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Updated: Aug 2, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
ConsAlign: simultaneous RNA structural aligner based on rich transfer learning and thermodynamic ensemble model of
1Department of Computational Biology and Medical Sciences, University of Tokyo, Chiba 277-8561, Japan.
Motivation:
To capture structural homology in RNAs, alignment and folding (AF) of RNA homologs has been a fundamental framework around RNA science. Learning sufficient scoring parameters for simultaneous AF (SAF) is an undeveloped subject because evaluating them is computationally expensive.
Results:
We developed ConsTrain-a gradient-based machine learning method for rich SAF scoring. We also implemented ConsAlign-a SAF tool composed of ConsTrain's learned scoring parameters. To aim for better AF quality, ConsAlign employs (1) transfer learning from well-defined scoring models and (2) the ensemble model between the ConsTrain model and a well-established thermodynamic scoring model. Keeping comparable running time, ConsAlign demonstrated competitive AF prediction quality among current AF tools.
Availability And Implementation:
Our code and our data are freely available at https://github.com/heartsh/consalign and https://github.com/heartsh/consprob-trained.
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