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Contrastive learning on protein embeddings enlightens midnight zone.

Michael Heinzinger¹, Maria Littmann¹, Ian Sillitoe²

¹TUM (Technical University of Munich) Dept Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany.

NAR Genomics and Bioinformatics

|June 15, 2022

Summary

ProtTucker leverages protein Language Models (pLMs) to create new embeddings for improved protein similarity recognition, even in the

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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 bioinformatics
Machine learning in protein science

Background:

Homology-based inference (HBI) transfers protein annotations using sequence alignments.
Embedding-based annotation transfer (EAT) uses protein Language Models (pLMs) for improved HBI.
Current methods struggle with distant homologous relationships, known as the 'midnight zone' of protein similarity.

Purpose of the Study:

To introduce ProtTucker, a novel method for protein similarity recognition.
To enhance the ability to identify distant homologous relationships using pLM embeddings.
To develop a faster and more accurate sequence comparison technique.

Main Methods:

Utilizing single protein representations from pLMs for contrastive learning.
Training embeddings to optimize constraints from hierarchical protein 3D structure classifications (CATH resource).
Developing a novel combination of tools and sampling techniques for embedding generation.

Main Results:

ProtTucker demonstrates improved recognition of distant homologous relationships compared to traditional methods.
The method achieves performance comparable to or better than state-of-the-art sequence comparison techniques.
ProtTucker is significantly faster than alignment-based methods as it avoids alignment generation.

Conclusions:

ProtTucker's novel embedding strategy effectively bridges the gap in identifying distantly related protein sequences.
This approach advances sequence comparison into the 'midnight zone' of protein similarity.
The method offers a computationally efficient and highly accurate alternative for protein analysis.