AutoMatch: target-binding protein design and enzyme design by automatic pinpointing potential active sites in available protein scaffolds

Proteins perform their functions mainly via active sites, whereas other parts of the proteins comprise the scaffolds, which support the active sites. One strategy for protein functional design is transplanting active sites, such as catalytic sites for enzyme or binding hot spots for protein-protein interactions, onto a new scaffold. AutoMatch is a new program designed for efficiently elucidating suitable scaffolds and potential sites on the scaffolds. Backrub motions are used to treat backbone flexibility during the design process. A step-by-step checking strategy and cluster-representation examination strategy were developed to solve the large combinatorial problem for the matching of active-site conformations. In addition, a grid-based binding energy scoring method was used to filter the solutions. An enzyme design benchmark and a protein-protein interaction design benchmark were built to test the algorithm. AutoMatch could identify the hot spots in the nonbinding protein and rank them within the top five results for 8 of 10 target-binding protein design cases. In addition, among the 15 enzymes tested, AutoMatch can identify the catalytic active sites in the apoprotein and rank them within the top five results for 13 cases. AutoMatch was also tested for screening scaffold library in designing binding proteins targeting influenza hemagglutinin, HIV gp120, and epidermal growth factor receptor kinase, respectively. AutoMatch, and the two test sets, ActApo and ActFree, are available for noncommercial applications at http://mdl.ipc.pku.edu.cn/cgi-bin/down.cgi.     

蛋白质-蛋白质界面热点残基预测及其在线工具

蛋白质-蛋白质结合热点是界面中对结合自由能有着显著贡献的一小簇残基。捕捉和揭示这类热点残基可以加深对蛋白质间相互作用机制的理解,为蛋白质工程和药物设计提供指导。但实验技术费时费力且代价昂贵。计算工具可用于辅助和补充实验上的尝试。该文较详细、系统地介绍了蛋白质界面热点的特性、计算预测的策略与技术,并应用实例进一步说明这些方法学的特征;还介绍了界面热点的数据库和一些主要的在线预测工具,旨在为设计、挑选和应用这类工具解决特定问题的研究人员提供指南。     

FTMove: A Web Server for Detection and Analysis of Cryptic and Allosteric Binding Sites by Mapping Multiple Protein Structures

Protein mapping distributes many copies of different molecular probes on the surface of a target protein in order to determine binding hot spots, regions that are highly preferable for ligand binding. While mapping of X-ray structures by the FTMap server is inherently static, this limitation can be overcome by the simultaneous analysis of multiple structures of the protein. FTMove is an automated web server that implements this approach. From the input of a target protein, by PDB code, the server identifies all structures of the protein available in the PDB, runs mapping on them, and combines the results to form binding hot spots and binding sites. The user may also upload their own protein structures, bypassing the PDB search for similar structures. Output of the server consists of the consensus binding sites and the individual mapping results for each structure - including the number of probes located in each binding site, for each structure. This level of detail allows the users to investigate how the strength of a binding site relates to the protein conformation, other binding sites, and the presence of ligands or mutations. In addition, the structures are clustered on the basis of their binding properties. The use of FTMove is demonstrated by application to 22 proteins with known allosteric binding sites; the orthosteric and allosteric binding sites were identified in all but one case, and the sites were typically ranked among the top five. The FTMove server is publicly available at https://ftmove.bu.edu.     

Small-world network approach to identify key residues in protein-protein interaction

We show that protein complexes can be represented as small-world networks, exhibiting a relatively small number of highly central amino-acid residues occurring frequently at protein-protein interfaces. We further base our analysis on a set of different biological examples of protein-protein interactions with experimentally validated hot spots, and show that 83% of these predicted highly central residues, which are conserved in sequence alignments and nonexposed to the solvent in the protein complex, correspond to or are in direct contact with an experimentally annotated hot spot. The remaining 17% show a general tendency to be close to an annotated hot spot. On the other hand, although there is no available experimental information on their contribution to the binding free energy, detailed analysis of their properties shows that they are good candidates for being hot spots. Thus, highly central residues have a clear tendency to be located in regions that include hot spots. We also show that some of the central residues in the protein complex interfaces are central in the monomeric structures before dimerization and that possible information relating to hot spots of binding free energy could be obtained from the unbound structures.     

APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility

Background  

It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required.     

A new strategy for improved secondary screening and lead optimization using high-resolution SPR characterization of compound-target interactions

Biophysical label-free assays such as those based on SPR are essential tools in generating high-quality data on affinity, kinetic, mechanistic and thermodynamic aspects of interactions between target proteins and potential drug candidates. Here we show examples of the integration of SPR with bioinformatic approaches and mutation studies in the early drug discovery process. We call this combination 'structure-based biophysical analysis'. Binding sites are identified on target proteins using information that is either extracted from three-dimensional structural analysis (X-ray crystallography or NMR), or derived from a pharmacore model based on known binders. The binding site information is used for in silico screening of a large substance library (e.g. available chemical directory), providing virtual hits. The three-dimensional structure is also used for the design of mutants where the binding site has been impaired. The wild-type target and the impaired mutant are then immobilized on different spots of the sensor chip and the interactions of compounds with the wild-type and mutant are compared in order to identify selective binders for the binding site of the target protein. This method can be used as a cost-effective alternative to high-throughput screening methods in cases when detailed binding site information is available. Here, we present three examples of how this technique can be applied to provide invaluable data during different phases of the drug discovery process.     

Binding Hot Spots and Amantadine Orientation in the Influenza A Virus M2 Proton Channel

Structures of truncated versions of the influenza A virus M2 proton channel have been determined recently by x-ray crystallography in the open conformation of the channel, and by NMR in the closed state. The structures differ in the position of the bound inhibitors. The x-ray structure shows a single amantadine molecule in the middle of the channel, whereas in the NMR structure four drug molecules bind at the channel's outer surface. To study this controversy we applied computational solvent mapping, a technique developed for the identification of the most druggable binding hot spots of proteins. The method moves molecular probes—small organic molecules containing various functional groups—around the protein surface, finds favorable positions using empirical free energy functions, clusters the conformations, and ranks the clusters on the basis of the average free energy. The results of the mapping show that in both structures the primary hot spot is an internal cavity overlapping the amantadine binding site seen in the x-ray structure. However, both structures also have weaker hot spots at the exterior locations that bind rimantadine in the NMR structure, although these sites are partially due to the favorable interactions with the interfacial region of the lipid bilayer. As confirmed by docking calculations, the open channel binds amantadine at the more favorable internal site, in good agreement with the x-ray structure. In contrast, the NMR structure is based on a peptide/micelle construct that is able to accommodate the small molecular probes used for the mapping, but has a too narrow pore for the rimantadine to access the internal hot spot, and hence the drug can bind only at the exterior sites.     

Multiple solvent crystal structures of ribonuclease A: An assessment of the method

The multiple solvent crystal structures (MSCS) method uses organic solvents to map the surfaces of proteins. It identifies binding sites and allows for a more thorough examination of protein plasticity and hydration than could be achieved by a single structure. The crystal structures of bovine pancreatic ribonuclease A (RNAse A) soaked in the following organic solvents are presented: 50% dioxane, 50% dimethylformamide, 70% dimethylsulfoxide, 70% 1,6‐hexanediol, 70% isopropanol, 50% R,S,R‐bisfuran alcohol, 70% t‐butanol, 50% trifluoroethanol, or 1.0M trimethylamine‐N‐oxide. This set of structures is compared with four sets of crystal structures of RNAse A from the protein data bank (PDB) and with the solution NMR structure to assess the validity of previously untested assumptions associated with MSCS analysis. Plasticity from MSCS is the same as from PDB structures obtained in the same crystal form and deviates only at crystal contacts when compared to structures from a diverse set of crystal environments. Furthermore, there is a good correlation between plasticity as observed by MSCS and the dynamic regions seen by NMR. Conserved water binding sites are identified by MSCS to be those that are conserved in the sets of structures taken from the PDB. Comparison of the MSCS structures with inhibitor‐bound crystal structures of RNAse A reveals that the organic solvent molecules identify key interactions made by inhibitor molecules, highlighting ligand binding hot‐spots in the active site. The present work firmly establishes the relevance of information obtained by MSCS. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Unraveling hot spots in binding interfaces: progress and challenges

Protein interface hot spots, as revealed by alanine scanning mutagenesis, continue to stimulate interest in the biophysical basis of molecular recognition. Although these regions apparently constitute fertile grounds for intermolecular interactions, no general algorithm has yet been developed that can predict hot spots based solely on their shape or composition. The discovery of structural plasticity in hot spot regions indicates that dynamic simulation techniques may be essential for achieving a predictive understanding of binding interface energetics. Future progress will depend as much on the application of new computational approaches for dissecting protein interfaces as on expanding our empirical databank of mutagenic substitutions and their effects. Despite our current theoretical shortcomings, recent methodological advances provide efficient experimental means of probing hot spots and enable immediate applications for hot spots in drug discovery.     

Evidence of Conformational Selection Driving the Formation of Ligand Binding Sites in Protein-Protein Interfaces

Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.     

Protein-protein interactions: hot spots and structurally conserved residues often locate in complemented pockets that pre-organized in the unbound states: implications for docking

Energetic hot spots account for a significant portion of the total binding free energy and correlate with structurally conserved interface residues. Here, we map experimentally determined hot spots and structurally conserved residues to investigate their geometrical organization. Unfilled pockets are pockets that remain unfilled after protein-protein complexation, while complemented pockets are pockets that disappear upon binding, representing tightly fit regions. We find that structurally conserved residues and energetic hot spots are strongly favored to be located in complemented pockets, and are disfavored in unfilled pockets. For the three available protein-protein complexes with complemented pockets where both members of the complex were alanine-scanned, 62% of all hot spots (DeltaDeltaG>2kcal/mol) are within these pockets, and 60% of the residues in the complemented pockets are hot spots. 93% of all red-hot residues (DeltaDeltaG>/=4kcal/mol) either protrude into or are located in complemented pockets. The occurrence of hot spots and conserved residues in complemented pockets highlights the role of local tight packing in protein associations, and rationalizes their energetic contribution and conservation. Complemented pockets and their corresponding protruding residues emerge among the most important geometric features in protein-protein interactions. By screening the solvent, this organization shields backbone hydrogen bonds and charge-charge interactions. Complemented pockets often pre-exist binding. For 18 protein-protein complexes with complemented pockets whose unbound structures are available, in 16 the pockets are identified to pre-exist in the unbound structures. The root-mean-squared deviations of the atoms lining the pockets between the bound and unbound states is as small as 0.9A, suggesting that such pockets constitute features of the populated native state that may be used in docking.     

Hidden partners: Using cross‐docking calculations to predict binding sites for proteins with multiple interactions

Protein‐protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein‐protein interactions. Cross‐docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross‐docking simulations of 358 proteins with 2 different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity‐sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross‐docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, that is, partners not included in the original cross‐docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.     

Multiple solvent crystal structures: probing binding sites, plasticity and hydration

Multiple solvent crystal structures (MSCS) of porcine pancreatic elastase were used to map the binding surface the enzyme. Crystal structures of elastase in neat acetonitrile, 95% acetone, 55% dimethylformamide, 80% 5-hexene-1,2-diol, 80% isopropanol, 80% ethanol and 40% trifluoroethanol showed that the organic solvent molecules clustered in the active site, were found mostly unclustered in crystal contacts and in general did not bind elsewhere on the surface of elastase. Mixtures of 40% benzene or 40% cyclohexane in 50% isopropanol and 10% water showed no bound benzene or cyclohexane molecules, but did reveal bound isopropanol. The clusters of organic solvent probe molecules coincide with pockets occupied by known inhibitors. MSCS also reveal the areas of plasticity within the elastase binding site and allow for the visualization of a nearly complete first hydration shell. The pattern of organic solvent clusters determined by MSCS for elastase is consistent with patterns for hot spots in protein-ligand interactions determined from database analysis in general. The MSCS method allows probing of hot spots, plasticity and hydration simultaneously, providing a powerful complementary strategy to guide computational methods currently in development for binding site determination, ligand docking and design.     

BioGPS: Navigating biological space to predict polypharmacology,off‐targeting,and selectivity

The structural comparison of protein binding sites is increasingly important in drug design; identifying structurally similar sites can be useful for techniques such as drug repurposing, and also in a polypharmacological approach to deliberately affect multiple targets in a disease pathway, or to explain unwanted off‐target effects. Once similar sites are identified, identifying local differences can aid in the design of selectivity. Such an approach moves away from the classical “one target one drug” approach and toward a wider systems biology paradigm. Here, we report a semiautomated approach, called BioGPS, that is based on the software FLAP which combines GRID Molecular Interactions Fields (MIFs) and pharmacophoric fingerprints. BioGPS comprises the automatic preparation of protein structure data, identification of binding sites, and subsequent comparison by aligning the sites and directly comparing the MIFs. Chemometric approaches are included to reduce the complexity of the resulting data on large datasets, enabling focus on the most relevant information. Individual site similarities can be analyzed in terms of their Pharmacophoric Interaction Field (PIF) similarity, and importantly the differences in their PIFs can be extracted. Here we describe the BioGPS approach, and demonstrate its applicability to rationalize off‐target effects (ERα and SERCA), to classify protein families and explain polypharmacology (ABL1 kinase and NQO2), and to rationalize selectivity between subfamilies (MAP kinases p38α/ERK2 and PPARδ/PPARγ). The examples shown demonstrate a significant validation of the method and illustrate the effectiveness of the approach. Proteins 2015; 83:517–532. © 2015 Wiley Periodicals, Inc.     

Protein subunit interfaces: a statistical analysis of hot spots in Sm proteins

The distinguishing property of Sm protein associations is very high stability. In order to understand this property, we analyzed the interfaces and compared the properties of Sm protein interfaces with those of a test set, the Binding Interface Database (BID). The comparison revealed that the main differences between the interfaces of Sm proteins and those of the BID set are the content of charged residues, the coordination numbers of the residues, knowledge-based pair potentials, and the conservation scores of hot spots. In Sm proteins, the interfaces have more hydrophobic and fewer charged residues than the surfaces, which is also the case for the BID test set and other proteins. However, in the interfaces, the content of charged residues in Sm proteins (26%) is substantially larger than that in the BID set (22%). Hot spots are residues that make up a small fraction of the interfaces, but they contribute most of the binding energy. These residues are critical to protein–protein interactions. Analyses of knowledge-based pair potentials of hot spot and non-hot spot residues in Sm proteins show that they are significantly different; their mean values are 31.5 and 11.3, respectively. In the BID set, this difference is smaller; in this case, the mean values for hot spot and non-hot spot residues are 20.7 and 12.4, respectively. Hence, the pair potentials of hot spots differ significantly for the Sm and BID data sets. In the interfaces of Sm proteins, the amino acids are tightly packed, and the coordination numbers are larger in Sm proteins than in the BID set for both hot spots and non-hot spots. At the same time, the coordination numbers are higher for hot spots; the average coordination number of the hot spot residues in Sm proteins is 7.7, while it is 6.1 for the non-hot spot residues. The difference in the calculated average conservation score for hot spots and non-hot spots in Sm proteins is significantly larger than it is in the BID set. In Sm proteins, the average conservation score for the hot spots is 7.4. Hot spots are surrounded by residues that are moderately conserved (5.9). The average conservation score for the other interface residues is 5.6. The conservation scores in the BID set do not show a significant distinction between hot and non-hot spots: the mean values for hot and non-hot spot residues are 5.5 and 5.2, respectively. These data show that structurally conserved residues and hot spots are significantly correlated in Sm proteins.     

A compound‐based computational approach for the accurate determination of hot spots

A plethora of both experimental and computational methods have been proposed in the past 20 years for the identification of hot spots at a protein–protein interface. The experimental determination of a protein–protein complex followed by alanine scanning mutagenesis, though able to determine hot spots with much precision, is expensive and has no guarantee of success while the accuracy of the current computational methods for hot‐spot identification remains low. Here, we present a novel structure‐based computational approach that accurately determines hot spots through docking into a set of proteins homologous to only one of the two interacting partners of a compound capable of disrupting the protein–protein interaction (PPI). This approach has been applied to identify the hot spots of human activin receptor type II (ActRII) critical for its binding toward Cripto‐I. The subsequent experimental confirmation of the computationally identified hot spots portends a potentially accurate method for hot‐spot determination in silico given a compound capable of disrupting the PPI in question. The hot spots of human ActRII first reported here may well become the focal points for the design of small molecule drugs that target the PPI. The determination of their interface may have significant biological implications in that it suggests that Cripto‐I plays an important role in both activin and nodal signal pathways.     

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Ligand–protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED‐SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED‐SMA is an automated and fast method to classify binding sites. It is based on MED‐SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora‐A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED‐SMA approach is demonstrated as it gathers binding sites of proteins with similar structure‐activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target‐based drug design and prediction of cross‐reactivity and therefore potential toxic side effects.     

Interolog interfaces in protein–protein docking

Proteins are essential elements of biological systems, and their function typically relies on their ability to successfully bind to specific partners. Recently, an emphasis of study into protein interactions has been on hot spots, or residues in the binding interface that make a significant contribution to the binding energetics. In this study, we investigate how conservation of hot spots can be used to guide docking prediction. We show that the use of evolutionary data combined with hot spot prediction highlights near‐native structures across a range of benchmark examples. Our approach explores various strategies for using hot spots and evolutionary data to score protein complexes, using both absolute and chemical definitions of conservation along with refinements to these strategies that look at windowed conservation and filtering to ensure a minimum number of hot spots in each binding partner. Finally, structure‐based models of orthologs were generated for comparison with sequence‐based scoring. Using two data sets of 22 and 85 examples, a high rate of top 10 and top 1 predictions are observed, with up to 82% of examples returning a top 10 hit and 35% returning top 1 hit depending on the data set and strategy applied; upon inclusion of the native structure among the decoys, up to 55% of examples yielded a top 1 hit. The 20 common examples between data sets show that more carefully curated interolog data yields better predictions, particularly in achieving top 1 hits. Proteins 2015; 83:1940–1946. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.