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How to code a functional molecular machine?
Name관리자| Date2018-05-30 16:39| Hit106
- How life evolves the genes that code for molecular machines like protein - 


Evolution has shaped the living world we see around us for billions of years. Zillions of molecular machines work harmoniously to keep these life processes going. One type of molecular machine are proteins that are responsible for the smooth functioning of virtually all the life processes in an organism. The protein recognizes other molecules, binds to them and converts them to other useful molecules. They also transport matter and provide structure and support to the cells. Evolution has shaped them to perform these tasks efficiently. The genes store the design of these molecular machines, which is translated through a sequence of production processes into functional nano-machines. Decoding the gene is essential to understanding life. The “map” from the gene to a machine like protein is not at all trivial. Despite decades of research, the map is not close to being understood. Recently, a new hypothesis has emerged, that the functions of proteins relies on some kind of “floppy” regions. In collaboration with Prof. Albert Libchaber from Rockefeller University and Prof. Jean-Pierre Eckmann from University of Geneva -- Prof. Tsvi Tlusty and Dr. Sandipan Dutta at the Center for Soft and Living Matter, within the Institute for Basic Science (IBS, South Korea), have explored this idea to understand how evolution searches for the code that gives rise to these efficient molecular machines.
The strategy of their study, which was published in Proceedings of the National Academy of Sciences (PNAS), is to construct a simple conceptual framework to examine and understand the genetic coding of protein and how evolution searches and finds these codes effectively. They built a simple model of protein based as an elastic material. As shown in Figure 1, the protein is made of red and blue amino acids connected by molecular “springs”. The red amino acids are flexible while the blue ones are rigid. Therefore, higher occurrence of the red amino acids at the center of the protein gives rise to a floppy channel within the protein, which allows the protein to perform large scale “hinge” movements as shown in the Figure. This motion allows them to bind effectively to other molecules.
Figure 1.jpg

Figure 1: (A) When a protein binds to a ligand it undergoes large scale motion due to the presence of certain “floppy” region  (red  “shear band” ) across the protein. This “floppy” region separated the rigid blue regions of the protein. These large movements are signatures of functional proteins. (B)-(D) shows different stages of evolution of a protein: from a non-functional (B) to a functional protein (D). The protein is modeled as an elastic spring network with two kinds of amino acids: red amino acids are flexible and blue amino acids are rigid. Binding to a ligand “pinches” the protein at the binding site. However since the protein is mostly rigid (blue) the protein can not move making it non-functional. (C) During evolution more flexible (red) amino acids are added and it functions better. (D) The “floppy” (red) region forms at the center of the protein. The protein can move and bind to ligands easily. 
The gene stores the details of the protein design in binary numbers of zeros and ones. The red amino acids are stored as 0’s and blue ones as 1’s. As a result the entire protein structure can be stored in the genes as a simple codes like 11110001...111 similar to a to the digital memory of a computer. Not all codes give rise to functional proteins, for example a code of all 1’s: 111111….1111, would give rise to an entirely rigid protein as in Fig 1B. This protein is too rigid to move, hence it cannot function at all. A code that produces a red “floppy” region in the center of the protein in Fig1D is one of the few codes of a functional protein.
During evolution, the 0’s and 1’s in the gene are randomly flipped through a process called mutation. Most mutations end in non-functional proteins, however some rare mutations can give rise to a functional protein as in Fig 1D. How does evolution “know” how to form functional proteins? Essentially both functional and non-functional proteins are produced during evolution, however thanks to “the survival of the fittest” only the functional proteins are kept and the non-functional proteins eventually die out. In the current model, the motion or the displacements of the protein during the binding measures this fitness, how well they perform their biological function of binding. The fittest functional proteins show large movements. Using simple simulations with this “motion-fitness” of the principal concept of the survival of the fittest, the authors have found what kind of mutations will end in functional proteins.   
 How does a “functional” code look like? This does not have a simple answer. In fact, the number of codes of a functional protein is enormous (larger even than the size of the universe) even for simple proteins. However, using techniques of data analysis it is possible to search for hidden patterns in all functional codes to look for some unifying characteristics. Such analysis shows that in fact the functional proteins occupy a much smaller space. This is called “dimensional reduction”, which helps evolution to find the functional codes much more effectively. Another finding is the motion of the protein has strong resemblance to the its genetic code.
The “floppy” channel in the protein has another interesting and peculiar consequence. Any flipping of 0’s or 1’s at one end of the channel strongly affects the flipping at the other end of the channel. This correlation effect, called epistasis, is thus very long ranged and can affect amino acids along the channel and might provide insight into the drug development in medicine.
In the future, the research team aims to explore some possible application of their findings to certain real proteins like kinases.  The study open avenues to investigate the evolution of other functions of proteins like molecular recognition. Huge databases, which have been developed through years of research already exist on the evolution of proteins. Some simple underlying phenomena can probably be uncovered using the current theory. 


Journal Reference: Sandipan Dutta, Jean-Pierre Eckmann, Albert Libchaber, and Tsvi Tlusty. Green function of correlated genes in a minimal mechanical model of protein evolution. PNAS (2018). DOI: 10.1073/pnas.1716215115

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