The study of enzyme substrate specificity is vital for developing potential applications of enzymes. However, the routine experimental procedures require lot of resources in the discovery of novel substrates. This article reports an in silico structure-based algorithm called Crius, which predicts substrates for enzyme. The results of this fragment-based algorithm show good agreements between the simulated and experimental substrate specificities, using a lipase from Candida antarctica (CALB), a nitrilase from Cyanobacterium syechocystis sp. PCC6803 (Nit6803), and an aldo-keto reductase from Gluconobacter oxydans (Gox0644). This opens new prospects of developing computer algorithms that can effectively predict substrates for an enzyme.
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http://dx.doi.org/10.1002/pro.3437 | DOI Listing |
J Cheminform
January 2025
Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK.
Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that 'stitches' the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein-ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode.
View Article and Find Full Text PDFiScience
January 2025
Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
Drugs that interact with multiple therapeutic targets are potential high-value products in polypharmacology-based drug discovery, but the rational design remains a formidable challenge. Here, we present artificial intelligence (AI)-based methods to design the chemical structures of compounds that interact with multiple therapeutic target proteins. The molecular structure generation is performed by a fragment-based approach using a genetic algorithm with chemical substructures and a deep learning approach using reinforcement learning with stochastic policy gradients in the framework of generative adversarial networks.
View Article and Find Full Text PDFChem Sci
January 2025
College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
[This corrects the article DOI: 10.1039/D4SC04620J.].
View Article and Find Full Text PDFJ Am Chem Soc
December 2024
Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China.
A complex chemical system is often examined based on their fragments, so fragment-based analysis is the key to chemical understanding. We report the natural fragment bond orbital (NFBO) method for interfragment bonding interaction analysis, as an extension to the well-known natural bond orbital method. NFBOs together with their corresponding natural fragment hybrid orbitals (NFHOs) allow us to derive local bonding and antibonding orbitals among fragments from the delocalized canonical molecular orbitals.
View Article and Find Full Text PDFJ Cheminform
December 2024
Department of Chemistry, Seoul National University, Seoul, 08826, Republic of Korea.
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.
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