HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses
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Abstract
Background: The RNA-Recognition motif (RRM) is a protein domain that binds single-stranded RNA (ssRNA) and is present in as much as 2% of the human genome. Despite this important role in biology, RRM-ssRNA interactions are very challenging to study on the structural level because of the remarkable flexibility of ssRNA. In the absence of atomic-level experimental data, the only method able to predict the 3D structure of protein-ssRNA complexes with any degree of accuracy is ssRNA’TTRACT, an ssRNA fragment-based docking approach using ATTRACT. However, since ATTRACT parameters are not ssRNA-specific and were determined in 2010, there is substantial opportunity for enhancement. Results: Here we present HIPPO, a composite RRM-ssRNA scoring potential derived analytically from contact frequencies in near-native versus non-native docking models. HIPPO consists of a consensus of four distinct potentials, each extracted from a distinct reference pool of protein-trinucleotide docking decoys. To score a docking pose with one potential, for each pair of RNA–protein coarse-grained bead types, each contact is awarded or penalised according to the relative frequencies of this contact distance range among the correct and incorrect poses of the reference pool. Validated on a fragment-based docking benchmark of 57 experimentally solved RRM-ssRNA complexes, HIPPO achieved a threefold or higher enrichment for half of the fragments, versus only a quarter with the ATTRACT scoring function. In particular, HIPPO drastically improved the chance of very high enrichment (12-fold or higher), a scenario where the incremental modelling of entire ssRNA chains from fragments becomes viable. However, for the latter result, more research is needed to make it directly practically applicable. Regardless, our approach already improves upon the state of the art in RRM-ssRNA modelling and is in principle extendable to other types of protein-nucleic acid interactions.
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Akram, H., Rahman, M. U., Mazhar, S., Qamer, F., & Yousaf, A. (2025) · Future Medicinal Chemistry · doi:10.1080/17568919.2025.2533061
- Authors
- Kravchenko, A., de Vries, S. J., Smaïl-Tabbone, M., & Chauvot de Beauchene, I.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- ORCID
- Kravchenko, Anna
- Venue
- BMC Bioinformatics
- Published
- 2024-03-26
- Volume
- 25
- Issue
- 1
- Pages
- 129-129
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC10964654
- OpenAlex
- W4393200185
- Cited by
- 1
- References
- 40
- Citations by year
- 2025: 1
- FWCI
- 0.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (813239)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- RNA recognition motif, fragment-based docking, scoring functions, pseudopotentials, protein-nucleic acid interactions, hippopotamus
- MeSH terms
- Humans, Protein Binding, Protein Conformation, Proteins, RNA, Molecular Docking Simulation
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