publications

2024

1. TrustAffinity

   TrustAffinity: accurate, reliable and scalable out-of-distribution protein-ligand binding affinity prediction using trustworthy deep learning

   Amitesh Badkul, Li Xie, Shuo Zhang, and Lei Xie

   bioRxiv 2024

   Abs PDF

   Accurate, reliable and scalable predictions of protein-ligand binding affinity have a great potential to accelerate drug discovery. Despite considerable efforts, three challenges remain: out-of-distribution (OOD) generalizations for understudied proteins or compounds from unlabeled protein families or chemical scaffolds, uncertainty quantification of individual predictions, and scalability to billions of compounds. We propose a sequence-based deep learning framework, TrustAffinity, to address aforementioned challenges. TrustAffinity synthesizes a structure-informed protein language model, efficient uncertainty quantification based on residue-estimation and novel uncertainty regularized optimization. We extensively validate TrustAffinity in multiple OOD settings. TrustAffinity significantly outperforms state-of-the-art computational methods by a large margin. It achieves a Pearson’s correlation between predicted and actual binding affinities above 0.9 with a high confidence and at least three orders of magnitude of faster than protein-ligand docking, highlighting its potential in real-world drug discovery. We further demonstrate TrustAffinity’s practicality through an Opioid Use Disorder lead discovery case study.Competing Interest StatementThe authors have declared no competing interest.

2023

1. PortalCG

   End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins

   Tian Cai, Li Xie, Shuo Zhang, Muge Chen, Di He, Amitesh Badkul, Yang Liu, and 4 more authors

   PLOS Computational Biology 2023

   Abs PDF Code

   Discovering chemical-protein interactions for millions of chemicals across the entire human and pathogen genomes is instrumental for chemical genomics, protein function prediction, drug discovery, and other applications. However, more than 90% of gene families remain dark, i.e., their small molecular ligands are undiscovered due to experimental limitations and human biases. Existing computational approaches typically fail when the unlabeled dark protein of interest differs from those with known ligands or structures. To address this challenge, we developed a deep learning framework PortalCG. PortalCG consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to represent the whole universe of protein sequences in recognition of evolutionary linkage of ligand binding sites across gene families, (ii) an end-to-end pretraining-fine-tuning strategy to simulate the folding process of protein-ligand interactions and reduce the impact of inaccuracy of predicted structures on function predictions under a sequence-structure-function paradigm, (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family, and (iv) stress model selection that uses different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for off-target predictions and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the human design. Our results also suggested that a differentiable sequence-structure-function deep learning framework where protein structure information serve as an intermediate layer could be superior to conventional methodology where the use of predicted protein structures for predicting protein functions from sequences. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of Dopamine receptors for the treatment of Opioid Use Disorder, and illuminating the undruggable human genome for targeting diseases that do not have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring the understudied protein functional space.