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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments.

Claudio Mirabello¹, Björn Wallner¹

¹IFM Bioinformatics, Linköping University, Linköping, Sweden.

|August 16, 2019

Summary

This summary is machine-generated.

This study introduces rawMSA, a novel deep learning method for protein structure prediction. rawMSA processes raw Multiple Sequence Alignments (MSA) directly, outperforming traditional methods in predicting secondary structure and solvent accessibility.

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Area of Science:

Bioinformatics
Computational Biology
Structural Bioinformatics

Background:

Machine learning methods are crucial for predicting protein structural features, aiding in understanding protein function and disease association.
Deep Learning has spurred advancements in neural network architectures for these predictions.
Current methods often rely heavily on pre-processed data and manually designed features from Multiple Sequence Alignments (MSA).

Purpose of the Study:

To introduce a new paradigm, rawMSA, for predicting protein structural features by directly processing raw Multiple Sequence Alignments (MSA).
To leverage natural language processing techniques to map amino acid sequences into an adaptive, learned continuous space.
To eliminate the need for pre-calculated features like sequence profiles.

Main Methods:

Developed the rawMSA methodology, inspired by natural language processing, to map amino acid sequences into a learned continuous space.
Input the entire MSA into a Deep Network, bypassing traditional feature extraction.
Applied and benchmarked rawMSA on secondary structure prediction, relative solvent accessibility, and inter-residue contact map prediction.

Main Results:

rawMSA outperformed classical Position-Specific Scoring Matrix (PSSM) methods in predicting secondary structure and solvent accessibility.
rawMSA achieved performance comparable to methods using pre-calculated features for inter-residue contact map prediction in CASP12 and CASP13.
Demonstrated the efficacy of directly using raw MSA data for protein structure prediction.

Conclusions:

rawMSA represents a significant advancement in protein structure prediction by directly utilizing raw MSA data.
This approach obviates the need for laborious feature engineering, offering a more streamlined and potentially powerful prediction pipeline.
rawMSA paves the way for future methods that use raw MSA to represent evolutionary information, improving predictions of protein structure and function.