Multiscale Modeling of Protein Interactions (Chromatography and Biomanufacturability)

Molecular simulations, coursed grained modeling, mechanistic and hybrid models and fully data driven modeling are used to provide insight into the interactions in these complex bioseparation systems and to develop in silico prediction tools for process development and the discovery of new separation materials.

 

Projects

Big data ML models for protein surface and biophysics
Machine learning (ML) using big data is a powerful tool for predicting protein surface properties like hydrophobicity and binding affinity. Large amounts of data generated from diverse experimental techniques and molecular simulations provide an opportunity to build ML-based models capable of accurately predicting these properties, as well as identifying interaction hot spots and binding pockets.
Mechanistic and hybrid modeling of chromatographic processes

The Cramer group at Rensselaer Polytechnic Institute has a longstanding history of contributions to the field of column modeling over the course of over three decades. One of the earliest contributions was the development of the steric mass action (SMA) model [1], describing adsorption of ion exchange chromatography with applications to displacement. 

molecular dynamics
Molecular Dynamic Simulations

We have employed explicit molecular dynamics (MD) simulations as well as coarse-grained molecular approaches, including electrostatic potential (EP) calculations, docking, etc. to study the behavior of protein retention on ion-exchange and multimodal (MM) systems.m

Quantitative Structure Activity Relationship
Predictive Models for Chromatographic Retention and Biophysical Properties
Quantitative Structure Activity Relationship (QSAR) is a powerful predictive tool connecting the experimental behaviors (e.g. retention in chromatography, solubility etc.) to physiochemical properties of molecules (e.g. charge density, hydrophobic surface area, etc.). It is also known as structure activity relationship (SAR), quantitative structure property relationship (QSPR) or quantitative structure retention relationship (QSRR).

The Howard P. Isermann Department of Chemical and Biological Engineering
Ricketts Building
Rensselaer Polytechnic Institute
110 8th Street, Troy, NY 12180
Tel: (518) 276-6378
Fax: (518) 276-3089

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