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【分享】分子模擬一般性步驟 [原創(chuàng)]
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以下是做模擬的一般性步驟，具體的步驟和過程依賴于確定的系統(tǒng)或者是軟件，但這不影響我們把它當(dāng)成一個入門指南： 1）首先我們需要對我們所要模擬的系統(tǒng)做一個簡單的評估, 三個問題是我們必須要明確的： 做什么（what to do）為什么做（why to do）怎么做（how to do） 2）選擇合適的模擬工具，大前提是它能夠?qū)崿F(xiàn)你所感興趣的目標(biāo)，這需要你非常謹(jǐn)慎的查閱文獻(xiàn)，看看別人用這個工具都做了些什么，有沒有和你相關(guān)的，千萬不要做到一半才發(fā)現(xiàn)原來這個工具根本就不能實(shí)現(xiàn)你所感興趣的idea，切記！ 考慮1：軟件的選擇，這通常和軟件主流使用的力場有關(guān)，而軟件本身就具體一定的偏向性，比如說，做蛋白體系，Gromacs，Amber，Namd均可；做DNA, RNA體系，首選肯定是Amber；做界面體系，Dl_POLY比較強(qiáng)大，另外做材料體系，Lammps會是一個不錯的選擇 考慮2：力場的選擇。力場是來描述體系中最小單元間的相互作用的，是用量化等方法計算擬合后生成的經(jīng)驗(yàn)式，有人會嫌它粗糙，但是它確確實(shí)實(shí)給我們模擬大系統(tǒng)提供了可能，只能說關(guān)注的切入點(diǎn)不同罷了。常見的有三類力場：全原子力場，聯(lián)合力場，粗�；觯划�(dāng)然還有所謂第一代，第二代，第三代力場的說法，這里就不一一列舉了。 再次提醒注意：必須選擇適合于我們所關(guān)注體系和我們所感興趣的性質(zhì)及現(xiàn)象的力場。 3）通過實(shí)驗(yàn)數(shù)據(jù)或者是某些工具得到體系內(nèi)的每一個分子的初始結(jié)構(gòu)坐標(biāo)文件，之后，我們需要按我們的想法把這些分子按照一定的規(guī)則或是隨機(jī)的排列在一起，從而得到整個系統(tǒng)的初始結(jié)構(gòu)，這也是我們模擬的輸入文件。 4）結(jié)構(gòu)輸入文件得到了，我們還需要力場參數(shù)輸入文件，也就是針對我們系統(tǒng)的力場文件，這通常由所選用的力場決定，比如鍵參數(shù)和非鍵參數(shù)等勢能函數(shù)的輸入?yún)?shù)。 5）體系的大小通常由你所選用的box大小決定，我們必須對可行性與合理性做出評估，從而確定體系的大小，這依賴于具體的體系，這里不細(xì)說了。 6）由于初始構(gòu)象可能會存在兩個原子挨的太近的情況（稱之為bad contact），所以需要在正式模擬開始的第一步進(jìn)行體系能量最小化，比較常用的能量最小化有兩種，最速下降法和共軛梯度法，最速下降法是快速移除體系內(nèi)應(yīng)力的好方法，但是接近能量極小點(diǎn)時收斂比較慢，而共軛梯度法在能量極小點(diǎn)附近收斂相對效率高一些，所有我們一般做能量最小化都是在最速下降法優(yōu)化完之后再用共軛梯度法優(yōu)化，這樣做能有效的保證后續(xù)模擬的進(jìn)行。 7）以平衡態(tài)模擬為例，你需要設(shè)置適當(dāng)?shù)哪M參數(shù)，并且保證這些參數(shù)設(shè)置和力場的產(chǎn)生相一致，舉個簡單的例子，gromos力場是用的范德華勢雙截斷來定范德華參數(shù)的，若你也用gromos力場的話也應(yīng)該用雙截斷來處理范德華相互作用。常見的模擬思路是，先在NVT下約束住你的溶質(zhì)（劑）做限制性模擬，這是一個升溫的過程，當(dāng)溫度達(dá)到你的設(shè)定后, 接著做NPT模擬，此過程將調(diào)整體系的壓強(qiáng)進(jìn)而使體系密度收斂。 經(jīng)過一段時間的平衡模擬，在確定系統(tǒng)弛豫已經(jīng)完全消除之后，就可以開始取數(shù)據(jù)了。如何判斷體系達(dá)到平衡，這個問題是比較技術(shù)性的問題，簡單的講可以通過以下幾種方式，一，看能量（勢能，動能和總能）是否收斂；二，看系統(tǒng)的壓強(qiáng)，密度等等是否收斂；三看系統(tǒng)的RMSD是否達(dá)到你能接受的范圍，等等。 8）運(yùn)行足夠長時間的模擬以確定我們所感興趣的現(xiàn)象或是性質(zhì)能夠被觀測到，并且務(wù)必確保此現(xiàn)象出現(xiàn)的可重復(fù)性。 9）數(shù)據(jù)拿到手后，很容易通過一些可視化軟件得到軌跡動畫，但這并不能拿來發(fā)文章。真正的工作才剛剛開始——分析數(shù)據(jù)，你所感興趣的現(xiàn)象或性質(zhì)只是表面，隱含在它們之中的機(jī)理才是文章中的主題。 參考文獻(xiàn): 1. 陳正隆《分子模擬的理論與實(shí)踐》講習(xí)班教材 2. Steps to Perform a Simulation (GMX)http://www.mdbbs.org/thread-54-1-1.html 附錄：分子模擬的簡要介紹 Molecular modelling is a collective term that refers to theoretical methods and computational techniques to model or mimic the behaviour of molecules. The techniques are used in the fields of computational chemistry, computational biology and materials science for studying molecular systems ranging from small chemical systems to large biological molecules and material assemblies. The simplest calculations can be performed by hand, but inevitably computers are required to perform molecular modelling of any reasonably sized system. The common feature of molecular modelling techniques is the atomistic level description of the molecular systems; the lowest level of information is individual atoms (or a small group of atoms). This is in contrast to quantum chemistry (also known as electronic structure calculations) where electrons are considered explicitly. The benefit of molecular modelling is that it reduces the complexity of the system, allowing many more particles (atoms) to be considered during simulations. Molecular mechanics is one aspect of molecular modelling, as it is refers to the use of classical mechanics/Newtonian mechanics to describe the physical basis behind the models. Molecular models typically describe atoms (nucleus and electrons collectively) as point charges with an associated mass. The interactions between neighbouring atoms are described by spring-like interactions (representing chemical bonds) and van der Waals forces. The Lennard-Jones potential is commonly used to describe van der Waals forces. The electrostatic interactions are computed based on Coulomb's law. Atoms are assigned coordinates in Cartesian space or in internal coordinates, and can also be assigned velocities in dynamical simulations. The atomic velocities are related to the temperature of the system, a macroscopic quantity. The collective mathematical expression is known as a potential function and is related to the system internal energy (U), a thermodynamic quantity equal to the sum of potential and kinetic energies. Methods which minimize the potential energy are known as energy minimization techniques (e.g., steepest descent and conjugate gradient), while methods that model the behaviour of the system with propagation of time are known as molecular dynamics. E = E_bonds + E_angle + E_dihedral + E_non − bonded E_non − bonded = E_electrostatic + E_vanderWaals This function, referred to as a potential function, computes the molecular potential energy as a sum of energy terms that describe the deviation of bond lengths, bond angles and torsion angles away from equilibrium values, plus terms for non-bonded pairs of atoms describing van der Waals and electrostatic interactions. The set of parameters consisting of equilibrium bond lengths, bond angles, partial charge values, force constants and van der Waals parameters are collectively known as a force field. Different implementations of molecular mechanics use slightly different mathematical expressions, and therefore, different constants for the potential function. The common force fields in use today have been developed by using high level quantum calculations and/or fitting to experimental data. The technique known as energy minimization is used to find positions of zero gradient for all atoms, in other words, a local energy minimum. Lower energy states are more stable and are commonly investigated because of their role in chemical and biological processes. A molecular dynamics simulation, on the other hand, computes the behaviour of a system as a function of time. It involves solving Newton's laws of motion, principally the second law, F = ma. Integration of Newton's laws of motion, using different integration algorithms, leads to atomic trajectories in space and time. The force on an atom is defined as the negative gradient of the potential energy function. The energy minimization technique is useful for obtaining a static picture for comparing between states of similar systems, while molecular dynamics provides information about the dynamic processes with the intrinsic inclusion of temperature effects. Molecules can be modelled either in vacuum or in the presence of a solvent such as water. Simulations of systems in vacuum are referred to as gas-phase simulations, while those that include the presence of solvent molecules are referred to as explicit solvent simulations. In another type of simulation, the effect of solvent is estimated using an empirical mathematical expression; these are known as implicit solvation simulations. Molecular modelling methods are now routinely used to investigate the structure, dynamics and thermodynamics of inorganic, biological, and polymeric systems. The types of biological activity that have been investigated using molecular modelling include protein folding, enzyme catalysis, protein stability, conformational changes associated with biomolecular function, and molecular recognition of proteins, DNA, and membrane complexes. from: http://en.wikipedia.org/wiki/Molecular_modelling 如需轉(zhuǎn)載請注明出處 http://www.mdbbs.org/viewthread.php?tid=5535 [ Last edited by zeoliters on 2009-12-25 at 16:20 ] |
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