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Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

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SVSBI: sequence-based virtual screening of biomolecular interactions.

Li Shen¹, Hongsong Feng¹, Yuchi Qiu¹

¹Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA.

Communications Biology

|May 18, 2023

Summary

Sequence-based virtual screening (SVS) offers a new generation of models for drug discovery. This method uses natural language processing (NLP) to analyze biomolecular interactions, outperforming traditional 3D structure-based approaches.

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Area of Science:

Computational biology
Bioinformatics
Drug discovery

Background:

Virtual screening (VS) is crucial for drug design but often limited by the inaccuracy of 3D structure-based molecular docking.
Current VS models struggle with predicting biomolecular interactions accurately due to reliance on potentially flawed structural data.

Purpose of the Study:

To introduce sequence-based virtual screening (SVS) as a novel approach to VS.
To overcome the limitations of 3D structure-dependent methods in predicting biomolecular interactions.
To enhance the accuracy and applicability of VS in drug discovery and protein engineering.

Main Methods:

Developed SVS models utilizing advanced natural language processing (NLP) algorithms.
Employed optimized deep K-embedding strategies to encode biomolecular interactions directly from sequences.
Avoided the need for 3D structure-based molecular docking in the screening process.

Main Results:

SVS demonstrated superior performance compared to state-of-the-art methods on four regression datasets (protein-ligand, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions).
SVS achieved high accuracy on five classification datasets for protein-protein interactions across five different biological species.
The sequence-based approach proved effective in capturing complex biomolecular interaction patterns.

Conclusions:

SVS represents a significant advancement in virtual screening, offering a more reliable and accurate alternative to traditional methods.
This novel approach has the potential to revolutionize drug discovery and protein engineering by improving the efficiency and success rates of identifying candidate molecules and interactions.
The reliance on sequence data and NLP makes SVS broadly applicable and scalable for various biomolecular interaction studies.