An AI Pipeline to Investigate the Binding Properties of Food Bioactive Small Molecules

Binding between proteins and small molecules plays a crucial role in predicting various biological processes, including metabolic reactions, regulatory mechanisms, and signal transduction pathways. However, binding information is limited, covering only a small fraction of the potential protein-molecule combinations. For example, in the Foodome project, we have data on over 135,000 food-related small molecules, yet only 4.58% of these molecules have known binding annotations with proteins1,2,3. Although deep learning models have been suggested to expedite this identification process, our research demonstrates that state-of-the-art models struggle to generalize to unfamiliar molecular structures, often learning unintended shortcuts.

In this presentation, I will introduce AI-Bind4, a novel pipeline that combines network-based sampling strategies with unsupervised pre-training, enhancing binding predictions for proteins and ligands with insufficient or no prior annotations, exemplified by food-related small molecules. AI-Bind predictions were validated via docking simulations and comparison with recent experimental evidence, offering a framework to interpret machine learning prediction of protein-ligand binding by identifying potential active binding sites on the amino acid sequence.

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Publisher: REPO4EU

Publication date (Electronic, pub): 14 September 2023

Affiliations

[1 ] Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA ( https://ror.org/04b6nzv94)

[2 ] Network Science Institute, Northeastern University, Boston, MA, USA ( https://ror.org/04t5xt781)

Author information

Giulia Menichetti https://orcid.org/0000-0001-5201-6774

Article

DOI: 10.58647/REXPO.23008

SO-VID: 813b37b7-3363-4a03-b669-c03456e1d30c

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Published under Creative Commons Attribution 4.0 International ( CC BY 4.0). Users are allowed to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material for any purpose, even commercially), as long as the authors and the publisher are explicitly identified and properly acknowledged as the original source.

Conference name: RExPO23

Conference number: 2

Conference location: Stockholm, Sweden

Conference date: 25-26 October 2023

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REPO4EU

References

Menichetti Giulia. An AI pipeline to investigate the binding properties of poorly annotated molecules. Nature Reviews Physics. Vol. 4(6)2022. Springer Science and Business Media LLC. [Cross Ref]
Nasirian Farzaneh, Menichetti Giulia. Molecular Interaction Networks and Cardiovascular Disease Risk: The Role of Food Bioactive Small Molecules. Arteriosclerosis, Thrombosis, and Vascular Biology. Vol. 43(6):813–823. 2023. Ovid Technologies (Wolters Kluwer Health). [Cross Ref]
Menichetti Giulia, Barabási Albert-László. Nutrient concentrations in food display universal behaviour. Nature Food. Vol. 3(5):375–382. 2022. Springer Science and Business Media LLC. [Cross Ref]
Chatterjee Ayan, Walters Robin, Shafi Zohair, Ahmed Omair Shafi, Sebek Michael, Gysi Deisy, Yu Rose, Eliassi-Rad Tina, Barabási Albert-László, Menichetti Giulia. Improving the generalizability of protein-ligand binding predictions with AI-Bind. Nature Communications. Vol. 14(1)2023. Springer Science and Business Media LLC. [Cross Ref]

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[1] Menichetti Giulia. An AI pipeline to investigate the binding properties of poorly annotated molecules. Nature Reviews Physics. Vol. 4(6)2022. Springer Science and Business Media LLC. [Cross Ref]

[2] Nasirian Farzaneh, Menichetti Giulia. Molecular Interaction Networks and Cardiovascular Disease Risk: The Role of Food Bioactive Small Molecules. Arteriosclerosis, Thrombosis, and Vascular Biology. Vol. 43(6):813–823. 2023. Ovid Technologies (Wolters Kluwer Health). [Cross Ref]

[3] Menichetti Giulia, Barabási Albert-László. Nutrient concentrations in food display universal behaviour. Nature Food. Vol. 3(5):375–382. 2022. Springer Science and Business Media LLC. [Cross Ref]

[4] Chatterjee Ayan, Walters Robin, Shafi Zohair, Ahmed Omair Shafi, Sebek Michael, Gysi Deisy, Yu Rose, Eliassi-Rad Tina, Barabási Albert-László, Menichetti Giulia. Improving the generalizability of protein-ligand binding predictions with AI-Bind. Nature Communications. Vol. 14(1)2023. Springer Science and Business Media LLC. [Cross Ref]