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While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they’re computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance in docking and virtual screening benchmarks. Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify cryptic pockets in unseen protein targets. As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.

 

Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science, and drug discovery. This paper develops a novel algorithm for optimizing molecular properties via an Expectation- Maximization (EM) like explainable evolutionary process. The algorithm is designed to mimic human experts in the process of searching for desirable molecules and alternate between two stages: the first stage on explainable local search which identifies rationales, i.e., critical subgraph patterns accounting for desired molecular properties, and the second stage on molecule completion which explores the larger space of molecules containing good rationales. We test our approach against various baselines on a real-world multi-property optimization task where each method is given the same number of queries to the property oracle. We show that our evolution-by-explanation algorithm is 79% better than the best baseline in terms of a generic metric combining aspects such as success rate, novelty, and diversity. Human expert evaluation on optimized molecules shows that 60% of top molecules obtained from our methods are deemed successful. 

Definitive diagnosis to sudden cardiac death (SCD) is often challenging since the postmortem examination on SCD victims could hardly demonstrate an adequate cause of death. It is therefore important to uncover the inherited risk component to SCD. Signal transducer and activators of transcription 5 A (STAT5A) is a member of the STAT family and a transcription factor that is activated by many cell ligands and associated with various cardiovascular processes. In this study, we performed a systematic variant screening on the STAT5A to filter potential functional genetic variations. Based on the screening results, an insertion/deletion polymorphism (rs3833144) in 3’UTR of STAT5A was selected as the candidate variant. A total of 159 SCD cases and 668 SCD matched healthy controls was enrolled to perform a case-control study and evaluate the association between rs3833144 and SCD susceptibility in Chinese populations. Logistic regression analysis showed that the deletion allele of rs3833144 had significantly increased the SCD risk (odds ratio (OR) = 1.54; 95% confidence interval (CI) = 1.18–2.01; P = 0.000955). Further genotype-expression eQTL analysis showed that samples with deletion allele appeared to lower expression of STAT5A, and in silico prediction suggested the local 3 D structure changes of STAT5A mRNA caused by the variant. On the other hand, the bioinformatic analysis presented that promoters of RARA and PTGES3L-AARSD1 could interact with rs3833144, and eQTL analysis showed the higher expression of both genes in samples with deletion allele. Dual-luciferase activity assays also suggested the significant regulatory role of rs3833144 in gene transcription. Our current data thus suggested a possible involvement of rs3833144 to SCD predisposition in Chinese populations and rs3833144 with potential function roles may become a candidate marker for SCD diagnosis and prevention.