A combined steepest descent and genetic algorithm (SD/GA) approach for the optimization of solvation parameters

An accurate solvation model is essential for computer modeling of protein folding and other biomolecular self-assembly processes. Compared to explicit solvent models, implicit solvent models, such as the Poisson-Boltzmann (PB) with solvent accessible surface area model (PB/SA), offer a much faster speed—the most compelling reason for the popularity of these implicit solvent models. Since these implicit solvent models typically use empirical parameters, such as atomic radii and the surface tensions, an optimal fit of these parameters is crucial for the final accuracy of properties such as solvation free energy and folding free energy. In this paper, we proposed a combined approach, namely SD/GA, which takes the advantage of both local optimization with the steepest descent (SD), and global optimization with the genetic algorithm (GA), for parameters optimization in multi-dimensional space. The SD/GA method is then applied to the optimization of solvation parameters in the non-polar cavity term of the PB/SA model. The results show that the newly optimized parameters from SD/GA not only increase the accuracy in the solvation free energies for ～200 organic molecules, but also significantly improve the free energy landscape of a β-hairpin folding. The current SD/GA method can be readily applied to other multi-dimensional parameter space optimization as well.     

Colon cancer prediction with genetic profiles using intelligent techniques

Micro array data provides information of expression levels of thousands of genes in a cell in a single experiment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. In our present study we have used the benchmark colon cancer data set for analysis. Feature selection is done using t‐statistic. Comparative study of class prediction accuracy of 3 different classifiers viz., support vector machine (SVM), neural nets and logistic regression was performed using the top 10 genes ranked by the t‐statistic. SVM turned out to be the best classifier for this dataset based on area under the receiver operating characteristic curve (AUC) and total accuracy. Logistic Regression ranks as the next best classifier followed by Multi Layer Perceptron (MLP). The top 10 genes selected by us for classification are all well documented for their variable expression in colon cancer. We conclude that SVM together with t-statistic based feature selection is an efficient and viable alternative to popular techniques.     

Random forests-based differential analysis of gene sets for gene expression data

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients.     

农田生态条件下玉米秸杆腐解过程腐解物的热解变化特征

用热分析法研究了农田生态条件下玉米秸杆腐解过程腐解物的热解变化特征 ,并探讨了腐解物中不同组分对腐解物热解特征的影响。结果表明 ,腐解物 DTA曲线的 2 80℃、3 3 0℃、4 50℃放热峰 ,DTG曲线的第二失重峰和 h330℃ /h4 50℃ 值可作为表征腐解进程的特征峰和特征值。由腐解物 DTA、DTG所得能量各参数 ( ΔH,E)与文献 [3 ]所述腐解物能态 ( Qv)呈现波动起伏 ,趋于稳定 2个阶段相符 ,二者相互印证 ,显示热分析方法用于植物残体腐解进程研究的可行性。 DTA的2 80℃放热峰主要由苯 -醇溶性物、水溶性物引起。苯 -醇溶性物、水溶性物和纤维素、半纤维素是影响腐解物热解及其能态变化特征的主要物质组分。木质素组分对腐解后期腐解物的热解及其能态特征趋于稳定具有重要作用     

Protein fold recognition without Boltzmann statistics or explicit physical basis.

We present a fast method for finding optimal parameters for a low-resolution (threading) force field intended to distinguish correct from incorrect folds for a given protein sequence. In contrast to other methods, the parameterization uses information from >10(7) misfolded structures as well as a set of native sequence-structure pairs. In addition to testing the resulting force field's performance on the protein sequence threading problem, results are shown that characterize the number of parameters necessary for effective structure recognition.     

A CLIQUE algorithm using DNA computing techniques based on closed-circle DNA sequences

DNA computing has been applied in broad fields such as graph theory, finite state problems, and combinatorial problem. DNA computing approaches are more suitable used to solve many combinatorial problems because of the vast parallelism and high-density storage. The CLIQUE algorithm is one of the gird-based clustering techniques for spatial data. It is the combinatorial problem of the density cells. Therefore we utilize DNA computing using the closed-circle DNA sequences to execute the CLIQUE algorithm for the two-dimensional data. In our study, the process of clustering becomes a parallel bio-chemical reaction and the DNA sequences representing the marked cells can be combined to form a closed-circle DNA sequences. This strategy is a new application of DNA computing. Although the strategy is only for the two-dimensional data, it provides a new idea to consider the grids to be vertexes in a graph and transform the search problem into a combinatorial problem.     

Prediction of potential drivers connecting different dysfunctional levels in lung adenocarcinoma via a protein–protein interaction network

Lung cancer is a serious disease that threatens an affected individual's life. Its pathogenesis has not yet to be fully described, thereby impeding the development of effective treatments and preventive measures. “Cancer driver” theory considers that tumor initiation can be associated with a number of specific mutations in genes called cancer driver genes. Four omics levels, namely, (1) methylation, (2) microRNA, (3) mutation, and (4) mRNA levels, are utilized to cluster cancer driver genes. In this study, the known dysfunctional genes of these four levels were used to identify novel driver genes of lung adenocarcinoma, a subtype of lung cancer. These genes could contribute to the initiation and progression of lung adenocarcinoma in at least two levels. First, random walk with restart algorithm was performed on a protein–protein interaction (PPI) network constructed with PPI information in STRING by using known dysfunctional genes as seed nodes for each level, thereby yielding four groups of possible genes. Second, these genes were further evaluated in a test strategy to exclude false positives and select the most important ones. Finally, after conducting an intersection operation in any two groups of genes, we obtained several inferred driver genes that contributed to the initiation of lung adenocarcinoma in at least two omics levels. Several genes from these groups could be confirmed according to recently published studies. The inferred genes reported in this study were also different from those described in a previous study, suggesting that they can be used as essential supplementary data for investigations on the initiation of lung adenocarcinoma. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.     

Limited proteolysis of native proteins: the interaction between avidin and proteinase K.

Avidin is a tetramer of 16-kDa subunits that have a high affinity for biotin. Proteolysis of native apoavidin by proteinase K results in a limited attack at the loop between beta-strands 3 and 4, involving amino acids 38-43. Specifically, sites of proteolysis are at Thr 40-Ser 41 and Asn 42-Glu 43. The limited proteolysis results in an avidin product that remains otherwise intact and which has enhanced binding for 4'-hydroxyazobenzene-2-benzoic acid (HABA), a chromogenic reporter that can occupy the biotin-binding site. Saturation of the biotin-binding site with the natural ligand protects avidin from proteolysis, but saturation with HABA enhances the rate of proteolysis of the same site. Analysis of the three-dimensional structures of apoavidin and holoavidin reveals that the 3-4 loop is accessible to solvent and scores highly in an algorithm developed to identify sites of proteolytic attack. The structure of holoavidin is almost identical to the apoprotein. In particular, the 3-4 loop has the same structure in the apo and holo forms, yet there are marked differences in proteolytic susceptibility of this region. Evidence suggests that the 3-4 loop is rather mobile and flexible in the apoprotein, and that it becomes constrained upon ligand binding. In one crystal structure of the apoprotein, this loop appears constrained by contacts with symmetry-related molecules. Structural analyses suggest that the "lid" to the biotin-binding site, formed by the 3-4 loop, is displaced and made more accessible by HABA binding, thereby enhancing its proteolytic susceptibility.