The 2024 Nobel Prize in Chemistry will be awarded to Dr. David Baker of the University of Washington and Dr. Demis Hassabis and Dr. John Jumper of Google DeepMind.
Dr. David Baker was recognized for “artificial design of proteins using computation,” while Dr. Demis Hassabis and Dr. John Jumper were recognized for “prediction of protein structures using computation. Dr. David Baker receives the award for his work in artificial protein design, but he has also been a world leader in the field of structure prediction research for many years. Dr. Baker has been a world leader in structure prediction research for many years and has successfully developed AI-based structure prediction methods and has also incorporated AI technology into protein artificial design.
The three-dimensional structure of a protein molecule is important for its functional expression, and the three-dimensional structure is determined by the sequence of amino acid residues that make up the protein. However, predicting how a certain amino acid sequence will fold into a certain conformation and, conversely, artificially designing an amino acid sequence that will fold into that conformation when a certain conformation is considered are known as the folding problem and the reverse folding problem, respectively, and have been a problem that has preoccupied researchers for more than half a century. The former is the folding problem and the latter is the reverse folding problem, which has puzzled researchers for more than half a century. One way of looking at the Nobel Prize in Chemistry is that it is awarded for achievements that have significantly led to the solution of these “folding problems” using computers. In addition, the Nobel Prize in Chemistry is awarded to David Baker, Demis Hassabis, and John Jumper. The chemistry prize is not awarded for a single discovery (or a few discoveries), but rather for “a point of achievement” in the total body of knowledge that has been created by many scientists involved in protein science over more than half a century of history.
And this achievement is about to transform life science research to date. In order to examine a biological problem at the molecular level, it is necessary to experimentally elucidate the three-dimensional structure of the protein involved in the problem, but this requires time in the unit of “years” for some proteins, and this has been a major barrier to elucidating biology at the molecular level. However, with the use of structure prediction technology represented by AlphaFold, it is now possible to obtain highly accurate predicted structures in as little as a few dozen minutes, and this barrier has been lowered considerably (Note 1*). In the future, the number of situations in which many biological problems can be clarified by considering them at the molecular structure level will increase dramatically, and there is no doubt that molecular simulations using computers will make a significant contribution. In addition, in conventional biology, when one tries to understand the function of a protein whose structure is unknown in an organism, the method of knocking out the function of the protein (knockout) has been used. However, from now on, instead of knocking out a protein, we will try to predict its structure and analogize its function, modify it using artificial design techniques to modulate its function, and add new functions to it, thereby increasing the number of attempts to give new characteristics to organisms born in the process of evolution. This will probably lead to an increase in the number of attempts to give new characteristics to organisms born during evolution. Furthermore, the day may come when we will be able to create life with proteins that are artificially designed from scratch and do not exist in nature at all.
From these perspectives, this Nobel Prize in Chemistry may be evaluated as an achievement for the total body of knowledge generated by many scientists, which has largely removed the barriers between biology and chemistry.
Now that protein structures can be deduced with a high degree of accuracy, life science research is expected to move further toward elucidating their functions than ever before. The development of techniques to directly observe more complex systems, such as the relationship between dynamic conformational changes (fluctuations) of proteins in cells and their functional expression, is becoming increasingly important. The Institute for Molecular Science hopes to contribute to new developments in life science research through the development of multimodal optical measurement techniques that combine the wide range of light (terahertz, infrared, visible, ultraviolet, vacuum ultraviolet, soft x-rays and gamma rays, etc.) covered by the UVSOR synchrotron facility with various types of light, such as higher harmonic laser light and chiral light, in a new cross-disciplinary research project called “Photon Life Science (tentative name).
Nobuyasu Koga (Institute for Protein Research, Osaka University;
ExCELLS, National Institutes of Natural Sciences)
Yoshihito Watanabe (Institute for Molecular Science)
(Note 1*) It is important to note that structure prediction by experiment is not unnecessary, since there are some proteins, such as protein mutants, proteins that do not have naturally similar sequences, and newly artificially designed proteins, for which structure prediction is less accurate, and many proteins dynamically change their steric structures (and even quaternary structures) depending on solvent conditions and small molecules with which they interact, and such dynamic structure prediction is also still It is important to note that this does not mean that experimental structural analysis is no longer necessary.
