Predicting Capsid Protein Binding Sites in Single-Stranded RNA Viruses Using Machine Learning from Local Geometric Features
Selective recognition of viral RNA by capsid proteins is essential for genome packaging during the assembly of single-stranded RNA (ssRNA) viruses. However, identification of capsid protein binding sites in the RNA genome remains challenging because current experimental techniques are labor-intensive and low-throughput, motivating the development of computational approaches. Here, we present a…
Predicting capsid protein binding sites in ssRNA viruses relies heavily on computational methods due to labor-intensive experimental techniques. Researchers present a sequence-based framework that combines RNA tertiary modeling, geometric feature extraction, and machine learning. The Qbeta bacteriophage serves as a proof-of-concept system for benchmarking against experimentally identified binding and non-binding RNA fragments.
A neural network trained on geometric descriptors shows high accuracy in distinguishing binding sites, achieving an AUC of 0.88. Applying the model to AlphaFold-predicted RNA structures also maintains predictive performance, though failed predictions suggest additional dynamic factors beyond static conformations influence viral genome packaging.
This study offers insights into RNA-capsid interactions and lays groundwork for applying the approach to other ssRNA viruses.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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