Discovery of highly active kynureninases for cancer immunotherapy through protein language model.

Tailor-made enzymes empower a wide range of versatile applications, although searching for the desirable enzymes often requires high throughput screening and thus poses significant challenges. In this study, we employed homology searches and protein language models to discover and prioritize enzymes by their kinetic parameters. We aimed to discover kynureninases as a potentially versatile therapeutic enzyme, which hydrolyses L-kynurenine, a potent immunosuppressive metabolite, to overcome the immunosuppressive tumor microenvironment in anticancer therapy.

CSearch: chemical space search via virtual synthesis and global optimization.

The two key components of computational molecular design are virtually generating molecules and predicting the properties of these generated molecules. This study focuses on an effective method for molecular generation through virtual synthesis and global optimization of a given objective function. Using a pre-trained graph neural network (GNN) objective function to approximate the docking energies of compounds for four target receptors, we generated highly optimized compounds with 300-400 times less computational effort compared to virtual compound library screening.

GalaxySagittarius-AF: Predicting Targets for Drug-Like Compounds in the Extended Human 3D Proteome.

In recent years, advancements in deep learning techniques have significantly expanded the structural coverage of the human proteome. GalaxySagittarius-AF translates these achievements in structure prediction into target prediction for druglike compounds by incorporating predicted structures. This web server searches the database of human protein structures using both similarity- and structure-based approaches, suggesting potential targets for a given druglike compound.

GalaxyDock-DL: Protein-Ligand Docking by Global Optimization and Neural Network Energy.

With the recent introduction of deep learning techniques into the prediction of biomolecular structures, structure prediction performance has significantly improved, and the potential for biomedical applications has increased considerably. The prediction of protein-ligand complex structures, applicable to the atomistic understanding of biomolecular functions and the effective design of drug molecules, has also improved with the introduction of deep learning. In this paper, it is demonstrated that docking performance can be greatly enhanced by training an energy function that encapsulates physical effects using deep learning within the framework of the traditional protein-ligand docking method.

Ebastine impairs metastatic spread in triple-negative breast cancer by targeting focal adhesion kinase.

We sought to investigate the utility of ebastine (EBA), a second-generation antihistamine with potent anti-metastatic properties, in the context of breast cancer stem cell (BCSC)-suppression in triple-negative breast cancer (TNBC). EBA binds to the tyrosine kinase domain of focal adhesion kinase (FAK), blocking phosphorylation at the Y397 and Y576/577 residues. FAK-mediated JAK2/STAT3 and MEK/ERK signaling was attenuated after EBA challenge in vitro and in vivo.

GalaxyDock2-HEME: Protein-ligand docking for heme proteins.

Prediction of protein-ligand binding poses is an essential component for understanding protein-ligand interactions and computer-aided drug design. Various proteins involve prosthetic groups such as heme for their functions, and adequate consideration of the prosthetic groups is vital for protein-ligand docking. Here, we extend the GalaxyDock2 protein-ligand docking algorithm to handle ligand docking to heme proteins.

GalaxyDomDock: An Ab Initio Domain-domain Docking Web Server for Multi-domain Protein Structure Prediction.

A significant proportion of proteins comprise multiple domains. Domain-domain docking is a tool that predicts multi-domain protein structures when individual domain structures can be accurately predicted but when domain orientations cannot be predicted accurately. GalaxyDomDock predicts an ensemble of domain orientations from given domain structures by docking.

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment.

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers).

Protein oligomer structure prediction using GALAXY in CASP14.

Proteins perform their functions by interacting with other biomolecules. For these interactions, proteins often form homo- or hetero-oligomers as well. Thus, oligomer protein structures provide important clues regarding the biological roles of proteins.

Symmetry-related residues as promising hotspots for the evolution of oligomeric enzymes.

Directed evolution has provided us with great opportunities and prospects in the synthesis of tailor-made proteins. It, however, often requires at least mid to high throughput screening, necessitating more effective strategies for laboratory evolution. We herein demonstrate that protein symmetry can be a versatile criterion for searching for promising hotspots for the directed evolution of oligomeric enzymes.

Aldehyde-alcohol dehydrogenase undergoes structural transition to form extended spirosomes for substrate channeling.

Aldehyde-alcohol dehydrogenase (AdhE) is an enzyme responsible for converting acetyl-CoA to ethanol via acetaldehyde using NADH. AdhE is composed of two catalytic domains of aldehyde dehydrogenase (ALDH) and alcohol dehydrogenase (ADH), and forms a spirosome architecture critical for AdhE activity. Here, we present the atomic resolution (3.

GalaxySagittarius: Structure- and Similarity-Based Prediction of Protein Targets for Druglike Compounds.

Computational techniques for predicting interactions of proteins and druglike molecules have often been used to search for compounds that bind a given protein with high affinity. More recently, such tools have also been applied to the reverse procedure of searching protein targets for a given compound. Among methods for predicting protein-ligand interactions, ligand-based methods relying on similarity to ligands of known interactions are effective only when similar protein-ligand interactions are known.

Structure prediction of biological assemblies using GALAXY in CAPRI rounds 38-45.

We participated in CARPI rounds 38-45 both as a server predictor and a human predictor. These CAPRI rounds provided excellent opportunities for testing prediction methods for three classes of protein interactions, that is, protein-protein, protein-peptide, and protein-oligosaccharide interactions. Both template-based methods (GalaxyTBM for monomer protein, GalaxyHomomer for homo-oligomer protein, GalaxyPepDock for protein-peptide complex) and ab initio docking methods (GalaxyTongDock and GalaxyPPDock for protein oligomer, GalaxyPepDock-ab-initio for protein-peptide complex, GalaxyDock2 and Galaxy7TM for protein-oligosaccharide complex) have been tested.

GalaxyDock3: Protein-ligand docking that considers the full ligand conformational flexibility.

Predicting conformational changes of both the protein and the ligand is a major challenge when a protein-ligand complex structure is predicted from the unbound protein and ligand structures. Herein, we introduce a new protein-ligand docking program called GalaxyDock3 that considers the full ligand conformational flexibility by explicitly sampling the ligand ring conformation and allowing the relaxation of the full ligand degrees of freedom, including bond angles and lengths. This method is based on the previous version (GalaxyDock2) which performs the global optimization of a designed score function.