Biomedical subjects

Curt M Breneman

Publications and source records attributed to Curt M Breneman.

10 recordsLinked to original sources

PubMed2006

Classification of protein adsorption and recovery at low salt conditions in hydrophobic interaction chromatographic systems.

There is significant interest in establishing appropriate bioprocessing conditions for protein adsorption in hydrophobic interaction chromatographic (HIC) systems without the need for high salt concentrations. In this paper, the adsorption and recovery of proteins under low salt conditions in HIC systems was investigated using a variety of experimental and computational techniques. Parallel batch screening was employed to determine protein adsorption and recovery. Experiments were carried out with twenty six proteins using five resins with different ligand chemistry, ligand density and backbone chemistry. Proteins were classified based on various combinations of adsorption and recovery behavior. In order to gain insight into the effect of protein properties on this behavior, molecular descriptors were computed based on protein crystal structure and primary sequence information as well as a set of hydrophobicity descriptors based on the solvent accessible surface area of the proteins. Finally, classification software CART was employed to determine the key molecular descriptors associated with various types of adsorption behavior.

Adsorption↗

PubMed2006

Investigation of protein retention and selectivity in HIC systems using quantitative structure retention relationship models.

In the present work, the effect of stationary phase resin chemistry and protein physicochemical properties on protein binding affinity in hydrophobic interaction chromatography (HIC) was investigated using linear gradient chromatography and quantitative structure-retention relationship (QSRR) modeling. Linear gradient experiments were carried out for a set of model proteins on four different HIC resins having different backbone and ligand chemistry. The retention data exhibited significant differences in protein binding affinity, not only across the phenyl and butyl ligand chemistries, but also for the different backbone chemistries found in the Sepharose (cross-linked agarose) and the Toyopearl 650 M (polymethacrylate) series of resins. QSRR models based on a Support Vector Machine (SVM) approach were developed for the linear retention data using molecular descriptors based on protein crystal structure and primary sequence information as well as a set of new hydrophobicity descriptors based on the solvent accessible protein surface area. The results indicate that the QSRR models were successfully able to capture and selectivity predict the changes observed in these systems. Furthermore, the new descriptors resulted in physically interpretable models of protein retention and provided insights into the factors influencing protein affinity in these different HIC systems. The approach put forth in this study provides a framework for developing predictive tools and for gaining insight into protein selectivity in hydrophobic interaction chromatography.

Chromatography, Gel↗

PubMed2004

Parallel screening of selective and high-affinity displacers for proteins in ion-exchange systems.

This paper employs a parallel batch screening technique for the identification of both selective and high-affinity displacers for a model binary mixture of proteins in a cation-exchange system. A variety of molecules were screened as possible displacers for the proteins ribonuclease A (RNAseA) and alpha-chymotrypsinogen A (alpha-chyA) on high performance Sepharose SP. The batch screening data for each protein was used to select leads for selective and high-affinity displacers and column experiments were carried out to evaluate the performance of the selected leads. The data from the batch displacements was also employed to generate quantitative structure-efficacy relationship (QSER) models based on a support vector machine regression approach. The resulting models had high correlation coefficients and were able to predict the behaviour of molecules not included in the training set. The descriptors selected in the QSER models for both proteins were examined to provide insights into factors influencing displacer selectivity in ion-exchange systems. The results presented in this paper demonstrate that this parallel batch screening-QSER approach can be employed for the identification of selective and high-affinity displacers for protein mixtures.

Cation Exchange Resins↗

PubMed2004

Three-dimensional quantitative structure-activity relationship analyses of piperidine-based CCR5 receptor antagonists.

The CCR5 chemokine receptor has recently been found to play a crucial role in the viral entry stage of HIV infection and has therefore become an attractive potential target for anti-HIV therapeutics. On the other hand, the lack of CCR5 crystal structure data has impeded the development of structure-based CCR5 antagonist design. In this paper, we compare two three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) methods: Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) on a series of piperidine-based CCR5 antagonists as an alternative approach to investigate the interaction between CCR5 antagonists and their receptor. Superimposition of antagonist structures was performed using two alignment rules: atomic/centroid rms fit and rigid body field fit techniques. The 3D QSAR models were derived from a training set of 72 compounds, and were found to have predictive capability for a set of 19 holdout test compounds. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to biological activity in this series of compounds. Further analyses of these interaction-field contour maps also showed a high level of internal consistency.

Animals↗

PubMed2003

Identification of chemically selective displacers using parallel batch screening experiments and quantitative structure efficacy relationship models.

Parallel batch screening experiments were carried out to examine how displacer chemistry and salt counterions affect the selectivity of batch protein displacements in anion exchange chromatographic systems. The results indicate that both salt type and displacer chemistry can have a significant impact on the amount of protein displaced. Importantly, the results indicate that, by changing the displacer, salt counterion, or both, one can induce significant selectivity changes in the relative displacement of two model proteins. This indicates that highly selective separations can be developed in ion exchange systems by the appropriate selection of displacer chemistry and salt counterion. The experimental batch screening data were also used in conjunction with various molecular descriptors to generate quantitative structure efficacy relationship (QSER) models based on a support vector machine feature selection and regression tool. The models resulted in good correlations and successful predictions for an external test set of displacers. A star plot approach was shown to be a powerful tool to aid in the interpretation of the QSER models. These results indicate that this modeling approach can be employed for the a priori prediction of displacer efficacy as well as for providing insight into displacer design and the selection of proper mobile-phase conditions for highly selective separations.

Algorithms↗

PubMed2003

Prediction of the effect of mobile-phase salt type on protein retention and selectivity in anion exchange systems.

This study examines the effect of different salt types on protein retention and selectivity in anion exchange systems. Particularly, linear retention data for various proteins were obtained on two structurally different anion exchange stationary-phase materials in the presence of three salts with different counterions. The data indicated that the effects are, for the most part, nonspecific, although various specific effects could also be observed. Quantitative structure retention relationship (QSRR) models based on support vector machine feature selection and regression models were developed using the experimental chromatographic data in conjunction with various molecular descriptors computed from protein crystal structure geometries. Star plots for each descriptor used in the final model were generated to aid in interpretation. The resulting QSRR models were predictive, with cross-validated r2 values of 0.9445, 0.9676, and 0.8897 for Source 15Q and 0.9561, 0.9876, and 0.9760 for Q Sepharose resins in the presence of three different salts. The predictive power of these models was validated using a set of test proteins that were not used in the generation of these models. Interpretation of the models revealed that particular trends for proteins and salts could be captured using QSRR techniques.

Algorithms↗

PubMed2002

High-throughput screening and quantitative structure-efficacy relationship models of potential displacer molecules for ion-exchange systems.

A technique is presented for the high-throughput screening of ion-exchange displacers. Potential displacers were employed to displace proteins in parallel batch ion-exchange experiments. The percentage of protein displaced from a particular stationary phase was then used as a parameter to rank the displacers. By employing this technique, a large number of molecules possessing a range of affinities and properties could be rapidly evaluated. This data was then used together with traditional and electron density-based transferable atom equivalent (TAE) molecular descriptors computed for the displacer molecules to produce quantitative structure-efficacy relationship (QSER) models using a genetic algorithm/partial least squares (GA/PLS) regression approach. The QSER models were generated using a portion of the protein-displacement data, with the remainder serving as a test set. Descriptor selection and model building was accomplished using a genetic algorithm/partial least squares approach. The resulting models were found to have high-correlation coefficients and could be used to accurately predict the behavior of molecules not included in the training set. In addition, the models were employed to examine a virtual library of displacers based on modifications of neomycin to provide further insight into displacer design. The results presented here indicate that it may be possible to design displacers that can dramatically improve the effective selectivity of ion-exchange chromatographic materials.

Algorithms↗

PubMedDate not supplied

Prediction of protein retention times in anion-exchange chromatography systems using support vector regression.

Quantitative Structure-Retention Relationship (QSRR) models are developed for the prediction of protein retention times in anion-exchange chromatography systems. Topological, subdivided surface area, and TAE (Transferable Atom Equivalent) electron-density-based descriptors are computed directly for a set of proteins using molecular connectivity patterns and crystal structure geometries. A novel algorithm based on Support Vector Machine (SVM) regression has been employed to obtain predictive QSRR models using a two-step computational strategy. In the first step, a sparse linear SVM was utilized as a feature selection procedure to remove irrelevant or redundant information. Subsequently, the selected features were used to produce an ensemble of nonlinear SVM regression models that were combined using bootstrap aggregation (bagging) techniques, where various combinations of training and validation data sets were selected from the pool of available data. A visualization scheme (star plots) was used to display the relative importance of each selected descriptor in the final set of "bagged" models. Once these predictive models have been validated, they can be used as an automated prediction tool for virtual high-throughput screening (VHTS).

Anion Exchange Resins↗

PubMedDate not supplied

Investigation of DNA-binding properties of an aminoglycoside-polyamine library using quantitative structure-activity relationship (QSAR) models.

We have recently developed a novel multivalent cationic library based on the derivatization of aminoglycosides by linear polyamines. In the current study, we describe the DNA-binding activity of this library. Screening results indicated that several candidates from the library showed high DNA-binding activities with some approaching those of cationic polymers. Quantitative Structure-Activity Relationship (QSAR) models of the screening data were employed to investigate the physicochemical effects governing polyamine-DNA binding. The utility of these models for the a priori prediction of polyamine-DNA-binding affinity was also demonstrated. Molecular descriptors selected in the QSAR modeling indicated that molecular size, basicity, methylene group spacing between amine centers, and hydrogen-bond donor groups of the polyamine ligands were important contributors to their DNA-binding efficacy. The research described in this paper has led to the development of new multivalent ligands with high DNA-binding activity and improved our understanding of structure-activity relationships involved in polyamine-DNA binding. These results have implications for the discovery of novel polyamine ligands for nonviral gene delivery, plasmid DNA purification, and anticancer therapeutics.

Aminoglycosides↗

PubMedDate not supplied

New developments in PEST shape/property hybrid descriptors.

Recent investigations have shown that the inclusion of hybrid shape/property descriptors together with 2D topological descriptors increases the predictive capability of QSAR and QSPR models. Property-Encoded Surface Translator (PEST) descriptors may be computed using ab initio or semi-empirical electron density surfaces and/or electronic properties, as well as atomic fragment-based TAE/RECON property-encoded surface reconstructions. The RECON and PEST algorithms also include rapid fragment-based wavelet coefficient descriptor (WCD) computation. These descriptors enable a compact encoding of chemical information. We also briefly discuss the use of the RECON/PEST methodology in a virtual high-throughput mode, as well as the use of TAE properties for molecular surface autocorrelation analysis.

Algorithms↗