Näytä suppeat kuvailutiedot

dc.contributor.authorKurkinen, Sami T.
dc.contributor.authorLätti, Sakari
dc.contributor.authorPentikäinen, Olli T.
dc.contributor.authorPostila, Pekka A.
dc.date.accessioned2019-09-04T10:22:09Z
dc.date.available2019-09-04T10:22:09Z
dc.date.issued2019
dc.identifier.citationKurkinen, S. T., Lätti, S., Pentikäinen, O. T., & Postila, P. A. (2019). Getting Docking into Shape Using Negative Image-Based Rescoring. <i>Journal of Chemical Information and Modeling</i>, <i>59</i>(8), 3584-3599. <a href="https://doi.org/10.1021/acs.jcim.9b00383" target="_blank">https://doi.org/10.1021/acs.jcim.9b00383</a>
dc.identifier.otherCONVID_32108459
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/65419
dc.description.abstractThe failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in the structure-based drug discovery. To remedy this problem, elaborate rescoring and post-processing schemes have been developed with a varying degree of success, specificity, and cost. The negative imagebased rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets.The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein’s ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is directly used in the similarity comparison to rank the explicit docking poses. Here, the PANTHER/ShaEP-based R-NiB methodology is tested with six popular docking software, including GLIDE, PLANTS, GOLD, DOCK, AUTODOCK, and AUTODOCK VINA, using five validated benchmark sets. Overall, the results indicate that the R-NiB outperforms the default docking scoring consistently and inexpensively; i.e., demonstrating that the methodology is ready for wide-scale virtual screening usage.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherAmerican Chemical Society
dc.relation.ispartofseriesJournal of Chemical Information and Modeling
dc.rightsCC BY 4.0
dc.subject.otherdrugs
dc.subject.othermolecular docking
dc.subject.otherPANTHER/ShaEP-based R-NiB methodology
dc.titleGetting Docking into Shape Using Negative Image-Based Rescoring
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201909044022
dc.contributor.laitosBio- ja ympäristötieteiden laitosfi
dc.contributor.laitosDepartment of Biological and Environmental Scienceen
dc.contributor.oppiaineSolu- ja molekyylibiologiafi
dc.contributor.oppiaineCell and Molecular Biologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange3584-3599
dc.relation.issn1549-9596
dc.relation.numberinseries8
dc.relation.volume59
dc.type.versionpublishedVersion
dc.rights.copyright© 2019 American Chemical Society
dc.rights.accesslevelopenAccessfi
dc.format.contentfulltext
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1021/acs.jcim.9b00383
jyx.fundinginformationThe Turku University Foundation and Finnish Cultural Foundation are acknowledged for personal grant to STK. The Finnish IT Center for Science (CSC) is acknowledged for generous computational resources (OTP; Project Nos. jyy2516 and jyy2585). Visual Molecular Dynamics (VMD) was developed by the Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign.
dc.type.okmA1


Aineistoon kuuluvat tiedostot

Thumbnail

Aineisto kuuluu seuraaviin kokoelmiin

Näytä suppeat kuvailutiedot

CC BY 4.0
Ellei muuten mainita, aineiston lisenssi on CC BY 4.0