Exploring the potential of 3D Zernike descriptors and SVM for protein\u2013protein interface prediction

Abstract Background The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Results In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). Conclusions The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class

Computational analysis and prediction of protein-RNA interactions

Protein-RNA interactions are essential for many important processes including all phases of protein production, regulation of gene expression, and replication and assembly of many viruses. This dissertation has two related goals: 1) predicting RNA-binding sites in proteins from protein sequence, structure, and conservation information, and 2) characterizing protein-RNA interactions. We present several machine learning classifiers for predicting RNA-binding sites in proteins based on the protein sequence, protein structure, and conservation information. Our first classifier uses only amino acid sequence information as input and predicts RNA-binding sites with an area under the receiver operator characteristic curve (AUC) of 0.74. Using the neighboring amino acids in the protein structure improves prediction performance over using sequence alone. We show that using evolutionary information in the form of position specific scoring matrices provides a further significant improvement in predictions. Finally, we create an ensemble classifier that combines the predictions of the sequence, structure, and PSSM based classifiers and gives the best prediction performance, with an AUC of 0.81. We construct the Protein-RNA Interaction Database, PRIDB, a comprehensive collection of all protein-RNA complexes in the PDB. PRIDB focuses on characterizing the molecular interaction at the protein-RNA interface in terms of van der Waals contacts, direct hydrogen bonds, and water-mediated hydrogen bonds. We perform an extensive analysis of the RNA-binding characteristics of a non-redundant dataset of 181 proteins to determine general characteristics of protein-RNA binding sites. We find that the overall interaction propensities for Watson-Crick paired nucleotides and non Watson-Crick paired nucleotides are very similar, with the propensities for amino acids binding to single stranded nucleotides showing more differences. We find that van der Waals contacts are more numerous than hydrogen bonds and amino acids interact with RNA through their side chain atoms more frequently than their main chain atoms. We also find that contacts to the RNA base are not as frequent as contacts to the RNA backbone. Together, the prediction and characterization presented in this dissertation have increased our understanding of how proteins and RNA interact

Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins

<p>Abstract</p> <p>Background</p> <p>Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the results are often not satisfactory. Although different amino acid compositions among the interaction sites of different complexes have been observed, such differences have not been integrated into the prediction process. Furthermore, the evolution information has not been exploited to achieve a more powerful propensity.</p> <p>Result</p> <p>In this study, the residue interface propensities of four kinds of complexes (homo-permanent complexes, homo-transient complexes, hetero-permanent complexes and hetero-transient complexes) are investigated. These propensities, combined with sequence profiles and accessible surface areas, are inputted to the support vector machine for the prediction of protein binding sites. Such propensities are further improved by taking evolutional information into consideration, which results in a class of novel propensities at the profile level, i.e. the binary profiles interface propensities. Experiment is performed on the 1139 non-redundant protein chains. Although different residue interface propensities among different complexes are observed, the improvement of the classifier with residue interface propensities can be negligible in comparison with that without propensities. The binary profile interface propensities can significantly improve the performance of binding sites prediction by about ten percent in term of both precision and recall.</p> <p>Conclusion</p> <p>Although there are minor differences among the four kinds of complexes, the residue interface propensities cannot provide efficient discrimination for the complicated interfaces of proteins. The binary profile interface propensities can significantly improve the performance of binding sites prediction of protein, which indicates that the propensities at the profile level are more accurate than those at the residue level.</p

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.</p> <p>Results</p> <p>We propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.</p> <p>Conclusions</p> <p>The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.</p> <p>Availability</p> <p>Datasets and software are available at <url>http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.htm</url>.</p

Structure-based Prediction of Protein-protein Interaction Networks across Proteomes

Protein-protein interactions (PPIs) orchestrate virtually all cellular processes, therefore, their exhaustive exploration is essential for the comprehensive understanding of cellular networks. Significant efforts have been devoted to expand the coverage of the proteome-wide interaction space at molecular level. A number of experimental techniques have been developed to discover PPIs, however these approaches have some limitations such as the high costs and long times of experiments, noisy data sets, and often high false positive rate and inter-study discrepancies. Given experimental limitations, computational methods are increasingly becoming important for detection and structural characterization of PPIs. In that regard, we have developed a novel pipeline for high-throughput PPI prediction based on all-to-all rigid body docking of protein structures. We focus on two questions, ‘how do proteins interact?’ and ‘which proteins interact?’. The method combines molecular modeling, structural bioinformatics, machine learning, and functional annotation data to answer these questions and it can be used for genome-wide molecular reconstruction of protein-protein interaction networks. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Further, we validated our method against a few human pathways. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques

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

Joint Evolutionary Trees: A Large-Scale Method To Predict Protein Interfaces Based on Sequence Sampling

The Joint Evolutionary Trees (JET) method detects protein interfaces, the core residues involved in the folding process, and residues susceptible to site-directed mutagenesis and relevant to molecular recognition. The approach, based on the Evolutionary Trace (ET) method, introduces a novel way to treat evolutionary information. Families of homologous sequences are analyzed through a Gibbs-like sampling of distance trees to reduce effects of erroneous multiple alignment and impacts of weakly homologous sequences on distance tree construction. The sampling method makes sequence analysis more sensitive to functional and structural importance of individual residues by avoiding effects of the overrepresentation of highly homologous sequences and improves computational efficiency. A carefully designed clustering method is parametrized on the target structure to detect and extend patches on protein surfaces into predicted interaction sites. Clustering takes into account residues' physical-chemical properties as well as conservation. Large-scale application of JET requires the system to be adjustable for different datasets and to guarantee predictions even if the signal is low. Flexibility was achieved by a careful treatment of the number of retrieved sequences, the amino acid distance between sequences, and the selective thresholds for cluster identification. An iterative version of JET (iJET) that guarantees finding the most likely interface residues is proposed as the appropriate tool for large-scale predictions. Tests are carried out on the Huang database of 62 heterodimer, homodimer, and transient complexes and on 265 interfaces belonging to signal transduction proteins, enzymes, inhibitors, antibodies, antigens, and others. A specific set of proteins chosen for their special functional and structural properties illustrate JET behavior on a large variety of interactions covering proteins, ligands, DNA, and RNA. JET is compared at a large scale to ET and to Consurf, Rate4Site, siteFiNDER|3D, and SCORECONS on specific structures. A significant improvement in performance and computational efficiency is shown