA-MD用于多级评分情境下Q矩阵修正更合理;(ii)在多种条件中,SA-MD 均有较优的修正效果,适用范围更广,稳定性更高;(iii)当小样本测验进行Q矩阵修正时,使用SA-MD方法可获得更优的效果.     

基于几何不变量的蛋白质三维分子结构比对

为了解决蛋白质三维结构比对需要处理大量的旋转、平移变换，直接用动态规划将变得十分繁琐这一问题，在保留蛋白质空间结构属性特征的基础上，对蛋白质三维数据进行了预先的处理．通过计算蛋白质结构在旋转和平移下的几何不变量，将蛋白质的三维结构坐标变换为具有旋转、平移不变性的一维序列．进一步给出了“距离”以及“相似得分”的定义．在此基础上采用动态规划方法给出了新的蛋白质结构比对算法．对专家分类的蛋白质结构数据库进行测试，结果显示准确、快速．     

Reverse-docking as a computational tool for the study of asymmetric organocatalysis

A novel methodology for 'reverse-docking' a cationic peptide-based organocatalyst to a rigid anionic transition state (TS) model for the conjugate addition of azide to alpha,beta-unsaturated carbonyl substrates is described. The resulting docking poses serve as simplified TS models for enantioselective catalysis. Molecular mechanics-based scoring and ranking of the docking poses, followed by clustering and structural analysis, reveal a clear energetic preference for docking to the S-enantiomeric azidation TS model, in agreement with experiment. Clear energetic trends emerged from docking the catalyst to both enantiomers of all six azidation TS models of this study. Structural analysis of the most favorable pose suggests a mechanism for enantioselective catalysis that is consistent with principles of molecular recognition, catalysis, and experimental data.     

Evaluation of docking/scoring approaches: A comparative study based on MMP3 inhibitors

An increasing number of docking/scoring programs are available that use different sampling and scoring algorithms. A reliable scoring function is the crucial element of such approaches. Comparative studies are needed to evaluate their current capabilities. DOCK4 with force field and PMF scoring as well as FlexX were used to evaluate the predictive power of these docking/scoring approaches to identify the correct binding mode of 61 MMP-3 inhibitors in a crystal structure of stromelysin and also to rank them according to their different binding affinities. It was found that DOCK4/PMF scoring performs significantly better than FlexX and DOCK4/FF in both ranking ligands and predicting their binding modes. Most notably, DOCK4/PMF was the only scoring/docking approach that found a significant correlation between binding affinity and predicted score of the docked inhibitors. However, comparing only those cases where the correct binding mode was identified (scoring highest among sampled poses), FlexX showed the best `fine tuning' (lowest rmsd) in predicted binding modes. The results suggest that not so much the sampling procedure but rather the scoring function is the crucial element of a docking program.     

Modified AutoDock for accurate docking of protein kinase inhibitors

Protein kinases are an important class of enzymes controlling virtually all cellular signaling pathways. Consequently, selective inhibitors of protein kinases have attracted significant interest as potential new drugs for many diseases. Computational methods, including molecular docking, have increasingly been used in the inhibitor design process [1]. We have considered several docking packages in order to strengthen our kinase inhibitor work with computational capabilities. In our experience, AutoDock offered a reasonable combination of accuracy and speed, as opposed to methods that specialize either in fast database searches or detailed and computationally intensive calculations.However, AutoDock did not perform well in cases where extensive hydrophobic contacts were involved, such as docking of SB203580 to its target protein kinase p38. Another shortcoming was a hydrogen bonding energy function, which underestimated the attraction component and, thus, did not allow for sufficiently accurate modeling of the key hydrogen bonds in the kinase-inhibitor complexes.We have modified the parameter set used to model hydrogen bonds, which increased the accuracy of AutoDock and appeared to be generally applicable to many kinase-inhibitor pairs without customization. Binding to largely hydrophobic sites, such as the active site of p38, was significantly improved by introducing a correction factor selectively affecting only carbon and hydrogen energy grids, thus, providing an effective, although approximate, treatment of solvation.     

Reverse fragment based drug discovery approach via simple estimation of fragment contributions

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Water PMF for predicting the properties of water molecules in protein binding site

Water is an important component in living systems and deserves better understanding in chemistry and biology. However, due to the difficulty of investigating the water functions in protein structures, it is usually ignored in computational modeling, especially in the field of computer‐aided drug design. Here, using the potential of mean forces (PMFs) approach, we constructed a water PMF (wPMF) based on 3946 non‐redundant high resolution crystal structures. The extracted wPMF potential was first used to investigate the structure pattern of water and analyze the residue hydrophilicity. Then, the relationship between wPMF score and the B factor value of crystal waters was studied. It was found that wPMF agrees well with some previously reported experimental observations. In addition, the wPMF score was also tested in parallel with 3D‐RISM to measure the ability of retrieving experimentally observed waters, and showed comparable performance but with much less computational cost. In the end, we proposed a grid‐based clustering scheme together with a distance weighted wPMF score to further extend wPMF to predict the potential hydration sites of protein structure. From the test, this approach can predict the hydration site at the accuracy about 80% when the calculated score lower than ?4.0. It also allows the assessment of whether or not a given water molecule should be targeted for displacement in ligand design. Overall, the wPMF presented here provides an optional solution to many water related computational modeling problems, some of which can be highly valuable as part of a rational drug design strategy. © 2012 Wiley Periodicals, Inc.     

Taba: A Tool to Analyze the Binding Affinity

Evaluation of ligand-binding affinity using the atomic coordinates of a protein-ligand complex is a challenge from the computational point of view. The availability of crystallographic structures of complexes with binding affinity data opens the possibility to create machine-learning models targeted to a specific protein system. Here, we describe a new methodology that combines a mass-spring system approach with supervised machine-learning techniques to predict the binding affinity of protein-ligand complexes. The combination of these techniques allows exploring the scoring function space, generating a model targeted to a protein system of interest. The new model shows superior predictive performance when compared with classical scoring functions implemented in the programs Molegro Virtual Docker, AutoDock4, and AutoDock Vina. We implemented this methodology in a new program named Taba. Taba is implemented in Python and available to download under the GNU license at https://github.com/azevedolab/taba . © 2019 Wiley Periodicals, Inc.     

基于位点特异性打分矩阵的卷积神经网络预测SARS-CoV-2核衣壳蛋白的蛋白质二级结构

新型冠状病毒(SARS-CoV-2)有4种关键的结构蛋白,而核衣壳蛋白就是其中的1种.本实验从公开数据库NCBI上选取的SARS-CoV-2核衣壳蛋白质序列数据,分析SARS-CoV-2核衣壳蛋白与SARS-CoV核衣壳蛋白的序列相似性,对SARS-CoV-2核衣壳蛋白的理化性质和疏水性进行分析;在此基础上提出基于位点...     

基于支持向量机的信用评估模型及风险评价

运用基于支持向量机理来建立一个新的个人信用评估预测模型,以期取得更好的预测分类能力.并对SVM分类结果与三层全连接BPN分类结果进行了比较.结果表明,在判别潜在的贷款申请者中支持向量的判别结果比神经网络的要好.为了减小训练集偏差及为了验证两种方法的鲁棒性,基于两种策略(平衡样本与非平衡样本)交叉验证来进一步评价SVM分类准确性,并对两种方法基于两种策略的误分类作了风险代价分析.