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Chatterjee A, Walters R, Shafi Z, Ahmed OS, Sebek M, Gysi D, Yu R, Eliassi-Rad T, Barabási AL, Menichetti G. Improving the generalizability of protein-ligand binding predictions with AI-Bind. Nat Commun 2023; 14:1989. [PMID: 37031187 PMCID: PMC10082765 DOI: 10.1038/s41467-023-37572-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 03/23/2023] [Indexed: 04/10/2023] Open
Abstract
Identifying novel drug-target interactions is a critical and rate-limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, here we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We unveil the mechanisms responsible for this shortcoming, demonstrating how models rely on shortcuts that leverage the topology of the protein-ligand bipartite network, rather than learning the node features. Here we introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training to improve binding predictions for novel proteins and ligands. We validate AI-Bind predictions via docking simulations and comparison with recent experimental evidence, and step up the process of interpreting machine learning prediction of protein-ligand binding by identifying potential active binding sites on the amino acid sequence. AI-Bind is a high-throughput approach to identify drug-target combinations with the potential of becoming a powerful tool in drug discovery.
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Affiliation(s)
- Ayan Chatterjee
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Robin Walters
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Zohair Shafi
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Omair Shafi Ahmed
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Michael Sebek
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
| | - Deisy Gysi
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rose Yu
- Department of Computer Science and Engineering, University of California, San Diego, CA, USA
| | - Tina Eliassi-Rad
- Network Science Institute, Northeastern University, Boston, MA, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
- Santa Fe Institute, Santa Fe, NM, USA
- The Institute for Experiential AI, Northeastern University, Boston, MA, USA
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
- Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Giulia Menichetti
- Network Science Institute, Northeastern University, Boston, MA, USA.
- Department of Physics, Northeastern University, Boston, MA, USA.
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Martínez-Jiménez F, Marti-Renom MA. Ligand-target prediction by structural network biology using nAnnoLyze. PLoS Comput Biol 2015; 11:e1004157. [PMID: 25816344 PMCID: PMC4376866 DOI: 10.1371/journal.pcbi.1004157] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Accepted: 01/27/2015] [Indexed: 11/24/2022] Open
Abstract
Target identification is essential for drug design, drug-drug interaction prediction, dosage adjustment and side effect anticipation. Specifically, the knowledge of structural details is essential for understanding the mode of action of a compound on a target protein. Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. The method was benchmarked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for “anonymous” compounds or compounds not present in the training set. nAnnoLyze resulted in higher accuracies than its predecessor, AnnoLyze. We applied the method to predict interactions for all the compounds in the DrugBank database with each human protein structure and provide examples of target identification for known drugs against human diseases. The accuracy and applicability of our method to any compound indicate that a comparative docking approach such as nAnnoLyze enables large-scale annotation and analysis of compound–protein interactions and thus may benefit drug development. Description of the “mode-of-action” of a small chemical compound against a protein target is essential for the drug discovery process. Such description relies on three main steps: i) the identification of the target protein within the thousands of proteins in an organism, ii) the localization of the binding interaction site in the identified target protein, and iii) the molecular characterization of the compound’s binding mode in the binding site of the target protein. Here, we introduce a new computational method, called nAnnoLyze, which uses graph theory principles to relate compounds and target proteins based on comparative principles. nAnnoLyze aims at addressing two of the three previous steps, that is, target identification and binding site localization. Our results suggest that the nAnnoLyze accuracy and proteome-wide applicability enables the large-scale annotation and analysis of compound–protein interaction and thus may benefit drug development.
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Affiliation(s)
- Francisco Martínez-Jiménez
- Genome Biology Group, Centre Nacional d’Aanàlisi Genòmica (CNAG), Barcelona, Spain
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), Barcelona, Spain
| | - Marc A. Marti-Renom
- Genome Biology Group, Centre Nacional d’Aanàlisi Genòmica (CNAG), Barcelona, Spain
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- * E-mail:
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