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A new super-secondary structure dataset with sequence identity

Protein structure prediction is a challenging field strongly associated with protein function and evolution determination, which is crucial for biologists. Despite significant process made in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. In this study, we have developed a method for protein structure prediction based on 7-state HMM which can reduce the number of states using secondary structure information about proteins for each fold. The QPSO is an efficient optimization algorithm which is used to train profile HMM. Experiment results show that the proposed method is reasonable.

Vehicle scheduling belongs to a typical TSP (Traveling salesman problem) problem in the field of combinatorial optimization. We design a vehicle scheduling model with soft time window and non-full load constraints. The model belongs to single-objective optimization with multiple constrains. This article uses binary particle swarm optimization algorithm (BPSO) to solve the vehicle scheduling model, combining constraint deviation value method for solving constraint. Cross experiments are designed based on the number of customers and the number of delivery vehicles. The experiment results show that BPSO algorithm in short running time can resolve vehicle scheduling problem, and get a more satisfactory solution to objective function, the number of vehicles customers required, the distribution distance and path of the delivery vehicle. Thus it lays a technical foundation for intelligent vehicle scheduling.

Multiple sequence alignment (MSA) is one of the most essential tools in bioinformatics. Genetic algorithm is used to simulate biological multiple sequence alignment problem, the initial population and crossover is the most critical part of the genetic algorithm. In this paper, we construct three initial populations and a simple horizontal crossover with respect to the vertical crossover. The experimental results showed that the initial population adding an appropriate proportion MAFFT excellent seed can optimize the population, horizontal crossover can reduce the computing time and the computational complexity. Combination of those two methods can improve the computational efficiency of multiple sequence alignment.

Efficiency and effectiveness of the logistics network is directly dependent on network topology and flow distribution of logistics nodes and transport routes. Flow distribution model based on multi-objective function belongs to NP- difficult problem. The model consists of supply, demand and constraints of network capacity and so on. The number of network paths will exponentially expand with the size of the network, that is, when the number of various types of nodes increases, the overall transportation route will rapidly increase. So we design a “sliced, two-phase” flow distribution model of logistics network. Combining with penalty-parameter-less method, we use particle swarm optimization (PSO) algorithm to optimize the model so as to find the solution of multi-objective function. Experiment results show that PSO algorithm with penalty function method can obtain satisfactory solution of flow distribution and network usage in a short period of time.

This study aimed to optimize the culture conditions (agitation speed, aeration rate and stirrer number) of hyaluronic acid production by Streptococcus zooepidemicus. Two optimization algorithms were used for comparison: response surface methodology (RSM) and genetic programming coupling Quantum-behaved particle swarm optimization algorithm (GP-QPSO). In GP -QPSO approach, GP is employed to model the microbial HA production and QPSO algorithm is used to find out the optimal culture conditions with the established GP estimator as the objective function. The maximum predicted value of HA production by RSM and GP-QPSO was 5.27 and 5.57 g/l, respectively. Here though both RSM and GP-QPSO approach provided good predictions, yet the proposed GP-QPSO method showed a clear superiority over RSM for both data fitting and optimization capabilities

Multiple sequence alignment (MSA) is an NP-complete and important problem in bioinformatics. For MSA, Hidden Markov Models (HMMs) are known to be powerful tools. However, the training of HMMs is computationally hard so that metaheuristic methods such as simulated annealing (SA), evolutionary algorithms (EAs) and particle swarm optimization (PSO), have been employed to tackle the training problem. In this paper, quantum-behaved particle swarm optimization (QPSO), a variant of PSO, is analyzed mathematically firstly, and then an improved version is proposed to train the HMMs for MSA. The proposed method, called diversity-maintained QPSO (DMQPO), is based on the analysis of QPSO and integrates a diversity control strategy into QPSO to enhance the global search ability of the particle swarm. To evaluate the performance of the proposed method, we use DMQPSO, QPSO and other algorithms to train the HMMs for MSA on three benchmark datasets. The experiment results show that the HMMs trained with DMQPSO and QPSO yield better alignments for the benchmark datasets than other most commonly used HMM training methods such as Baum-Welch and PSO.

Multiple sequence alignment (MSA) is an NP-complete and important problem in bioinformatics. Currently, profile hidden Markov model (HMM) is widely used for multiple sequence alignment. In this paper, Quantum-behaved Particle Swarm Optimization with selection operation (SQPSO) is presented, which is used to train profile HMM. Furthermore, an integration algorithm based on the profile HMM and SQPSO for the MSA is constructed. The approach is examined by using multiple nucleotides and protein sequences and compared with other algorithms. The results of the comparisons show that the HMM trained with SQPSO and QPSO yield better alignments than other most commonly used HMM training methods such as Baum–Welch and PSO.

A set of descriptors was derived from a matrix of three structural variables of the natural amino acid, including Van Der Waal's volume, net charge index and hydrophobic parameter of side residues. They were selected from many properties of amino acid residues, which were the primary properties to influence the interaction between peptides and its protein receptor. They were then applied to structure characterization and QSAR analysis on agiotensin-converting enzyme inhibitor di-peptides by using orthogonal signal correction (OSC) method combined with partial least squares (PLS) method. The results of QSAR analysis on di-peptides obtained are as following: the cross-validation correlative coefficient (Q 2 ) was 0.817, and the correlative coefficient (R 2 ) was 0.838, and the root mean-square (RMS) was 0.410. Test sets of peptides were used to validate the quantitative model, and it was shown that all these QSAR models had good predictability for outside samples. The results showed that, in comparison with the conventional descriptors, this descriptors was a useful structure characterization parameter for peptide QSAR analysis, and also showed that the OSC-PLS method is better than the traditional PLS method.

Quantum-behaved particle swarm optimization (QPSO) algorithm is a global-convergence-guaranteed algorithm, which outperforms original PSO in search ability but has fewer parameters to control. But QPSO algorithm is to be easily trapped into local optima as a result of the rapid decline in diversity. So this paper describes diversity-maintained into QPSO (QPSO-DM) to enhance the diversity of particle swarm and then improve the search ability of QPSO. The experiment results on benchmark functions show that QPSO-DM has stronger global search ability than QPSO and standard PSO.

According to the data of the 18 cities and counties pesticide usage and tropical crops area of HaiNan province from 2003 to 2010, the prediction model is established based on the particle swarm optimization algorithm. From the fitted curve the trend direction about pesticides usage with the time course of change is clearly observed. The result can be used to provide a reference for department of pesticide monitoring, management and decision-making.

The aim of this study was to discuss the clinical effectiveness of high intensity focused ultrasound (HIFU) combined with gemcitabine administered by intra-arterial infusion on intermediate and advanced pancreatic cancer.Forty-eight patients with intermediate and advanced pancreatic cancer were divided into two groups. Twenty-four patients of the experimental group were treated by HIFU combined with gemcitabine, and 24 patients of the the HIFU group were treated by HIFU alone. Then the curative effect, extent of pain relief, and survival time were compared in the course of the treatment between the two groups.As compared with those in the control group, the overall response rate, level of pain relief, and 12-month survival rate after therapy were higher and the median survival time was longer in the joint group (P0.05).Ultrasound imaging, CT and associated tumor marker detection can make effective measurement to evaluate curative effect on pancreatic carcinoma. HIFU plus gemcitabine administered by intra-arterial infusion can improve the clinical therapeutic efficacy, pain relief, quality of life and long-term survival rate of patients with pancreatic cancer.

To isolate and identify the cancer stem cells from primary human ovarian cancer tissues.Fresh tumor tissues from five cases of pathologically diagnosed ovarian cancers were taken, minced and then digested with collagenase and hyaluronidase to obtain single cell suspension. The erythrocytes were removed with ACK Lysis buffer. The suspensions were sorted by magnetic activated cell sorting (MACS) using CD133-binding microbeads. Then the sorted CD133(+) cells were verified by flow cytometry. The cells were cultured in serum-free medium supplemented with EGF, bFGF, insulin and BSA, and grew into spheroids. Immunofluorescence, differentiation and tumor formation tests of the cells were performed to characterize the properties of cancer stem cells.The ovarian cancer stem cells were successfully isolated from primary human ovarian tumors, which formed typical spheroids in serum-free medium and had stronger ability of tumorigenesis. The results of related experiments verified that CD133 positive cells owned the properties of cancer stem cells.The ovarian cancer stem cells presenting the characteristics of stemness in vitro and in vivo, have been successfully isolated from primary human ovarian tumor tissues by MACS. The isolated ovarian cancer stem cells could be used in future researches of their biological functions.

Metabolic flux estimation through 13C trace experiment is crucial for quantifying the intracellular metabolic fluxes. In fact, it corresponds to a constrained optimization problem that minimizes a weighted distance between measured and simulated results. In this paper, we propose particle swarm optimization (PSO) with penalty function to solve 13C-based metabolic flux estimation problem. The stoichiometric constraints are transformed to an unconstrained one, by penalizing the constraints and building a single objective function, which in turn is minimized using PSO algorithm for flux quantification. The proposed algorithm is applied to estimate the central metabolic fluxes of Corynebacterium glutamicum. From simulation results, it is shown that the proposed algorithm has superior performance and fast convergence ability when compared to other existing algorithms.

Multiple sequence alignment (MSA), known as NP-complete problem, is one of the basic problems in computational biology. Presently, profile hidden Markov model (HMM) is widely used for multiple sequence alignment. In this paper, Quantum-behaved Particle Swarm Optimization (QPSO) is used to train profile HMM. Furthermore, an integration algorithm based on the profile HMM and QPSO for the MSA is proposed. In order to evaluate the approach protein sequences are taken. Finally, compared with other algorithms, the results show that the proposed algorithm not only finds out perfect profile HMM, but also produces the optimal alignment of multiple sequences.

Multiple sequence alignment (MSA) is a fundamental and challenging problem in the analysis of biologic sequence. The MSA problem is hard to be solved directly, for it always results in exponential complexity with the scale of the problem. In this paper, we propose mutation-based binary particle swarm optimization (M-BPSO) for MSA solving. In the proposed M-BPSO algorithm, BPSO algorithm is conducted to provide alignments. Thereafter, mutation operator is performed to move out of local optima and speed up convergence. From simulation results of nucleic acid and amino acid sequences, it is shown that the proposed M-BPSO algorithm has superior performance when compared to other existing algorithms. Furthermore, this algorithm can be used quickly and efficiently for smaller and medium size sequences.

A new set of descriptors was derived from a matrix of three structural variables of the natural amino acid, including van der Waal's volume, net charge index and hydrophobic parameter of side residues. They were selected from many properties of amino acid residues, which have been validated being the key factors to influence the interaction between peptides and its protein receptor. They were then applied to structure characterization and QSAR analysis on bitter tasting di-peptide, agiotensin-converting enzyme inhibitor and bactericidal peptides by using multiple linear regression (MLR) method. The leave one out cross validation values ( Q 2 ) were 0.921, 0.943 and 0.773. The multiple correlation coefficients ( R 2 ) were 0.948, 0.970 and 0.926, the root mean square (RMS) error for estimated error were 0.165, 0.154 and 0.41, respectively for bitter tasting di-peptide, angiotensin-converting enzyme inhibitor and bactericidal peptides. Test sets of peptides were used to validate the quantitative model, and it was shown that all these QSAR models had good predictability for outside samples. The results showed that, in comparison with the conventional descriptors, the new set of descriptors is a useful structure characterization method for peptide QSAR analysis, which has multiple advantages, such as definite physical and chemical meaning, easy to get, and good structural characterization ability.

Quantum-behaved particle swarm optimisation (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. This paper describes two selection mechanisms into QPSO to improve the search ability of QPSO. One is the QPSO with tournament selection (QPSO-TS) and the other is the QPSO with roulette-wheel selection (QPSO-RS). While the centre of position distribution of each particle in QPSO is determined by global best position and personal best position, in the QPSO with selection operation, the global best position is substituted by a candidate solution through selection. The QPSO with selection operation also maintains the mean best position of the swarm as in the previous QPSO to make the swarm more efficient in global search. The experiment results on benchmark functions show that both QPSO-RS and QPSO-TS have better performance and stronger global search ability than QPSO and standard PSO.