Nobel Prizes, as stipulated in Alfred Nobel’s will, are decided and awarded by the Swedish Academy of Sciences (for Chemistry) and the Karolinska Institute in Stockholm (for Medicine). Chemistry prizes are dedicated for those “who made the most important chemical discovery or improvement” and medicine prizes are awarded to those “who made the most important discovery within the domain of physiology or medicine”. While connections between public health and medicine prizes can be very clearly drawn, chemical discoveries’ contributions are less obvious. Hence, chemistry prizes are increasingly being presented to researchers in the field of molecular biology, rather than physical or nuclear chemistry: breakthroughs in biochemistry have unobscured connections to pharmaceutics and therapeutics.
This year, prizes were revealed on the week of October 7. The 2024 Chemistry Prize was awarded to Demis Hassabis and John M. Jumper “for protein structure prediction” and David Baker “for computational protein design”. The story of Hassabis and Jumper was particularly inspiring–I recommend you watch a video about their work here. To set the context for their discovery, we must go back in time to 1972, the year chemist Christian B. Anfinsen won the chemistry prize. Through his research on ribonuclease, he found that the specific order of amino acids determines the final shape–that is, the same amino acid strand will always fold into the same shape spontaneously. Then, it is reasonable to assume that given a polypeptide chain, we can calculate and model how the protein folds. However, molecular biologist Cyrus Levinthal proposed his famous paradox: the sheer amount of possible configurations a protein can take on would force a computer billions of years to produce a prediction, rendering a brute-force calculation method nearly impossible to implement. Thus birthed the “protein problem” and an era of scientists tirelessly attempting to devise a solution. A team at Google DeepMind worked on this problem using neural networks. Drawing from a large database of amino acid chains and corresponding structures, they trained a model, AlphaFold2, to recognize inputted sequences to be able to generate a 3D structure.
The existence of the protein problem–identifying structure based on sequence–also implies the existence of an opposite: identifying sequence based on structure. Baker was awarded the Chemistry Prize just for that. Baker worked in the University of Washington in Seattle, where he helped develop Rosetta, software originally intended to solve the original, “forward” protein problem. However, the field of de novo design, where completely new proteins are constructed from scratch, called for a different use of Rosetta: the “inverse” protein problem. The efficacy of the novel application of the software was tested in the protein Top7. The researchers introduced the prediction into bacteria to produce the protein whose 3D structure was then analyzed with X-ray crystallography. Extraordinarily, Rosetta accurately predicted the sequence of Top7–indicating the inverse protein problem had been solved.
These two discoveries in biochemistry will fundamentally alter the pace at which researchers can name structures and sequences of proteins. The implications in medicine are obvious–what once took months of careful data collection to generate sequences of previously unidentified proteins can now be run by AI in a few days. An application of this discovery is in antigen identification: contributing heavily in vaccine development, recognizing the sequence of proteins on novel diseases would allow immunizations to move faster down the production pipeline. Perhaps, these computer softwares could even help prevent the next COVID-19. De novo design has increasingly seen larger applications in medicine, especially as more novel–not produced by existing organisms–need to be synthesized to treat diseases which previously had no cure.
Also incredibly additive to biology research was the discovery of Victor Ambros and Gary Ruvkun, awarded the 2024 Physiology or Medicine “for the discovery of microRNA and its role in post-transcriptional gene regulation”. Ambros and Ruvkun, in the midst of their work, individually noted unusual data regarding the lin-4 and lin-14 genes in Caenorhabditis elegans. The combination of their findings indicated that lin-4 codes for a tiny segment of RNA, hence, microRNA, that binds to transcribed mRNA from the lin-14 gene. The complementary base-pairing prevents translation of the lin-14 mRNA, thus blocking the production of the protein in a post-transcriptional manner. Irregular microRNA interactions contribute to cancer, hearing loss, eye and skeletal disorders. The clear identification of microRNA interactions would allow better treatment of such syndromes and cleaner disease diagnosis and prognosis.
However, one who is well-versed in Nobel laureates may note that in 2006, Andrew Fire and Craig Mello were presented the award for their discovery of gene silencing by double-stranded RNA (dsRNA), or RNA interference (RNAi). From this, some might assume that the 2024 prize this year may be redundant–but the reality is far from that. Although both of their discoveries revealed reliance on RNA as a basis of gene expression regulation, both are distinct and incredibly relevant in biology research. RNAi can be manipulated into a useful tool that allows specific knockout of a gene without needing to remove it endogenously. The mechanism of RNAi originated from an antiviral defense mechanism; since dsRNA usually only exist in virus replication pathways, the recognition of dsRNA by the protein RISC will trigger RISC-mediated degradation of mRNA molecules matching the sequence of the dsRNA. I actually got to use RNAi constructs in my summer research on D. melanogaster–demonstrating the abundance and accessibility of this tool in research. While RNAi evolved from defending oneself from exogenous dsRNA, microRNA is an endogenous mechanism for gene expression. It plays a crucial role in organismal development. Both RNAi and microRNA contribute to a cell’s transcriptome, however performing different functions.
This year was the first year I closely looked at the work of Nobel Prize winners. In years prior, most of my biology learning came from textbooks and classroom settings. I thought of the biology we knew as static and far removed into the past. After beginning my research over the summer, I felt like a completely new source of science learning had opened up to me: the present biology research community. Science is incredibly dynamic–we are able to observe the development of a single discovery into a widely-used tool in research and medicine in real time. To me, Nobel Prizes are a perfect way to observe this phenomenon, since they are often awarded years after the discovery’s original publication date. To think that, perhaps I could one day cause ripples in the ocean that is science inspires me all the more to pursue biology research as a career. This year’s Nobel Prize awards were truly amazing–I strongly suggest you read the plentiful literature around microRNAs and the protein problem!
(To begin your reading, might I suggest you direct press releases from the Nobel Prize website! 2024 Chemistry Prize 2024 Medicine Prize)