Search

Abstract Nuclear Magnetic Resonance Spectroscopy (most commonly known as NMR Spectroscopy) is used to generate approximate and partial distances between pairs of atoms of the native structure of a protein. To predict protein structure from these partial distances by solving the Euclidean distance geometry problem from the partial distances obtained from NMR Spectroscopy, we can predict three-dimensional (3D) structure of a protein. In this paper, a new genetic algorithm is proposed to efficiently address the Euclidean distance geometry problem towards building 3D structure of a given protein applying NMR's sparse data. Our genetic algorithm uses (i) a greedy mutation and crossover operator to intensify the search; (ii) a twin removal technique for diversification in the population; (iii) a random restart method to recover from stagnation; and (iv) a compaction factor to reduce the search space. Reducing the search space drastically, our approach improves the quality of the search. We tested our algorithms on a set of standard benchmarks. Experimentally, we show that our enhanced genetic algorithms significantly outperforms the traditional genetic algorithms and a previously proposed state-of-the-art method. Our method is capable of producing structures that are very close to the native structures and hence, the experimental biologists could adopt it to determine more accurate protein structures from NMR data. [ABSTRACT FROM AUTHOR]

Literature contains over fifty years of accumulated methods proposed by researchers for predicting the secondary structures of proteins in silico. A large part of this collection is comprised of artificial neural network-based approaches, a field of artificial intelligence and machine learning that is gaining increasing popularity in various application areas. The primary objective of this paper is to put together the summary of works that are important but sparse in time, to help new researchers have a clear view of the domain in a single place. An informative introduction to protein secondary structure and artificial neural networks is also included for context. This review will be valuable in designing future methods to improve protein secondary structure prediction accuracy. The various neural network methods found in this problem domain employ varying architectures and feature spaces, and a handful stand out due to significant improvements in prediction. Neural networks with larger feature scope and higher architecture complexity have been found to produce better protein secondary structure prediction. The current prediction accuracy lies around the 84% marks, leaving much room for further improvement in the prediction of secondary structures in silico. It was found that the estimated limit of 88% prediction accuracy has not been reached yet, hence further research is a timely demand. [ABSTRACT FROM AUTHOR]

Pseudomonas aeruginosa ( P. aeruginosa ) is frequently associated with infections with high mortality rates. The intrinsically high resistance to many antibiotics and multidrug resistance in the hospital setting is considered to be among the reasons for high pathogenicity of P. aeruginosa . In this study, a total of 200 wound and burn swabs were collected from patients. The collected specimens were examined for P. aeruginosa through biochemical and antibacterial sensitivity tests performed in the Microbiology Laboratory in College of Medicine, University of Kirkuk, Kirkuk, Iraq. The polymerase chain reaction was then used to detect mexA, mexB, mexR, and oprD genes. In total, 31 isolates of P. aeruginosa were collected from 200 patients with wounds and burns. Most cases were isolated from 23 (74.19%) and 8 (25.80%) wound and burn swabs, respectively. Antibiotic sensitivity was tested on all isolates against 17 antimicrobial agents. The obtained results revealed a high resistance rate to gentamicin, trimethoprim, amikacin, and amoxicillin, and a low resistance rate was observed to ceftazidime, tobramycin, levofloxacin, cotrimoxazole, ciprofloxacin, and aztreonam. Regarding antibiotic resistance, mexB, mexR, and oprD genes were observed in three isolates, in which mexB and mexR were detected in two isolates, and only one isolate carried mexA gene.