Deciphering the structural and energetic determinants of protein-RNA interactions harbors the potential to understand key cell processes at molecular level, such as gene expression and regulation. With this purpose, computational methods like docking aim to complement current biophysical and structural biology efforts. However, the few reported docking algorithms for protein-RNA interactions show limited predictive success rates, mainly due to incomplete sampling of the conformational space of both the protein and the RNA molecules, as well as to the difficulties of the scoring function in identifying the correct docking models. Here, we have tested the predictive value of a variety of knowledge-based and energetic scoring functions on a recently published protein-RNA docking benchmark and developed a scoring function able to efficiently discriminate docking decoys. We first performed docking calculations with the bound conformation, which allowed us to analyze the problem in optimal conditions. We found that geometry-based terms and electrostatics were the most important scoring terms, while binding propensities and desolvation were much less relevant for the scoring of protein-RNA models. This is in contrast with what we observed for protein-protein docking. The results also showed an interesting dependence of the predictive rates on the flexibility of the protein molecule, which arises from the observed higher positive charge of flexible interfaces and provides hints for future development of more efficient protein-RNA docking methods.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.ymeth.2016.11.001 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
FTDMP: A Framework for Protein-Protein, Protein-DNA, and Protein-RNA Docking and Scoring.
Proteins
January 2025
Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania.
FTDMP is a software framework for biomolecular docking and scoring. It can perform docking of subunits containing one or more protein, DNA, or RNA chains, followed by subsequent scoring of the resulting models. FTDMP can also be used for the ranking of user-provided models of biomolecular complexes, generated by any structure prediction method.
View Article and Find Full Text PDFIdentification of deleterious non-synonymous single nucleotide polymorphisms in the mRNA decay activator ZFP36L2.
RNA Biol
December 2025
Biochemistry and Biophysics Department, School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
More than 4,000 single nucleotide polymorphisms (SNP) variants have been identified in the human gene, however only a few have been studied in the context of protein function. The tandem zinc finger domain of ZFP36L2, an RNA binding protein, is the functional domain that binds to its target mRNAs. This protein/RNA interaction triggers mRNA degradation, controlling gene expression.
View Article and Find Full Text PDFProtein-nucleic acid complexes: Docking and binding affinity.
Curr Opin Struct Biol
November 2024
Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India.
Protein-nucleic interactions play essential roles in several biological processes, such as gene regulation, replication, transcription, repair and packaging. The knowledge of three-dimensional structures of protein-nucleic acid complexes and their binding affinities helps to understand these functions. In this review, we focus on two major aspects namely, (i) deciphering the three-dimensional structures of protein-nucleic acid complexes and (ii) predicting their binding affinities.
View Article and Find Full Text PDFSynchronized motion of interface residues for evaluating protein-RNA complex binding affinity: Application to aptamer-mediated inhibition of TDP-43 aggregates.
Protein Sci
December 2024
Department of Physics, Sapienza University, Rome, Italy.
Investigating the binding between proteins and aptamers, such as peptides or RNA molecules, is of crucial importance both for understanding the molecular mechanisms that regulate cellular activities and for therapeutic applications in several pathologies. Here, a new computational procedure, employing mainly docking, clustering analysis, and molecular dynamics simulations, was designed to estimate the binding affinities between a protein and some RNA aptamers, through the investigation of the dynamical behavior of the predicted molecular complex. Using the state-of-the-art software catRAPID, we computationally designed a set of RNA aptamers interacting with the TAR DNA-binding protein 43 (TDP-43), a protein associated with several neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS).
View Article and Find Full Text PDFInvestigation of RNA-binding protein NOVA1 in silico: Comparison of the modern human V197 with the archaic I197 variant present in Neanderthals.
Comput Biol Med
December 2024
Epigenetics in Human Health and Disease Program, Baker Heart and Diabetes Institute, 75 Commercial Road, Prahran, VIC, 3004, Australia; yΘμ Study Group, ProspED Polytechnic, Carlton, VIC, 3053, Australia; Baker Department of Cardiometabolic Health, The University of Melbourne, Parkville, VIC 3010, Australia; Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia. Electronic address:
Article Synopsis
- Researchers have identified specific genetic differences between archaic and modern human genomes, notably a switch in the NOVA1 gene linked to RNA regulation in neurons, changing an amino acid from isoleucine to valine.
- Using advanced modeling techniques, they compared the structures and binding interactions of the archaic and modern NOVA1 variants, finding similar binding free energies for protein-RNA complexes.
- The study suggests that although structural changes are modest, more research is needed to understand how mutations in NOVA1 affect alternative splicing and contribute to diseases, especially considering the roles of multiple mutations.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!