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.     

Presaging Critical Residues in Protein interfaces-Web Server (PCRPi-W): A Web Server to Chart Hot Spots in Protein Interfaces

Background

It is well established that only a portion of residues that mediate protein-protein interactions (PPIs), the so-called hot spot, contributes the most to the total binding energy, and thus its identification is an important and relevant question that has clear applications in drug discovery and protein design. The experimental identification of hot spots is however a lengthy and costly process, and thus there is an interest in computational tools that can complement and guide experimental efforts.

Principal Findings

Here, we present Presaging Critical Residues in Protein interfaces-Web server (http://www.bioinsilico.org/PCRPi), a web server that implements a recently described and highly accurate computational tool designed to predict critical residues in protein interfaces: PCRPi. PRCPi depends on the integration of structural, energetic, and evolutionary-based measures by using Bayesian Networks (BNs).

Conclusions

PCRPi-W has been designed to provide an easy and convenient access to the broad scientific community. Predictions are readily available for download or presented in a web page that includes among other information links to relevant files, sequence information, and a Jmol applet to visualize and analyze the predictions in the context of the protein structure.     

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。     

Hot regions in protein--protein interactions: the organization and contribution of structurally conserved hot spot residues

Structurally conserved residues at protein-protein interfaces correlate with the experimental alanine-scanning hot spots. Here, we investigate the organization of these conserved, computational hot spots and their contribution to the stability of protein associations. We find that computational hot spots are not homogeneously distributed along the protein interfaces; rather they are clustered within locally tightly packed regions. Within the dense clusters, they form a network of interactions and consequently their contributions to the stability of the complex are cooperative; however the contributions of independent clusters are additive. This suggests that the binding free energy is not a simple summation of the single hot spot residue contributions. As expected, around the hot spot residues we observe moderately conserved residues, further highlighting the crucial role of the conserved interactions in the local densely packed environment. The conserved occurrence of these organizations suggests that they are advantageous for protein-protein associations. Interestingly, the total number of hydrogen bonds and salt bridges contributed by hot spots is as expected. Thus, H-bond forming residues may use a "hot spot for water exclusion" mechanism. Since conserved residues are located within highly packed regions, water molecules are easily removed upon binding, strengthening electrostatic contributions of charge-charge interactions. Hence, the picture that emerges is that protein-protein associations are optimized locally, with the clustered, networked, highly packed structurally conserved residues contributing dominantly and cooperatively to the stability of the complex. When addressing the crucial question of "what are the preferred ways of proteins to associate", these findings point toward a critical involvement of hot regions in protein-protein interactions.     

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.     

Predicting permanent and transient protein–protein interfaces

Protein–protein interactions (PPIs) are involved in diverse functions in a cell. To optimize functional roles of interactions, proteins interact with a spectrum of binding affinities. Interactions are conventionally classified into permanent and transient, where the former denotes tight binding between proteins that result in strong complexes, whereas the latter compose of relatively weak interactions that can dissociate after binding to regulate functional activity at specific time point. Knowing the type of interactions has significant implications for understanding the nature and function of PPIs. In this study, we constructed amino acid substitution models that capture mutation patterns at permanent and transient type of protein interfaces, which were found to be different with statistical significance. Using the substitution models, we developed a novel computational method that predicts permanent and transient protein binding interfaces (PBIs) in protein surfaces. Without knowledge of the interacting partner, the method uses a single query protein structure and a multiple sequence alignment of the sequence family. Using a large dataset of permanent and transient proteins, we show that our method, BindML+, performs very well in protein interface classification. A very high area under the curve (AUC) value of 0.957 was observed when predicted protein binding sites were classified. Remarkably, near prefect accuracy was achieved with an AUC of 0.991 when actual binding sites were classified. The developed method will be also useful for protein design of permanent and transient PBIs. © Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Analysis and network representation of hotspots in protein interfaces using minimum cut trees

We propose a novel approach to analyze and visualize residue contact networks of protein interfaces by graph‐based algorithms using a minimum cut tree (mincut tree). Edges in the network are weighted according to an energy function derived from knowledge‐based potentials. The mincut tree, which is constructed from the weighted residue network, simplifies and summarizes the complex structure of the contact network by an efficient and informative representation. This representation offers a comprehensible view of critical residues and facilitates the inspection of their organization. We observed, on a nonredundant data set of 38 protein complexes with experimental hotspots that the highest degree node in the mincut tree usually corresponds to an experimental hotspot. Further, hotspots are found in a few paths in the mincut tree. In addition, we examine the organization of hotspots (hot regions) using an iterative clustering algorithm on two different case studies. We find that distinct hot regions are located on specific sites of the mincut tree and some critical residues hold these clusters together. Clustering of the interface residues provides information about the relation of hot regions with each other. Our new approach is useful at the molecular level for both identification of critical paths in the protein interfaces and extraction of hot regions by clustering of the interface residues. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

PresCont: predicting protein-protein interfaces utilizing four residue properties

An important task of computational biology is to identify those parts of a polypeptide chain, which are involved in interactions with other proteins. For this purpose, we have developed the program PresCont, which predicts in a robust manner amino acids that constitute protein-protein interfaces (PPIs). PresCont reaches state-of-the-art classification quality on the basis of only four residue properties that can be readily deduced from the 3D structure of an individual protein and a multiple sequence alignment (MSA) composed of homologs. The core of PresCont is a support vector machine, which assesses solvent-accessible surface area, hydrophobicity, conservation, and the local environment of each amino acid on the protein surface. For training and performance testing, we compiled three nonoverlapping datasets consisting of permanently formed or transient complexes, respectively. A comparison with SPPIDER, ProMate, and meta-PPISP showed that PresCont compares favorably with these highly sophisticated programs, and that its prediction quality is less dependent on the type of protein complex being considered. This balance is due to a mutual compensation of classification weaknesses observed for individual properties: For PPIs of permanent complexes, solvent-accessible surface and hydrophobicity contribute most to classification quality, for PPIs of transient complexes, the assessment of the local environment is most significant. Moreover, we show that for permanent complexes a segmentation of PPIs into core and rim residues has only a moderate influence on prediction quality. PresCont is available as a web service at http://www-bioinf.uni-regensburg.de/.     

Z-score biological significance of binding hot spots of protein interfaces by using crystal packing as the reference state

Characterization of binding hot spots of protein interfaces is a fundamental study in molecular biology. Many computational methods have been proposed to identify binding hot spots. However, there are few studies to assess the biological significance of binding hot spots. We introduce the notion of biological significance of a contact residue for capturing the probability of the residue occurring in or contributing to protein binding interfaces. We take a statistical Z-score approach to the assessment of the biological significance. The method has three main steps. First, the potential score of a residue is defined by using a knowledge-based potential function with relative accessible surface area calculations. A null distribution of this potential score is then generated from artifact crystal packing contacts. Finally, the Z-score significance of a contact residue with a specific potential score is determined according to this null distribution. We hypothesize that residues at binding hot spots have big absolute values of Z-score as they contribute greatly to binding free energy. Thus, we propose to use Z-score to predict whether a contact residue is a hot spot residue. Comparison with previously reported methods on two benchmark datasets shows that this Z-score method is mostly superior to earlier methods. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.     

Protein-protein interactions; coupling of structurally conserved residues and of hot spots across interfaces. Implications for docking

Hot spot residues contribute dominantly to protein-protein interactions. Statistically, conserved residues correlate with hot spots, and their occurrence can distinguish between binding sites and the remainder of the protein surface. The hot spot and conservation analyses have been carried out on one side of the interface. Here, we show that both experimental hot spots and conserved residues tend to couple across two-chain interfaces. Intriguingly, the local packing density around both hot spots and conserved residues is higher than expected. We further observe a correlation between local packing density and experimental deltadeltaG. Favorable conserved pairs include Gly coupled with aromatics, charged and polar residues, as well as aromatic residue coupling. Remarkably, charged residue couples are underrepresented. Overall, protein-protein interactions appear to consist of regions of high and low packing density, with the hot spots organized in the former. The high local packing density in binding interfaces is reminiscent of protein cores.     

Magnitude of the hydrophobic effect at central versus peripheral sites in protein-protein interfaces

Hydrophobic interactions are essential for stabilizing protein-protein complexes, whose interfaces generally consist of a central cluster of hot spot residues surrounded by less important peripheral residues. According to the O-ring hypothesis, a condition for high affinity binding is solvent exclusion from interacting residues. This hypothesis predicts that the hydrophobicity at the center is significantly greater than at the periphery, which we estimated at 21 cal mol(-1) A(-2). To measure the hydrophobicity at the center, structures of an antigen-antibody complex where a buried phenylalanine was replaced by smaller hydrophobic residues were determined. By correlating structural changes with binding free energies, we estimate the hydrophobicity at this central site to be 46 cal mol(-1) A(-2), twice that at the periphery. This context dependence of the hydrophobic effect explains the clustering of hot spots at interface centers and has implications for hot spot prediction and the design of small molecule inhibitors.     

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.     

Geometrically centered region: A “wet” model of protein binding hot spots not excluding water molecules

A protein interface can be as “wet” as a protein surface in terms of the number of immobilized water molecules. This important water information has not been explicitly taken by computational methods to model and identify protein binding hot spots, overlooking the water role in forming interface hydrogen bonds and in filing cavities. Hot spot residues are usually clustered at the core of the protein binding interfaces. However, traditional machine learning methods often identify the hot spot residues individually, breaking the cooperativity of the energetic contribution. Our idea in this work is to explore the role of immobilized water and meanwhile to capture two essential properties of hot spots: the compactness in contact and the far distance from bulk solvent. Our model is named geometrically centered region (GCR). The detection of GCRs is based on novel tripartite graphs, and atom burial levels which are a concept more intuitive than SASA. Applying to a data set containing 355 mutations, we achieved an F measure of 0.6414 when ΔΔG ≥ 1.0 kcal/mol was used to define hot spots. This performance is better than Robetta, a benchmark method in the field. We found that all but only one of the GCRs contain water to a certain degree, and most of the outstanding hot spot residues have water‐mediated contacts. If the water is excluded, the burial level values are poorly related to the ΔΔG, and the model loses its performance remarkably. We also presented a definition for the O‐ring of a GCR as the set of immediate neighbors of the residues in the GCR. Comparative analysis between the O‐rings and GCRs reveals that the newly defined O‐ring is indeed energetically less important than the GCR hot spot, confirming a long‐standing hypothesis. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Hot spot prediction in protein-protein interactions by an ensemble system

Background

Hot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structural features, sequence characteristics, and/or other protein features.

Results

This paper proposed an ensemble learning method to predict hot spot residues that only uses sequence features and the relative accessible surface area of amino acid sequences. In this work, a novel feature selection technique was developed, an auto-correlation function combined with a sliding window technique was applied to obtain the characteristics of amino acid residues in protein sequence, and an ensemble classifier with SVM and KNN base classifiers was built to achieve the best classification performance.

Conclusion

The experimental results showed that our model yields the highest F1 score of 0.92 and an MCC value of 0.87 on ASEdb dataset. Compared with other machine learning methods, our model achieves a big improvement in hot spot prediction.

Availability

http://deeplearner.ahu.edu.cn/web/HotspotEL.htm.

     

Global investigation of protein-protein interactions in yeast Saccharomyces cerevisiae using re-occurring short polypeptide sequences

Protein-protein interaction (PPI) maps provide insight into cellular biology and have received considerable attention in the post-genomic era. While large-scale experimental approaches have generated large collections of experimentally determined PPIs, technical limitations preclude certain PPIs from detection. Recently, we demonstrated that yeast PPIs can be computationally predicted using re-occurring short polypeptide sequences between known interacting protein pairs. However, the computational requirements and low specificity made this method unsuitable for large-scale investigations. Here, we report an improved approach, which exhibits a specificity of approximately 99.95% and executes 16,000 times faster. Importantly, we report the first all-to-all sequence-based computational screen of PPIs in yeast, Saccharomyces cerevisiae in which we identify 29,589 high confidence interactions of approximately 2 x 10(7) possible pairs. Of these, 14,438 PPIs have not been previously reported and may represent novel interactions. In particular, these results reveal a richer set of membrane protein interactions, not readily amenable to experimental investigations. From the novel PPIs, a novel putative protein complex comprised largely of membrane proteins was revealed. In addition, two novel gene functions were predicted and experimentally confirmed to affect the efficiency of non-homologous end-joining, providing further support for the usefulness of the identified PPIs in biological investigations.     

Artificial intelligence based methods for hot spot prediction

Proteins interact through their interfaces to fulfill essential functions in the cell. They bind to their partners in a highly specific manner and form complexes that have a profound effect on understanding the biological pathways they are involved in. Any abnormal interactions may cause diseases. Therefore, the identification of small molecules which modulate protein interactions through their interfaces has high therapeutic potential. However, discovering such molecules is challenging. Most protein–protein binding affinity is attributed to a small set of amino acids found in protein interfaces known as hot spots. Recent studies demonstrate that drug-like small molecules specifically may bind to hot spots. Therefore, hot spot prediction is crucial. As experimental data accumulates, artificial intelligence begins to be used for computational hot spot prediction. First, we review machine learning and deep learning for computational hot spot prediction and then explain the significance of hot spots toward drug design.     

Charged residues at protein interaction interfaces: unexpected conservation and orchestrated divergence

Protein-protein interactions play an essential role in the functioning of cell. The importance of charged residues and their diverse role in protein-protein interactions have been well studied using experimental and computational methods. Often, charged residues located in protein interaction interfaces are conserved across the families of homologous proteins and protein complexes. However, on a large scale, it has been recently shown that charged residues are significantly less conserved than other residue types in protein interaction interfaces. The goal of this work is to understand the role of charged residues in the protein interaction interfaces through their conservation patterns. Here, we propose a simple approach where the structural conservation of the charged residue pairs is analyzed among the pairs of homologous binary complexes. Specifically, we determine a large set of homologous interactions using an interaction interface similarity measure and catalog the basic types of conservation patterns among the charged residue pairs. We find an unexpected conservation pattern, which we call the correlated reappearance, occurring among the pairs of homologous interfaces more frequently than the fully conserved pairs of charged residues. Furthermore, the analysis of the conservation patterns across different superkingdoms as well as structural classes of proteins has revealed that the correlated reappearance of charged residues is by far the most prevalent conservation pattern, often occurring more frequently than the unconserved charged residues. We discuss a possible role that the new conservation pattern may play in the long-range electrostatic steering effect.     

Cold spots are universal in protein–protein interactions

Proteins interact with each other through binding interfaces that differ greatly in size and physico‐chemical properties. Within the binding interface, a few residues called hot spots contribute the majority of the binding free energy and are hence irreplaceable. In contrast, cold spots are occupied by suboptimal amino acids, providing possibility for affinity enhancement through mutations. In this study, we identify cold spots due to cavities and unfavorable charge interactions in multiple protein–protein interactions (PPIs). For our cold spot analysis, we first use a small affinity database of PPIs with known structures and affinities and then expand our search to nearly 4000 homo‐ and heterodimers in the Protein Data Bank (PDB). We observe that cold spots due to cavities are present in nearly all PPIs unrelated to their binding affinity, while unfavorable charge interactions are relatively rare. We also find that most cold spots are located in the periphery of the binding interface, with high‐affinity complexes showing fewer centrally located colds spots than low‐affinity complexes. A larger number of cold spots is also found in non‐cognate interactions compared to their cognate counterparts. Furthermore, our analysis reveals that cold spots are more frequent in homo‐dimeric complexes compared to hetero‐complexes, likely due to symmetry constraints imposed on sequences of homodimers. Finally, we find that glycines, glutamates, and arginines are the most frequent amino acids appearing at cold spot positions. Our analysis emphasizes the importance of cold spot positions to protein evolution and facilitates protein engineering studies directed at enhancing binding affinity and specificity in a wide range of applications.     

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.     

Residue frequencies and pairing preferences at protein-protein interfaces

We used a nonredundant set of 621 protein-protein interfaces of known high-resolution structure to derive residue composition and residue-residue contact preferences. The residue composition at the interfaces, in entire proteins and in whole genomes correlates well, indicating the statistical strength of the data set. Differences between amino acid distributions were observed for interfaces with buried surface area of less than 1,000 A(2) versus interfaces with area of more than 5,000 A(2). Hydrophobic residues were abundant in large interfaces while polar residues were more abundant in small interfaces. The largest residue-residue preferences at the interface were recorded for interactions between pairs of large hydrophobic residues, such as Trp and Leu, and the smallest preferences for pairs of small residues, such as Gly and Ala. On average, contacts between pairs of hydrophobic and polar residues were unfavorable, and the charged residues tended to pair subject to charge complementarity, in agreement with previous reports. A bootstrap procedure, lacking from previous studies, was used for error estimation. It showed that the statistical errors in the set of pairing preferences are generally small; the average standard error is approximately 0.2, i.e., about 8% of the average value of the pairwise index (2.9). However, for a few pairs (e.g., Ser-Ser and Glu-Asp) the standard error is larger in magnitude than the pairing index, which makes it impossible to tell whether contact formation is favorable or unfavorable. The results are interpreted using physicochemical factors and their implications for the energetics of complex formation and for protein docking are discussed. Proteins 2001;43:89-102.