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1. Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction

   You, Yuning; Shen, Yang (December 2020, Machine Learning for Structure Biology (MLSB) Workshop at the 34th Conference on Neural Information Processing Systems)

   null (Ed.)

   Compound-protein pairs dominate FDA-approved drug-target pairs and the prediction of compound-protein affinity and contact (CPAC) could help accelerate drug discovery. In this study we consider proteins as multi-modal data including 1D amino-acid sequences and (sequence-predicted) 2D residue-pair contact maps. We empirically evaluate the embeddings of the two single modalities in their accuracyand generalizability of CPAC prediction (i.e. structure-free interpretable compound-protein affinity prediction). And we rationalize their performances in both challenges of embedding individual modalities and learning generalizable embedding-label relationship. We further propose two models involving cross-modality protein embedding and establish that the one with cross interaction (thus capturing correlations among modalities) outperforms SOTAs and our single modality models in affinity, contact, and binding-site predictions for proteins never seen in the training set. 

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2. Generalized Protein Pocket Generation with Prior-Informed Flow Matching

   Zhang, Zaixi; Zitnik, Marinka; Qi, Liu (December 2024, 38th Conference on Neural Information Processing Systems (NeurIPS 2024).)

   Globerson, A; Mackey, L; Belgrave, D; Fan, A; Paquet, U; Tomczak, J; Zhang, C (Ed.)

   Designing ligand-binding proteins, such as enzymes and biosensors, is essential in bioengineering and protein biology. One critical step in this process involves designing protein pockets, the protein interface binding with the ligand. Current approaches to pocket generation often suffer from time-intensive physical computations or template-based methods, as well as compromised generation quality due to the overlooking of domain knowledge. To tackle these challenges, we propose PocketFlow, a generative model that incorporates protein-ligand interaction priors based on flow matching. During training, PocketFlow learns to model key types of protein-ligand interactions, such as hydrogen bonds. In the sampling, PocketFlow leverages multi-granularity guidance (overall binding affinity and interaction geometry constraints) to facilitate generating high-affinity and valid pockets. Extensive experiments show that PocketFlow outperforms baselines on multiple benchmarks, e.g., achieving an average improvement of 1.29 in Vina Score and 0.05 in scRMSD. Moreover, modeling interactions make PocketFlow a generalized generative model across multiple ligand modalities, including small molecules, peptides, and RNA. 

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3. Protein‐Ligand Structure and Affinity Prediction in CASP16 Using a Geometric Deep Learning Ensemble and Flow Matching

   https://doi.org/10.1002/prot.26827  

   Morehead, Alex; Liu, Jian; Neupane, Pawan; Giri, Nabin; Cheng, Jianlin (April 2025, Proteins: Structure, Function, and Bioinformatics)

   ABSTRACT Predicting the structure of ligands bound to proteins is a foundational problem in modern biotechnology and drug discovery, yet little is known about how to combine the predictions of protein‐ligand structure (poses) produced by the latest deep learning methods to identify the best poses and how to accurately estimate the binding affinity between a protein target and a list of ligand candidates. Further, a blind benchmarking and assessment of protein‐ligand structure and binding affinity prediction is necessary to ensure it generalizes well to new settings. Towards this end, we introduceMULTICOM_ligand, a deep learning‐based protein‐ligand structure and binding affinity prediction ensemble featuring structural consensus ranking for unsupervised pose ranking and a new deep generative flow matching model for joint structure and binding affinity prediction. Notably,MULTICOM_ligand ranked among the top‐5 ligand prediction methods in both protein‐ligand structure prediction and binding affinity prediction in the 16th Critical Assessment of Techniques for Structure Prediction (CASP16), demonstrating its efficacy and utility for real‐world drug discovery efforts. The source code for MULTICOM_ligand is freely available on GitHub. 

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4. Improving the generalizability of protein-ligand binding predictions with AI-Bind

   https://doi.org/10.1038/s41467-023-37572-z  

   Chatterjee, Ayan; Walters, Robin; Shafi, Zohair; Ahmed, Omair Shafi; Sebek, Michael; Gysi, Deisy; Yu, Rose; Eliassi-Rad, Tina; Barabási, Albert-László; Menichetti, Giulia (December 2023, Nature Communications)

   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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5. Many-to-one binding by intrinsically disordered protein regions

   https://doi.org/10.1142/9789811215636_0015  

   Alterovitz, W L; Faraggi, E; Oldfield, C J; Meng, J W; Xue, B; Huang, F; Romero, P; Kloczkowski, A; Uversky, V N; Dunker, A K. (January 2020, Pacific symposium on biocomputing)

   Disordered binding regions (DBRs), which are embedded within intrinsically disordered proteins or regions (IDPs or IDRs), enable IDPs or IDRs to mediate multiple protein-protein interactions. DBR-protein complexes were collected from the Protein Data Bank for which two or more DBRs having different amino acid sequences bind to the same (100% sequence identical) globular protein partner, a type of interaction herein called many-to-one binding. Two distinct binding profiles were identified: independent and overlapping. For the overlapping binding profiles, the distinct DBRs interact by means of almost identical binding sites (herein called “similar”), or the binding sites contain both common and divergent interaction residues (herein called “intersecting”). Further analysis of the sequence and structural differences among these three groups indicate how IDP flexibility allows different segments to adjust to similar, intersecting, and independent binding pockets. 

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