Structure-based drug design is an integral part of modern day drug discovery and requires detailed structural characterization of protein-ligand interactions, which is most commonly performed by X-ray crystallography. However, the success rate of generating these costructures is often variable, in particular when working with dynamic proteins or weakly binding ligands. As a result, structural information is not routinely obtained in these scenarios, and ligand optimization is challenging or not pursued at all, representing a substantial limitation in chemical scaffolds and diversity. To overcome this impediment, we have developed a robust NMR restraint guided docking protocol to generate high-quality models of protein-ligand complexes. By combining the use of highly methyl-labeled protein with experimentally determined intermolecular distances, a comprehensive set of protein-ligand distances is generated which then drives the docking process and enables the determination of the correct ligand conformation in the bound state. For the first time, the utility and performance of such a method is fully demonstrated by employing the generated models for the successful, prospective optimization of crystallographically intractable fragment hits into more potent binders.
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http://dx.doi.org/10.1021/jacs.7b07171 | DOI Listing |
Proteins
October 2024
Department of Bioinformatics and Computational Biology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task.
View Article and Find Full Text PDFJ Biomol Struct Dyn
February 2024
Department of Molecular Biology and Genetics, Gebze Technical University, Gebze, Kocaeli, Turkey.
Ataxia represents a heterogeneous group of neurodegenerative disorders characterized by a loss of balance and coordination, often resulting from mutations in genes vital for cerebellar function and maintenance. Recent advances in genomics have identified gene fusion events as critical contributors to various cancers and neurodegenerative diseases. However, their role in ataxia pathogenesis remains largely unexplored.
View Article and Find Full Text PDFbioRxiv
December 2023
Department of Bioinformatics and Computational Biology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task.
View Article and Find Full Text PDFNat Commun
March 2023
Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA.
Advances in cryo-electron microscopy (cryoEM) and deep-learning guided protein structure prediction have expedited structural studies of protein complexes. However, methods for accurately determining ligand conformations are lacking. In this manuscript, we develop EMERALD, a tool for automatically determining ligand structures guided by medium-resolution cryoEM density.
View Article and Find Full Text PDFNucleic Acids Res
January 2023
Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea.
Atomic-level knowledge of protein-ligand interactions allows a detailed understanding of protein functions and provides critical clues to discovering molecules regulating the functions. While recent innovative deep learning methods for protein structure prediction dramatically increased the structural coverage of the human proteome, molecular interactions remain largely unknown. A new database, HProteome-BSite, provides predictions of binding sites and ligands in the enlarged 3D human proteome.
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