Publication:
Fine tuning rigid body docking results using the Dreiding force field: A computational study of 36 known nanobody-protein complexes

dc.contributor.coauthorHacisuleyman, Aysima
dc.contributor.departmentDepartment of Chemical and Biological Engineering
dc.contributor.kuauthorErman, Burak
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.date.accessioned2025-01-19T10:30:44Z
dc.date.issued2023
dc.description.abstractThis paper aims to understand the binding strategies of a nanobody-protein pair by studying known complexes. Rigid body protein-ligand docking programs produce several complexes, called decoys, which are good candidates with high scores of shape complementarity, electrostatic interactions, desolvation, buried surface area, and Lennard-Jones potentials. However, the decoy that corresponds to the native structure is not known. We studied 36 nanobody-protein complexes from the single domain antibody database, sd-Ab DB, . For each structure, a large number of decoys are generated using the Fast Fourier Transform algorithm of the software ZDOCK. The decoys were ranked according to their target protein-nanobody interaction energies, calculated by using the Dreiding Force Field, with rank 1 having the lowest interaction energy. Out of 36 protein data bank (PDB) structures, 25 true structures were predicted as rank 1. Eleven of the remaining structures required angstrom ngstrom size rigid body translations of the nanobody relative to the protein to match the given PDB structure. After the translation, the Dreiding interaction (DI) energies of all complexes decreased and became rank 1. In one case, rigid body rotations as well as translations of the nanobody were required for matching the crystal structure. We used a Monte Carlo algorithm that randomly translates and rotates the nanobody of a decoy and calculates the DI energy. Results show that rigid body translations and the DI energy are sufficient for determining the correct binding location and pose of ZDOCK created decoys. A survey of the sd-Ab DB showed that each nanobody makes at least one salt bridge with its partner protein, indicating that salt bridge formation is an essential strategy in nanobody-protein recognition. Based on the analysis of the 36 crystal structures and evidence from existing literature, we propose a set of principles that could be used in the design of nanobodies.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.issue10
dc.description.openaccesshybrid, Green Submitted
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.volume91
dc.identifier.doi10.1002/prot.26529
dc.identifier.eissn1097-0134
dc.identifier.issn0887-3585
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-85160631518
dc.identifier.urihttps://doi.org/10.1002/prot.26529
dc.identifier.urihttps://hdl.handle.net/20.500.14288/26106
dc.identifier.wos994924200001
dc.keywordsCase-study
dc.keywordsDreiding energy
dc.keywordsNanobody
dc.keywordsSalt-bridge
dc.keywordsZDOCK
dc.language.isoeng
dc.publisherWiley
dc.relation.ispartofProteins-Structure Function and Bioinformatics
dc.subjectBiochemistry and molecular biology
dc.subjectBiophysics
dc.titleFine tuning rigid body docking results using the Dreiding force field: A computational study of 36 known nanobody-protein complexes
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorErman, Burak
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Chemical and Biological Engineering
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relation.isParentOrgUnitOfPublication8e756b23-2d4a-4ce8-b1b3-62c794a8c164
relation.isParentOrgUnitOfPublication.latestForDiscovery8e756b23-2d4a-4ce8-b1b3-62c794a8c164

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