Accelerating discovery of bioactive ligands with pharmacophore-informed generative models.

Deep generative models have advanced drug discovery but often generate compounds with limited structural novelty, providing constrained inspiration for medicinal chemists. To address this, we develop TransPharmer, a generative model that integrates ligand-based interpretable pharmacophore fingerprints with a generative pre-training transformer (GPT)-based framework for de novo molecule generation. TransPharmer excels in unconditioned distribution learning, de novo generation, and scaffold elaboration under pharmacophoric constraints.

Integrated computational analysis identifies therapeutic targets with dual action in cancer cells and T cells.

Many cancer drugs that target cancer cell pathways also impair the immune system. We developed a computational target discovery platform to enable examination of both cancer and immune cells so as to identify pathways that restrain tumor progression and potentiate anti-tumor immunity. Immune-related CRISPR screen analyzer of functional targets (ICRAFT) integrates immune-related CRISPR screen datasets, single-cell RNA sequencing (scRNA-seq) data, and pre-treatment RNA-seq data from clinical trials, enabling a systems-level approach to therapeutic target discovery.

MMFuncPhos: A Multi-Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types.

Protein phosphorylation plays a crucial role in regulating a wide range of biological processes, and its dysregulation is strongly linked to various diseases. While many phosphorylation sites have been identified so far, their functionality and regulatory effects are largely unknown. Here, a deep learning model MMFuncPhos, based on a multi-modal deep learning framework, is developed to predict functional phosphorylation sites.

Learning a generalized graph transformer for protein function prediction in dissimilar sequences.

Background: In the face of a growing disparity between high-throughput sequence data and low-throughput experimental studies, the emerging field of deep learning stands as a promising alternative. Generally, many data-driven approaches are capable of facilitating fast and accurate predictions of protein functions. Nevertheless, the inherent statistical nature of deep learning techniques may limit their generalization capabilities when applied to novel nonhomologous proteins that diverge significantly from existing ones.

Reacon: a template- and cluster-based framework for reaction condition prediction.

Computer-assisted synthesis planning has emerged as a valuable tool for organic synthesis. Prediction of reaction conditions is crucial for applying the planned synthesis routes. However, achieving diverse suggestions while ensuring the reasonableness of predictions remains an underexplored challenge.

All-Atom Protein Sequence Design Based on Geometric Deep Learning.

Designing sequences for specific protein backbones is a key step in creating new functional proteins. Here, we introduce GeoSeqBuilder, a deep learning framework that integrates protein sequence generation with side chain conformation prediction to produce the complete all-atom structures for designed sequences. GeoSeqBuilder uses spatial geometric features from protein backbones and explicitly includes three-body interactions of neighboring residues.

Mapping Full Conformational Transition Dynamics of Intrinsically Disordered Proteins Using a Single-Molecule Nanocircuit.

Intrinsically disordered proteins (IDPs) are emerging therapeutic targets for human diseases. However, probing their transient conformations remains challenging because of conformational heterogeneity. To address this problem, we developed a biosensor using a point-functionalized silicon nanowire (SiNW) that allows for real-time sampling of single-molecule dynamics.

Target-Specific Peptide Binder Design with DiffPepBuilder.

Despite the exciting progress in target-specific protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic data set, referred to as PepPC-F, from the abundant protein-protein interface data and developed DiffPepBuilder, a target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to codesign peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures.

Challenging Complexity with Simplicity: Rethinking the Role of Single-Step Models in Computer-Aided Synthesis Planning.

Computer-assisted synthesis planning has become increasingly important in drug discovery. While deep-learning models have shown remarkable progress in achieving high accuracies for single-step retrosynthetic predictions, their performances in retrosynthetic route planning need to be checked. This study compares the intricate single-step models with a straightforward template enumeration approach for retrosynthetic route planning on a real-world drug molecule data set.

DiffBindFR: an SE(3) equivariant network for flexible protein-ligand docking.

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of protein-ligand binding mode is essential. Conventional docking approaches perform well in redocking tasks with known protein binding pocket conformation in the complex state. However, in real-world docking scenario without knowing the protein binding conformation for a new ligand, accurately modeling the binding complex structure remains challenging as flexible docking is computationally expensive and inaccurate.

Discovery of 3-oxo-1,2,3,4-tetrahydropyrido[1,2-a]pyrazin derivatives as SARS-CoV-2 main protease inhibitors through virtual screening and biological evaluation.

The COVID-19 caused by SARS-CoV-2 has led to a global pandemic that continues to impact societies and economies worldwide. The main protease (M) plays a crucial role in SARS-CoV-2 replication and is an attractive target for anti-SARS-CoV-2 drug discovery. Herein, we report a series of 3-oxo-1,2,3,4-tetrahydropyrido[1,2-a]pyrazin derivatives as non-peptidomimetic inhibitors targeting SARS-CoV-2 M through structure-based virtual screening and biological evaluation.

Photoaffinity labeling coupled with proteomics identify PDI-ADAM17 module is targeted by (-)-vinigrol to induce TNFR1 shedding and ameliorate rheumatoid arthritis in mice.

Various biological agents have been developed to target tumor necrosis factor alpha (TNF-α) and its receptor TNFR1 for the rheumatoid arthritis (RA) treatment, whereas small molecules modulating such cytokine receptors are rarely reported in comparison to the biologicals. Here, by revealing the mechanism of action of vinigrol, a diterpenoid natural product, we show that inhibition of the protein disulfide isomerase (PDI, PDIA1) by small molecules activates A disintegrin and metalloprotease 17 (ADAM17) and then leads to the TNFR1 shedding on mouse and human cell membranes. This small-molecule-induced receptor shedding not only effectively blocks the inflammatory response caused by TNF-α in cells, but also reduces the arthritic score and joint damage in the collagen-induced arthritis mouse model.

Designing Cyclic-Constrained Peptides to Inhibit Human Phosphoglycerate Dehydrogenase.

Although loop epitopes at protein-protein binding interfaces often play key roles in mediating oligomer formation and interaction specificity, their binding sites are underexplored as drug targets owing to their high flexibility, relatively few hot spots, and solvent accessibility. Prior attempts to develop molecules that mimic loop epitopes to disrupt protein oligomers have had limited success. In this study, we used structure-based approaches to design and optimize cyclic-constrained peptides based on loop epitopes at the human phosphoglycerate dehydrogenase (PHGDH) dimer interface, which is an obligate homo-dimer with activity strongly dependent on the oligomeric state.

Visualizing single-molecule conformational transition and binding dynamics of intrinsically disordered proteins.

Intrinsically disordered proteins (IDPs) play crucial roles in cellular processes and hold promise as drug targets. However, the dynamic nature of IDPs remains poorly understood. Here, we construct a single-molecule electrical nanocircuit based on silicon nanowire field-effect transistors (SiNW-FETs) and functionalize it with an individual disordered c-Myc bHLH-LZ domain to enable label-free, in situ, and long-term measurements at the single-molecule level.

ssDNA accessibility of Rad51 is regulated by orchestrating multiple RPA dynamics.

The eukaryotic single-stranded DNA (ssDNA)-binding protein Replication Protein A (RPA) plays a crucial role in various DNA metabolic pathways, including DNA replication and repair, by dynamically associating with ssDNA. While the binding of a single RPA molecule to ssDNA has been thoroughly studied, the accessibility of ssDNA is largely governed by the bimolecular behavior of RPA, the biophysical nature of which remains unclear. In this study, we develop a three-step low-complexity ssDNA Curtains method, which, when combined with biochemical assays and a Markov chain model in non-equilibrium physics, allow us to decipher the dynamics of multiple RPA binding to long ssDNA.

Hierarchical graph transformer with contrastive learning for protein function prediction.

Motivation: In recent years, high-throughput sequencing technologies have made large-scale protein sequences accessible. However, their functional annotations usually rely on low-throughput and pricey experimental studies. Computational prediction models offer a promising alternative to accelerate this process.

CavityPlus 2022 Update: An Integrated Platform for Comprehensive Protein Cavity Detection and Property Analyses with User-friendly Tools and Cavity Databases.

Ligand binding sites provide essential information for uncovering protein functions and structure-based drug discovery. To facilitate cavity detection and property analysis process, we developed a comprehensive web server, CavityPlus in 2018. CavityPlus applies the CAVITY program to detect potential binding sites in a given protein structure.

Small molecules in regulating protein phase separation.

Biomolecular condensates formed by phase separation are involved in many cellular processes. Dysfunctional or abnormal condensates are closely associated with neurodegenerative diseases, cancer and other diseases. Small molecules can effectively regulate protein phase separation by modulating the formation, dissociation, size and material properties of condensates.

Structure of the Spring Viraemia of Carp Virus Ribonucleoprotein Complex Reveals Its Assembly Mechanism and Application in Antiviral Drug Screening.

Spring viremia of carp virus (SVCV) is a highly pathogenic infecting the common carp, yet neither a vaccine nor effective therapies are available to treat spring viremia of carp (SVC). Like all negative-sense viruses, SVCV contains an RNA genome that is encapsidated by the nucleoprotein (N) in the form of a ribonucleoprotein (RNP) complex, which serves as the template for viral replication and transcription. Here, the three-dimensional (3D) structure of SVCV RNP was resolved through cryo-electron microscopy (cryo-EM) at a resolution of 3.

How protein topology controls allosteric regulations.

Allostery is an important regulatory mechanism of protein functions. Among allosteric proteins, certain protein structure types are more observed. However, how allosteric regulation depends on protein topology remains elusive.

Coevolution-based prediction of key allosteric residues for protein function regulation.

Allostery is fundamental to many biological processes. Due to the distant regulation nature, how allosteric mutations, modifications, and effector binding impact protein function is difficult to forecast. In protein engineering, remote mutations cannot be rationally designed without large-scale experimental screening.

miDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies.

The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural networks, we developed three subdivisional models to individually predict the capacity of a compound to enter in vivo testing, clinical trials, and market approval stages.

QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets.

The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target protein-identification algorithms are limited by the insufficient number of proteins with experimentally resolved structures. Moreover, sequence-based algorithms cannot effectively extract information from protein sequences and thus display insufficient accuracy.