RECCR Rensselaer Exploratory Center for Cheminformatics Research

Bioseparations

Modeling

Descriptors

Homology Modeling

References

Co-PI: Steven Cramer

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Bioseparations

Modeling

In the proposed work we will focus on the development of novel screening techniques and quantitative structure-based models for investigating the binding of small molecules, such as displacers, and larger biological molecules, such as proteins, in various chromatographic modes. We will examine the identification of selective and/or high-affinity displacers through high throughput screening (HTS) of compound libraries. The percent protein data obtained from the HTS will be employed to generate predictive QSER models. Insights gained through model interpretation will be employed for the design of virtual libraries of molecules, which will be screened in silico against the QSER models for the identification of new, potential high-affinity and selective displacer leads.

The QSPR modeling strategy will be extended to understand and predict protein adsorption in hydrophobic interaction chromatography (HIC). The influence of stationary phase resin chemistry on the affinity and selectivity of protein separations in HIC will be investigated using column experiments with different HIC media. Novel surface hydrophobicity and hydration density descriptors will be developed through interaction with the Protein Descriptor Modules, and employed to generate more physically interpretable QSPR models. Also, insights into the physicochemical effects responsible for protein adsorption in HIC will be obtained through model interpretation.

The MD-HTS screening protocol offers an excellent opportunity for screening large displacer libraries on different resin materials under a wide variety of mobile phase conditions. In addition, we have also demonstrated the utility of these screens for the identification of selective displacers for the purification of mixtures of varying complexity. The development of appropriate labeling techniques and/or the use of genetically modified naturally fluorescent proteins (such as green fluorescent protein and yellow fluorescent protein) for rapid sample analysis in a multicomponent setting will enhance the reliability of the leads identified from the MD-HTS technique. In addition, the availability of robotic systems capable of automated fluid and resin handling are expected to significantly reduce the time and effort involved in screening displacers and conditions for developing displacement separations.

QSER models generated from the HTS screening data have been shown to yield good predictions for the efficacies of new, untested molecules. An important aspect of the QCD approach is the use of the QSER models for the identification of new molecules as displacer as well as for displacer lead optimization. This may be achieved via the screening of large virtual libraries of potential displacer compounds so as to identify molecules with desirable efficacies and selectivities for subsequent synthesis. In addition, it may be advantageous to employ virtual high throughput screening (VHTS) software packages that automate the process of virtual library generation and can generate hundreds of virtual compounds for a given scaffold molecule. VHTS has the potential to bridge the gap between the chromatographic screening and synthetic chemistry arms of the QCD project. Therefore, there is an urgent need to explore available VHTS approaches and link these with available combinatorial synthesis strategies so as to accelerate the pace of development of new displacer molecules. While the first pass may not yield the best displacers, the refinement of the QSER models with each successive iteration through the QCD loop will yield increasingly reliable predictions. Consequently, it is expected that molecules with desirable characteristics may be identified within a relatively small number of iterations.

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