In this issue: What is a protein 3D structure and why it matters; New article on the structure and dynamics of the SBDS protein; DeepMind is now publishing the predicted structures of over 200 million proteins
Welcome to our weekly updates on all things SDS, science, and advocacy. We bring you a digest of recent scientific publications, conferences, and other newsworthy content - all relevant to SDS - with links to more details and learning opportunities. Are you interested in anything specific? Did we miss something? Let us know. Email connect@SDSAlliance.org or message us on Facebook! This is all for you!
What is a protein 3D structure and why it matters
Biological processes in both health and disease are mostly mediated by proteins in our cells. Proteins are long chains of amino acids (the building blocks) that fold up into specific 3D structures. This structure along with their chemical properties on the surface is responsible for the proteins' function. The exact sequence of the amino acids is determined by the nucleotide sequence encoded in our DNA. Each gene - a defined stretch of DNA - encodes one protein. As you can imagine, if there is a mutation in a gene, it can result in a change in the amino acid sequence - which in turn can change or disrupt the resulting protein structure and function.
Here is a great overview on how the 3D structure of a protein comes to be:
Plus, a handy overview of how amino acid chains fold into 3D protein structures:
And what does this all have to do with Shwachman-Diamond Syndrome?
Shwachman-Diamond Syndrome (SDS) is an inherited (aka genetic) disorder that is (in over 90% of patients) a result of mutations in the SBDS gene. When mutated, the SBDS gene gives rise to either not enough SBDS protein, or an SBDS protein that has lost its function. The SBDS protein is responsible for catalyzing the assembly of ribosomes. If there is not enough SBDS protein, then there is not enough ribosome, and the cell is not able to keep up with overall protein production.
We created a video overview on this topic, here:
SBDS structure and why it is important as a therapeutic target for SDS
Since SBDS takes center stage in Shwachman-Diamond Syndrome, it is not a surprise that understanding the details of its function and what it looks like in 3D is critical when it comes to developing strategies for therapies and cures.
Dr. Alan Warren has championed this effort for many years since the SBDS gene was identified to be the main cause of SDS by Dr. Johanna Rommens' group in 2003. In this recent video interview, Dr. Warren explains the importance of a thorough analysis of the structure and how these insights feed into small molecule drug development - championed in his lab. Read his detailed article about SBDS and SDS from 2018, here.
New article on SBDS structure and dynamics
In this new article published this month by Dr. Mangiatordi's group in Italy, the authors report their work using comparative Molecular Dynamics simulations to analyze the impact of three different point mutations in SBDS on the protein function. The results indicate that both the open and closed forms of wild type SBDS are necessary for proper SBDS function, and support the hypothesis that SBDS function is governed by an allosteric mechanism involving domains I and III.
Read the full article (open access), here:
Spinetti E, Delre P, Saviano M, Siliqi D, Lattanzi G, Mangiatordi GF. Int J Mol Sci. 2022 Jul 19;23(14):7938.
doi: 10.3390/ijms23147938. PMID: 35887285
On the news: DeepMind's protein-folding AI cracks biology's biggest problem
Coincidentally with our theme "protein structure" in this issue, there was big news in the science world this week. Google’s AI outfit and the European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI) announced Thursday that DeepMind’s AlphaFold database now contains the structures of more than 200 million proteins. It’s a substantial jump from where it was a year ago when DeepMind announced that it had predicted the structure of only about 350,000 proteins.
The two companies said in a statement announcing the database expansion that it now contains the structure of essentially every protein that has been sequenced — and is designed to function essentially like a Google search. On top of that, the companies are keeping it free for use for the scientific community at large.
Understanding protein structure is an overarching challenge in research and therapeutic development. Learning about structure can teach us about disease mechanisms and creating effective treatments - including for Shwachman-Diamond Syndrome as we discussed above. But as you can see, this process is anything but trivial. Long sequences of amino acids can take on many shapes and structures (conformations), which can change as they bind with other proteins or ligands, or through changes in their environment.
The gold standard for "looking at" protein structure is X-ray crystallography - a complex and resource intensive technology that is hard to scale. That's where Artificial Intelligence (AI) comes in. To accelerate our understanding of protein structures, DeepMind has developed AlphaFold, an AI approach that predicts protein structure based on existing observations of the protein and some basic rules about protein folding. DeepMind is now publishing the predicted structures of over 200 million proteins.
These predictions aren’t perfect: AlphaFold doesn’t always predict structural changes in response to a mutation, for example. But by providing likely structures of so many proteins, this technology has the potential to significantly accelerate molecular research of proteins in health and disease.
DeepMind has predicted the structure of almost every protein so far catalogued by science, cracking one of the grand challenges of biology in just 18 months thanks to an artificial intelligence called AlphaFold. Researchers say that the work has already led to advances in combating malaria, antibiotic resistance and plastic waste, and could speed up the discovery of new drugs.