分子对接的随机聚点搜索算法

随机聚点搜索算法是一种普遍的全局极小化方法,在目标函数自变量数目不很大时,计算效率较高。将该算法应用于分子对接,首先要通过模型分子对接,反复调整算法各控制参数使效率最高。对于HIV－1蛋白酶与苯甲醚配体的刚性对接,算法成功的找到了相互作用能量全局极小,与晶体结构的均方根偏差(RMSD)仅0.2?。这表明,该算法可高效率找到分子对接的能量最适构型。     

RNA二级结构预测系统构建

运用下列RNA二级结构预测算法:碱基最大配对方法、Zuker极小化自由能方法、螺旋区最优堆积、螺旋区随机堆积和所有可能组合方法与基于一级螺旋区的RNA二级结构绘图技术, 构建了RNA二级结构预测系统Rnafold. 另外, 通过随机选取20个tRNA序列, 从自由能和三叶草结构两个方面比较了前4种二级结构预测算法, 并运用t检验方法分析了自由能的统计学差别. 从三叶草结构来看, 以随机堆积方法最好, 其次是螺旋区最优堆积方法和Zuker算法, 以碱基最大配对方法最差. 最后, 分析了两种极小化自由能方法之间的差别.     

基于混沌优化算法的多信号分类脑磁图源定位方法

如何利用实验测得的脑磁图数据准确定位脑磁图源的真实活动位置是脑功能研究和临床应用中的一个关键问题.在脑磁活动源定位问题中,多信号分类算法是被广泛研究和采用的一类方法.为了克服多信号分类算法及其改进算法--递归多信号分类算法全局扫描时速度太慢的缺点,提出了一种基于混沌优化算法的脑磁图源定位新方法.该方法利用混沌运动遍历性的特点估计目标函数的全局最大值,进行初步的脑磁图源定位;然后,在小范围内结合网格的方法,进一步进行精确的定位.实验结果表明,此方法可实现多个脑磁图源的定位,并且定位速度大大加快,同时又能达到所要求的定位精度.     

一个生物网络比对的启发式算法

随着各种高通量生物实验技术的发明和广泛应用,越来越多的分子生物网络数据被公布.有效而且可靠的比对这些网络对检测分子生物网络的保守性功能模块和推测物种间的进化关系有着十分重要的意义.然而,由于网络比对在理论上是NP-困难（nondeterministic polynomialtime-hard）问题,它已经成为当前计算生物学需要攻克的主要难点之一.本文提出了一个比对两个蛋白质相互作用网络的启发式算法.该算法首先通过比较两个网络中所有顶点的邻域相似性给出这两个网络的顶点相似性矩阵,然后利用该矩阵将全局网络比对问题转化为一个二部图匹配问题.众所周知,二部图匹配问题具有多项式时间复杂度算法,本文利用ILOG CPLEX软件进行求解.为了验证该算法的优越性,作者比对了水痘病毒（varicella-zoster,VZV）和卡波济（氏）肉瘤病毒（kaposi＇s sarcomaassociated herpesvirus,KSHV）的蛋白质相互作用网络,并且把比对结果同其它网络比对算法进行比较.结果证明该算法显著提高了全局网络比对的精确度.     

基于混沌优化算法的MUSIC脑磁图源定位方法

如何利用实验测得的脑磁图数据准确定位脑磁图源的真实活动位置是脑功能研究和临床应用中的一个关键问题.在脑磁活动源定位问题中,多信号分类算法是被广泛研究和采用的一类方法.为了克服多信号分类算法及其改进算法--递归多信号分类算法全局扫描时速度太慢的缺点,提出了一种基于混沌优化算法的脑磁图源定位新方法.该方法利用混沌运动遍历性的特点估计目标函数的全局最大值,进行初步的脑磁图源定位;然后,在小范围内结合网格的方法,进一步进行精确的定位.实验结果表明,此方法可实现多个脑磁图源的定位,并且定位速度大大加快,同时又能达到所要求的定位精度.     

蛋白质复合物结构预测的集成分子对接方法

蛋白质分子间相互作用与识别是当前生命科学研究的热点,分子对接方法是研究这一问题的有效手段．为了推进分子对接方法的发展,欧洲生物信息学中心组织了国际蛋白质复合物结构预测（CAPRI）竞赛．通过参加CAPRI竞赛,逐步摸索出了一套用于蛋白质复合物结构预测的集成蛋白质一蛋白质分子对接方法HoDock,它包括结合位点预测、初始复合物结构采集、精细复合物结构采集、结构成簇和打分排序以及最终复合物结构挑选等主要步骤．本文以最近的CAPRI Target 39为例,具体说明该方法的主要步骤和应用．该方法在CAPRI Target 39竞赛中取得了比较好的结果,预测结构Model 10是所有参赛小组提交的366个结构中仅有的3个正确结构之一,其配体均方根偏差（L_Rmsd）为0．25nm．在对接过程中,首先用理论预测和实验信息相结合的方法来寻找蛋白质结合位点残基,确认CAPRI Target 39A链的A31TRP和A191HIS,B链的B512ARG和B531ARG为可能结合位点残基．同时,用ZDock程序做不依赖结合位点的初步全局刚性对接．然后,根据结合位点信息进行初步局部刚性对接,从全局和局部对接中挑出了11个初始对接复合物结构．进而,用改进的Rosetta Dock程序做精细位置约束对接,并对每组对接中打分排序前200的结构进行成簇聚类．最后,综合分析打分、成簇和结合位点三方面的信息,得到10个蛋白质复合物结构．竞赛结果表明,A191HIS,B512ARG和B531ARG三个结合位点残基预测正确,提交的10个蛋白质复合物结构中有5个复合物受体一配体界面残基预测成功率较高．与其他参赛小组的对接结果比较,表明HoDock方法具有一定优势．这些结果说明我们提出的集成分子对接方法有助于提高蛋白质复合物结构预测的准确率．     

一种在格点模型上模拟蛋白质折叠结构的优化算法

蛋白质折叠问题是生物信息学中一个经典的多项式复杂程度的非确定性(non-deterministic polynomial,NP)难度问题.势能曲面变平法(ELP)是一种启发式的全局优化算法.通过对ELP方法中的直方图函数提出一种新的更新机制,并将基于贪心策略的初始构象的产生,基于牵引移动的邻域搜索策略与ELP方法相结合,为面心立方体(FCC)格点模型的蛋白质折叠问题提出一种改进的势能曲面变平(ELP+)算法.采用文献中9条常用序列作为测试集.对于每条序列,ELP+算法均能找到与文献中的算法所得到的最低能量相等或更低的能量.实验结果表明,ELP+算法是求解FCC格点模型的蛋白质折叠问题的一种有效算法.     

颗粒粒径测量中约束正则CONTIN算法分析

介绍用于光子相关光谱法颗粒粒径分布反演计算的约束正则化方法及基于它的CONTIN算法,通过数值模拟反演理想单峰和双峰分布,分析CONTIN算法的展宽效应、该效应与粒径的关系.利用实验测量比较了指数采样法、非负约束最小二乘法和CONTIN算法的反演结果.研究指出CONTIN的分辨力与粒径有关,总体较弱.合理选择小正则化参数或者缩小反演范围能改善反演质量.     

一种预测细胞凋亡蛋白的亚细胞定位的新方法

细胞凋亡蛋白对生物体的发育、维持内环境稳定及人们理解细胞凋亡机制非常重要。文中提出了一种新的蛋白质序列特征提取方法—三肽离散源方法。计算了蛋白质序列中紧邻三联体的出现个数,利用离散增量极小化对凋亡蛋白进行定位预测;同时推广了张春霆等提出的内容平衡精度指数,使其能评估任意类的分类问题。实验结果表明:在凋亡蛋白定位预测研究中,三肽离散源方法在提高总体预测精度的同时,能够较好的解决样本不均衡问题;而内容平衡精度指数能比传统的总体预测精度更准确的评估预测算法的预测能力,有效的反映预测算法对样本不均衡问题的相容能力。     

一种新的蛋白质结构字母序列优化算法

基于蛋白质结构字母的预测和分析方法,一个必然的步聚,是将目标蛋白质离散成结构字母序列。本文在对蛋白质结构字母序列空间,及其最小根均方偏差变化,穷举分析的基础上,提出了一种新的蛋白质结构字母序列优化算法,全局贪婪算法。全局贪婪算法避免了基本贪婪算法过度依赖候选集大小,计算量过大、以及过早收缩于局部最小等缺点。经实验分析,全局贪婪算法在性能上优于基本贪婪算法和局部最优方法。。     

A simple reference state makes a significant improvement in near-native selections from structurally refined docking decoys

Near-native selections from docking decoys have proved challenging especially when unbound proteins are used in the molecular docking. One reason is that significant atomic clashes in docking decoys lead to poor predictions of binding affinities of near native decoys. Atomic clashes can be removed by structural refinement through energy minimization. Such an energy minimization, however, will lead to an unrealistic bias toward docked structures with large interfaces. Here, we extend an empirical energy function developed for protein design to protein-protein docking selection by introducing a simple reference state that removes the unrealistic dependence of binding affinity of docking decoys on the buried solvent accessible surface area of interface. The energy function called EMPIRE (EMpirical Protein-InteRaction Energy), when coupled with a refinement strategy, is found to provide a significantly improved success rate in near native selections when applied to RosettaDock and refined ZDOCK docking decoys. Our work underlines the importance of removing nonspecific interactions from specific ones in near native selections from docking decoys.     

The identification of complementarity of molecular surfaces using fuzzy set theory

Fuzzy logic-based algorithms for the quantitative treatment of complementarity of molecular surfaces are presented. The identification of complementary surface patches can be considered as a first step for the solution of molecular docking problems. Based on these initial guesses, docking structures can be further optimized by standard technologies. In this work a simple downhill simplex method for the optimization is used. The algorithms are applied to various biomolecular complexes. For all these complexes, at least one structure was found to be in very good agreement with the experimental data.     

ATTRACT: protein-protein docking in CAPRI using a reduced protein model

Protein-protein complex structures have been predicted for CAPRI Rounds 3 and 5 using a reduced protein model. Proteins are represented by up to 3 pseudoatoms per amino acid. The docking approach termed ATTRACT is based on energy minimization in translational and rotational degrees of freedom of one protein with respect to another protein. The reduced protein model allows one to perform systematic docking minimization of many thousand start structures in reasonable computer time. Flexibility of critical surface side-chains can be accounted for by a multiple conformational copy approach. The multicopy approach allows simultaneous adjustment of side-chain conformations and optimization of translational and rotational degrees of freedom of one protein with respect to the partner during docking. For 3 (Targets 8, 14, and 19) out of 5 CAPRI targets, the approach resulted in predictions in close agreement with experiment [root-mean-square deviation (RMSD) of backbone atoms within 10 A of the protein-protein interface < 1.8 A]. The comparison of predicted and experimental structures of the CAPRI targets indicates that besides local conformational changes (e.g., changes in side-chain conformations), global conformational changes of the protein backbone can be critical for complex formation. These conformational changes not accounted for during docking are a likely reason for the unrealistic predictions in 2 cases (Targets 9 and 18).     

Protein-protein docking in CAPRI using ATTRACT to account for global and local flexibility

A reduced protein model combined with a systematic docking approach has been employed to predict protein-protein complex structures in CAPRI rounds 6-11. The docking approach termed ATTRACT is based on energy minimization in translational and rotational degrees of freedom of one protein with respect to the second protein starting from many thousand initial protein partner placements. It also allows for approximate inclusion of global flexibility of protein partners during systematic docking by conformational relaxation of the partner proteins in precalculated soft collective backbone degrees of freedom. We have submitted models for six targets, achieved acceptable docking solutions for two targets, and predicted >20% correct contacts for five targets. Possible improvements of the docking approach in particular at the scoring and refinement steps are discussed.     

Identification of hotspot regions of MurB oxidoreductase enzyme using homology modeling,molecular dynamics and molecular docking techniques

Despite the availability of effective chemotherapy and a moderately protective vaccine, new anti-tuberculosis agents are urgently needed to decrease the global incidence of tuberculosis (TB) disease. The MurB gene belongs to the bacterial cell wall biosynthesis pathway and is an essential drug target in Mycobacterium tuberculosis (Mtb) that has no mammalian counterparts. Here, we present an integrated approach involving homology modeling, molecular dynamics and molecular docking studies on Mtb-MurB oxidoreductase enzyme. A homology model of Mtb-MurB enzyme was built for the first time in order to carry out structure-based inhibitor design. The accuracy of the model was validated using different techniques. The molecular docking study on this enzyme was undertaken using different classes of well known MurB inhibitors. Estimation of binding free energy by docking analysis indicated the importance of Tyr155, Arg156, Ser237, Asn241 and His304 residues within the Mtb-MurB binding pocket. Our computational analysis is in good agreement with experimental results of site-directed mutagenesis. The present study should therefore play a guiding role in the experimental design of Mtb-MurB inhibitors for in vitro/in vivo analysis.     

ZDOCK and RDOCK performance in CAPRI rounds 3, 4, and 5

We present an evaluation of the results of our ZDOCK and RDOCK algorithms in Rounds 3, 4, and 5 of the protein docking challenge CAPRI. ZDOCK is a Fast Fourier Transform (FFT)-based, initial-stage rigid-body docking algorithm, and RDOCK is an energy minimization algorithm for refining and reranking ZDOCK results. Of the 9 targets for which we submitted predictions, we attained at least acceptable accuracy for 7, at least medium accuracy for 6, and high accuracy for 3. These results are evidence that ZDOCK in combination with RDOCK is capable of making accurate predictions on a diverse set of protein complexes.     

Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule

Ligand-protein docking has been developed and used in facilitating new drug discoveries. In this approach, docking single or multiple small molecules to a receptor site is attempted to find putative ligands. A number of studies have shown that docking algorithms are capable of finding ligands and binding conformations at a receptor site close to experimentally determined structures. These algorithms are expected to be equally applicable to the identification of multiple proteins to which a small molecule can bind or weakly bind. We introduce a ligand-protein inverse-docking approach for finding potential protein targets of a small molecule by the computer-automated docking search of a protein cavity database. This database is developed from protein structures in the Protein Data Bank (PDB). Docking is conducted with a procedure involving multiple-conformer shape-matching alignment of a molecule to a cavity followed by molecular-mechanics torsion optimization and energy minimization on both the molecule and the protein residues at the binding region. Scoring is conducted by the evaluation of molecular-mechanics energy and, when applicable, by the further analysis of binding competitiveness against other ligands that bind to the same receptor site in at least one PDB entry. Testing results on two therapeutic agents, 4H-tamoxifen and vitamin E, showed that 50% of the computer-identified potential protein targets were implicated or confirmed by experiments. The application of this approach may facilitate the prediction of unknown and secondary therapeutic target proteins and those related to the side effects and toxicity of a drug or drug candidate. Proteins 2001;43:217-226.     

Energy minimization in low-frequency normal modes to efficiently allow for global flexibility during systematic protein-protein docking

Protein-protein association can frequently involve significant backbone conformational changes of the protein partners. A computationally rapid method has been developed that allows to approximately account for global conformational changes during systematic protein-protein docking starting from many thousands of start configurations. The approach employs precalculated collective degrees of freedom as additional variables during protein-protein docking minimization. The global collective degrees of freedom are obtained from normal mode analysis using a Gaussian network model for the protein. Systematic docking searches were performed on 10 test systems that differed in the degree of conformational change associated with complex formation and in the degree of overlap between observed conformational changes and precalculated flexible degrees of freedom. The results indicate that in case of docking searches that minimize the influence of local side chain conformational changes inclusion of global flexibility can significantly improve the agreement of the near-native docking solutions with the corresponding experimental structures. For docking of unbound protein partners in several cases an improved ranking of near native docking solutions was observed. This was achieved at a very modest ( approximately 2-fold) increase of computational demands compared to rigid docking. For several test cases the number of docking solutions close to experiment was also significantly enhanced upon inclusion of soft collective degrees of freedom. This result indicates that inclusion of global flexibility can facilitate in silico protein-protein association such that a greater number of different start configurations results in favorable complex formation.     

Protein social behavior makes a stronger signal for partner identification than surface geometry

Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico‐chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross‐docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S‐index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface‐based (ranking) score to discriminate partners from non‐interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137–154. © 2016 Wiley Periodicals, Inc.