Pattern recognition strategies for molecular surfaces. I. Pattern generation using fuzzy set theory

A new method for the characterization of molecules based on the model approach of molecular surfaces is presented. We use the topographical properties of the surface as well as the electrostatic potential, the local lipophilicity/hydrophilicity, and the hydrogen bond density on the surface for characterization. The definition and the calculation method for these properties are reviewed shortly. The surface is segmented into overlapping patches with similar molecular properties. These patches can be used to represent the characteristic local features of the molecule in a way that is beyond the atomistic resolution but can nevertheless be applied for the analysis of partial similarities of different molecules as well as for the identification of molecular complementarity in a very general sense. The patch representation can be used for different applications, which will be demonstrated in subsequent articles.     

运用模糊神经网络表达和预测链烷烃pVT性质

采用一种基于遗传算法的新型模糊神经网络方法研究链烷烃类化合物的pVT性质。该方法综合神经网络、遗传算法与模糊系统三种柔性智能计算技术的优点,具有良好的学习能力,不易陷入局部最小区域,学习速度较快,网络知识以模糊语言变量的形式加以表达,易于理解。用分子连接性指数对24种链烷烃化合物结构和pVT数据进行学习,进而预测另外14种未知化合物的pVT性质,较好地揭示出化合物分子结构与pVT性质之间的关系,并给出了良好的关联与预测结果。     

Mapping protein pockets through their potential small-molecule binding volumes: QSCD applied to biological protein structures

Previously we demonstrated a method, Quantized Surface Complementarity Diversity (QSCD), of defining molecular diversity by measuring shape and functional complementarity of molecules to a basis set of theoretical target surfaces [Wintner E.A. and Moallemi C.C., J. Med. Chem., 43 (2000) 1993]. In this paper we demonstrate a method of mapping actual protein pockets to the same basis set of theoretical target surfaces, thereby allowing categorization of protein pockets by their properties of shape and functionality. The key step in the mapping is a `dissection' algorithm that breaks any protein pocket into a set of potential small molecule binding volumes. It is these binding volumes that are mapped to the basis set of theoretical target surfaces, thus measuring a protein pocket not as a single surface but as a collection of molecular recognition environments.     

Structure-based identification of small molecule binding sites using a free energy model

We separately have shown that the maximal druglike affinity of a given binding site on a protein can be calculated on the basis of the binding-site structure alone by using a desolvation-based free energy model along with the notion that druglike ligands fall into certain physiochemical property ranges. Here, we present an approach where we reformulate the calculated druggability affinity as an additive free energy to facilitate the searching of whole protein surfaces for druglike binding sites. The highest-scoring patches in many cases represent known ligand-binding sites for druggable targets, but not for difficult targets. This approach differs from other approaches in that it does not simply identify pockets with the greatest volume but instead identifies pockets that are likely to be amenable to druglike small-molecule binding. Combining the method with a functional residue prediction method called SCA (statistical coupling analysis) results in the prediction of potentially druggable allosteric binding sites on p38alpha kinase.     

Self-Similarity of solvent-accessible surfaces of biological and synthetical macromolecules

The quantification of surface roughness of globular proteins and synthetical macromolecules in the globular state is discussed using the concept of fractality. The Hausdorff dimension as a measure for local and global fractality of surfaces is applied. To calculate the Hausdorff dimension of any surface at a high level of accuracy, a new algorithm is presented that is based on a triangulated solvent-accessible molecular surface. It can be demonstrated that protein surfaces (as calculated on the basis of experimentally determined structures) as well as surfaces of globular polyethylene (PE) conformers (calculated on the basis of structural information basing on extensive Monte Carlo and molecular dynamics simulations) in fact show self-similarity within a reasonable yardstick range, at least in a global statistical sense. The same is true for parts of a protein surface provided that these regions are not too small. The concept of self-similarity breaks down when individual surface points are considered. The results obtained for the fractal dimension of PE surfaces (average fractal dimension D = 2.23) lead to the conclusion that protein surfaces probably do not exhibit a unique and specific degree of geometrical complexity (or surface roughness) characterized by a fractal dimension of approximately D = 2.2 as was argued in the past. It is clear that the concept of self-similarity is helpful for the classification of surface roughness of large molecules, but it seems questionable whether this concept is useful for the identification of active sites or other questions related to the field of molecular recognition. © John Wiley & Sons, Inc.     

The role of protein “Stability patches” in molecular recognition: A case study of the human growth hormone‐receptor complex

Dynamic characteristics of protein surfaces are among the factors determining their functional properties, including their potential participation in protein‐protein interactions. The presence of clusters of static residues—“stability patches” (SPs)—is a characteristic of protein surfaces involved in intermolecular recognition. The mechanism, by with SPs facilitate molecular recognition, however, remains unclear. Analyzing the surface dynamic properties of the growth hormone and of its high‐affinity variant we demonstrated that reshaping of the SPs landscape may be among the factors accountable for the improved affinity of this variant to the receptor. We hypothesized that SPs facilitate molecular recognition by moderating the conformational entropy of the unbound state, diminishing enthalpy–entropy compensation upon binding, and by augmenting the favorable entropy of desolvation. SPs mapping emerges as a valuable tool for investigating the structural basis of the stability of protein complexes and for rationalizing experimental approaches, such as affinity maturation, aimed at improving it. © 2015 Wiley Periodicals, Inc.     

Protein-protein binding sites prediction by 3D structural similarities

Identifying the location of binding sites on proteins is of fundamental importance for a wide range of applications including molecular docking, de novo drug design, structure identification, and comparison of functional sites. In this paper, we develop an efficient approach for finding binding sites between proteins. Our approach consists of four steps: local sequence alignment, protein surface detection, 3D structure comparison, and candidate binding site selection. A comparison of our method with the LSA algorithm shows that the binding sites predicted by our method are somewhat closer to the actual binding sites in the protein-protein complexes. The software package is available at http://sites.google.com/site/guofeics/pro-bs for noncommercial use.     

蛋白质表面模块划分及其在结合位点预测中的应用

蛋白质-蛋白质复合物的结合位点预测是计算分子生物学的一个难题. 本文对蛋白质-蛋白质复合物数据集Benchmark 3.0 中的双链蛋白质复合物进行了研究, 计算了单体的残基溶剂可接近表面积和残基间的接触面积, 并据此提出了蛋白质表面模块划分方法. 发现模块的溶剂可接近表面积与其内部接触面积的乘积(PSAIA)值能够提供结合位点的信息. 在78 个双链蛋白质复合物中, 有74 个体系其受体或配体上具有最大或次大PSAIA值的模块是界面模块. 将该方法获得的结合位点信息应用在CAPRI竞赛Target 39 的复合物结构预测中取得了较好的结果. 本文提出的基于模块的蛋白质结合位点预测方法不同于以残基为基础且仅考虑表面残基的传统预测方法, 为蛋白质-蛋白质复合物结合位点预测提供了新思路.     

Protein-protein binding-sites prediction by protein surface structure conservation

A new algorithm to predict protein-protein binding sites using conservation of both protein surface structure and physical-chemical properties in structurally similar proteins is developed. Binding-site residues in proteins are known to be more conserved than the rest of the surface, and finding local surface similarities by comparing a protein to its structural neighbors can potentially reveal the location of binding sites on this protein. This approach, which has previously been used to predict binding sites for small ligands, is now extended to predict protein-protein binding sites. Examples of binding-site predictions for a set of proteins, which have previously been studied for sequence conservation in protein-protein interfaces, are given. The predicted binding sites and the actual binding sites are in good agreement. Our algorithm for finding conserved surface structures in a set of similar proteins is a useful tool for the prediction of protein-protein binding sites.     

Pattern recognition strategies for molecular surfaces. II. Surface complementarity

Fuzzy logic based algorithms for the quantitative treatment of complementarity of molecular surfaces are presented. Therein, the overlapping surface patches defined in article I1 of this series are used. The identification of complementary surface patches can be considered as a first step for the solution of molecular docking problems. Standard technologies can then be used for further optimization of the resulting complex structures. The algorithms are applied to 33 biomolecular complexes. After the optimization with a downhill simplex method, for all these complexes one structure was found, which is in very good agreement with the experimental results.     

Selectivity of neutral/weakly basic P1 group inhibitors of thrombin and trypsin by a molecular dynamics study

Molecular dynamics (MD) simulations followed by molecular mechanics generalized Born surface area (MM-GBSA) analyses have been carried out to study the selectivity of two neutral and weakly basic P1 group inhibitors (177 and CDA) to thrombin and trypsin. Detailed binding free energies between these inhibitors and individual protein residues are calculated by using a per-residue basis decomposition method. The analysis of the detailed interaction energies provides insight on the protein-inhibitor-binding mechanism and helps to elucidate the basis for achieving selectivity through interpretation of the structural and energetic results from the simulations. The study shows that the dominant factor of selectivity for both inhibitors is van der Waals energy, which suggests better shape complementarity and packing with thrombin. Nonpolar solvation free energy and total entropy contribution are also in favor of selectivity, but the contributions are much smaller. Binding mode and structural analysis show that 177 binds to thrombin and trypsin in a similar binding mode. In contrast, the CDA binds to thrombin and trypsin in very different modes.     

Blind docking method combining search of low‐resolution binding sites with ligand pose refinement by molecular dynamics‐based global optimization

This study describes the development of a new blind hierarchical docking method, bhDock, its implementation, and accuracy assessment. The bhDock method uses two‐step algorithm. First, a comprehensive set of low‐resolution binding sites is determined by analyzing entire protein surface and ranked by a simple score function. Second, ligand position is determined via a molecular dynamics‐based method of global optimization starting from a small set of high ranked low‐resolution binding sites. The refinement of the ligand binding pose starts from uniformly distributed multiple initial ligand orientations and uses simulated annealing molecular dynamics coupled with guided force‐field deformation of protein–ligand interactions to find the global minimum. Assessment of the bhDock method on the set of 37 protein–ligand complexes has shown the success rate of predictions of 78%, which is better than the rate reported for the most cited docking methods, such as AutoDock, DOCK, GOLD, and FlexX, on the same set of complexes. © 2009 Wiley Periodicals, Inc. J Comput Chem 2010     

The comparative molecular surface analysis (COMSA)--a nongrid 3D QSAR method by a coupled neural network and PLS system: predicting pK(a) values of benzoic and alkanoic acids

A self-organizing neural network was used to design a novel method capable of the quantitative prediction of molecular properties. The method is based on the comparison of molecular surfaces performed by the coupled neural network and PLS system. Unlike CoMFA and related methods it does not compare the properties describing a discrete set of points but the average property values calculated for a certain area of the molecular surface. It has been found that the results of the PLS analysis of the series of the comparative matrices of the molecular electrostatic potential (MEP) are quite stable. Also the results only slightly depend on such parameters as the number of points sampled at the molecular surface (D) or a winning distance (MD) of the self-organizing neurons. The influence of these parameters for modeling the effects limited by steric and electronic effects was determined and the pK(a) values of the ortho-, meta-, and para- (o-, m-, p-) analogues of benzoic acid and selected alkanoic acids were predicted. We generally found that for the series analyzed CoMSA gave better models than CoMFA.     

The comparison of geometric and electronic properties of molecular surfaces by neural networks: Application to the analysis of corticosteroid-binding globulin activity of steroids

Summary It is shown how a self-organizing neural network such as the one introduced by Kohonen can be used to analyze features of molecular surfaces, such as shape and the molecular electrostatic potential. On the one hand, two-dimensional maps of molecular surface properties can be generated and used for the comparison of a set of molecules. On the other hand, the surface geometry of one molecule can be stored in a network and this network can be used as a template for the analysis of the shape of various other molecules. The application of these techniques to a series of steroids exhibiting a range of binding activities to the corticosteroid-binding globulin receptor allows one to pinpoint the essential features necessary for biological activity.