A motif-based method for predicting interfacial residues in both the RNA and protein components of protein-RNA complexes

Efforts to predict interfacial residues in protein-RNA complexes have largely focused on predicting RNA-binding residues in proteins. Computational methods for predicting protein-binding residues in RNA sequences, however, are a problem that has received relatively little attention to date. Although the value of sequence motifs for classifying and annotating protein sequences is well established, sequence motifs have not been widely applied to predicting interfacial residues in macromolecular complexes. Here, we propose a novel sequence motif-based method for “partner-specific” interfacial residue prediction. Given a specific protein-RNA pair, the goal is to simultaneously predict RNA binding residues in the protein sequence and protein-binding residues in the RNA sequence. In 5-fold cross validation experiments, our method, PS-PRIP, achieved 92% Specificity and 61% Sensitivity, with a Matthews correlation coefficient (MCC) of 0.58 in predicting RNA-binding sites in proteins. The method achieved 69% Specificity and 75% Sensitivity, but with a low MCC of 0.13 in predicting protein binding sites in RNAs. Similar performance results were obtained when PS-PRIP was tested on two independent “blind” datasets of experimentally validated protein- RNA interactions, suggesting the method should be widely applicable and valuable for identifying potential interfacial residues in protein-RNA complexes for which structural information is not available. The PS-PRIP webserver and datasets are available at: http://pridb.gdcb.iastate.edu/PSPRIP/

Computational prediction of RNA-protein interaction partners and interfaces

RNA-protein interactions play important roles in fundamental cellular processes involved in human diseases, viral replication and defense against pathogens in plants, animals and microbes. However, the detailed recognition mechanisms underlying these interactions are poorly understood. To gain a better understanding of the molecular recognition code for RNA-protein interactions, this dissertation has three related goals: i) to develop methods for predicting RNA-protein interaction partners; ii) to develop an approach for predicting interfacial residues in both the RNA and protein components of RNA-protein complexes; and iii) to develop computational tools and resources for investigating RNA-protein interactions. First, we present machine learning classifiers for predicting RNA-protein interaction partners. The classifiers use the amino acid composition of proteins and the ribonucleotide composition of RNAs as input to predict whether a given RNA-protein pair interacts. We show that protein and RNA sequences alone (i.e., in the absence of any structural information) contain enough signal to allow reliable prediction of interaction partners. Second, we present RPISeq, a webserver that predicts the interaction probabilities of input RNA-protein pairs, using the above-mentioned machine learning classifiers. A comprehensive database of RNA-protein interactions, RPIntDB, is integrated with the webserver to allow users to search for homologous proteins and their known interacting RNA partners. Finally, we perform an analysis of contiguous interfacial amino acids and ribonucleotides in RNA-protein complexes for which structures are known. We generate a dataset of bipartite RNA-protein motifs that can be used to predict interfacial residues in both the RNA and protein sequences of a given RNA-protein pair simultaneously. We show that taking binding partner information into account leads to higher precision in the prediction of RNA-binding residues in proteins. Taken together, these studies have increased our understanding of how RNA and proteins interact

Sequence-Based Prediction of RNA-Binding Residues in Proteins

Identifying individual residues in the interfaces of protein–RNA complexes is important for understanding the molecular determinants of protein–RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein–RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein–RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner

Modelling the Effects of Disease-Associated Single Amino Acid Variants and Rescuing the Effects by Small Molecules

Single nucleotide polymorphism (SNP) is a variation of a single nucleotide in the genome. Some of these variations can cause a change of single amino acid in the corresponding protein, resulting in single amino acid variation (SAV). SAVs can lead to profound alterations of the corresponding biological processes and thus can be associated with many human diseases. This dissertation focuses on integration of existing and development of new computational approaches to model the effects of SAVs with the goal to reveal molecular mechanism of human diseases. Since proton transfer and pKa shifts are frequently attributed to disease causality, the proton transfers in the protein-nucleic acid interactions are investigated and along with development of a new computational approach to predict the SAV’s effect on the protein-DNA binding affinity. The SAVs in four proteins: Lysine-specific demethylase 5C (KDM5C), Spermine Synthase (SpmSyn), 7-Dehydrocholesterol reductase (DHCR7) and methyl CpG binding protein 2 (MeCP2) are extensively studied using numerous computational approaches to reveal molecular details of disease-associated effects. In case of MeCP2 protein, the effects of the most commonly occurring disease-causing mutation, R133C, was targeted by structure-based virtual screening to identify the small molecules potentially to rescue the malfunctioning R133C mutant

Sequence-based prediction of RNA-protein interactions

The interaction of RNAs with proteins is fundamental for executing many of the key roles they play in living systems, including translation, post-transcriptional regulation of gene expression, RNA splicing, and viral replication. Recently, new roles for RNA-protein interactions have emerged, following the discovery that the human genome is pervasively transcribed and produces thousands of non-coding RNAs (ncRNAs). Although the functions of many ncRNAs are not yet known, one emerging theme is that long non-coding RNAs (lncRNAs) often drive the formation of ribonucleoprotein (RNP) complexes, which in turn influence the regulation of gene expression. Although the human genome is predicted to encode almost as many different RNA-binding proteins as DNA-binding transcription factors, our current understanding of the cellular roles of RNA-binding proteins, how they recognize their targets, and how they are regulated, lags far behind our understanding of transcription factors. To improve our comprehension of RNA-protein recognition and the regulation of RNA-protein interaction networks within cells, this dissertation has four related goals: (i) performing a rigorous and systematic evaluation of sequence- and structure-based methods for predicting RNA-binding residues in proteins; (ii) developing improved method for predicting interfacial residues in RNA-binding proteins, using only sequence information; (iii) generating a comprehensive collection of RNA-protein interaction motifs (RPIMs); and (iv) developing improved methods for RNA-protein interaction partner prediction. First, we present a systematic evaluation of state-of-the-art machine learning methods for predicting RNA-binding residues in proteins, using three carefully curated benchmark datasets and a rich set of data representations. We show that sequence-based methods trained using position-specific scoring matrices (PSSMs) perform better than structure-based methods, which use more complex features extracted from the 3D structures of proteins. Second, we present RNABindRPlus, a new method for predicting RNA-binding residues in proteins, using only sequence information. The predictor combines output from an optimized Support Vector Machine (SVM) classifier with the output from a novel homology-based method (HomPRIP). We show that RNABindRPlus performs better than all currently available methods for predicting interfacial residues in proteins. Third, we extract more than 30,000 unique RNA-protein interfacial motifs (RPIMs), consisting of contiguous residues from both the RNA and protein chains of characterized RNA-protein complexes. Lastly, we demonstrate the utility of RPIMs in predicting RNA-protein interaction partners. We employ them in an innovative and significantly improved method for partner prediction and show that it has both a high true positive rate and a much lower false positive rate than other available methods. Taken together, the results presented here provide important new insights into the determinants of RNA-protein recognition, in addition to valuable new software tools for interrogating and predicting RNA-protein complexes and interaction networks

Computational Tools for Investigating RNA-Protein Interaction Partners

RNA-protein interactions are important in a wide variety of cellular and developmental processes. Recently, high-throughput experiments have begun to provide valuable information about RNA partners and binding sites for many RNA-binding proteins (RBPs), but these experiments are expensive and time consuming. Thus, computational methods for predicting RNA-Protein interactions (RPIs) can be valuable tools for identifying potential interaction partners of a given protein or RNA, and for identifying likely interfacial residues in RNA-protein complexes. This review focuses on the “partner prediction” problem and summarizes available computational methods, web servers and databases that are devoted to it. New computational tools for addressing the related “interface prediction” problem are also discussed. Together, these computational methods for investigating RNA-protein interactions provide the basis for new strategies for integrating RNA-protein interactions into existing genetic and developmental regulatory networks, an important goal of future research

PRETICTIVE BIOINFORMATIC METHODS FOR ANALYZING GENES AND PROTEINS

Since large amounts of biological data are generated using various high-throughput technologies, efficient computational methods are important for understanding the biological meanings behind the complex data. Machine learning is particularly appealing for biological knowledge discovery. Tissue-specific gene expression and protein sumoylation play essential roles in the cell and are implicated in many human diseases. Protein destabilization is a common mechanism by which mutations cause human diseases. In this study, machine learning approaches were developed for predicting human tissue-specific genes, protein sumoylation sites and protein stability changes upon single amino acid substitutions. Relevant biological features were selected for input vector encoding, and machine learning algorithms, including Random Forests and Support Vector Machines, were used for classifier construction. The results suggest that the approaches give rise to more accurate predictions than previous studies and can provide valuable information for further experimental studies. Moreover, seeSUMO and MuStab web servers were developed to make the classifiers accessible to the biological research community. Structure-based methods can be used to predict the effects of amino acid substitutions on protein function and stability. The nonsynonymous Single Nucleotide Polymorphisms (nsSNPs) located at the protein binding interface have dramatic effects on protein-protein interactions. To model the effects, the nsSNPs at the interfaces of 264 protein-protein complexes were mapped on the protein structures using homology-based methods. The results suggest that disease-causing nsSNPs tend to destabilize the electrostatic component of the binding energy and nsSNPs at conserved positions have significant effects on binding energy changes. The structure-based approach was developed to quantitatively assess the effects of amino acid substitutions on protein stability and protein-protein interaction. It was shown that the structure-based analysis could help elucidate the mechanisms by which mutations cause human genetic disorders. These new bioinformatic methods can be used to analyze some interesting genes and proteins for human genetic research and improve our understanding of their molecular mechanisms underlying human diseases

COMPUTATIONAL MODELING OF RNA-SMALL MOLECULE AND RNA-PROTEIN INTERACTIONS

The past decade has witnessed an era of RNA biology; despite the considerable discoveries nowadays, challenges still remain when one aims to screen RNA-interacting small molecule or RNA-interacting protein. These challenges imply an immediate need for cost-efficient while predictive computational tools capable of generating insightful hypotheses to discover novel RNA-interacting small molecule or RNA-interacting protein. Thus, we implemented novel computational models in this dissertation to predict RNA-ligand interactions (Chapter 1) and RNA-protein interactions (Chapter 2). Targeting RNA has not garnered comparable interest as protein, and is restricted by lack of computational tools for structure-based drug design. To test the potential of translating molecular docking tools designed for protein to RNA-ligand docking and virtual screening, we benchmarked 5 docking software and 11 scoring functions to assess their performances in pose reproduction, pose ranking, score-RMSD correlation and virtual screening. From this benchmark, we proposed a three-step docking pipelines optimized for virtual screening against RNAs with different flexibility properties. Using this pipeline, we have successfully identified a selective compound binding to GA:UU motif. Both NMR and the subsequent MD simulation proved its selective binding to GA:UU motif flanked by two tandem flexible base pairs next to GA. Consistent to the 3D model, SAR analysis revealed that any R-group substitution would abolish the binding. Current computational methods for RNA-protein interaction prediction (sequence-based or structure-based) are either short of interpretability or robustness. Aware of these pitfalls, we implemented RNA-Protein interaction prediction through Interface Threading (RPIT), which identifies and references a known RNA-protein interface as the template to infer the region where the interaction occurs and predict the interacting propensity based on the interface profiles. To estimate the propensity more accurately, we implemented five statistical scoring functions based our unique collection of non-redundant protein-RNA interaction database. Our benchmark using leave-protein-out cross validation and two external validation sets resulted in overall 70%-80% accuracy of RPIT. Compared with other methods, RPIT offers an inexpensive but robust method for in silico prediction of RNA-protein interaction networks, and for prioritizing putative RNA-protein pairs using virtual screening