Motivation: The binding sites of proteins generally contain smaller regions that provide major contributions to the binding free energy and hence are the prime targets in drug design. Screening libraries of fragment-sized compounds by NMR or X-ray crystallography demonstrates that such ‘hot spot’ regions bind a large variety of small organic molecules, and that a relatively high ‘hit rate’ is predictive of target sites that are likely to bind drug-like ligands with high affinity. Our goal is to determine the ‘hot spots’ computationally rather than experimentally.
Results: We have developed the FTMAP algorithm that performs global search of the entire protein surface for regions that bind a number of small organic probe molecules. The search is based on the extremely efficient fast Fourier transform (FFT) correlation approach which can sample billions of probe positions on dense translational and rotational grids, but can use only sums of correlation functions for scoring and hence is generally restricted to very simple energy expressions. The novelty of FTMAP is that we were able to incorporate and represent on grids a detailed energy expression, resulting in a very accurate identification of low-energy probe clusters. Overlapping clusters of different probes are defined as consensus sites (CSs). We show that the largest CS is generally located at the most important subsite of the protein binding site, and the nearby smaller CSs identify other important subsites. Mapping results are presented for elastase whose structure has been solved in aqueous solutions of eight organic solvents, and we show that FTMAP provides very similar information. The second application is to renin, a long-standing pharmaceutical target for the treatment of hypertension, and we show that the major CSs trace out the shape of the first approved renin inhibitor, aliskiren.
Availability: FTMAP is available as a server at http://ftmap.bu.edu/.
Supplementary information: Supplementary Material is available at Bioinformatics online.
Binding hot spots, protein regions with high binding affinity, can be identified by using X-ray crystallography or NMR spectroscopy to screen libraries of small organic molecules that tend to cluster at such hot spots. FTMap, a direct computational analogue of the experimental screening approaches, uses 16 different probe molecules for global sampling of the surface of a target protein on a dense grid and evaluates the energy of interaction using an empirical energy function that includes a continuum electrostatic term. Energy evaluation is based on the fast Fourier transform correlation approach, which allows for the sampling of billions of probe positions. The grid sampling is followed by off-grid minimization that uses a more detailed energy expression with a continuum electrostatics term. FTMap identifies the hot spots as consensus clusters formed by overlapping clusters of several probes. The hot spots are ranked on the basis of the number of probe clusters, which predicts their binding propensity. We applied FTMap to nine structures of hen egg-white lysozyme (HEWL), whose hot spots have been extensively studied by both experimental and computational methods. FTMap found the primary hot spot in site C of all nine structures, in spite of conformational differences. In addition, secondary hot spots in sites B and D that are known to be important for the binding of polysaccharide substrates were found. The predicted probe–protein interactions agree well with those seen in the complexes of HEWL with various ligands and also agree with an NMR-based study of HEWL in aqueous solutions of eight organic solvents. We argue that FTMap provides more complete information on the HEWL binding site than previous computational methods and yields fewer false-positive binding locations than the X-ray structures of HEWL from crystals soaked in organic solvents.
Computational solvent mapping globally samples the surface of target proteins using molecular probes – small molecules or functional groups – to identify potentially favorable binding positions. The method is based on X-ray and NMR screening studies showing that the binding sites of proteins also bind a large variety of fragment-sized molecules. We have developed the multi-stage mapping algorithm FTMap (available as a server at http://ftmap.bu.edu/) based on the fast Fourier transform (FFT) correlation approach. Identifying regions of low free energy rather than individual low energy conformations, FTMap reproduces the available experimental mapping results. Applications to a variety of proteins show that the probes always cluster in important subsites of the binding site, and the amino acid residues that interact with many probes also bind the specific ligands of the protein. The “consensus” sites at which a number of different probes cluster are likely to be “druggable” sites, capable of binding drug-size ligands with high affinity. Due to its sensitivity to conformational changes the method can also be used for comparing the binding sites in different structures of a protein.
Protein structure; protein-ligand interactions; binding site; binding hot spots; fragment-based ligand design; druggability; binding site comparison; docking
Fragment based drug design (FBDD) starts with finding fragment-sized compounds that are highly ligand efficient and can serve as a core moiety for developing high affinity leads. Although the core-bound structure of a protein facilitates the construction of leads, effective design is far from straightforward. We show that protein mapping, a computational method developed to find binding hot spots and implemented as the FTMap server, provides information that complements the fragment screening results and can drive the evolution of core fragments into larger leads with a minimal loss or, in some cases, even a gain in ligand efficiency. The method places small molecular probes, the size of organic solvents, on a dense grid around the protein, and identifies the hot spots as consensus clusters formed by clusters of several probes. The hot spots are ranked based on the number of probe clusters, which predicts the binding propensity of the subsites and hence their importance for drug design. Accordingly, with a single exception the main hot spot identified by FTMap binds the core compound found by fragment screening. The most useful information is provided by the neighboring secondary hot spots, indicating the regions where the core can be extended to increase its affinity. To quantify this information, we calculate the density of probes from mapping, which describes the binding propensity at each point, and show that the change in the correlation between a ligand position and the probe density upon extending or repositioning the core moiety predicts the expected change in ligand efficiency.
Protein mapping; protein docking; drug design; ligand efficiency; affinity prediction
We report a comprehensive analysis of binding energy hot spots at the protein-protein interaction (PPI) interface between NF-κB Essential Modulator (NEMO) and IκB kinase subunit β (IKKβ), an interaction that is critical for NF-κB pathway signaling, using experimental alanine scanning mutagenesis and also the FTMap method for computational fragment screening. The experimental results confirm that the previously identified NBD region of IKKβ contains the highest concentration of hot spot residues, the strongest of which are W739, W741 and L742 (ΔΔG = 4.3, 3.5 and 3.2 kcal/mol, respectively). The region occupied by these residues defines a potentially druggable binding site on NEMO that extends for ~16 Å to additionally include the regions that bind IKKβ L737 and F734. NBD residues D738 and S740 are also important for binding but do not make direct contact with NEMO, instead likely acting to stabilize the active conformation of surrounding residues. We additionally found two previously unknown hot spot regions centered on IKKβ residues L708/V709 and L719/I723. The computational approach successfully identified all three hot spot regions on IKKβ. Moreover, the method was able to accurately quantify the energetic importance of all hot spots residues involving direct contact with NEMO. Our results provide new information to guide the discovery of small molecule inhibitors that target the NEMO/IKKβ interaction. They additionally clarify the structural and energetic complementarity between “pocket-forming” and “pocket occupying” hot spot residues, and further validate computational fragment mapping as a method for identifying hot spots at PPI interfaces.
IKKγ; alanine scanning mutagenesis; protein-protein interactions; IKKγ; fluorescence polarization; fluorescence anisotropy
Lysine specific demethylase-1 (LSD1/KDM1A) in complex with its corepressor protein CoREST is a promising target for epigenetic drugs. No therapeutic that targets LSD1/CoREST, however, has been reported to date. Recently, extended molecular dynamics (MD) simulations indicated that LSD1/CoREST nanoscale clamp dynamics is regulated by substrate binding and highlighted key hinge points of this large-scale motion as well as the relevance of local residue dynamics. Prompted by the urgent need for new molecular probes and inhibitors to understand LSD1/CoREST interactions with small-molecules, peptides, protein partners, and chromatin, we undertake here a configurational ensemble approach to expand LSD1/CoREST druggability. The independent algorithms FTMap and SiteMap and our newly developed Druggable Site Visualizer (DSV) software tool were used to predict and inspect favorable binding sites. We find that the hinge points revealed by MD simulations at the SANT2/Tower interface, at the SWIRM/AOD interface, and at the AOD/Tower interface are new targets for the discovery of molecular probes to block association of LSD1/CoREST with chromatin or protein partners. A fourth region was also predicted from simulated configurational ensembles and was experimentally validated to have strong binding propensity. The observation that this prediction would be prevented when using only the X-ray structures available (including the X-ray structure bound to the same peptide) underscores the relevance of protein dynamics in protein interactions. A fifth region was highlighted corresponding to a small pocket on the AOD domain. This study sets the basis for future virtual screening campaigns targeting the five novel regions reported herein and for the design of LSD1/CoREST mutants to probe LSD1/CoREST binding with chromatin and various protein partners.
Protein dynamics plays a major role in determining the molecular interactions available to molecular binding partners, including druggable hot spots. The LSD1/CoREST complex is one of the most relevant epigenetic targets discovered and was shown to be a highly dynamic nanoscale clamp using molecular dynamics simulations. The general relationship between LSD1/CoREST dynamics and the molecular sites available for non-covalent interactions with an array of known binding partners (from relatively small drug-like molecules and peptides, to larger proteins and chromatin) remains relatively unexplored. We employed an integrated experimental and computational biology approach to effectively capture the nature of non-covalent binding interactions available to the LSD1/CoREST nanoscale complex. This ensemble approach relies on the newly developed graphical visualization by Druggable Site Visualizer (DSV) that allows treatment of large-size protein configurational ensembles data and is freely distributed to the public and readily transferable to other protein targets of pharmacological interest.
To address the problem of specificity in G-protein coupled receptor (GPCR) drug discovery, there has been tremendous recent interest in allosteric drugs that bind at sites topographically distinct from the orthosteric site. Unfortunately, structure-based drug design of allosteric GPCR ligands has been frustrated by the paucity of structural data for allosteric binding sites, making a strong case for predictive computational methods. In this work, we map the surfaces of the β1 (β1AR) and β2 (β2AR) adrenergic receptor structures, to detect a series of five potentially druggable allosteric sites. We employ the FTMAP algorithm to identify “hot spots” with affinity for a variety of organic probe molecules corresponding to drug fragments. Our work is distinguished by an ensemble-based approach, whereby we map diverse receptor conformations taken from Molecular Dynamics (MD) simulations totalling ~0.5 μs. Our results reveal distinct pockets formed at both solvent-exposed and lipid-exposed cavities, which we interpret in the light of experimental data and which may constitute novel targets for GPCR drug discovery. This mapping data can now serve to drive a combination of fragment-based and virtual screening approaches for the discovery of small molecules that bind at these sites and which may offer highly selective therapies.
molecular dynamics; allosteric; GPCR; docking; fragment-based
To address the problem of specificity in G-protein coupled receptor (GPCR) drug discovery, there has been tremendous recent interest in allosteric drugs that bind at sites topographically distinct from the orthosteric site. Unfortunately, structure-based drug design of allosteric GPCR ligands has been frustrated by the paucity of structural data for allosteric binding sites, making a strong case for predictive computational methods. In this work, we map the surfaces of the β1 (β1AR) and β2 (β2AR) adrenergic receptor structures to detect a series of five potentially druggable allosteric sites. We employ the FTMAP algorithm to identify ‘hot spots’ with affinity for a variety of organic probe molecules corresponding to drug fragments. Our work is distinguished by an ensemble-based approach, whereby we map diverse receptor conformations taken from molecular dynamics (MD) simulations totaling approximately 0.5 μs. Our results reveal distinct pockets formed at both solvent-exposed and lipid-exposed cavities, which we interpret in light of experimental data and which may constitute novel targets for GPCR drug discovery. This mapping data can now serve to drive a combination of fragment-based and virtual screening approaches for the discovery of small molecules that bind at these sites and which may offer highly selective therapies.
allosteric; docking; fragment-based; GPCR; molecular dynamics
The identification of hot spots, i.e. binding regions that contribute substantially to the free energy of ligand binding, is a critical step for structure-based drug design. Here we present the application of two fragment-based methods to the detection of hot spots for DJ-1 and glucocerebrosidase (GCase), targets for the development of therapeutics for Parkinson’s and Gaucher’s diseases respectively. While the structures of these two proteins are known, binding information is lacking. In this study we employ both the multiple solvent crystal structures (MSCS) method and the FTMap algorithm to identify regions suitable for the development of pharmacological chaperones for DJ-1 and GCase. Comparison of data derived via MSCS and FTMap also shows that FTMap, a computational method for the identification of fragment binding hot spots, is an accurate and robust alternative to the performance of expensive and difficult MSCS experiments.
fragment-based drug design; structure-based drug design; hot spot identification; DJ-1; glucocerebrosidase; Parkinson’s disease; Gaucher’s disease; pharmacological chaperones
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.
Computational solvent mapping finds binding hot spots, determines their druggability and provides information for drug design. While mapping of a ligand-bound structure yields more accurate results, usually the apo structure serves as the starting point in design. The FTFlex algorithm, implemented as a server, can modify an apo structure to yield mapping results that are similar to those of the respective bound structure. Thus, FTFlex is an extension of our FTMap server, which only considers rigid structures. FTFlex identifies flexible residues within the binding site and determines alternative conformations using a rotamer library. In cases where the mapping results of the apo structure were in poor agreement with those of the bound structure, FTFlex was able to yield a modified apo structure, which lead to improved FTMap results. In cases where the mapping results of the apo and bound structures were in good agreement, no new structure was predicted.
Availability: FTFlex is freely available as a web-based server at http://ftflex.bu.edu/.
email@example.com or firstname.lastname@example.org
Supplementary data are available at Bioinformatics online.
We have recently discovered an allosteric switch in Ras, bringing an additional level of complexity to this GTPase whose mutants are involved in nearly 30% of cancers. Upon activation of the allosteric switch, there is a shift in helix 3/loop 7 associated with a disorder to order transition in the active site. Here, we use a combination of multiple solvent crystal structures and computational solvent mapping (FTMap) to determine binding site hot spots in the “off” and “on” allosteric states of the GTP-bound form of H-Ras. Thirteen sites are revealed, expanding possible target sites for ligand binding well beyond the active site. Comparison of FTMaps for the H and K isoforms reveals essentially identical hot spots. Furthermore, using NMR measurements of spin relaxation, we determined that K-Ras exhibits global conformational dynamics very similar to those we previously reported for H-Ras. We thus hypothesize that the global conformational rearrangement serves as a mechanism for allosteric coupling between the effector interface and remote hot spots in all Ras isoforms. At least with respect to the binding sites involving the G domain, H-Ras is an excellent model for K-Ras and probably N-Ras as well. Ras has so far been elusive as a target for drug design. The present work identifies various unexplored hot spots throughout the entire surface of Ras, extending the focus from the disordered active site to well-ordered locations that should be easier to target.
Ras isoforms; drug target; binding site hot spots; Ras dynamics; allosteric switch
Peptide-mediated interactions, in which a short linear motif binds to a globular domain, play major roles in cellular regulation. An accurate structural model of this type of interaction is an excellent starting point for the characterization of the binding specificity of a given peptide-binding domain. A number of different protocols have recently been proposed for the accurate modeling of peptide-protein complex structures, given the structure of the protein receptor and the binding site on its surface. When no information about the peptide binding site(s) is a priori available, there is a need for new approaches to locate peptide-binding sites on the protein surface. While several approaches have been proposed for the general identification of ligand binding sites, peptides show very specific binding characteristics, and therefore, there is a need for robust and accurate approaches that are optimized for the prediction of peptide-binding sites.
Here we present PeptiMap, a protocol for the accurate mapping of peptide binding sites on protein structures. Our method is based on experimental evidence that peptide-binding sites also bind small organic molecules of various shapes and polarity. Using an adaptation of ab initio ligand binding site prediction based on fragment mapping (FTmap), we optimize a protocol that specifically takes into account peptide binding site characteristics. In a high-quality curated set of peptide-protein complex structures PeptiMap identifies for most the accurate site of peptide binding among the top ranked predictions. We anticipate that this protocol will significantly increase the number of accurate structural models of peptide-mediated interactions.
protein peptide interactions; FFT sampling; binding site detection; mapping; PeptiDB
We present a new database of computational hot spots in protein interfaces: HotSprint. Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. HotSprint contains data for 35 776 protein interfaces among 49 512 protein interfaces extracted from the multi-chain structures in Protein Data Bank (PDB) as of February 2006. The conserved residues in interfaces with certain buried accessible solvent area (ASA) and complex ASA thresholds are flagged as computational hot spots. The predicted hot spots are observed to correlate with the experimental hot spots with an accuracy of 76%. Several machine-learning methods (SVM, Decision Trees and Decision Lists) are also applied to predict hot spots, results reveal that our empirical approach performs better than the others. A web interface for the HotSprint database allows users to browse and query the hot spots in protein interfaces. HotSprint is available at http://prism.ccbb.ku.edu.tr/hotsprint; and it provides information for interface residues that are functionally and structurally important as well as the evolutionary history and solvent accessibility of residues in interfaces.
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.
In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods.
We have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. A major contribution of this study is to propose several new features based on the protrusion index of amino acid residues, which has been shown to significantly improve the prediction performance of hot spots. Moreover, we identify a compact and useful feature subset that has an important implication for identifying hot spot residues. Our results indicate that these features are more effective than the conventional evolutionary conservation, pairwise residue potentials and other traditional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues. The data and source code are available on web site http://home.ustc.edu.cn/~jfxia/hotspot.html.
Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need.
In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes.
Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots.
Hot spots are residues contributing the most of binding free energy yet accounting for a small portion of a protein interface. Experimental approaches to identify hot spots such as alanine scanning mutagenesis are expensive and time-consuming, while computational methods are emerging as effective alternatives to experimental approaches.
In this study, we propose a semi-supervised boosting SVM, which is called sbSVM, to computationally predict hot spots at protein-protein interfaces by combining protein sequence and structure features. Here, feature selection is performed using random forests to avoid over-fitting. Due to the deficiency of positive samples, our approach samples useful unlabeled data iteratively to boost the performance of hot spots prediction. The performance evaluation of our method is carried out on a dataset generated from the ASEdb database for cross-validation and a dataset from the BID database for independent test. Furthermore, a balanced dataset with similar amounts of hot spots and non-hot spots (65 and 66 respectively) derived from the first training dataset is used to further validate our method. All results show that our method yields good sensitivity, accuracy and F1 score comparing with the existing methods.
Our method boosts prediction performance of hot spots by using unlabeled data to overcome the deficiency of available training data. Experimental results show that our approach is more effective than the traditional supervised algorithms and major existing hot spot prediction methods.
The energy distribution along the protein–protein interface is not homogenous; certain residues contribute more to the binding free energy, called ‘hot spots’. Here, we present a web server, HotPoint, which predicts hot spots in protein interfaces using an empirical model. The empirical model incorporates a few simple rules consisting of occlusion from solvent and total knowledge-based pair potentials of residues. The prediction model is computationally efficient and achieves high accuracy of 70%. The input to the HotPoint server is a protein complex and two chain identifiers that form an interface. The server provides the hot spot prediction results, a table of residue properties and an interactive 3D visualization of the complex with hot spots highlighted. Results are also downloadable as text files. This web server can be used for analysis of any protein–protein interface which can be utilized by researchers working on binding sites characterization and rational design of small molecules for protein interactions. HotPoint is accessible at http://prism.ccbb.ku.edu.tr/hotpoint.
For decades, classical crossover studies and linkage disequilibrium (LD) analysis of genomic regions suggested that human meiotic crossovers may not be randomly distributed along chromosomes but are focused instead in “hot spots.” Recent sperm typing studies provided data at very high resolution and accuracy that defined the physical limits of a number of hot spots. The data were also used to test whether patterns of LD can predict hot spot locations. These sperm typing studies focused on several small regions of the genome already known or suspected of containing a hot spot based on the presence of LD breakdown or previous experimental evidence of hot spot activity. Comparable data on target regions not specifically chosen using these two criteria is lacking but is needed to make an unbiased test of whether LD data alone can accurately predict active hot spots. We used sperm typing to estimate recombination in 17 almost contiguous ~5 kb intervals spanning 103 kb of human Chromosome 21. We found two intervals that contained new hot spots. The comparison of our data with recombination rates predicted by statistical analyses of LD showed that, overall, the two datasets corresponded well, except for one predicted hot spot that showed little crossing over. This study doubles the experimental data on recombination in men at the highest resolution and accuracy and supports the emerging genome-wide picture that recombination is localized in small regions separated by cold areas. Detailed study of one of the new hot spots revealed a sperm donor with a decrease in recombination intensity at the canonical recombination site but an increase in crossover activity nearby. This unique finding suggests that the position and intensity of hot spots may evolve by means of a concerted mechanism that maintains the overall recombination intensity in the region.
Meiotic crossover events are not randomly distributed across the human genome, but are concentrated in many small regions of a few kb with high recombination rates compared to surrounding regions. How the distribution of recombination events affects the association of different alleles along the chromosome (linkage disequilibrium, or LD) was recently addressed using sperm typing in regions already known or suspected to contain unusually high recombination intensities. In the current paper, the authors used sperm typing to examine recombination in a region not known or suspected of containing recombination hot spots. They first established the crossover distribution pattern within a 103-kb region of human Chromosome 21. Then, they compared their data to predictions of crossover distributions estimated by statistical analyses of polymorphism in the region. They found a good concordance between the two, although it was not perfect. To the authors' knowledge, this work is the first to compare LD-based estimates of recombination to sperm-typing data from regions not previously known or suspected of containing recombination hot spots. In addition, one of the studied hot spots revealed an example of a decrease in recombination intensity with a concurrent increase at a nearby site. This unique observation suggests that the activity of hot spots may evolve in a concerted fashion such that the overall recombination activity of the region is maintained.
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.
Many protein-protein interfaces (PPIs) are biologically compelling drug targets. Disrupting the interaction between two large proteins by a small inhibitor requires forming a high affinity binding site in the interface that generally can bind both peptides and drug-like compounds. Here we investigate whether such sites are induced by peptide or ligand binding, or already exist in the unbound state. The analysis requires comparing ligand-free and ligand-bound structures. To avoid any potential bias, we study ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) rather than generated by simulations. The analysis is based on computational solvent mapping, which explores the surface of the target protein by docking a large number of small “probe” molecules. Results show that ensembles of ligand-free models always include conformations that are fairly similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. The analysis also identifies the models that are the most similar to a bound state, and shows the maximum level of similarity that is achieved without any influence from a ligand. While forming the binding site may require a combination of recognition mechanisms, there is preference for the spontaneous formation of bound-like structures.
A protein binding hot spot is a cluster of residues in the interface that are energetically important for the binding of the protein with its interaction partner. Identifying protein binding hot spots can give useful information to protein engineering and drug design, and can also deepen our understanding of protein-protein interaction. These residues are usually buried inside the interface with very low solvent accessible surface area (SASA). Thus SASA is widely used as an outstanding feature in hot spot prediction by many computational methods. However, SASA is not capable of distinguishing slightly buried residues, of which most are non hot spots, and deeply buried ones that are usually inside a hot spot.
We propose a new descriptor called “burial level” for characterizing residues, atoms and atomic contacts. Specifically, burial level captures the depth the residues are buried. We identify different kinds of deeply buried atomic contacts (DBAC) at different burial levels that are directly broken in alanine substitution. We use their numbers as input for SVM to classify between hot spot or non hot spot residues. We achieve F measure of 0.6237 under the leave-one-out cross-validation on a data set containing 258 mutations. This performance is better than other computational methods.
Our results show that hot spot residues tend to be deeply buried in the interface, not just having a low SASA value. This indicates that a high burial level is not only a necessary but also a more sufficient condition than a low SASA for a residue to be a hot spot residue. We find that those deeply buried atoms become increasingly more important when their burial levels rise up. This work also confirms the contribution of deeply buried interfacial atomic contacts to the energy of protein binding hot spot.
The interaction between complement fragment C3d and complement receptor 2 (CR2) is a key aspect of complement immune system activation, and is a component in a link between innate and adaptive immunities. The complement immune system is an ancient mechanism for defense, and can be found in species that have been on Earth for the last 600 million years. However, the link between the complement system and adaptive immunity, which is formed through the association of the B-cell co-receptor complex, including the C3d-CR2 interaction, is a much more recent adaptation. Human C3d and CR2 have net charges of −1 and +7 respectively, and are believed to have evolved favoring the role of electrostatics in their functions. To investigate the role of electrostatics in the function and evolution of human C3d and CR2, we have applied electrostatic similarity methods to identify regions of evolutionarily conserved electrostatic potential based on 24 homologues of complement C3d and 4 homologues of CR2. We also examine the effects of structural perturbation, as introduced through molecular dynamics and mutations, on spatial distributions of electrostatic potential to identify perturbation resistant regions, generated by so-called electrostatic “hot-spots”. Distributions of electrostatic similarity based on families of perturbed structures illustrate the presence of electrostatic “hot-spots” at the two functional sites of C3d, while the surface of CR2 lacks electrostatic “hot-spots” despite its excessively positive nature. We propose that the electrostatic “hot-spots” of C3d have evolved to optimize its dual-functionality (covalently attaching to pathogen surfaces and interaction with CR2), which are both necessary for the formation B-cell co-receptor complexes. Comparison of the perturbation resistance of the electrostatic character of the homologues of C3d suggests that there was an emergence of a new role of electrostatics, and a transition in the function of C3d, after the divergence of jawless fish.
Complement fragment C3d is a thioester-containing protein that is a key component/domain in the complement system, an ancient line of defense, due to its ability to covalently attach to pathogen cell surfaces, such as bacteria. As the immune system evolved in complexity, from acellular defense mechanisms to multicellular systems with memory, so has the function of C3d. In humans, but not lower species such as invertebrates, C3d attached to pathogen surfaces binds B-cell co-receptor CR2, in conjunction with an antibody/antigen complex, forming a link between the innate and adaptive immune systems. The C3d-CR2 interaction ultimately increases B-cell sensitivity to the C3d tagged pathogen by 1,000–10,000 fold, and is known to be driven by electrostatic forces. Since electrostatics are crucial to the C3d-CR2 interaction, it is likely that probing the evolution of the electrostatics of C3d and CR2 will provide insight into this gained function. To this end, we employ a novel computational approach for identifying the electrostatic “hot-spots” of C3d and CR2, which are produced by clusters of like-charged residues found on the surface of the protein. Electrostatic “hot-spots” are often evolutionarily favored and in this study provide new insight into the evolution of C3d in its role in a link between innate and adaptive immunity.
The steroid and xenobiotic-responsive human pregnane X receptor (PXR) binds a broad range of structurally diverse compounds. The structures of the apo and ligand-bound forms of PXR are very similar, in contrast to most promiscuous proteins that generally adapt their shape to different ligands. We investigated the structural origins of PXR's recognition promiscuity using computational solvent mapping, a technique developed for the identification and characterization of hot spots, i.e., regions of the protein surface that are major contributors to the binding free energy. Results reveal that the smooth and nearly spherical binding site of PXR has a well-defined hot spot structure, with four hot spots located on four different sides of the pocket and a fifth close to its center. Three of these hot spots are already present in the ligand-free protein. The most important hot spot is defined by three structurally and sequentially conserved residues, W299, F288, and Y306. This largely hydrophobic site is not very specific, and interacts with all known PXR ligands. Depending on their sizes and shapes, individual PXR ligands extend into 2, 3, or 4 more hot spot regions. The large number of potential arrangements within the binding site explains why PXR is able to accommodate a large variety of compounds. All five hot spots include at least one important residue, which is conserved in all mammalian PXRs, suggesting that the hot spot locations have remained largely invariant during mammalian evolution. The same side chains also show a high level of structural conservation across hPXR structures. However, each of the hPXR hot spots also includes residues with moveable side chains, further increasing the size variation in ligands that PXR can bind. Results also suggest a unique signal transduction mechanism between the PXR homodimerization interface and its co-activator binding site.
Motivation: The O-ring theory reveals that the binding hot spot at a protein interface is surrounded by a ring of residues that are energetically less important than the residues in the hot spot. As this ring of residues is served to occlude water molecules from the hot spot, the O-ring theory is also called ‘water exclusion’ hypothesis. We propose a ‘double water exclusion’ hypothesis to refine the O-ring theory by assuming the hot spot itself is water-free. To computationally model a water-free hot spot, we use a biclique pattern that is defined as two maximal groups of residues from two chains in a protein complex holding the property that every residue contacts with all residues in the other group.
Methods and Results: Given a chain pair A and B of a protein complex from the Protein Data Bank (PDB), we calculate the interatomic distance of all possible pairs of atoms between A and B. We then represent A and B as a bipartite graph based on these distance information. Maximal biclique subgraphs are subsequently identified from all of the bipartite graphs to locate biclique patterns at the interfaces. We address two properties of biclique patterns: a non-redundant occurrence in PDB, and a correspondence with hot spots when the solvent-accessible surface area (SASA) of a biclique pattern in the complex form is small. A total of 1293 biclique patterns are discovered which have a non-redundant occurrence of at least five, and which each have a minimum two and four residues at the two sides. Through extensive queries to the HotSprint and ASEdb databases, we verified that biclique patterns are rich of true hot residues. Our algorithm and results provide a new way to identify hot spots by examining proteins' structural data.
Availability: The biclique mining algorithm is available at http://www.ntu.edu.sg/home/jyli/dwe.html.
Supplementary information: Supplementary data are available at Bioinformatics online.
Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (ΔΔG) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.
We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which ΔΔG ≥ 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.
We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration.