ArticleJournal of chemical information and modeling2015
AlphaSpace: Fragment-Centric Topographical Mapping To Target Protein-Protein Interaction Interfaces.
Article in Journal of chemical information and modeling, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
40 citing papers in PubMed, 65 citations in OpenAlex.
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- From Concepts to Inhibitors: A Blueprint for Targeting Protein-Protein Interactions.Chemical reviews · 2025Review
- Mapping the Binding Hotspots and Transient Binding Pockets on V-Domain Immunoglobulin Suppressor of T Cell Activation Protein Surface.ACS omega · 2024Article
- Diffusion Models in De Novo Drug Design.Journal of chemical information and modeling · 2024Review
- A Repurposed Drug Interferes with Nucleic Acid to Inhibit the Dual Activities of Coronavirus Nsp13.ACS chemical biology · 2024Article
- Current Computational Methods for Protein-peptide Complex Structure Prediction.Current medicinal chemistry · 2024Review
- Integrated Molecular Modeling and Machine Learning for Drug Design.Journal of chemical theory and computation · 2023Review
- Upregulation of WDR6 drives hepatic de novo lipogenesis in insulin resistance in mice.Nature metabolism · 2023Article
- DataDTA: a multi-feature and dual-interaction aggregation framework for drug-target binding affinity prediction.Bioinformatics (Oxford, England) · 2023Article
- Protein-Ligand Docking in the Machine-Learning Era.Molecules (Basel, Switzerland) · 2022Review
- Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.Journal of chemical information and modeling · 2022Review
- Peptide Tethering: Pocket-Directed Fragment Screening for Peptidomimetic Inhibitor Discovery.Journal of the American Chemical Society · 2022Article
- A 3D structural SARS-CoV-2-human interactome to explore genetic and drug perturbations.Nature methods · 2021Article
- Co-Evolution of Opioid and Adrenergic Ligands and Receptors: Shared, Complementary Modules Explain Evolution of Functional Interactions and Suggest Novel Engineering Possibilities.Life (Basel, Switzerland) · 2021Article
- Impact of DNA sequences on DNA 'opening' by the Rad4/XPC nucleotide excision repair complex.DNA repair · 2021Article
- Identification of Secondary Binding Sites on Protein Surfaces for Rational Elaboration of Synthetic Protein Mimics.ACS chemical biology · 2021Article
- Recent advances in user-friendly computational tools to engineer protein function.Briefings in bioinformatics · 2021Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Inhibition of protein-protein interactions (PPIs) is emerging as a promising therapeutic strategy despite the difficulty in targeting such interfaces with drug-like small molecules. PPIs generally feature large and flat binding surfaces as compared to typical drug targets. These features pose a challenge for structural characterization of the surface using geometry-based pocket-detection methods. An attractive mapping strategy--that builds on the principles of fragment-based drug discovery (FBDD)--is to detect the fragment-centric modularity at the protein surface and then characterize the large PPI interface as a set of localized, fragment-targetable interaction regions. Here, we introduce AlphaSpace, a computational analysis tool designed for fragment-centric topographical mapping (FCTM) of PPI interfaces. Our approach uses the alpha sphere construct, a geometric feature of a protein's Voronoi diagram, to map out concave interaction space at the protein surface. We introduce two new features--alpha-atom and alpha-space--and the concept of the alpha-atom/alpha-space pair to rank pockets for fragment-targetability and to facilitate the evaluation of pocket/fragment complementarity. The resulting high-resolution interfacial map of targetable pocket space can be used to guide the rational design and optimization of small molecule or biomimetic PPI inhibitors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.