Docking flexible molecules: A case study of three proteins

A genetic algorithm (GA) conformation search method is used to dock a series of flexible molecules into one of three proteins. The proteins examined are thermolysin (tmn), carboxypeptidase A (cpa), and dihydrofolate reductase (dfr). In the latter two proteins, the crystal ligand was redocked. For thermolysin, we docked eight ligands into a protein conformation derived from a single crystal structure. The bound conformations of the other ligands in tmn are known. In the cpa and dfr cases, and in seven of the eight tmn ligands, the GA docking method found conformations within 1.6 Å root mean square (rms) of the relaxed crystal conformation. © 1995 John Wiley & Sons, Inc.     

Genetic algorithms: A robust scheme for geometry optimizations and global minimum structure problems

A Genetic Algorithm for Geometry Optimizations (GALGO) program has been developed to study the efficiency of this method of finding global minimum structures. Using a semiempirical tight-binding potential, the behavior of different genetic algorithm (GA) operators has been tested for the linear chain isomer of a C₈ cluster. An optimum set of parameters for the GA operators is proposed for this problem and afterward is used to obtain the global minimum structure of rare-gas atomic clusters of up to 13 atoms using the 12–6 Lennard-Jones interatomic pair potential. © 1995 by John Wiley & Sons, Inc.     

径向基函数神经网络用于重叠色谱峰解析

 在高斯基径向基函数神经网络 (RBFNN)学习算法中引入了鲁棒性和随机全局寻优的两阶段遗传算法 :结构学习和参数优化。通过两阶段学习算法的交替使用 ,使网络具有结构自学习和参数优化的能力 ,而后将网络应用于组分数未知的重叠色谱峰解析。该方法具有不需人为干预 ,可自动确定网络结构即组分数的优点 ;并且解析精度较高 ,适用于多组分重叠色谱峰的解析 ;对完全重叠色谱峰也具有良好的解析能力。     

Protein-protein binding-sites prediction by protein surface structure conservation

A new algorithm to predict protein-protein binding sites using conservation of both protein surface structure and physical-chemical properties in structurally similar proteins is developed. Binding-site residues in proteins are known to be more conserved than the rest of the surface, and finding local surface similarities by comparing a protein to its structural neighbors can potentially reveal the location of binding sites on this protein. This approach, which has previously been used to predict binding sites for small ligands, is now extended to predict protein-protein binding sites. Examples of binding-site predictions for a set of proteins, which have previously been studied for sequence conservation in protein-protein interfaces, are given. The predicted binding sites and the actual binding sites are in good agreement. Our algorithm for finding conserved surface structures in a set of similar proteins is a useful tool for the prediction of protein-protein binding sites.     

Use of a novel Hill-climbing genetic algorithm in protein folding simulations

We have developed a novel Hill-climbing genetic algorithm (GA) for simulation of protein folding. The program (written in C) builds a set of Cartesian points to represent an unfolded polypeptide’s backbone. The dihedral angles determining the chain’s configuration are stored in an array of chromosome structures that is copied and then mutated. The fitness of the mutated chain’s configuration is determined by its radius of gyration.

A four-helix bundle was used to optimise simulation conditions, and the program was compared with other, larger, genetic algorithms on a variety of structures. The program ran 50% faster than other GA programs. Overall, tests on 100 non-redundant structures gave comparable results to other genetic algorithms, with the Hill-climbing program running from between 20 and 50% faster. Examples including crambin, cytochrome c, cytochrome B and hemerythrin gave good secondary structure fits with overall alpha carbon atom rms deviations of between 5 and 5.6 Å with an optimised hydrophobic term in the fitness function.     

A novel approach for protein identification from complex cell proteome using modified peptide mass fingerprinting algorithm

A unique peptide based search algorithm for identification of protein mixture using PMF is proposed. The proposed search algorithm utilizes binary search and heapsort programs to generate frequency chart depicting the unique peptides corresponding to all proteins in a proteome. The use of binary search program significantly reduces the time for frequency chart preparation to ～2 s for a proteome comprising ～23 000 proteins. The algorithm was applied to a three‐protein mixture identification, host cell protein (HCP) analysis, and a simulation‐generated data set. It was found that the algorithm could identify at least one unique peptide of a protein even in the presence of fourfold higher concentration of another protein. In addition, two HCPs that are known to be difficult to remove were missed by MS/MS approach and were exclusively identified using the presented algorithm. Thus, the proposed algorithm when used along with standard proteomic approaches present avenues for enhanced protein identification efficiency, particularly for applications such as HCP analysis in biopharmaceutical research, where identification of low‐abundance proteins are generally not achieved due to dynamic range limitations between the target product and HCPs.     

Machine learning based pattern recognition applied to microarray data

Motivation: Microarrays have allowed the expression level of thousands of genes or proteins to be measured simultaneously. Data sets generated by these arrays consist of a small number of observations (e.g., 20-100 samples) on a very large number of variables (e.g., 10,000 genes or proteins). The observations in these data sets often have other attributes associated with them such as a class label denoting the pathology of the subject. Finding the genes or proteins that are correlated to these attributes is often a difficult task since most of the variables do not contain information about the pathology and as such can mask the identity of the relevant features. We describe a genetic algorithm (GA) that employs both supervised and unsupervised learning to mine gene expression and proteomic data. The pattern recognition GA selects features that increase clustering, while simultaneously searching for features that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the features chosen by the GA contain information primarily about differences between classes in the data set. The principal component analysis routine embedded in the fitness function of the GA acts as an information filter, significantly reducing the size of the search space since it restricts the search to feature sets whose principal component plots show clustering on the basis of class. The algorithm integrates aspects of artificial intelligence and evolutionary computations to yield a smart one pass procedure for feature selection, clustering, classification, and prediction.     

基于混合型遗传算法的森林可燃物热解动力学参数优化方法

森林可燃物热解动力学参数的优化计算是构建综合热解模型的关键步骤。传统的基于梯度的优化方法收敛速度快但全局寻优能力不足,基于“生物进化理论”的遗传算法具有全局寻优能力但收敛速度慢。本研究首先探讨了单纯的遗传算法对初始值设置的依赖,发现设定合适的初始值能够稳定计算结果,加快算法的收敛速度。针对初始值未知的情况,本文提出了将单纯的遗传算法与迭代算法相结合构建混合型遗传算法的流程。然后以樟子松松枝为例,采用热重分析仪开展了森林可燃物热解实验。假设可燃物热解失重过程遵循三步一级平行反应模型,通过对比单纯遗传算法和混合型遗传算法的收敛过程,发现混合型遗传算法能够快速地获取全局最优的动力学参数,显著地提高遗传算法的优化性能。     

LocaPep: localization of epitopes on protein surfaces using peptides from phage display libraries

The use of peptides from a phage display library selected by binding to a given antibody is a widespread technique to probe epitopes of antigenic proteins. However, the identification of interaction sites mimicked by these peptides on the antigen surface is a difficult task. LocaPep is a computer program developed to localize epitopes using a new clusters algorithm that focuses on protein surface properties. The program is constructed with the aim of providing a flexible computational tool for predicting the location of epitopes on protein structures. As a first set of testing results, the localization of epitope regions in eight different antigenic proteins for which experimental data on their antibody interactions exist is correctly identified by LocaPep. These results represent a disparate sample of biologically different systems. The program is freely available at http://atenea.montes.upm.es.     

Triangulation algorithms for the representation of molecular surface properties

Summary A triangulation algorithm for a dotted surface (i.e. a surface defined by point coordinates in three dimensions) is given. The individual triangles are generated on the basis of a hierarchy of strategies according to increasing surface complexity. While for small molecules an elementary algorithm is sufficient to triangulate the surface, large molecules-like proteins-generally need all steps of the hierarchy. Although this program has been developed with the aim of triangulating molecular surfaces, it can in principle be applied to any surface defined by 3D point coordinates.     

Parallel internally contracted multireference configuration interaction

A parallel implementation of the internally contracted (IC) multireference configuration (MRCI) module of the MOLPRO quantum chemistry program is described. The global array (GA) toolkit has been used in order to map an existing disk-paging small-memory algorithm onto a massively parallel supercomputer, where disk storage is replaced by the combined memory of all processors. This model has enabled a rather complicated code to be ported to the parallel environment without the need for the wholesale redesign of algorithms and data structures. Examples show that the parallel ICMRCI program can deliver results in a fraction of the time needed for equivalent uncontracted MRCI computations. Further examples demonstrate that ICMRCI computations with up to 10⁷ variational parameters, and equivalent to uncontracted MRCI with 10⁹ configurations, are feasible. The largest calculation demonstrates a parallel efficiency of about 80% on 128 nodes of a Cray T3E-300. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1215–1228, 1998     

A chaotic approach to maintain the population diversity of genetic algorithm in network training

The concept of chaos being radically different from statistical randomness is introduced into chemometrics research. The chaotic system that is deterministic with underlying patterns and inherent ability in searching the space of interest has been employed to improve the performance of chemometric algorithms. In this paper, a chaotic mutation is introduced into the genetic algorithm (GA) which is used for artificial neural network (ANN) training. The chaotic algorithm is very efficient in maintaining the population diversity during the evolution process of GA. The proposed algorithm CGANN has been testified by prediction of vibrational frequencies of octahedral hexahalides from some selected molecular parameters.     

Mixed Langmuir monolayers of gramicidin A and fluorinated alcohols

Mixed monolayers of gramicidin A (GA) and three alcohols, differing in the degree of fluorination, namely C18OH, F18OH, and F8H10OH have been investigated by means of: surface manometry (pi-A isotherms) and Brewster angle microscopy (BAM) aiming at finding appropriate molecules for incorporating gramicidin A for a biosensor design. Our results proved that only the semifluorinated alcohol is appropriate material for this purpose since it forms miscible and homogeneous monolayers with GA within the whole concentration range. The experimental results have been supported by the calculations of van der Waals energy profiles using the Insight II program. Both the hydrogenated and perfluorinated alcohols were found to aggregate at higher surface pressures, which exclude their application for gramicidin-based biosensor construction.     

基于分子表面的水化自由能预测方法

报导了一种基于加权原子表面的水合自由能预测（SAWSA）.对于不同原子类型的溶剂化参数，其参数化分为三个步骤：首先用SMARTS 语言确定不同的原子类型；然后计算每个原子的溶剂可及化表面；最后用遗传算法来优化不同原子类型的溶剂化参数.采用该模型，计算了18个蛋白质分子的水合自由能，预测结果和PB/SA的计算结果呈现了很好的线性关系（r=0.99).计算表明，SAWSA模型对有机小分子和生物大分子都具有很好的预测能力.     

MSEED: A program for the rapid analytical determination of accessible surface areas and their derivatives

An algorithm for the rapid analytical determination of the accessible surface areas of solute molecules is described. The accessible surface areas as well as the derivatives with respect to the Cartesian coordinates of the atoms are computed by a program called “MSEED,” which is based in part on Connolly's analytical formulas for determining surface area. Comparisons of the CPU time required for MSEED, Connolly's numerical algorithm DOT, and a program for surface area determination (ANA) based on Connolly's analytical algorithm, are presented. MSEED is shown to be as much as 70 times faster than ANA and up to 11 times faster than DOT for several proteins. The greater speed of MSEED is achieved partially because nonproductive computation of the surface areas of internal atoms is avoided. A sample minimization of an energy function, which included a term for hydration, was carried out on MET-enkephalin using MSEED to compute the solvent-accessible surface area and its derivatives. The potential employed was ECEPP/2 plus an empirical potential for solvation based on the solvent-accessible surface area of the peptide. The CPU time required for 150 steps of minimization with the potential that included solvation was approximately twice as great as the CPU time required for 150 steps of minimization with the ECEPP/2 potential only.     

Improved strategy in analytic surface calculation for molecular systems: Handling of singularities and computational efficiency

Computer methods for analytic surface calculations of molecular systems suffer from numerical instabilities and are CPU time consuming. In this article, we present proposals toward the solution of both problems. Singularities arise when nearly collinear triples of neighboring atoms or multiple vertices are encountered during the calculation. Topological decisions in analytic surface calculation algorithms (accessibility of vertices and arcs) are based upon the comparison of distances or angles. If two such numbers are nearly equal, then currently used computer programs may not resolve this ambiguity correctly and can subsequently fail. In this article, modifications in the analytic surface calculation algorithm are described that recognize singularities automatically and treat them appropriately without restarting parts of the computation. The computing time required to execute these alterations is minimal. The basic modification consists in defining an accuracy limit within which two values may be assumed as equal. The search algorithm has been reformulated to reduce the computational effort. A new set of formulas makes it possible to avoid mostly the extraction of square roots. Tests for small-and medium-sized intersection circles and for pairs of vertices with small vertex height help recognize fully buried circles and vertex pairs at an early stage. The new program can compute the complete topology of the surface and accessible surface area of the protein crambin in 1.50–4.29 s (on a single R3000 processor of an SGI 4D/480) depending on the compactness of the conformation where the limits correspond to the fully extended or fully folded chain, respectively. The algorithm, implemented in a computer program, will be made available on request. © John Wiley & Sons, Inc.     

Selective adsorption of poly-His tagged glutaryl acylase on tailor-made metal chelate supports.

A poly-His tag was fused in the glutaryl acylase (GA) from Acinetobacter sp. strain YS114 cloned in E. coli yielding a fully active enzyme. Biochemical analyses showed that the tag did not alter the maturation of the chimeric GA (poly-His GA) that undergoes a complex post-translational processing from an inactive monomeric precursor to the active heterodimeric enzyme. This enzyme has been used as a model to develop a novel and very simple procedure for one-step purification of poly-His proteins via immobilized metal-ion affinity chromatography on tailor-made supports. It was intended to improve the selectivity of adsorption of the target protein on tailor-made chelate supports instead of performing a selective desorption. The rate and extent of the adsorption of proteins from a crude extract from E. coli and of pure poly-His tagged GA on different metal chelate supports was studied. Up to 90% of proteins from E. coli were adsorbed on commercial chelate supports having a high density of ligands attached to the support through long spacer arms, while this adsorption becomes almost negligible when using low ligand densities, short spacer arms and Zn2+ or Co2+ as cations. On the contrary, poly-His GA adsorbs strongly enough on all supports. A strong affinity interaction between the poly-His tail and a single chelate moiety seems to be the responsible for the adsorption of poly-His GA. By contrast, multipoint weak interactions involving a number of chelate moieties seem to be mainly responsible for adsorption of natural proteins. By using tailor-made affinity supports, a very simple procedure for one-step purification of GA with minimal adsorption of host proteins could be performed. Up to 20 mg of GA were adsorbed on each ml of chelate support while most of accompanying proteins were hardly adsorbed on such supports. Following few washing steps, the target enzyme was finally recovered (80% yield) by elution with 50 mM imidazole with a very high increment of specific activity (up to a 120 purification factor).