3DLigandSite: structure-based prediction of protein–ligand binding sites

Abstract

3DLigandSite is a web tool for the prediction of ligand-binding sites in proteins. Here, we report a significant update since the first release of 3DLigandSite in 2010. The overall methodology remains the same, with candidate binding sites in proteins inferred using known binding sites in related protein structures as templates. However, the initial structural modelling step now uses the newly available structures from the AlphaFold database or alternatively Phyre2 when AlphaFold structures are not available. Further, a sequence-based search using HHSearch has been introduced to identify template structures with bound ligands that are used to infer the ligand-binding residues in the query protein. Finally, we introduced a machine learning element as the final prediction step, which improves the accuracy of predictions and provides a confidence score for each residue predicted to be part of a binding site. Validation of 3DLigandSite on a set of 6416 binding sites obtained 92% recall at 75% precision for non-metal binding sites and 52% recall at 75% precision for metal binding sites. 3DLigandSite is available at https://www.wass-michaelislab.org/3dligandsite. Users submit either a protein sequence or structure. Results are displayed in multiple formats including an interactive Mol* molecular visualization of the protein and the predicted binding sites.

AlphaFold, Artificial Intelligence (AI), and Allostery

“…It also points to research projects that should be reconsidered. The richness of high quality data that are being compiled in databases (e.g., refs and − ) is already strengthening studies that require protein structures, such as mapping binding sites and interactions in signaling pathways, and identification of hot spots, including latent and rare cancer driver mutations. − The most profound impact will likely be in accelerating and improving production of new medications (e.g., ref ), and in generating data that can be used toward this vital aim (e.g., refs , , , and − ). AI developments and applications may further help foretell whether the signal propagating downstream will be strong enough to reach its genomic target to activate (suppress) gene expression, and predict pathways. − Altogether, these powerful approaches and the databases that they create revamp and transform traditional and ongoing research involving the use of structures.…”

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

“…Furthermore, there are evolutionary methods that could be applied that use structure and/or sequence alignments to identify conserved regions that are likely to serve as binding pockets [115] . Examples of methods that incorporate evolutionary information include 3DLigandSite and FINDSITE [127] , [128] .…”

Estimating the Similarity between Protein Pockets

“…Although the available methods for binding site detection covers the different applications above, the majority relates to small molecule pocket identification as a testimony of efforts to structure-based drug design of small chemical entities in recent decades. The accessibility to binding site identification is possible via standalone tools [ 16 ], websites [ 41 ], or databases of precomputed sites [ 35 , 42 ].…”

“…Template or sequence-based methods such as ConSeq [ 43 ] identifies functionally important residues in protein sequences by searching for evolutionary relations with other proteins. Another approach is 3DLigandSite, which takes a protein sequence as input, although it relies on homology models or de novo structure predictions [ 41 ]. Structure-based pocket identification uses only the 3D coordinates of the structures as input and benefits from the augmentation of structural data [ 7 ].…”