Assessing interface accuracy in macromolecular complexes DOI Creative Commons

Olgierd Ludwiczak,

Maciej Antczak, Marta Szachniuk

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0319917 - e0319917

Published: April 2, 2025

Accurately predicting the 3D structures of macromolecular complexes is becoming increasingly important for understanding their cellular functions. At same time, reliably assessing prediction quality remains a significant challenge in bioinformatics. To address this, various methods analyze and evaluate silico models from multiple perspectives, accounting both reconstructed components’ arrangement within complex. In this work, we introduce Intermolecular Interaction Network Fidelity (I-INF), normalized similarity measure that quantifies intermolecular interactions multichain complexes. Adapted well-established score RNA field, I-INF provides clear intuitive way to predicted against reference structure, with specific focus on interchain interaction sites. Additionally, implement F 1 assess interfaces assemblies, further enriching evaluation framework. Tested 72 RNA-protein decoys, as well exemplary DNA-DNA, RNA-RNA, protein-protein complexes, these measures deliver reliable scores enable straightforward ranking predictions. The tool computing publicly available Zenodo, facilitating large-scale analysis integration other computational systems.

Language: Английский

Advances and Mechanisms of RNA–Ligand Interaction Predictions DOI Creative Commons
Zhuo Chen, Chengwei Zeng,

Haoquan Liu

et al.

Life, Journal Year: 2025, Volume and Issue: 15(1), P. 104 - 104

Published: Jan. 15, 2025

The diversity and complexity of RNA include sequence, secondary structure, tertiary structure characteristics. These elements are crucial for RNA's specific recognition other molecules. With advancements in biotechnology, RNA-ligand structures allow researchers to utilize experimental data uncover the mechanisms complex interactions. However, determining these complexes experimentally can be technically challenging often results low-resolution data. Many machine learning computational approaches have recently emerged learn multiscale-level features predict Predicting interactions remains an unexplored area. Therefore, studying is essential understanding biological processes. In this review, we analyze interaction characteristics by examining structure. Our goal clarify how specifically recognizes ligands. Additionally, systematically discuss methods predicting guide future research directions. We aim inspire creation more reliable prediction tools.

Language: Английский

Citations

0
Assessing interface accuracy in macromolecular complexes DOI Creative Commons

Olgierd Ludwiczak,

Maciej Antczak, Marta Szachniuk

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0319917 - e0319917

Published: April 2, 2025

Accurately predicting the 3D structures of macromolecular complexes is becoming increasingly important for understanding their cellular functions. At same time, reliably assessing prediction quality remains a significant challenge in bioinformatics. To address this, various methods analyze and evaluate silico models from multiple perspectives, accounting both reconstructed components’ arrangement within complex. In this work, we introduce Intermolecular Interaction Network Fidelity (I-INF), normalized similarity measure that quantifies intermolecular interactions multichain complexes. Adapted well-established score RNA field, I-INF provides clear intuitive way to predicted against reference structure, with specific focus on interchain interaction sites. Additionally, implement F 1 assess interfaces assemblies, further enriching evaluation framework. Tested 72 RNA-protein decoys, as well exemplary DNA-DNA, RNA-RNA, protein-protein complexes, these measures deliver reliable scores enable straightforward ranking predictions. The tool computing publicly available Zenodo, facilitating large-scale analysis integration other computational systems.

Language: Английский

Citations

0