ProBiS-Fold Approach for Annotation of Human Structures from the AlphaFold Database with No Corresponding Structure in the PDB to Discover New Druggable Binding Sites

Konc, Janez; Janežič, Dušanka

doi:10.1021/acs.jcim.2c00947

Cited by 10 publications

(7 citation statements)

References 35 publications

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“…Both the web server and the ProBiS algorithm have received numerous updates through the years, ,− including a database of precalculated binding site similarities and local alignments of PDB structures . Recently, the ProBiS-Fold approach has been developed, it can annotate structures from the AlphaFold database lacking corresponding structures in the PDB, with the goal of discovering new druggable sites …”

Section: Methodsmentioning

confidence: 99%

“…58 Recently, the ProBiS-Fold approach has been developed, it can annotate structures from the AlphaFold database lacking corresponding structures in the PDB, with the goal of discovering new druggable sites. 59 Clustering. After protein superposition, the MADE approach applies a clustering algorithm to detect dense areas where a particular species is present in many of the homologous protein chains.…”

Section: ■ Methodsmentioning

confidence: 99%

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Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach

Ravnik,

Jukič,

Bren

2023

J. Chem. Inf. Model.

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In order to identify the locations of metal ions in the binding sites of proteins, we have developed a method named the MADE (MAcromolecular DEnsity and Structure Analysis) approach. The MADE approach represents an evolution of our previous toolset, the ProBiS H2O (MD) methodology, for the identification of conserved water molecules. Our method uses experimental structures of proteins homologous to a query, which are subsequently superimposed upon it. Areas with a particular species present in a similar location among many homologous protein structures are identified using a clustering algorithm. Dense clusters likely represent positions containing species important to the query protein structure or function. We analyze well-characterized apo protein structures and show that the MADE approach can identify clusters corresponding to the expected positions of metal ions in their binding sites. The greatest advantage of our method lies in its generality. It can in principle be applied to any species found in protein records; it is not only limited to metal ions. We additionally demonstrate that the MADE approach can be successfully applied to predict the location of cofactors in computer-modeled structures, e.g., via AlphaFold. We also conduct a careful protein superposition method comparison and find our methodology robust and the results largely independent of the selected protein superposition algorithm. We postulate that with increasing structural data availability, additional applications of the MADE approach will be possible such as non-protein systems, water network identification, protein binding site elaboration, and analysis of binding events, all in a dynamic manner. We have implemented the MADE approach as a plugin for the PyMOL molecular visualization tool. The MADE plugin is available free of charge at .

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Section: Methodsmentioning

confidence: 99%

Section: ■ Methodsmentioning

confidence: 99%

Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach

Ravnik,

Jukič,

Bren

2023

J. Chem. Inf. Model.

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“…This web server and database ( http://probis-fold.insilab.org ) enables annotation of human structures from the AlphaFold database (Varadi et al 2022 ) with no corresponding structure in the PDB to discover new druggable binding sites (Konc and Janežič 2022 ). It contains predictions of binding sites and their corresponding ligands from the whole human structural proteome (Fig.…”

Section: Probis Tools Developmentmentioning

confidence: 99%

Protein binding sites for drug design

Konc

Janežič

2022

Biophys Rev

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Drug development is a lengthy and challenging process that can be accelerated at early stages by new mathematical approaches and modern computers. To address this important issue, we are developing new mathematical solutions for the detection and characterization of protein binding sites that are important for new drug development. In this review, we present algorithms based on graph theory combined with molecular dynamics simulations that we have developed for studying biological target proteins to provide important data for optimizing the early stages of new drug development. A particular focus is the development of new protein binding site prediction algorithms (ProBiS) and new web tools for modeling pharmaceutically interesting molecules—ProBiS Tools (algorithm, database, web server), which have evolved into a full-fledged graphical tool for studying proteins in the proteome. ProBiS differs from other structural algorithms in that it can align proteins with different folds without prior knowledge of the binding sites. It allows detection of similar binding sites and can predict molecular ligands of various types of pharmaceutical interest that could be advanced to drugs to treat a disease, based on the entire Protein Data Bank (PDB) and AlphaFold database, including proteins not yet in the PDB. All ProBiS Tools are freely available to the academic community at http://insilab.org and https://probis.nih.gov .

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“…A recent analog to this work is the ProBis-Score scoring function’s identification of template ligands for binding pockets. ProBis-Dock – identifies template ligands using local surface similarities from other binding sites in PDB. Similarly, in this work, we gather PDB ligand IDs from structurally matched templates in PDB.…”

Section: Introductionmentioning

confidence: 99%

Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method

Stevenson,

Kirshner,

Bennion

et al. 2023

J. Chem. Inf. Model.

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Protein−ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein−ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.

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ProBiS-Fold Approach for Annotation of Human Structures from the AlphaFold Database with No Corresponding Structure in the PDB to Discover New Druggable Binding Sites

Cited by 10 publications

References 35 publications

Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach

Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach

Protein binding sites for drug design

Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method

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