There is no right way to do science, that's what makes science awesome. However, when you are entering a new field, or trying something new the first thing you do is find a current review. I remember in grad school whenever it was my turn to present literature at group meeting, I would search the topic in TIBS (Trends In Biochemical Sciences). That was always the best starting point. However, when you want to really do something, as in practical applications you looked for a Methods in Enzymology paper or Current Protocols. This always give you a way to do something, with in depth technical hints and tricks of the trade. On this blog, we discuss a lot of different techniques and oftentimes it is out of the users area of expertise. We try to make it understandable and I think we are largely successful.
In this paper, yours truly, Darren Begley, and colleagues from Emerald put forth one way to run and analyze Saturation Transfer Difference NMR for fragment campaigns. STD is the subject of MANY posts here. One of the things I want to point out is that opinions are like belly buttons, everyone has one. So, in this Protocol we put forth a way to perform STD it is not the only way to do, but I think it is a rather robust method. Not everything will translate to every company. For example, most companies don't have extremely small, highly soluble fragments like the Fragments of Life and thus 500mM stocks will not be achievable. I believe 100 mM is a better generic concentration. However, I would love to hear in the comments what other people think. Additionally, there are computational approaches that make the manual creation of pools unnecessary. In terms of analysis, there are a million different approaches. Most companies that make NMR software have some sort of automation. I really like the implementation from Mnova. However, it is important to keep in mind that your results are really only as good as your understanding of the experiment.
What this all really comes down to is that NMR, and STD in particular, is not a black box. You still need an expert user running the experiments and analyzing the data. My goal with this paper though is to enable better understanding of the STD experiment for the lay user. Hopefully, this leads to greater use of the experiment and concomitant increased success in screening. I would really like to hear comments about what people do differently and why.