Antibody sequence-based prediction of pH gradient elution in multimodal chromatography

Hess, Rudger; Faessler, Jan; Yun, Doil; Saleh, David; Grosch, Jan-Hendrik; Schwab, Thomas; Hubbuch, Jürgen

doi:10.1016/j.chroma.2023.464437

Cited by 7 publications

(1 citation statement)

References 65 publications

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“…Protein structure prediction is the computational task of determining the three-dimensional structure of a protein from its amino acid sequence. This process is crucial for understanding protein function, interactions, and designing novel therapeutics [1][2][3][4][5][6][7][8]. By definition, accurate prediction of protein structure from its amino acid sequence is a formidable challenge in computational structural biology, with profound implications for understanding biological function and designing novel therapeutics [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

2024

Preprint

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Due to the vast conformational space proteins can adopt, accurate and efficient prediction of protein structure remains still a challenging task, coupled with the intricacies of interatomic interactions and the limitations of current computational models in effectively navigating this complex molecular landscape. Additionally, the lack of comprehensive experimental data for all protein structures further exacerbates the difficulty in reliable machine learning-based prediction of the three-dimensional conformations of the proteios building block of life. Geometrically, Cartesian coordinate system (CCS, X, Y and Z) and spherical coordinate system (SCS, ρ, θ and φ) are two interconvertible coordinate systems, and are like two sides of one coin. Since the beginning of Protein Data Bank (PDB) in 1971, CCS has been the default approach to specify atomic positions with X, Y and Z in PDB. In this manuscript, therefore, I present a novel method for the reversible spherical geometric conversion of protein backbone structure coordinate matrices to three independent vectors: ρ, θ and φ. This reversible conversion facilitates lossless extraction of essential structural features from protein backbone structural data, enabling the development of advanced novel algorithms for protein structure prediction in future. In short, this inter-atomic SCS approach offers a comprehensive yet efficient means of representing protein backbone geometry, leveraging spherical coordinates to capture spatial relationships in a compact and intuitive inter-atomic manner, and to provide a robust framework for reversible feature extraction for the ongoing efforts in advancing the field of protein structure prediction, the holy grail of computational structural biology.

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Section: Introductionmentioning

confidence: 99%

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

2024

Preprint

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From protein structure to an optimized chromatographic capture step using multiscale modeling

Keulen,

Neijenhuis,

Lazopoulou

et al. 2024

Biotechnology Progress

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Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost‐effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR‐based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.

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Preparation of dicationic ionic liquid modified silica stationary phase for mixed-mode liquid chromatography and its application for food additive detection

Wang,

et al. 2024

Analytica Chimica Acta

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Antibody sequence-based prediction of pH gradient elution in multimodal chromatography

Cited by 7 publications

References 65 publications

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

From protein structure to an optimized chromatographic capture step using multiscale modeling

Preparation of dicationic ionic liquid modified silica stationary phase for mixed-mode liquid chromatography and its application for food additive detection

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