Global and local prediction of protein folding rates based on sequence autocorrelation information

To understand the folding behavior of proteins is an important and challenging problem in modern molecular biology. In the present investigation, a large number of features representing protein sequences were developed based on sequence autocorrelation weighted by properties of amino acid residues. Genetic algorithm (GA) combined with multiple linear regression (MLR) was employed to select significant features related to protein folding rates, and to build global predictive model. Moreover, local lazy regression (LLR) method was also used to predict the protein folding rates. The obtained results indicated that LLR performed much better than the global MLR model. The important properties of amino acid residues affecting protein folding rates were also analyzed. The results of this study will be helpful to understand the mechanism of protein folding. Our results also demonstrate that the features of amino acid sequence autocorrelation is effective in representing the relationship between protein sequence and folding rates, and the local method is a powerful tool to predict the protein folding rates.     

Optimal HP configurations of proteins by combining local search with elastic net algorithm

The prediction of protein conformation from its amino-acid sequence is one of the most prominent problems in computational biology. But it is NP-hard. Here, we focus on an abstraction widely studied of this problem, the two-dimensional hydrophobic-polar protein folding problem (2D HP PFP). Mathematical optimal model of free energy of protein is established. Native conformations are often sought using stochastic sampling methods, but which are slow. The elastic net (EN) algorithm is one of fast deterministic methods as travelling salesman problem (TSP) strategies. However, it cannot be applied directly to protein folding problem, because of fundamental differences in the two types of problems. In this paper, how the 2D HP protein folding problem can be framed in terms of TSP is shown. Combination of the modified elastic net algorithm and novel local search method is adopted to solve this problem. To our knowledge, this is the first application of EN algorithm to 2D HP model. The results indicate that our approach can find more optimal conformations and is simple to implement, computationally efficient and fast.     

A note on the measurement of redundancy

Plant Ecology - The present paper points out a simpler method for computing the redundancy coefficient proposed by Orlóci (1975) using the inverse of the variance-covariance matrix. It is...     

Research note: A method for studying local differences in ruminal fermentation in dairy cattle

A method was developed for studying local differences in ruminai fermentation. The developed sampler consisted of an acrylic glass container (460 cm³) with an aperture for digesta sampling, which could be opened and closed by the scaled “T”; rod. The scale was a reference for defined rumen layers: top, middle, 5 to 10 cm and 25 to 35 cm beneath the top of particles mat, respectively, and bottom 5 to 10 cm above the rumen floor. The repeatability of the method was proved in two rumen cannulated cows. Particle/fluid ratio, pH and sample amount were measured 2 to 2¹/₂ h after morning feeding in four replicates each day (over 5 days), rumen layer and animal. No significant differences between replicates were observed. The coefficients of variation (CV) of the particle/fluid ratio varied between 8.7% and 13.6%. Top layer had higher CV than middle and bottom layer. CV of pH ranged between 0.59% and 1.27%. The developed method of sampling showed satisfactory repeatability for investigation of digesta properties and fermentation in different rumen layers.     

A note on key factor analysis

Various aspects of a model for key factor analysis (Manly , 1977a) are discussed. Equations for estimating the parameters of the model are given. The jackknife method is suggested for obtaining standard errors for estimators. The case of circular populations (where the adults in one generation produce all the individuals alive at the start of the next, generation) is considered. A computer program is described. Varley et al.'s (1973) data on the winter moth is used for illustrative purposes.     

Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm

The ultimate goal of the Recommender System (RS) is to offer a proposal that is very close to the user's real opinion. Data clustering can be effective in increasing the accuracy of production proposals by the RS. In this paper, single-objective hybrid evolutionary approach is proposed for clustering items in the offline collaborative filtering RS. This method, after generating a population of randomized solutions, at each iteration, improves the population of solutions first by Genetic Algorithm (GA) and then by using the Gravitational Emulation Local Search (GELS) algorithm. Simulation results on standard datasets indicate that although the proposed hybrid meta-heuristic algorithm requires a relatively high run time, it can lead to more appropriate clustering of existing data and thus improvement of the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Coverage criteria.     

Protein structure prediction in a 210-type lattice model: parameter optimization in the genetic algorithm using orthogonal array

We have applied the orthogonal array method to optimize the parameters in the genetic algorithm of the protein folding problem. Our study employed a 210-type lattice model to describe proteins, where the orientation of a residue relative to its neighboring residue is described by two angles. The statistical analysis and graphic representation show that the two angles characterize protein conformations effectively. Our energy function includes a repulsive energy, an energy for the secondary structure preference, and a pairwise contact potential. We used orthogonal array to optimize the parameters of the population, mating factor, mutation factor, and selection factor in the genetic algorithm. By designing an orthogonal set of trials with representative combinations of these parameters, we efficiently determined the optimal set of parameters through a hierarchical search. The optimal parameters were obtained from the protein crambin and applied to the structure prediction of cytochrome B562. The results indicate that the genetic algorithm with the optimal parameters reduces the computing time to reach a converged energy compared to nonoptimal parameters. It also has less chance to be trapped in a local energy minimum, and predicts a protein structure which is closer to the experimental one. Our method may also be applicable to many other optimization problems in computational biology.