Multimodal Pre-Training Model for Sequence-based Prediction of
Protein-Protein Interaction
- URL: http://arxiv.org/abs/2112.04814v1
- Date: Thu, 9 Dec 2021 10:21:52 GMT
- Title: Multimodal Pre-Training Model for Sequence-based Prediction of
Protein-Protein Interaction
- Authors: Yang Xue, Zijing Liu, Xiaomin Fang, Fan Wang
- Abstract summary: Pre-training a protein model to learn effective representation is critical for protein-protein interactions.
Most pre-training models for PPIs are sequence-based, which naively adopt the language models used in natural language processing to amino acid sequences.
We propose a multimodal protein pre-training model with three modalities: sequence, structure, and function.
- Score: 7.022012579173686
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Protein-protein interactions (PPIs) are essentials for many biological
processes where two or more proteins physically bind together to achieve their
functions. Modeling PPIs is useful for many biomedical applications, such as
vaccine design, antibody therapeutics, and peptide drug discovery. Pre-training
a protein model to learn effective representation is critical for PPIs. Most
pre-training models for PPIs are sequence-based, which naively adopt the
language models used in natural language processing to amino acid sequences.
More advanced works utilize the structure-aware pre-training technique, taking
advantage of the contact maps of known protein structures. However, neither
sequences nor contact maps can fully characterize structures and functions of
the proteins, which are closely related to the PPI problem. Inspired by this
insight, we propose a multimodal protein pre-training model with three
modalities: sequence, structure, and function (S2F). Notably, instead of using
contact maps to learn the amino acid-level rigid structures, we encode the
structure feature with the topology complex of point clouds of heavy atoms. It
allows our model to learn structural information about not only the backbones
but also the side chains. Moreover, our model incorporates the knowledge from
the functional description of proteins extracted from literature or manual
annotations. Our experiments show that the S2F learns protein embeddings that
achieve good performances on a variety of PPIs tasks, including cross-species
PPI, antibody-antigen affinity prediction, antibody neutralization prediction
for SARS-CoV-2, and mutation-driven binding affinity change prediction.
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