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Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

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NABP-BERT: NANOBODY®-antigen binding prediction based on bidirectional encoder representations from transformers

Fatma S Ahmed^1,2, Saleh Aly³, Xiangrong Liu¹

¹Department of Computer Science and Technology, Xiamen University, Xiamen 361005, China.

Briefings in Bioinformatics

|December 17, 2024

Summary

NABP-BERT predicts nanobody-antigen binding using sequence data. This deep learning model accelerates the development of nanobodies (Nbs) for diverse applications by overcoming limitations in experimental identification.

Keywords:

BERT NANOBODY®antigen binding prediction deep learning sequence embedding

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Selection of Transporter-Targeted Inhibitory Nanobodies by Solid-Supported-Membrane SSM-Based Electrophysiology

Published on: May 3, 2021

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Antibody-mediated immunity is vital in vertebrates.
Nanobodies (Nbs), or single-domain antibodies (sdAbs), offer advantages over traditional antibodies but face limited availability for specific antigens (Ags).
Predicting Nb-Ag interactions is crucial for improving Nb efficacy but is experimentally challenging and time-consuming.

Purpose of the Study:

To develop a computational method for predicting nanobody-antigen binding from sequence data.
To create a deep learning model, NABP-BERT, for accurate Nb-Ag interaction prediction.
To establish a general pre-trained model for protein-related tasks, including protein-protein interactions.

Main Methods:

Utilized a BERT-based deep learning architecture (NABP-BERT).
Focused on analyzing amino acid sequence contexts for binding prediction.
Developed a general pre-trained model with transfer learning capabilities for protein tasks.

Main Results:

NABP-BERT accurately predicts nanobody-antigen binding using only sequence information.
Achieved high performance metrics: AUROC of 0.986 and AUPR of 0.985.
Demonstrated superior performance compared to existing methods.

Conclusions:

NABP-BERT effectively predicts nanobody-antigen interactions, addressing the challenge of limited Nb availability.
The model's sequence-based approach offers a cost-effective and efficient alternative to experimental methods.
The developed pre-trained model has broad applicability in protein-protein interaction studies.