(6.2.1)--17.Karplus-computationalchemistr.pdf

卡普拉斯卡普拉斯-计算化学计算化学 Martin Karplus-Computational Chemistry When you recall your chemistry class,teachers often like to use these sticks and balls to show you the structure of chemicals.But,nowadays,we more like to use computer to display these models.In addition,the understanding of detailed chemical process enabled chemists to reveal the mysterious world of chemistry.But,because chemical reactions are so rapid that electrons move rapidly between nuclei,traditional experiment process cant snap-shot every reaction step that happens so fast.So,we need reveal the mysterious world of chemistry with the help of computer.Martin Karplus,Michael Levitt and Arieh Warshel was awarded jointly to“for the development of multiscale models for complex chemical systems.”for the Nobel Prize in Chemistry 2013.Their work helps us improve the processes of catalysts,drugs and even solar panels.Now,chemists all over the world design experiments on computers every day.The world around us is made up of atoms that are joined together to form molecules.During chemical reactions,atoms change places and new molecules are formed.To accurately predict the course of the reactions at the sites where the reaction occurs,advanced calculations based on quantum mechanics are required.For other parts of the molecules,it is possible to use the less complicated calculations of classical mechanics.In the 1970s,Martin Karplus,Michael Levitt,and Arieh Warshe successfully developed methods that combined quantum and classical mechanics to calculate the courses of chemical reactions using computers.The research of Professor Martin Karplus and his group is directed toward understanding the electronic structure,geometry,and dynamics of chemical and biological molecules.PART TWO Achievement and Application In each study a problem that needs to be solved is isolated and the methods required are developed and applied.In recent years,techniques of ab initio and semi-empirical quantum mechanics,theoretical and computational statistical mechanics,classical and quantum dynamics and other approaches,including NMR,have been used.In developing computational methods to study complex chemical systems,the essential element has been to introduce classical concepts wherever possible,to replace the much more time-consuming quantum mechanical calculations.In 1929,Paul Dirac(the Nobel Prize winner in Physics,in 1933)wrote the following statement:How to develop of Multiscale Models for Complex Chemical Systems From simple mode,such as H+H2,to Biomolecules?“The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known,and the difficulty is only that the exact application of these laws leads to equations that are much too complicated to be soluble.”It therefore becomes desirable that approximate practical methods of applying quantum mechanics should be developed,which can lead to explanation of the main features of complex atomic systems without too much computation.To understand the behavior of complex systems need:First of all,The potential surface on which the atoms move Secondly,the laws of motion for the atoms The most important approaches for representing the potential surface of complex systems which do not use quantum mechanics(the so-called force fields)were developed.To study chemical reactions,the classical force fields were extended to treat part of the system by quantum mechanics,the so-called QM/MM method.The laws of motion for the atoms Although the laws governing the motions of atoms are quantum mechanical,the essential realization that made possible the treatment of the dynamics of complex systems was classical mechanics.Such description of the atomic motions is adequate in most cases.This realization was derived from simulations of the H+H2 exchange reaction.Dynamics Based on the Integrating Newtons Classical Equation of Motion This video shows a non-collinear reactive collision.In this diagram shows the atom distances during reactive collision.The yellow box indicates the strong interaction region.And,this video shows a non-collinear non-reactive collision.In this diagram shows the atom distances during non-reactive collision.The yellow box indicates the strong interaction region.Classical mechanical dynamics based on generalization of the H+H2 methodology was applied to a large number of atoms.Obviously,the best way to illustrate the motions would have been a film of the trajectory.However,the computer graphics facilities available to us were not advanced enough to treat a 458(pseudo)-atom system in a finite time.Instead,Bruce Gelin made two drawings of the structure of BPTI(Fig.13),one at the beginning of the simulation(left)and the other(right)after 3.2 picoseconds.If you look carefully at the figure,you can see that although the two structures are very similar,every residue has moved by a small amount.Given that computer graphics can now make the desired film of the trajectory very easily,Victor Ovchinnikov produced a film for the Nobel Lecture using the corresponding representation(Fig.14).These quotations raise the question:how Nature through evolution has developed the structures of proteins so that their“jigglings and wigglings”have a functional role.As Fig.20 indicates,there are two aspects to this.First,evolution determines the protein structure,which in many cases,is made up of relatively rigid units that are connected by hinges.They allow the units to move with respect to one another.Second,there is a signal,usually the binding of a ligand,that changes the equilibrium between two structures with the rigid units in different positions.As an example,adenylate kinase,an enzyme,has two major conformations,shown in this picture.On the left of the figure is shown the open structure,which permits the substrates to come in and the product to go out,and on the right is shown the closed structure.The closed structure creates a reaction“chamber,”which is isolated from the solvent and has the catalytic residues in position for the reaction to take place.As we can see,the top figure 22 shows a series of snapshots from a cartoon movie with the substrate coming in and the enzyme closing;And the bottom figure shows the reaction taking place and the enzyme opening up to allow the products to escape.How about the future of computational chemistry?Experimentalists use simulations as a tool like any other Applications of simulations to ever more complex systems(viruses,ribosomes,cells,the brain,.)Always with cautions that simulations,like experiments,have their limitations and inherent errors.Todays class tells us,with the development of computers,how does it help us to predict and mimic the chemistry experiments.We have to thank Karplus!思思 考考 卡普拉斯的得奖对我们有什么启示？第一第一、敢于想象敢于想象，不怕风险不怕风险。用七十年代的笨拙计算机来做小分子量子化学计算已经很不容易，但他就已开始计算蛋白质大分子了，更不用说他居然把量子化学和分子动力学结合起来计算大分子而不管当时计算机只能模拟几个皮秒的运动。做费时大计算量的工作，几个月甚至几年的反复计算才会有结果，但结果如果不符合实验就等于白做了。不怕风险，虽然这不是成功的保证但只有这样才有真正成功的可能。第二第二、不要不要怕繁怕繁。学物理出身的人往往想把问题简化，但对于生物现象，关键之处经常在于细节，简化马虎不得。一个分子动力学程序里用的蛋白质能量函数有上千个参数。卡普拉斯的得奖是对全世界做计算生物学的一个鼓励。计算生物学，特别是分子动力学，不像量子化学，还不是一个被普遍认可的学科。这主要是由于它往往用于解释已知现象而不是一个很好的预测工具。也许只有等有了远超于现在的计算机，一切从量子力学开始，生物高分子的全部秘密才能向我们展开。在这样的超级计算机来到之前，大部分生物高分子的研究只好靠对已知数据的挖掘来完成，这就是生物信息学，一门方兴待艾的学科。Martin Karplus The Nobel Prize in Chemistry 2013 Born:15 March 1930,Vienna,Austria Affiliation at the time of the award:Universit de Strasbourg,Strasbourg,France,Harvard University,Cambridge,MA,USA Prize motivation:“for the development of multiscale models for complex chemical systems.”Prize share:1/3 PART ONE Biography Life Martin Karplus was born in Vienna,Austria.His family moved to the U.S.prior to the German occupation in 1938.After studying at Harvard College in Cambridge,Massachusetts in the United States,he moved to the California Institute of Technology,Pasadena,where he received his Ph.D.in 1953.He worked at the University of Illinois in Urbana-Champaign,at Columbia University in New York,and later at Harvard University from 1967.He is also associated with the University of Strasbourg,France.Its function is to transfer one phosphate group from adenosine diphosphate(A-P-P)to another A-P-P to produce adenosine triphosphate(A-P-P-P)and adenosine monophosphate(A-P).