Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning | Zendy

Tushar Jain | Zendy; Todd Boland | Zendy; Asparouh Lilov | Zendy; Irina Burnina | Zendy; Michael E. Brown | Zendy; Yingda Xu | Zendy; Maximiliano Vásquez | Zendy

Open Access

Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning

Author(s) -

Tushar Jain,

Todd Boland,

Asparouh Lilov,

Irina Burnina,

Michael E. Brown,

Yingda Xu,

Maximiliano Vásquez

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx519

Subject(s) - hydrophilic interaction chromatography , computer science , prioritization , receiver operating characteristic , artificial intelligence , sequence (biology) , accessible surface area , machine learning , hydrophobic effect , biological system , chemistry , chromatography , biology , high performance liquid chromatography , biochemistry , management science , economics

The hydrophobicity of a monoclonal antibody is an important biophysical property relevant for its developability into a therapeutic. In addition to characterizing heterogeneity, Hydrophobic Interaction Chromatography (HIC) is an assay that is often used to quantify the hydrophobicity of an antibody to assess downstream risks. Earlier studies have shown that retention times in this assay can be correlated to amino-acid or atomic propensities weighted by the surface areas obtained from protein 3-dimensional structures. The goal of this study is to develop models to enable prediction of delayed HIC retention times directly from sequence.

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