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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

ManyFold: an efficient and flexible library for training and validating protein folding models.

Amelia Villegas-Morcillo^1,2, Louis Robinson¹, Arthur Flajolet¹

¹InstaDeep, London W2 1AY, UK.

Bioinformatics (Oxford, England)

|December 10, 2022

Summary

This summary is machine-generated.

ManyFold is a new deep learning library for protein structure prediction. It supports multiple input types and model training, offering a flexible and efficient alternative for researchers.

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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
Deep learning

Background:

Protein structure prediction is crucial for understanding biological function.
Existing deep learning models like AlphaFold and OpenFold have advanced the field.
There is a need for flexible and trainable libraries to facilitate further research.

Purpose of the Study:

Introduce ManyFold, a novel deep learning library for protein structure prediction.
Enable support for diverse input modalities, including MSAs and pLM embeddings.
Facilitate the training and fine-tuning of new protein structure prediction models.

Main Methods:

Developed ManyFold using Jax for efficient distributed computation.
Implemented support for multiple sequence alignments (MSAs) and protein language model (pLM) embeddings.
Enabled inference of existing models like AlphaFold and OpenFold within the library.

Main Results:

Trained a proof-of-concept pLM-based model, pLMFold, from scratch.
pLMFold achieved reasonable protein structure prediction results.
Demonstrated reduced computational overhead compared to AlphaFold.

Conclusions:

ManyFold provides a flexible and trainable platform for protein structure prediction.
The library supports diverse inputs and existing models, accelerating research.
pLMFold shows promise for efficient protein structure prediction with deep learning.