2016
DOI: 10.1002/btpr.2219
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Prediction of protein retention times in hydrophobic interaction chromatography by robust statistical characterization of their atomic‐level surface properties

Abstract: The correlation between the dimensionless retention times (DRT) of proteins in hydrophobic interaction chromatography (HIC) and their surface properties were investigated. A ternary atomic-level hydrophobicity scale was used to calculate the distribution of local average hydrophobicity across the proteins surfaces. These distributions were characterized by robust descriptive statistics to reduce their sensitivity to small changes in the three-dimensional structure. The applicability of these statistics for the… Show more

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Cited by 17 publications
(11 citation statements)
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“…All DLS studies were performed at 25°C on the undiluted samples in glass bottom 96-well plates using a DynaPro Plate Reader II (Wyatt, Santa Barbara, CA). Twenty (20) acquisitions (5 seconds each) per well were averaged and the hydrodynamic radius (Rh), % polydispersity, and % mass was modeled using Dynamics version 7.1.9.3 (Wyatt Technology) to assess aggregation.…”
Section: Assessment Of Aggregation and Self-interaction By Dynamic LImentioning
confidence: 99%
See 1 more Smart Citation
“…All DLS studies were performed at 25°C on the undiluted samples in glass bottom 96-well plates using a DynaPro Plate Reader II (Wyatt, Santa Barbara, CA). Twenty (20) acquisitions (5 seconds each) per well were averaged and the hydrodynamic radius (Rh), % polydispersity, and % mass was modeled using Dynamics version 7.1.9.3 (Wyatt Technology) to assess aggregation.…”
Section: Assessment Of Aggregation and Self-interaction By Dynamic LImentioning
confidence: 99%
“…Machine learning methods (e.g., random forest) to predict the hydrophobic chromatography (HIC) retention time of a given antibody sequence were applied to develop predictive models. 19,20 Progress is being made toward establishing correlations between biophysical assays and computationally predictive behavior for downstream and manufacturing endpoints using data gathered for a large number of antibody molecules. In 2017, Jain et al reported the production and characterization using a dozen biophysical property assays of a panel of 137 monoclonal antibodies currently in advanced clinical stages.…”
Section: Introductionmentioning
confidence: 99%
“…Reports indicate that mAbs with greater retention in HIC studies may also exhibit greater F o r P e e r R e v i e w context of sequence 8 and structure 25 indicate that low hydrophobicity correlates with reduced retention time. Recent work attempts to predict solvent accessible surface area (SASA), and associated non-polar area, from amino acid sequence, and finds reasonable performance of a binary classifier for lower and higher retention times in HIC 26 .…”
mentioning
confidence: 99%
“…Currently, there are also quite a few existing methods for predicting the hydrophobicity of proteins including mAbs [21][22][23]. These methods are mostly based on threedimensional structures of proteins.…”
Section: Introductionmentioning
confidence: 99%