SITEHOUND-web: a server for ligand binding site identification in protein structures

<div> A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding sites on the 3D structure of the protein. Although several methods for detecting binding sites have been developed beforehand, a majority of them surprisingly fail in the identification and ranking of binding sites accurately. The rapid adoption and success of deep learning algorithms in various sections of structural biology beckons the usage of such algorithms for accurate binding site detection. As a combination of geometry based software and deep learning, we report a novel framework, DeepPocket that utilises 3D convolutional neural networks for the rescoring of pockets identified by Fpocket and further segments these identified cavities on the protein surface. Apart from this, we also propose another dataset SC6K containing protein structures submitted in the Protein Data Bank (PDB) from January 2018 till February 2020 for ligand binding site (LBS) detection. DeepPocket's results on various binding site datasets and SC6K highlights its better performance over current state-of-the-art methods and good generalization ability over novel structures. </div><div><br></div>

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Highly accurate method for ligand-binding site prediction in unbound state (apo) protein structures

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.22067 ◽

2008 ◽

Vol 73 (2) ◽

pp. 468-479 ◽

Cited By ~ 31

Author(s):

Mizuki Morita ◽

Shugo Nakamura ◽

Kentaro Shimizu

Keyword(s):

Ligand Binding ◽

Binding Site ◽

Protein Structures ◽

Accurate Method ◽

Ligand Binding Site ◽

Site Prediction ◽

Binding Site Prediction ◽

Ligand Binding Site Prediction

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Dynamic protein structures. Effects of pH on conformer stabilities at the ligand-binding site of bovine heart myoglobin carbonyl.

Journal of Biological Chemistry ◽

10.1016/s0021-9258(18)33650-0 ◽

1982 ◽

Vol 257 (20) ◽

pp. 11893-11900 ◽

Cited By ~ 7

Author(s):

H Shimada ◽

W S Caughey

Keyword(s):

Ligand Binding ◽

Binding Site ◽

Protein Structures ◽

Ligand Binding Site ◽

Bovine Heart ◽

Effects Of Ph

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De novo protein fold families expand the designable ligand binding site space

10.1101/2021.01.13.426598 ◽

2021 ◽

Author(s):

Xingjie Pan ◽

Tanja Kortemme

Keyword(s):

Ligand Binding ◽

Binding Site ◽

Binding Sites ◽

Method Development ◽

De Novo ◽

Protein Structures ◽

Protein Fold ◽

Ligand Binding Site ◽

Protein Families ◽

Ligand Binding Sites

AbstractA major challenge in designing proteins de novo to bind user-defined ligands with high specificity and affinity is finding backbones structures that can accommodate a desired binding site geometry with high precision. Recent advances in methods to generate protein fold families de novo have expanded the space of accessible protein structures, but it is not clear to what extend de novo proteins with diverse geometries also expand the space of designable ligand binding functions. We constructed a library of 25,806 high-quality ligand binding sites and developed a fast protocol to place (“match”) these binding sites into both naturally occurring and de novo protein families with two fold topologies: Rossman and NTF2. 5,896 and 7,475 binding sites could be matched to the Rossmann and NTF2 fold families, respectively. De novo designed Rossman and NTF2 protein families can support 1,791 and 678 binding sites that cannot be matched to naturally existing structures with the same topologies, respectively. While the number of protein residues in ligand binding sites is the major determinant of matching success, ligand size and primary sequence separation of binding site residues also play important roles. The number of matched binding sites are power law functions of the number of members in a fold family. Our results suggest that de novo sampling of geometric variations on diverse fold topologies can significantly expand the space of designable ligand binding sites for a wealth of possible new protein functions.Author summaryDe novo design of proteins that can bind to novel and highly diverse user-defined small molecule ligands could have broad biomedical and synthetic biology applications. Because ligand binding site geometries need to be accommodated by protein backbone scaffolds at high accuracy, the diversity of scaffolds is a major limitation for designing new ligand binding functions. Advances in computational protein structure design methods have significantly increased the number of accessible stable scaffold structures. Understanding how many new ligand binding sites can be accommodated by the de novo scaffolds is important for designing novel ligand binding proteins. To answer this question, we constructed a large library of ligand binding sites from the Protein Data Bank (PDB). We tested the number of ligand binding sites that can be accommodated by de novo scaffolds and naturally existing scaffolds with same fold topologies. The results showed that de novo scaffolds significantly expanded the ligand binding space of their respective fold topologies. We also identified factors that affect difficulties of binding site accommodation, as well as the relationship between the number of scaffolds and the accessible ligand binding site space. We believe our findings will benefit future method development and applications of ligand binding protein design.

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