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Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs, Model Ranking, and Deep

Yuki Kagaya¹, Tsukasa Nakamura¹, Jacob Verburgt¹

¹Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA.

|August 2, 2025

Summary

This summary is machine-generated.

KiharaLab achieved top rankings in CASP16 protein complex (1st) and RNA structure (3rd) prediction. Their deep learning approach leveraged enhanced multiple sequence alignments (MSAs) and consensus scoring for superior protein and RNA structure modeling.

Keywords:

CASP CASP16 MSA RNA structure prediction kiharalab model ranking protein structure prediction

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Computational biology
Structural biology
Bioinformatics

Background:

Accurate prediction of protein complex and RNA structures is crucial for understanding biological functions.
CASP16 provides a benchmark for evaluating the performance of structure prediction methods.

Purpose of the Study:

To present the methods and results of KiharaLab's protein complex and RNA structure predictions at CASP16.
To evaluate the effectiveness of integrated deep learning models, enhanced multiple sequence alignments (MSAs), and consensus scoring strategies.

Main Methods:

Integration of multiple state-of-the-art deep learning models with consensus-based scoring.
Enhancement of MSAs using a large metagenomic sequence database.
Ensemble approach for RNA structure prediction centered around the NuFold framework.
Manual refinement of predictions based on literature evidence.

Main Results:

KiharaLab group ranked first in protein complex prediction.
KiharaLab group ranked third in RNA structure prediction.
Analysis revealed strengths in MSA and scoring strategies, alongside areas for future development.

Conclusions:

The developed computational approach demonstrates high accuracy in protein complex and RNA structure prediction.
The study highlights the importance of deep learning, comprehensive MSAs, and robust scoring for structural predictions.
Further improvements are identified for future iterations of the prediction models.