BIASED PROBABILITY MONTE-CARLO CONFORMATIONAL SEARCHES AND ELECTROSTATIC CALCULATIONS FOR PEPTIDES AND PROTEINS
BIASED PROBABILITY MONTE-CARLO CONFORMATIONAL SEARCHES AND ELECTROSTATIC CALCULATIONS FOR PEPTIDES AND PROTEINS
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DOI：
10.1006/jmbi.1994.1052
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发表时间：
1994-01-21
影响因子：
5.6
通讯作者：
TOTROV, M
中科院分区：
文献类型：
--
作者：
ABAGYAN, R;TOTROV, M
成功预测多肽和蛋白质的三维结构需要两个主要组成部分：一种高效的全局优化程序，能够为数百个变量的强各向异性函数找到适当的局部最小值，以及溶液中蛋白质分子的一组自由能分量，其计算成本低，足以用于搜索过程，但足够精确，以确保天然构象的唯一性。在给定构象子空间（例如φ-ψ区或侧链扭转角）的能量或统计性质的知识的情况下，我们在这里找到了一种有效的方法来在蒙特卡罗过程中做出随机步骤。该有偏概率蒙特卡罗算法首先随机选择子空间，然后根据预定义的连续概率分布，向一个与前一个位置无关的新的随机位置移动。随机步进之后是扭转角空间的局部极小化。从191个和161个蛋白质三维结构的代表集中，分别计算了不同残基类型在φ-ψ图和χ-角图上的高概率区位置、大小和偏好。建立了一种快速、精确的测定溶液中蛋白质静电能的方法，并将其与BPMC方法相结合。该方法基于改进的球面像电荷近似，有效地投射到任意形状的分子上。与泊松-玻尔兹曼方程的有限差分解比较表明，本文方法具有较高的精度。以链球菌蛋白G的免疫球蛋白结合域为例，成功地将BPMC方法应用于12-残基和16-残基合成肽的结构预测和核磁共振数据的蛋白质结构测定。与无偏模拟相比，BPMC运行显示出更好的收敛性能。核磁共振结构确定的真正全局优化过程的优势在于它能够处理由核磁共振数据误差和模糊性引起的局部最小值。
Two major components are required for a successful prediction of the three-dimensional structure of peptides and proteins: an efficient global optimization procedure which is capable of finding an appropriate local minimum for the strongly anisotropic function of hundreds of variables, and a set of free energy components for a protein molecule in solution which are computationally inexpensive enough to be used in the search procedure, yet sufficiently accurate to ensure the uniqueness of the native conformation. We here found an efficient way to make a random step in a Monte Carlo procedure given knowledge of the energy or statistical properties of conformational subspaces (e.g.φ-ψzones or side-chain torsion angles). This biased probability Monte Carlo (BPMC) procedure randomly selects the subspace first, then makes a step to a new random position independent of the previous position, but according to the predefined continuous probability distribution. The random step is followed by a local minimization in torsion angle space. The positions, sizes and preferences for high-probability zones onφ-ψmaps and χ-angle maps were calculated for different residue types from the representative set of 191 and 161 protein 3D-structures, respectively. A fast and precise method to evaluate the electrostatic energy of a protein in solution is developed and combined with the BPMC procedure. The method is based on the modified spherical image charge approximation, efficiently projected onto a molecule of arbitrary shape. Comparison with the finite-difference solutions of the Poisson-Boltzmann equation shows high accuracy for our approach. The BPMC procedure is applied successfully to the structure prediction of 12- and 16-residue synthetic peptides and the determination of protein structure from NMR data, with the immunoglobulin binding domain of streptococcal protein G as an example. The BPMC runs display much better convergence properties than the non-biased simulations. The advantage of a true global optimization procedure for NMR structure determination is its ability to cope with local minima originating from data errors and ambiguities in NMR data.