Our latest paper has just appeared in the open access journal Chemical Science. It's ultimately related to biotechnology and drug design so first some background.
Background
The New Study
In 2015 we published a new method for predicting NMR fingerprints and in the paper that just got published we combined it with a method for generating a lot of protein structures. We started with known x-ray structures and generated millions of relatively small variations of the structure and found the structure with the best match. We started from a known structure to answer the question: what is the best match we can hope for? The answer is: not perfect but good enough. Now that we know this the next step will be to start with a structure we know is wrong and see if the program can find the right structure. Also, our NMR fingerprint method does not generate fingerprints for all parts of the protein so we need to improve the model as well.
This work is licensed under a Creative Commons Attribution 4.0
Background
Most biotechnology and medicine involves proteins in some way. Many diseases involve mutations that alter the function of proteins, most drugs are molecules that bind to proteins and inhibit their actions, and a large part of industrial biotechnology involves making new types of proteins such as enzymes. Like with everything else, it is easier to deal with proteins of you know what they look like but protein structure determination can be very difficult and we don't know what many important proteins actually look like.
The most popular way of determining protein structure is a technique called x-ray crystallography where you basically take an x-ray of a crystal made from the protein. Unfortunately, it can be very difficult or impossible to grow crystals of some proteins and if you can't get the protein to crystallise you can't use x-ray crystallography to find the structure. The other main way for determining protein structure is a technique called NMR spectroscopy where you basically take an MRI of a solution containing the protein. The advantage is that there is no need for crystallisation, but the disadvantage it that it is difficult to extract enough information from the "NMR-MRI" go get a good structure.
The "NMR-MRI" of a protein actually provides a unique fingerprint of each protein so in principle all one has to do is generate a lot of possible structures of a protein, compute the NMR fingerprint for each, and compare to the measured fingerprint. The structure with the best fingerprint match should be the correct protein structure. The questions are how to best generate the structure and how to best predict the NMR fingerprint using the structure.
The most popular way of determining protein structure is a technique called x-ray crystallography where you basically take an x-ray of a crystal made from the protein. Unfortunately, it can be very difficult or impossible to grow crystals of some proteins and if you can't get the protein to crystallise you can't use x-ray crystallography to find the structure. The other main way for determining protein structure is a technique called NMR spectroscopy where you basically take an MRI of a solution containing the protein. The advantage is that there is no need for crystallisation, but the disadvantage it that it is difficult to extract enough information from the "NMR-MRI" go get a good structure.
The "NMR-MRI" of a protein actually provides a unique fingerprint of each protein so in principle all one has to do is generate a lot of possible structures of a protein, compute the NMR fingerprint for each, and compare to the measured fingerprint. The structure with the best fingerprint match should be the correct protein structure. The questions are how to best generate the structure and how to best predict the NMR fingerprint using the structure.
In 2015 we published a new method for predicting NMR fingerprints and in the paper that just got published we combined it with a method for generating a lot of protein structures. We started with known x-ray structures and generated millions of relatively small variations of the structure and found the structure with the best match. We started from a known structure to answer the question: what is the best match we can hope for? The answer is: not perfect but good enough. Now that we know this the next step will be to start with a structure we know is wrong and see if the program can find the right structure. Also, our NMR fingerprint method does not generate fingerprints for all parts of the protein so we need to improve the model as well.
This work is licensed under a Creative Commons Attribution 4.0
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