Dataset Information

Structural protein-ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study.

ABSTRACT: Accurate and affordable assessment of ligand-protein affinity for structure-based virtual screening (SB-VS) is a standing challenge. Hence, empirical postdocking filters making use of various types of structure-activity information may prove useful. Here, we introduce one such filter based upon three-dimensional structural protein-ligand interaction fingerprints (SPLIF). SPLIF permits quantitative assessment of whether a docking pose interacts with the protein target similarly to a known ligand and rescues active compounds penalized by poor initial docking scores. An extensive benchmark study on 10 diverse data sets selected from the DUD-E database has been performed in order to evaluate the absolute and relative efficiency of this method. SPLIF demonstrated an overall better performance than relevant standard methods.

SUBMITTER: Da C

PROVIDER: S-EPMC4170813 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Structural protein-ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study.

Da C C Kireev D D

Journal of chemical information and modeling 20140820 9

Accurate and affordable assessment of ligand-protein affinity for structure-based virtual screening (SB-VS) is a standing challenge. Hence, empirical postdocking filters making use of various types of structure-activity information may prove useful. Here, we introduce one such filter based upon three-dimensional structural protein-ligand interaction fingerprints (SPLIF). SPLIF permits quantitative assessment of whether a docking pose interacts with the protein target similarly to a known ligand ...[more]

PMID: 25116840

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Project description:BackgroundStructure-based drug repositioning has emerged as a promising alternative to conventional drug development. Regardless of the many success stories reported over the past years and the novel breakthroughs on the AI-based system AlphaFold for structure prediction, the availability of structural data for protein-drug complexes remains very limited. Whereas the chemical libraries contain millions of drug compounds, the vast majority of them do not have structures to crystallized targets,and it is, therefore, impossible to characterize their binding to targets from a structural view. However, the concept of building blocks offers a novel perspective on the structural problem. A drug compound is considered a complex of small chemical blocks or fragments, which confer the relevant properties to the drug and have a high proportion of functional groups involved in protein binding. Based on this, we propose a novel approach to expand the scope of structure-based repositioning approaches by transferring the structural knowledge from a fragment to a compound level.ResultsWe fragmented over 100,000 compounds in the Protein Data Bank (PDB) and characterized the structural binding mode of 153,000 fragments to their crystallized targets. Using the fragment's data, we were able to artificially reconstruct the binding mode of over 7,800 complexes between ChEMBL compounds and their known targets, for which no structural data is available. We proved that the conserved binding tendency of fragments, when binding to the same targets, highly influences the drug's binding specificity and carries the key information to reconstruct full drugs binding mode. Furthermore, our approach was able to reconstruct multiple compound-target pairs at optimal thresholds and high similarity to the actual binding mode.ConclusionsSuch reconstructions are of great value and benefit structure-based drug repositioning since they automatically enlarge the technique's scope and allow exploring the so far 'unexplored compounds' from a structural perspective. In general, the transfer of structural information is a promising technique that could be applied to any chemical library, to any compound that has no crystal structure available in PDB, and even to transfer any other feature that may be relevant for the drug discovery process and that due to data limitations is not yet fully available. In that sense, the results of this work document the full potential of structure-based screening even beyond PDB.

Dataset Information

Structural protein-ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study.

Publications

Structural protein-ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study.

Similar Datasets

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