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: III. Binding site prediction with a neural network

An algorithm for the identification of possible binding sites of biomolecules, which are represented as regions of the molecular surface, is introduced. The algorithm is based on the segmentation of the molecular surface into overlapping patches as described in the first article of this series.1 The properties of these patches (calculated on the basis of physical and chemical properties) are used for the analysis of the molecular surfaces of 7821 proteins and protein complexes. Special attention is drawn to known protein binding sites. A binding site identification algorithm is realized on the basis of the calculated data using a neural network strategy. The neural network is able to classify surface patches as protein-protein, protein-DNA, protein-ligand, or nonbinding sites. To show the capability of the algorithm, results of the surface analysis and the predictions are presented and discussed with representative examples.     

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.     

Module assembly for protein-surface recognition: geranylgeranyltransferase I bivalent inhibitors for simultaneous targeting of interior and exterior protein surfaces

Synthetic chemical probes designed to simultaneously targeting multiple sites of protein surfaces are of interest owing to their potential application as site specific modulators of protein-protein interactions. A new approach toward bivalent inhibitors of mammalian type I geranylgeranyltransferase (GGTase I) based on module assembly for simultaneous recognition of both interior and exterior protein surfaces is reported. The inhibitors synthesized in this study consist of two modules linked by an alkyl spacer; one is the tetrapeptide CVIL module for binding to the interior protein surface (active pocket) and the other is a 3,4,5-alkoxy substituted benzoyl motif that contains three aminoalkyl groups designed to bind to the negatively charged protein exterior surface near the active site. The compounds were screened by two distinct enzyme inhibition assays based on fluorescence spectroscopy and incorporation of a [(3)H]-labeled prenyl group onto a protein substrate. The bivalent inhibitors block GGTase I enzymatic activity with K(i) values in the submicromolar range and are approximately one order of magnitude and more than 150 times more effective than the tetrapeptide CVIL and the methyl benzoate derivatives, respectively. The bivalent compounds 6 and 8 were shown to be competitive inhibitors, suggesting that the CVIL module anchors the whole molecule to the GGTase I active site and delivers the other module to the targeting protein surface. Thus, our module-assembly approach resulted in simultaneous multiple-site recognition, and as a consequence, synergetic inhibition of GGTase I activity, thereby providing a new approach in designing protein-surface-directed inhibitors for targeting protein-protein interactions.     

WATGEN: an algorithm for modeling water networks at protein-protein interfaces

Water molecules at protein-protein interfaces contribute to the close packing of atoms and ensure complementarity between the protein surfaces, as well as mediating polar interactions. Therefore, modeling of interface water is of importance in understanding the structural basis of biomolecular association. We present an algorithm, WATGEN, which predicts locations for water molecules at a protein-protein or protein-peptide interface, given the atomic coordinates of the protein and peptide. A key element of the WATGEN algorithm is the prediction of water sites that can form multiple hydrogen bonds that bridge the binding interface. Trial calculations were performed on water networks predicted by WATGEN at 126 protein-peptide interfaces (X-ray resolutions 相似文献   

Protein-protein binding site prediction by local structural alignment

Generalization of an earlier algorithm has led to the development of new local structural alignment algorithms for prediction of protein-protein binding sites. The algorithms use maximum cliques on protein graphs to define structurally similar protein regions. The search for structural neighbors in the new algorithms has been extended to all the proteins in the PDB and the query protein is compared to more than 60,000 proteins or over 300,000 single-chain structures. The resulting structural similarities are combined and used to predict the protein binding sites. This study shows that the location of protein binding sites can be predicted by comparing only local structural similarities irrespective of general protein folds.     

Relationship between Hot Spot Residues and Ligand Binding Hot Spots in Protein-Protein Interfaces

In the context of protein-protein interactions, the term "hot spot" refers to a residue or cluster of residues that makes a major contribution to the binding free energy, as determined by alanine scanning mutagenesis. In contrast, in pharmaceutical research, a hot spot is a site on a target protein that has high propensity for ligand binding and hence is potentially important for drug discovery. Here we examine the relationship between these two hot spot concepts by comparing alanine scanning data for a set of 15 proteins with results from mapping the protein surfaces for sites that can bind fragment-sized small molecules. We find the two types of hot spots are largely complementary; the residues protruding into hot spot regions identified by computational mapping or experimental fragment screening are almost always themselves hot spot residues as defined by alanine scanning experiments. Conversely, a residue that is found by alanine scanning to contribute little to binding rarely interacts with hot spot regions on the partner protein identified by fragment mapping. In spite of the strong correlation between the two hot spot concepts, they fundamentally differ, however. In particular, while identification of a hot spot by alanine scanning establishes the potential to generate substantial interaction energy with a binding partner, there are additional topological requirements to be a hot spot for small molecule binding. Hence, only a minority of hot spots identified by alanine scanning represent sites that are potentially useful for small inhibitor binding, and it is this subset that is identified by experimental or computational fragment screening.     

Context-based identification of protein-protein interfaces and "hot-spot" residues

Reliable determination of protein-protein interaction sites is of critical importance for structure-based design of small molecules modulating protein function through macromolecular interfaces. We present an alignment-free computational method for prediction of protein-protein interface residues. The method ("iPred") is based on a knowledge-based scoring function adapted from the field of protein folding and small molecule docking. Based on a training set of 394 hetero-dimeric proteins iPred achieves sustained accuracy on an external unbound test set. Prediction robustness was assessed from more than 1500 diverse complexes containing homo- and hetero-dimers. The technique does not rely on sequence conservation, so that rapid interface identification is possible even for proteins for which homologs are unknown or lack conserved residue patterns in interface region. Functional "hot-spot" residues are enriched among the predicted interface residues, rendering the method predestined for macromolecular binding site identification and drug design studies aiming at modulating protein-protein interaction that might influence protein function. For a comparative structural model of peptidase HtrA from Helicobacter pylori, we performed mutation studies for predicted hot-spot residues, which were confirmed as functionally relevant for HtrA activity or oligomerization.     

Molecular aptamers for real-time protein-protein interaction study

Protein-protein interactions play critical roles in cellular functions, but current techniques for real-time study of these interactions are limited. We report the real-time monitoring of protein-protein interactions without labeling either of the two interacting proteins; this procedure poses minimum effects on the binding properties of the proteins. Our strategy uses a protein/aptamer complex to probe the interactions in a competitive assay where the binding of an aptamer to its target protein is altered by a second protein that interacts with the target protein. Two signal transduction strategies, fluorescence resonance energy transfer (FRET) and fluorescence anisotropy, have been designed to study the interactions of human alpha-thrombin with different proteins by using two aptamers specific for two binding sites on alpha-thrombin. Our method has been shown to be simple and effective, does not require labeling of proteins, makes use of easily obtainable aptamers, provides detailed protein-protein interaction information and has excellent sensitivity for protein detection and protein-protein interaction studies. The FRET and the fluorescent anisotropy approaches complement each other in providing insight into the kinetics, mechanisms, binding sites and binding dynamics of the interacting proteins.     

Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy

Probing protein surfaces to accurately predict the binding site and conformation of a small molecule is a challenge currently addressed through mainly two different approaches: blind docking and cavity detection-guided docking. Although cavity detection-guided blind docking has yielded high success rates, it is less practical when a large number of molecules must be screened against many detected binding sites. On the other hand, blind docking allows for simultaneous search of the whole protein surface, which however entails the loss of accuracy and speed. To bridge this gap, in this study, we developed and tested BLinDPyPr, an automated pipeline which uses FTMap and DOCK6 to perform a hybrid blind docking strategy. Through our algorithm, FTMap docked probe clusters are converted into DOCK6 spheres for determining binding regions. Because these spheres are solely derived from FTMap probes, their locations are contained in and specific to multiple potential binding pockets, which become the regions that are simultaneously probed and chosen by the search algorithm based on the properties of each candidate ligand. This method yields pose prediction results (45.2–54.3% success rates) comparable to those of site-specific docking with the classic DOCK6 workflow (49.7–54.3%) and is half as time-consuming as the conventional blind docking method with DOCK6.     

Bcl-2蛋白结合底物时的诱导契合及高效抑制剂的柔性对接研究

Bcl-2蛋白是目前抗肿瘤药物研究很具前景的新靶点. Bcl-2蛋白与底物作用的活性腔生理情况下是蛋白与蛋白作用接触面大而平坦, 与底物结合时发生明显的诱导契合. 通过分析和比较Bcl-2蛋白和其高同源的Bcl-x_L蛋白的自由状态及与底物复合时的9个相关三维结构模型, 明确了蛋白及活性腔的骨架结构特征, 活性腔内的重要位点和关键残基, 及它们结合底物时发生的诱导契合. 基于以上认识, 采用柔性对接的方法得到了高亲和力的小分子抑制剂与Bcl-2蛋白的合理结合模式, 为以后设计合成新型高效Bcl-2蛋白抑制剂打下了坚实基础.     

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.     

Bcl-XL-templated assembly of its own protein-protein interaction modulator from fragments decorated with thio acids and sulfonyl azides

Protein-protein interactions have key importance in various biological processes and modulation of particular protein-protein interactions has been shown to have therapeutic effects. However, disrupting or modulating protein-protein interactions with low-molecular-weight compounds is extremely difficult due to the lack of deep binding pockets on protein surfaces. Herein we describe the development of an unprecedented lead synthesis and discovery method that generates only biologically active compounds from a library of reactive fragments. Using the protein Bcl-XL, a central regulator of programmed cell death, we demonstrated that an amidation reaction between thio acids and sulfonyl azides is applicable for Bcl-XL-templated assembly of inhibitory compounds. We have demonstrated for the first time that kinetic target-guided synthesis can be applied not only on enzymatic targets but also for the discovery of small molecules modulating protein-protein interactions.     

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.     

Accelerating molecular docking calculations using graphics processing units

The generation of molecular conformations and the evaluation of interaction potentials are common tasks in molecular modeling applications, particularly in protein-ligand or protein-protein docking programs. In this work, we present a GPU-accelerated approach capable of speeding up these tasks considerably. For the evaluation of interaction potentials in the context of rigid protein-protein docking, the GPU-accelerated approach reached speedup factors of up to over 50 compared to an optimized CPU-based implementation. Treating the ligand and donor groups in the protein binding site as flexible, speedup factors of up to 16 can be observed in the evaluation of protein-ligand interaction potentials. Additionally, we introduce a parallel version of our protein-ligand docking algorithm PLANTS that can take advantage of this GPU-accelerated scoring function evaluation. We compared the GPU-accelerated parallel version to the same algorithm running on the CPU and also to the highly optimized sequential CPU-based version. In terms of dependence of the ligand size and the number of rotatable bonds, speedup factors of up to 10 and 7, respectively, can be observed. Finally, a fitness landscape analysis in the context of rigid protein-protein docking was performed. Using a systematic grid-based search methodology, the GPU-accelerated version outperformed the CPU-based version with speedup factors of up to 60.     

(alpha/beta+alpha)-peptide antagonists of BH3 domain/Bcl-x(L) recognition: toward general strategies for foldamer-based inhibition of protein-protein interactions

The development of molecules that bind to specific protein surface sites and inhibit protein-protein interactions is a fundamental challenge in molecular recognition. New strategies for approaching this challenge could have important long-term ramifications in biology and medicine. We are exploring the concept that unnatural oligomers with well-defined conformations ("foldamers") can mimic protein secondary structural elements and thereby block specific protein-protein interactions. Here, we describe the identification and analysis of helical peptide-based foldamers that bind to a specific cleft on the anti-apoptotic protein Bcl-xL by mimicking an alpha-helical BH3 domain. Initial studies, employing a fluorescence polarization (FP) competition assay, revealed that among several alpha/beta- and beta-peptide foldamer backbones only alpha/beta-peptides intended to adopt 14/15-helical secondary structure display significant binding to Bcl-xL. The most tightly binding Bcl-xL ligands are chimeric oligomers in which an N-terminal alpha/beta-peptide segment is fused to a C-terminal alpha-peptide segment ((alpha/beta + alpha)-peptides)). Sequence-affinity relationships were probed via standard and nonstandard techniques (alanine scanning and hydrophile scanning, respectively), and the results allowed us to construct a computational model of the ligand/Bcl-xL complex. Analytical ultracentrifugation with a high-affinity (alpha/beta + alpha)-peptide established 1:1 ligand:Bcl-xL stoichiometry under FP assay conditions. Binding selectivity studies with the most potent (alpha/beta + alpha)-peptide, conducted via surface plasmon resonance measurements, revealed that this ligand binds tightly to Bcl-w as well as to Bcl-xL, while binding to Bcl-2 is somewhat weaker. No binding could be detected with Mcl-1. We show that our most potent (alpha/beta + alpha)-peptide can induce cytochrome C release from mitochondria, an early step in apoptosis, in cell lysates, and that this activity is dependent upon inhibition of protein-protein interactions involving Bcl-xL.