- HPLC analysis and purification of peptides (2007)
- High-performance liquid chromatography (HPLC) has proved extremely versatile over the past 25 yr for the isolation and punfication of peptides varying widely in their sources, quantity and complexity. This article covers the major modes of HPLC utilized for peptides (size-exclusion, ion-exchange, and reversed-phase), as well as demonstrating the potential of a novel mixed-mode hydrophilic interaction/cation-exchange approach developed in this laboratory. In addition to the value of these HPLC modes for peptide separations, the value of various HPLC techniques for structural characterization of peptides and proteins will be addressed, e.g., assessment of oligomerization state of peptideslproteins by sizeexclusion chromatography and monitoring the hydrophilicitykydrophobicity of amphipathic cr-helical peptides, a vital precursor Tor the development of novel antimicrobial peptides. The value of capillary electrophoresis for peptide separations is also demonstrated. Preparative reversed-phase chromatography purification protocols for sample loads of up to 200 mg on analytical columns and instrumentation are introduced for both peptides and recombinant proteins. Key Words: Peptides; proteins; size-exclusion chromatography (SEC); anion-exchange chromatography (AEX); cation-exchange chromatography (CEX); mixed-mode hydrophilic interaction chromatography (HIL1C)/cation-exchange chromatography (CEX); reversed-phase high-performance liquid chromatography (RP-HPLC); preparative RP-HPLC of peptides and proteins; amino acid side-chain hydrophilicitylhydrophobicity coefficients; amino acid U-helical propensity values; amino acid side-chain stability coefficients
- A comparison of spike time prediction and receptive field mapping with point process generalized linear models, Wiener-Voltera kernels, and spike-triggered averaging methods (2009)
- Poster presentation: Characterizing neuronal encoding is essential for understanding information processing in the brain. Three methods are commonly used to characterize the relationship between neural spiking activity and the features of putative stimuli. These methods include: Wiener-Volterra kernel methods (WVK), the spike-triggered average (STA), and more recently, the point process generalized linear model (GLM). We compared the performance of these three approaches in estimating receptive field properties and orientation tuning of 251 V1 neurons recorded from 2 monkeys during a fixation period in response to a moving bar. The GLM consisted of two formulations of the conditional intensity function for a point process characterization of the spiking activity: one with a stimulus only component and one with the stimulus and spike history. We fit the GLMs by maximum likelihood using GLMfit in Matlab. Goodness-of-fit was assessed using cross-validation with Kolmogorov-Smirnov (KS) tests based on the time-rescaling theorem to evaluate the accuracy with which each model predicts the spiking activity of individual neurons and for each movement direction (4016 models in total, for 251 neurons and 16 different directions). The GLMs that considered spike history of up to 35 ms, accurately predicted neuronal spiking activity (95% confidence intervals for KS test) with a performance of 97.0% (3895/4016) for the training data, and 96.5% (3876/4016) for the test data. If spike history was not considered, performance dropped to 73,1% in the training and 71.3% in the testing data. In contrast, the WVF and the STA predicted spiking accurately for 24.2% and 44.5% of the test data examples respectively. The receptive field size estimates obtained from the GLM (with and without history), WVF and STA were comparable. Relative to the GLM orientation tuning was underestimated on average by a factor of 0.45 by the WVF and the STA. The main reason for using the STA and WVF approaches is their apparent simplicity. However, our analyses suggest that more accurate spike prediction as well as more credible estimates of receptive field size and orientation tuning can be computed easily using GLMs implemented in Matlab with standard functions such as GLMfit.