Exploration of interaction scoring criteria in the CANDO platform

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Abstract

Objective

Ascertain the optimal interaction scoring criteria for the Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing to improve benchmarking performance, thereby enabling more accurate prediction of novel therapeutic drug-indication pairs.

Results

We have investigated and enhanced the interaction scoring criteria in the bioinformatic docking protocol in the newest version of our platform (v1.5), with the best performing interaction scoring criterion yielding increased benchmarking accuracies from 11.7% in v1 to 12.8% in v1.5 at the top10 cutoff (the most stringent one) and correspondingly from 24.9 to 31.2% at the top100 cutoff.

Electronic supplementary material

The online version of this article (10.1186/s13104-019-4356-3) contains supplementary material, which is available to authorized users.

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Most cited references 17

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COFACTOR: an accurate comparative algorithm for structure-based protein function annotation

Ambrish Roy, Jianyi Yang, Yang Zhang (2012)

We have developed a new COFACTOR webserver for automated structure-based protein function annotation. Starting from a structural model, given by either experimental determination or computational modeling, COFACTOR first identifies template proteins of similar folds and functional sites by threading the target structure through three representative template libraries that have known protein–ligand binding interactions, Enzyme Commission number or Gene Ontology terms. The biological function insights in these three aspects are then deduced from the functional templates, the confidence of which is evaluated by a scoring function that combines both global and local structural similarities. The algorithm has been extensively benchmarked by large-scale benchmarking tests and demonstrated significant advantages compared to traditional sequence-based methods. In the recent community-wide CASP9 experiment, COFACTOR was ranked as the best method for protein–ligand binding site predictions. The COFACTOR sever and the template libraries are freely available at http://zhanglab.ccmb.med.umich.edu/COFACTOR.

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COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information

Chengxin Zhang, Peter Freddolino, Yang Zhang (2017)

Abstract The COFACTOR web server is a unified platform for structure-based multiple-level protein function predictions. By structurally threading low-resolution structural models through the BioLiP library, the COFACTOR server infers three categories of protein functions including gene ontology, enzyme commission and ligand-binding sites from various analogous and homologous function templates. Here, we report recent improvements of the COFACTOR server in the development of new pipelines to infer functional insights from sequence profile alignments and protein–protein interaction networks. Large-scale benchmark tests show that the new hybrid COFACTOR approach significantly improves the function annotation accuracy of the former structure-based pipeline and other state-of-the-art functional annotation methods, particularly for targets that have no close homology templates. The updated COFACTOR server and the template libraries are available at http://zhanglab.ccmb.med.umich.edu/COFACTOR/.

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Can we rationally design promiscuous drugs?

Andrew L Hopkins, Annaliese S. Mason, Donald L. Hopkins (2006)

Structure-based drug design is now used widely in modern medicinal chemistry. The application of structural biology to medicinal chemistry has heralded the "rational drug design" vision of discovering exquisitely selective ligands. However, recent advances in post-genomic biology are indicating that polypharmacology may be a necessary trait for the efficacy of many drugs, therefore questioning the "one drug, one target" assumption of current rational drug design. By combining advances in chemoinformatics and structural biology, it might be possible to rationally design the next generation of promiscuous drugs with polypharmacology.

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Author and article information

Contributors

Ram Samudrala: ram@compbio.org

Journal

Journal ID (nlm-ta): BMC Res Notes

Journal ID (iso-abbrev): BMC Res Notes

Title: BMC Research Notes

Publisher: BioMed Central (London )

ISSN (Electronic): 1756-0500

Publication date (Electronic): 7 June 2019

Publication date PMC-release: 7 June 2019

Publication date Collection: 2019

Volume: 12

Electronic Location Identifier: 318

Affiliations

ISNI 0000 0004 1936 9887, GRID grid.273335.3, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, , University at Buffalo, ; 77 Goodell St., Suite 540, Buffalo, NY 14203 USA

Author information

Zackary Falls http://orcid.org/0000-0003-4116-0441

Article

Publisher ID: 4356

DOI: 10.1186/s13104-019-4356-3

PMC ID: 6555930

PubMed ID: 31174591

SO-VID: 61c48191-20cd-4b85-afc0-4ac2cd0da7f6

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 22 April 2019

Date accepted : 31 May 2019

Funding

Funded by: National Institute of Health Director’s Pioneer Award

Award ID: DP1OD006779

Award Recipient : Ram Samudrala

Funded by: National Institute of Health Clinical and Translational Sciences Award

Award ID: UL1TR001412

Award Recipient : Ram Samudrala

Funded by: National Library of Medicine T15 Award

Award ID: T15LM012495

Award Recipient : Zackary Falls

Funded by: National Cancer Institute/Veterans Affairs Big Data-Scientist Training Enhancement Program Fellowship in Big Data Sciences

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: drug repurposing,drug-protein interaction,binding site similarity,protein structure docking,molecular fingerprinting

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ScienceOpen disciplines: Medicine

Keywords: drug repurposing, drug-protein interaction, binding site similarity, protein structure docking, molecular fingerprinting

Exploration of interaction scoring criteria in the CANDO platform

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Abstract

Objective

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Electronic supplementary material

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Drug Repurposing Research Collection

Most cited references 17

COFACTOR: an accurate comparative algorithm for structure-based protein function annotation

COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information

Can we rationally design promiscuous drugs?

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