改进的牵引移动的蚁群算法在2D HP模型中的应用

蛋白质折叠问题就是从氨基酸序列中预测蛋白质的构象,该问题是生物信息学的一个突出问题。主要研究二维HP格点模型,它是用于模拟蛋白质折叠问题的一个具有代表性的简化模型,并且将蚁群算法用于求解该二维HP蛋白质的折叠问题。此外,在局部搜索机制中引入一种改进的牵引移动方法,这是一个提高蛋白质构象的有效方法。实验结果表明,针对较长的氨基酸序列,改进的带牵引移动的蚁群算法(ACO+)比ACO能够获得更低能量的构象,证明了所提出的改进蚁群算法是预测蛋白质结构的有效方法。     

基于改进的禁忌搜索的蛋白质三维结构预测

禁忌搜索算法是一种局部搜索能力很强的全局迭代优化算法,已经被成功地应用到各种组合优化问题中。基于AB非格模型,该文将一种改进的禁忌搜索算法应用于蛋自质三维折叠结构预测。实验结果表明改进的禁忌算法求得的蛋白质三维最低能量构形的最低能量值比已有的算法求得的最低能量值要低,同时三维构形中形成了一个疏水核,被亲水残基包围,反映了真实蛋白质的结构特征。该算法效率高,可以有效地用于蛋白质三维折叠预测。     

一种基于片段组装的蛋白质构象空间优化算法

针对蛋白质构象空间优化问题,提出一种基于片段组装的构象空间优化算法。算法利用基于Rosetta粗粒度的知识能量模型有效地提高了收敛速度;同时,借助片段组装技术可以有效弥补因能量函数不精确而导致的预测精度不足的缺陷;此外,差分进化算法的引入使得算法具有较好的全局搜索能力。5种测试蛋白的实验结果表明,所提算法具有较好的搜索性能和预测精度。     

蛋白质构象空间局部增强差分进化搜索方法

针对蛋白质构象空间搜索问题,提出一种蛋白质构象空间局部增强差分进化搜索方法。在差分进化算法框架下,采用Rosetta Score3粗粒度知识能量模型有效降低构象空间的搜索维数,加快算法收敛速度;引入基于知识的片段组装技术可以有效提高预测精度;利用Monte Carlo算法良好的局部搜索性能对种群做局部增强,以得到更为优良的局部构象;结合差分进化算法较强的全局搜索能力,可以对构象空间进行更为有效的采样。5个测试蛋白实验结果表明,所提算法具有较好的搜索性能和预测精度。     

从头预测蛋白质骨架的一种并行蚁群方法及其在CASP8/9中的应用

从低同源关系的氨基酸序列预测蛋白质的三维结构被称为从头预测,它是计算生物学领域中的挑战之一.蛋白质骨架预测是从头预测的必要先导步骤.本文应用一种基于共享信息素的并行蚁群优化算法,在现有能量函数指导下,通过不同能量项之间的定性互补,构建具有最低能量的蛋白质骨架结构,并通过聚类选择构象候选集合中具有最低自由能的构象.在CASP8/9所公布的从头建模目标上应用了该方法,CASP8的13个从头建模目标中,模型1中有2个目标的预测结果超过CASP8中最好的结果,7个位列前10名;CASP9的29个从头建模目标中,候选集中的最佳结果中有20个进入Server组的前10名,模型1中有11个进入前10名.本文的结果说明融合多个不同的能量函数指导并行搜索,可以更好地模拟天然蛋白质的折叠行为.同时,在本算法载体上实现了不同种类搜索策略的融合并行,对于用非确定性算法解决类似的优化问题来说也是一种新颖的方法.     

基于副本交换的局部增强差分进化蛋白质结构从头预测方法

针对蛋白质高维构象空间搜索问题,提出一种基于副本交换的局部增强差分进化蛋白质结构从头预测方法(RLDE)。首先,采用基于知识的Rosetta粗粒度能量模型显著降低构象空间优化变量维数;其次,引入基于片段库知识的片段组装技术进一步减小构象搜索空间,有效避免搜索过程中的熵效应;此外,在每个副本层设置构象种群,采用差分进化算法对种群进行更新,然后利用Monte Carlo算法对种群做局部增强,以此得到全局和部分局部最优构象。综上,RLDE利用差分进化算法较强的全局搜索能力可以对构象空间进行有效的全局搜索;借助Monte Carlo算法局部搜索性能对构象空间局部极小区域进行更为充分的采样;副本交换策略保证了副本层中种群的多样性,同时能够增强算法跳出局部极小的能力,从而使得算法对构象空间的搜索能力进一步增强。15个目标蛋白测试结果表明,所提方法能够有效地对构象空间采样,得到高精度的近天然态蛋白质构象。     

基于Toy模型蛋白质折叠预测的多种群微粒群优化算法研究

基于Toy模型的蛋白质折叠结构预测问题是一个典型的NP问题.提出了多种群微粒群优化算法用于计算蛋白质能量最小值.该算法采用了一种新的算法结构,在该结构中,每一代的种群被分为精英子种群、开采子种群和勘探子种群三部分,通过改善种群的局部开采能力和全局勘探能力来提高算法的性能.分别采用Fibonacci蛋白质测试序列和真实蛋白质序列进行了折叠结构预测的仿真实验.实验结果表明该算法能够更精确地进行蛋白质折叠结构预测,为生物科学研究提供了一条有效途径.     

基于几何流形能量最小化的图像检索算法

提出了一种基于几何流形能量GEOMEN(geometric manifold energy)最小化的图像检索算法。许多基于流形的检索算法都是在图像的特征空间提取相应的语义流形空间,进而在语义空间中进行图像检索排序。将图像的检索看作一个图像数据库中搜索一个最优图像能量环的问题。图像能量环表示了图像之间的联系和相关性,通过最小化GEOMEN可以得到最优图像环。最小化的求解涉及到一个组合优化的问题,传统的禁忌搜索算法在选择最优候选集时非常耗时,提出一种智能的积极禁忌搜索算法求解最优环,实验表明提出的算法检索性能高,可以得到较高的查全率与准确率。     

基于粒子群优化算法的并行模拟退火算法

针对模拟退火（simulated annealing,SA）算法收敛速度慢,随机采样策略缺乏记忆能力,算法内在的串行性使其具有并行化问题依赖等缺点,提出了基于粒子群优化（particle swarm optimization,PSO）算法的并行模拟退火算法。该算法利用粒子群优化算法中个体的记忆功能引导算法在解空间中开展精细搜索,在反向学习算法基础上设计新的反向转动操作机制增加了算法的多样性,借助PSO的天然并行性克服了SA的并行问题依赖性,并在集群上实现了多Agent协同进化的改进算法。对Toy模型的蛋白质结构预测问题进行了仿真实验,结果表明该算法能有效提高求解问题的质量和效率。     

三角网格模型的快速树搜索算法及可设计性分析

在蛋白质折叠格子模型的可设计性特征研究中,为了克服以往方格模型具有奇偶问题这一缺点,本文利用三角网格模型来进行穷举搜索。在简化的网格模型中,序列折叠为某一结构的能量值为在结构心部疏水氨基酸的个数取负值。在蛋白质折叠模型的二维4 5 6 5 4三角网格中穷举了所有的序列和致密结构。其中序列由两类氨基酸(疏水氨基酸和亲水氨基酸)组成,排除正反对称序列共2~(12) 2~(23)=8392704种不同序列。在由24个格点组成的三角网格模型中共得到219093种简化结构串。在穷尽搜索算法中,为实现快速搜索,通过树结构将相似的结构串尽量聚类,通过计算各树结点的目标能量值以减少搜索算法中所需的计算量。经并行实验验证,利用该树结构可使快速搜索算法达到指数级加速比。最后对计算所得结果进行了统计分析。     

改进二进制量子粒子群算法在蛋白质折叠中的应用*

为了改善已有二维HP模型蛋白质折叠算法容易陷入局部最优、找不到理论最低能量构象的缺点,提出一种基于变异算子的改进二进制量子粒子群算法。采用二进制编码蛋白质序列,提出变异策略,并采用惩罚因子避免出现蛋白质重叠,最后将该算法应用于蛋白质序列进行测试。测试结果表明,改进算法能够找到更优的结果,算法具有一定的实用性和有效性。     

禁忌搜索算法在蛋白质结构预测中应用

一种氨基酸序列只可能有一种蛋白质结构,所以在蛋白质理论预测中,正确定义能量函数、精确选用的计算机搜寻算法来寻找能量最低值,是蛋白质结构预测的关键。基于此,本文以两两残基之间距离分布和二面角分布符合玻尔兹曼定理,提出了一种抽象的蛋白质三维结构连续物理数学模型。然后应用了禁忌搜索算法很好的计算了牛胰岛素B(D)主链走向;比较计算了氨基酸序列最低能量的全局最优点。     

Effect of increasing the energy gap between the two lowest energy states on the mixing time of the Metropolis algorithm

In order to understand what makes natural proteins fold rapidly, ?ali, Shakhnovich and Karplus (1994) [6], [7] had used the Metropolis algorithm to search for the minimum energy conformations of chains of beads in the lattice model of protein folding. Based on their computational experiments, they concluded that the Metropolis algorithm would find the minimum energy conformation of a chain of beads within an acceptable time scale if and only if there is a large gap between the energies of the minimum energy conformation and that of the second minimum. Clote (1999) [1] attempted to support this conclusion by a proof that the mixing time of the underlying Markov chain would decrease as the gap in energies of the minimum energy conformation and that of the second minimum increased. He was able to show that an upper bound on the mixing time does indeed decrease as the energy gap increases. We show in this paper that the mixing time itself, however, is a non-decreasing function of the value of the energy gap. Therefore, our result contradicts what Clote had attempted to prove.     

蛋白质HP模型的改进和PERM算法求解

HP模型是一种被广泛研究的简化的蛋白质折叠模型.在蛋白质螺旋结构的预测上有很高的可信度.但是HP的正方格点模型存在能量计算缺陷,影响其对链上特定结构的计算.针对这一缺陷,给出一种模型上的修正,引入了三角化的格点模型.利用求解格点蛋白质模型高效的算法PERM对通用算例进行了基于2-D三角格点模型的仿真计算.得到了紧密的蛋白质链折叠构型.构型最小能量比正方格点模型更低,得到的标准算例构型比较吻合实际蛋白质折叠的直观构型.计算结果验证了改进模型的有效性.对后续的研究提出了建议.     

The Protein Structure Prediction Problem: A Constraint Optimization Approach using a New Lower Bound

The protein structure prediction problem is one of the most (if not the most) important problem in computational biology. This problem consists of finding the conformation of a protein with minimal energy. Because of the complexity of this problem, simplified models like Dill's HP-lattice model [15], [16] have become a major tool for investigating general properties of protein folding. Even for this simplified model, the structure prediction problem has been shown to be NP-complete [5], [7]. We describe a constraint formulation of the HP-model structure prediction problem, and present the basic constraints and search strategy. Of course, the simple formulation would not lead to an efficient algorithm. We therefore describe redundant constraints to prune the search tree. Furthermore, we need bounding function for the energy of an HP-protein. We introduce a new lower bound based on partial knowledge about the final conformation (namely the distribution of H-monomers to layers).     

Determination of protein structure and dynamics combining immune algorithms and pattern search methods

Natural proteins quickly fold into a complicated three-dimensional structure. Evolutionary algorithms have been used to predict the native structure with the lowest energy conformation of the primary sequence of a given protein. Successful structure prediction requires a free energy function sufficiently close to the true potential for the native state, as well as a method for exploring the conformational space. Protein structure prediction is a challenging problem because current potential functions have limited accuracy and the conformational space is vast. In this work, we show an innovative approach to the protein folding (PF) problem based on an hybrid Immune Algorithm (IMMALG) and a quasi-Newton method starting from a population of promising protein conformations created by the global optimizer DIRECT. The new method has been tested on Met-Enkephelin peptide, which is a paradigmatic example of multiple–minima problem, 1POLY, 1ROP and the three helix protein 1BDC. DIRECT produces an initial population of promising candidate solutions within a potentially optimal rectangle for the funnel landscape of the PF problem. Hence, IMMALG starts from a population of promising protein conformations created by the global optimizer DIRECT. The experimental results show that such a multistage approach is a competitive and effective search method in the conformational search space of real proteins, in terms of solution quality and computational cost comparing the results of the current state-of-art algorithms.     

Simulation of coupled folding and binding of an intrinsically disordered protein in explicit solvent with metadynamics

The C-terminal domain of measles virus nucleoprotein is an intrinsically disordered protein that could bind to the X domain (XD) of phosphoprotein P to exert its physiological function. Experiments reveal that the minimal binding unit is a 21-residue α-helical molecular recognition element (α-MoRE-MeV), which adopts a fully helical conformation upon binding to XD. Due to currently limited computing power, direct simulation of this coupled folding and binding process with atomic force field in explicit solvent cannot be achieved. In this work, two advanced sampling methods, metadynamics and parallel tempering, are combined to characterize the free energy surface of this process and investigate the underlying mechanism. Starting from an unbound and partially folded state of α-MoRE-MeV, multiple folding and binding events are observed during the simulation and the energy landscape was well estimated. The results demonstrate that the isolated α-MoRE-MeV resembles a molten globule and rapidly interconverts between random coil and multiple partially helical states in solution. The coupled folding and binding process occurs through the induced fit mechanism, with the residual helical conformations providing the initial binding sites. Upon binding, α-MoRE-MeV can easily fold into helical conformation without obvious energy barriers. Two mechanisms, namely, the system tending to adopt the structure in which the free energy of isolated α-MoRE-MeV is the minimum, and the binding energy of α-MoRE-MeV to its partner protein XD tending to the minimum, jointly dominate the coupled folding and binding process. With the advanced sampling approach, more IDP systems could be simulated and common mechanisms concerning the coupled folding and binding process could be investigated in the future.