Optimized nonlinear gradients for RPLC-MS

Do you always wonder about which gradient to be used for LC-MS analysis? Although, there is no such thing as perfect LC gradient as each and every sample is somewhat different from the other and one particular gradient will not be suitable for every sample. However, users in the field, usually stick to one gradient that worked best for the most part.

This study recently published in Analytical Chemistry by Luminita Moruz et al., deals with optimizing the gradient in a non-linear way (article). In brief, one needs to analyze the sample with the linear gradient method and then use the gradient and peptide id - retention time information to get the non-linear gradient. The output is a text file containing an optimized non-linear gradient method. Now, this is based on the peptide id and their retention times that originally came from your linear gradient method. You can see the change in gradient in following image post-optimization. I obtained this by using the python script they provided on GoogleCode. I will have some data on this in a few days. In the article, they have shown that the peptides exclusively identified by nonlinear gradient method have somewhat different properties than with gradient method. So, you get two things, one is that even spread of peptides across the LC gradient and more coverage of peptides/proteins.

The python code is available at Google Code here.