Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

InteractoMIX:A suite of computational tools to exploit interactomes in biological and clinical research

Virtually all the biological processes that occur inside or outside cells are mediated by protein–protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com)

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

© 2018 The Author(s). 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

KFC Server: interactive forecasting of protein interaction hot spots

The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model—a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interface's; binding free energy. The server facilitates the automated analysis of a user submitted protein–protein or protein–DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org

Protein-protein interface: database, analysis and prediction

Protein-protein interaction plays a pivotal role in biological metabolism. It directs many cellular processes like signal transduction, DNA replication and RNA splicing, etc. Identification of protein-protein interaction sites is important to identification of protein functions, improvement of protein-protein docking and rational drug design. Experimental methods to identify protein-protein interaction sites are always time-consuming and costly, which calls for computational methods to be applied in this area. The research work focuses on three parts: We have built a Protein-Protein Interface Database (PPIDB) which extracted 71, 486 protein-protein interfaces from experimentally determined protein complex structures in the current version of Protein Data Bank. It facilitates construction of well-characterized datasets of protein-protein interface residues for computational analyses. The database is accessible through the Web Interface http://ppidb.cs.iastate.edu and a set of Web services. We have made a comprehensive analysis of protein-protein dimeric interfaces, which consists of thirteen physic-chemical properties. The results disclose that interface residues have side chains pointing inward; interfaces are rougher, tend to be flat, moderately convex or concave and protrude more relative to non-interface surface residues; interface residues tend to be surrounded by hydrophobic neighbors. We have developed NB PPIPS, a Naive Bayes method to predict protein-protein interaction sites on protein surfaces. Trained over a non-redundant data set consisting of 2, 383 proteins and fed with sequence, evolutionary and structural properties, NB PPIPS achieves 60.7% recall and 34.6% precision in 10 fold cross-validation, which greatly improves over the baseline classifier that only utilizes protein sequence information. Attempts are made to apply the NB PPIPS in a two stage prediction of protein-protein interfaces when only protein sequence is known. Modeled protein structures are generated via homologue modeling and fed as inputs into NB PPIPS. The results show that good predictions are obtained only for well modeled structures. NB PPIPS is implemented as an online server to facilitate its usage. It is accessible at http://watson.cs.iastate.edu/nb_ppips

HotPoint: hot spot prediction server for protein interfaces

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

Identification of interface residues involved in protein-protein and protein-DNA interactions from sequence using machine learning approaches

Identification of interface residues involved in protein-protein and protein-DNA interactions is critical for understanding the functions of biological systems. Because identifying interface residues using experimental methods cannot catch up with the pace at which protein sequences are determined, computational methods that can identify interface residues are urgently needed. In this study, we apply machine-learning methods to identify interface residues with the focus on the methods using amino acid sequence information alone. We have developed classifiers for identification of the residues involved in protein-protein and protein-DNA interactions using a window of primary sequence as input. The classifiers were evaluated using both representative datasets and specific cases of interest based on multiple measurements. The results have shown the feasibility of identifying interface residues from sequence. We have also explored information besides primary sequence to improve the performance of sequence-based classifiers. The results show that the performance of sequence-based classifiers can be improved by using solvent accessibility and sequence entropy of the target residue as additional inputs. We have developed a database of protein-protein interfaces that consists of all the protein-protein interfaces derived from the Protein Data Bank. This database, for the first time, makes possible the quick and flexible retrieval of interface sets and various interface features. We have systematically analyzed the characteristics of interfaces using the largest dataset available. In particular, we compared interfaces with the samples that had the same solvent accessibility as the interfaces. This strategy excludes the effect of solvent accessibility on the distributions of residues, secondary structure, and sequence entropy

ProMateus—an open research approach to protein-binding sites analysis

The development of bioinformatic tools by individual labs results in the abundance of parallel programs for the same task. For example, identification of binding site regions between interacting proteins is done using: ProMate, WHISCY, PPI-Pred, PINUP and others. All servers first identify unique properties of binding sites and then incorporate them into a predictor. Obviously, the resulting prediction would improve if the most suitable parameters from each of those predictors would be incorporated into one server. However, because of the variation in methods and databases, this is currently not feasible. Here, the protein-binding site prediction server is extended into a general protein-binding sites research tool, ProMateus. This web tool, based on ProMate's infrastructure enables the easy exploration and incorporation of new features and databases by the user, providing an evaluation of the benefit of individual features and their combination within a set framework. This transforms the individual research into a community exercise, bringing out the best from all users for optimized predictions. The analysis is demonstrated on a database of protein protein and protein-DNA interactions. This approach is basically different from that used in generating meta-servers. The implications of the open-research approach are discussed. ProMateus is available at http://bip.weizmann.ac.il/promate

Examination of Molecular Recognition in Protein-Ligand Interactions

This dissertation is a compilation of two main projects that were investigated during my thesis research. The first project was a prospective study which identified and characterized drug-like inhibitors of a prototype of bacterial two-component signal transduction response regulator using computational and experimental methods. The second project was the development and validation of a scoring function, PHOENIX, derived using high-resolution structures and calorimetry measurements to predict binding affinities of protein-ligand interactions. Collectively, my thesis research aimed to better understand the underlying driving forces and principles which govern molecular recognition and molecular design. A prospective study coupled computational predictions with experimental validation resulted in the discovery of first-in-class inhibitors targeting a signal transduction module important for bacterial virulence. Development and validation of the PHOENIX scoring function for binding affinity prediction derived using high-resolution structures and calorimetry measurements should guide future molecular recognition studies and endeavors in computer-aided molecular design. To request for an electronic copy of this dissertation, please email the author: yattang at gmail dot com)