Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

Nithin, Chandran; Ghosh, Pritha; Bujnicki, Janusz M.

doi:10.3390/genes9090432

Cited by 36 publications

(24 citation statements)

References 142 publications

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“…Experimental techniques have been developed to characterize protein-RNA interactions [44]. However, only a fraction of identified protein-RNA interactions has been structurally characterized, further increasing the importance of the structural modeling [45]. Similar to the protein interactome, most RNA structures in the RNA interactome have to be models.…”

Section: Protein-rna Interactionsmentioning

confidence: 99%

Computational approaches to macromolecular interactions in the cell

Vakser

Deeds

2019

Current Opinion in Structural Biology

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Structural modeling of a cell is an evolving strategic direction in computational structural biology. It takes advantage of new powerful modeling techniques, deeper understanding of fundamental principles of molecular structure and assembly, and rapid growth of the amount of structural data generated by experimental techniques. Key modeling approaches to principal types of macromolecular assemblies in a cell already exist. The main challenge, along with the further development of these modeling approaches, is putting them together in a consistent, unified whole cell model. This opinion piece addresses the fundamental aspects of modeling macromolecular assemblies in a cell, and the state-of-the-art in modeling of the principal types of such assemblies.

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Section: Protein-rna Interactionsmentioning

confidence: 99%

Computational approaches to macromolecular interactions in the cell

Vakser

Deeds

2019

Current Opinion in Structural Biology

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“…Computational power and toolkits must keep up with these new challenges. New databases, algorithms and user-friendly applications [206][207][208][209][210][211] are being developed to support the analysis of said data by experts in bioinformatics and physicians alike.…”

Section: Biological Data Integration and Network: The Way Forwardmentioning

confidence: 99%

Systems biology in inflammatory bowel diseases: on the way to precision medicine

Dovrolis

Filidou

Kolios

2019

aog

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Inflammatory bowel diseases (IBD) are chronic and recurrent inflammatory disorders of the gastrointestinal tract. The elucidation of their etiopathology requires complex and multiple approaches. Systems biology has come to fulfill this need in approaching the pathogenetic mechanisms of IBD and its etiopathology, in a comprehensive way, by combining data from different scientific sources. In combination with bioinformatics and network medicine, it uses principles from computer science, mathematics, physics, chemistry, biology, medicine and computational tools to achieve its purposes. Systems biology utilizes scientific sources that provide data from omics studies (e.g., genomics, transcriptomics, etc.) and clinical observations, whose combined analysis leads to network formation and ultimately to a more integrative image of disease etiopathogenesis. In this review, we analyze the current literature on the methods and the tools utilized by systems biology in order to cover an innovative and exciting field: IBD-omics.

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“…There are different data sets available for protein-RNA binding affinity values curated by multiple groups (Nithin et al 2018). With 73 cases, the first data set of affin-ity values was developed in 2013 by the Liu group (Yang et al 2013).…”

Section: The Data Set Of Protein-rna Binding Affinitymentioning

confidence: 99%

A structure-based model for the prediction of protein–RNA binding affinity

Nithin

Mukherjee

Bahadur

2019

RNA

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Protein-RNA recognition is highly affinity-driven and regulates a wide array of cellular functions. In this study, we have curated a binding affinity data set of 40 protein-RNA complexes, for which at least one unbound partner is available in the docking benchmark. The data set covers a wide affinity range of eight orders of magnitude as well as four different structural classes. On average, we find the complexes with single-stranded RNA have the highest affinity, whereas the complexes with the duplex RNA have the lowest. Nevertheless, free energy gain upon binding is the highest for the complexes with ribosomal proteins and the lowest for the complexes with tRNA with an average of −5.7 cal/mol/Å 2 in the entire data set. We train regression models to predict the binding affinity from the structural and physicochemical parameters of protein-RNA interfaces. The best fit model with the lowest maximum error is provided with three interface parameters: relative hydrophobicity, conformational change upon binding and relative hydration pattern. This model has been used for predicting the binding affinity on a test data set, generated using mutated structures of yeast aspartyl-tRNA synthetase, for which experimentally determined ΔG values of 40 mutations are available. The predicted ΔG empirical values highly correlate with the experimental observations. The data set provided in this study should be useful for further development of the binding affinity prediction methods. Moreover, the model developed in this study enhances our understanding on the structural basis of protein-RNA binding affinity and provides a platform to engineer protein-RNA interfaces with desired affinity.

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Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

Cited by 36 publications

References 142 publications

Computational approaches to macromolecular interactions in the cell

Computational approaches to macromolecular interactions in the cell

Systems biology in inflammatory bowel diseases: on the way to precision medicine

A structure-based model for the prediction of protein–RNA binding affinity

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