Assessment of fully automated antibody homology modeling protocols in molecular operating environment

Maier, Johannes; Labute, Paul

doi:10.1002/prot.24576

Cited by 80 publications

(70 citation statements)

References 23 publications

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“…28 Using the method described by Maier and Labute, 29 a homology search was performed and 2R8S was used as the framework template for both chains. For the CDRs, 4KZE, 4XVU, 4XVU were used as templates for light chain CDR1, 2, and 3, respectively, while 3PNW, 3S34, and 3AUV were used for the heavy chain CDR 1, 2, and 3, respectively.…”

Section: Methodsmentioning

confidence: 99%

Homology modeling and structure-based design improve hydrophobic interaction chromatography behavior of integrin binding antibodies

Jetha

Thorsteinson²,

Jmeian

et al. 2018

mAbs

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Monoclonal antibody (mAb) candidates from high-throughput screening or binding affinity optimization often contain mutations leading to liabilities for further development of the antibody, such as aggregation-prone regions and lack of solubility. In this work, we optimized a candidate integrin α11-binding mAb for developability using molecular modeling, rational design, and hydrophobic interaction chromatography (HIC). A homology model of the parental mAb Fv region was built, and this revealed hydrophobic patches on the surface of the complementarity-determining region loops. A series of 97 variants of the residues primarily responsible for the hydrophobic patches were expressed and their HIC retention times (RT) were measured. As intended, many of the computationally designed variants reduced the HIC RT compared to the parental mAb, and mutating residues that contributed most to hydrophobic patches had the greatest effect on HIC RT. A retrospective analysis was then performed where 3-dimentional protein property descriptors were evaluated for their ability to predict HIC RT using the current series of mAbs. The same descriptors were used to train a simple multi-parameter protein quantitative structure-property relationship model on this data, producing an improved correlation. We also extended this analysis to recently published HIC data for 137 clinical mAb candidates as well as 31 adnectin variants, and found that the surface area of hydrophobic patches averaged over a molecular dynamics sample consistently correlated to the experimental data across a diverse set of biotherapeutics.

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Section: Methodsmentioning

confidence: 99%

Homology modeling and structure-based design improve hydrophobic interaction chromatography behavior of integrin binding antibodies

Jetha

Thorsteinson²,

Jmeian

et al. 2018

mAbs

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show abstract

“…The tertiary structures of the Fv regions in the mAbs panel were modeled using the automated AutoFv/CCG3 protocol 50 in Molecular Operating Environment (MOE) obtained from the Chemical Computing Group as described previously. 50 The 3D homology models of the Fv regions were used to extract several features for machine learning, including the SASA at the residue level.…”

Section: Methodsmentioning

confidence: 99%

“…50 The 3D homology models of the Fv regions were used to extract several features for machine learning, including the SASA at the residue level. SASA values were calculated empirically using the POPS software.…”

Section: Methodsmentioning

confidence: 99%

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

Sankar¹,

Hoi²,

Yuan

et al. 2018

mAbs

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Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the manufacturing process, long-term storage, and in vivo circulation that can impair their potency. One such modification is the oxidation of methionine residues. Chemical modifications that occur in the complementarity-determining regions (CDRs) of mAbs can lead to the abrogation of antigen binding and reduce the drug’s potency and efficacy. Thus, it is highly desirable to identify and eliminate any chemically unstable residues in the CDRs during the therapeutic antibody discovery process. To provide increased throughput over experimental methods, we extracted features from the mAbs’ sequences, structures, and dynamics, used random forests to identify important features and develop a quantitative and highly predictive in silico methionine oxidation model.

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“…The articles included in the analysis relied on diverse sets of software tools and web servers. Several software tools, servers, and frameworks were explicitly applied (Maier and Labute, 2014;Webb and Sali, 2014). The most commonly used software were MODELLER and GROMACS.…”

Section: Study Characteristicsmentioning

confidence: 99%

A systematic review: Application of in silico models for antimalarial drug discovery

Cheoymang¹,

Na‐Bangchang²

2018

Afr. J. Pharm. Pharmacol.

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Malaria remains the global public health problem due to the reemergence of drug resistance. There is an urgent need for development of new antimalarial candidates which are effective against resistant malaria parasite. This systematic review evaluates the published research studies that applied in silico modeling during the discovery process of antimalarial drugs. Literature searches were conducted using PubMed, EBSCO, EMBASE, and Web of Science to identify the relevant articles using the search terms "Malaria" "In silico model", "Computer-based drug design", "Antimalarial drug", and "Drug discovery". Only the articles published in English between 2008 and May 2015 were included in the analysis. A total of 17 relevant articles met the search criteria. Most articles are studies specific to Plasmodium falciparum targets; 3 and 1 articles, respectively involve target for P. vivax and liver stage of Plasmodium. Both structure-based and ligand-based approaches were applied to obtain lead antimalarial candidates. Two articles also assessed absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Confirmation of activity of the candidate leads by in vitro and/or in vivo assays were reported in some studies. Homology modelling, molecular docking, 2D-or 3D-QSAR and pharmacophore modeling are commonly applied methods. One study used de novo synthesis for lead identification and one study applied phylogenetic analysis for target identification/validation.

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Assessment of fully automated antibody homology modeling protocols in molecular operating environment

Cited by 80 publications

References 23 publications

Homology modeling and structure-based design improve hydrophobic interaction chromatography behavior of integrin binding antibodies

Homology modeling and structure-based design improve hydrophobic interaction chromatography behavior of integrin binding antibodies

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

A systematic review: Application of in silico models for antimalarial drug discovery

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