No abstract

Computational methods are rapidly gaining importance in the field of structural biology, mostly due to the explosive progress in genome sequencing projects and the large disparity between the number of sequences and the number of structures. There has been an exponential growth in the number of available protein sequences and a slower growth in the number of structures. There is therefore an urgent need to develop computational methods to predict structures and identify their functions from the sequence. Developing methods that will satisfy these needs both efficiently and accurately is of paramount importance for advances in many biomedical fields, including drug development and discovery of biomarkers. A novel method called Fast Learning Optimized PREDiction Methodology (FLOPRED) is proposed for predicting protein secondary structure, using knowledge-based potentials combined with structure information from the CATH database. A Neural Network-based Extreme Learning Machine (ELM) and advanced Particle Swarm Optimization (PSO) are used with this data that yield better and faster convergence to produce more accurate results. Protein secondary structures are predicted efficiently, reliably, more efficiently and more accurately using FLOPRED. These techniques yield superior classification of secondary structure elements, with a training accuracy ranging between 83% and 87% over a wide range of hidden neurons and a cross-validated testing accuracy ranging between 81% and 84% and a Segment OVerlap (SOV) score of 78% that are obtained with different sets of proteins. These results are comparable to other recently published studies, but are obtained with greater efficiencies, in terms of time and cost.

A combination of Integer-Coded Genetic Algorithm (ICGA) and Particle Swarm Optimization (PSO), coupled with the neural-network-based Extreme Learning Machine (ELM), is used for gene selection and cancer classification. ICGA is used with PSO-ELM to select an optimal set of genes, which is then used to build a classifier to develop an algorithm (ICGA_PSO_ELM) that can handle sparse data and sample imbalance. We evaluate the performance of ICGA-PSO-ELM and compare our results with existing methods in the literature. An investigation into the functions of the selected genes, using a systems biology approach, revealed that many of the identified genes are involved in cell signaling and proliferation. An analysis of these gene sets shows a larger representation of genes that encode secreted proteins than found in randomly selected gene sets. Secreted proteins constitute a major means by which cells interact with their surroundings. Mounting biological evidence has identified the tumor microenvironment as a critical factor that determines tumor survival and growth. Thus, the genes identified by this study that encode secreted proteins might provide important insights to the nature of the critical biological features in the microenvironment of each tumor type that allow these cells to thrive and proliferate.

Loops in proteins connect secondary structures such as alpha-helix and beta-sheet, are often on the surface, and may play a critical role in some functions of a protein. The mobility of loops is central for the motional freedom and flexibility requirements of active-site loops and may play a critical role for some functions. The structures and behaviors of loops have not been much studied in the context of the whole structure and its overall motions, and especially how these might be coupled. Here we investigate loop motions by using coarse-grained structures (Cα atoms only) to solve for the motions of the system by applying Lagrange equations with elastic network models to learn about which loops move in an independent fashion and which move in coordination with domain motions, faster and slower, respectively. The normal modes of the system are calculated using eigen-decomposition of the stiffness matrix. The contribution of individual modes and groups of modes are investigated for their effects on all residues in each loop by using Fourier analyses. Our results indicate overall that the motions of functional sets of loops behave in similar ways as the whole structure. But, overall only a relatively few loops move in coordination with the dominant slow modes of motion, and that these are often closely related to function.

Background: Diabetes mellitus (DM) is a common chronic disorder in children and is caused by absolute or relative insulin deficiency, with or without insulin resistance. There are several different forms of childhood DM. Children can suffer from neonatal diabetes mellitus (NDM), type 1 diabetes (T1DM), type 2 diabetes (T2DM), Maturity Onset Diabetes of the Young (MODY), autoimmune monogenic, mitochondrial, syndromic and as yet unclassified forms of DM. The Middle East has one of the highest incidences of several types of DM in children; however, it is unclear whether pediatric diabetes is an active area of research in the Middle East and if ongoing, which research areas are of priority for DM in children.Objectives: To review the literature on childhood DM related to research in the Middle East, summarize results, identify opportunities for research and make observations and recommendations for collaborative studies in pediatric DM.Methods: We conducted a thorough and systematic literature review by adhering to a list recommended by PRISMA. We retrieved original papers written in English that focus on childhood DM research, using electronic bibliographic databases containing publications from the year 2000 until October 2018. For our final assessment, we retrieved 429 full-text articles and selected 95 articles, based on our inclusion and exclusion criteria.Results: Our literature review suggests that childhood DM research undertaken in the Middle East has focused mainly on reporting retrospective review of case notes, a few prospective case studies, systemic reviews, questionnaire-based studies, and case reports. These reported studies have focused mostly on the incidence/prevalence of different types of DM in childhood. No studies report on the establishment of National Childhood Diabetes Registries. There is a lack of consolidated studies focusing on national epidemiology data of different types of childhood DM (such as NDM, T1DM, T2DM, MODY, and syndromic forms) and no studies reporting on clinical trials in children with DM.Conclusions: Investing in and funding basic and translational childhood diabetes research and encouraging collaborative studies, will bring enormous benefits financially, economically, and socially for the whole of the Middle East region.

BackgroundProtein secondary structure prediction (SSP) has been an area of intense research interest. Despite advances in recent methods conducted on large datasets, the estimated upper limit accuracy is yet to be reached. Since the predictions of SSP methods are applied as input to higher-level structure prediction pipelines, even small errors may have large perturbations in final models. Previous works relied on cross validation as an estimate of classifier accuracy. However, training on large numbers of protein chains compromises the classifier ability to generalize to new sequences. This prompts a novel approach to training and an investigation into the possible structural factors that lead to poor predictions.Here, a small group of 55 proteins termed the compact model is selected from the CB513 dataset using a heuristics-based approach. In a prior work, all sequences were represented as probability matrices of residues adopting each of Helix, Sheet and Coil states, based on energy calculations using the C-Alpha, C-Beta, Side-chain (CABS) algorithm. The functional relationship between the conformational energies computed with CABS force-field and residue states is approximated using a classifier termed the Fully Complex-valued Relaxation Network (FCRN). The FCRN is trained with the compact model proteins.ResultsThe performance of the compact model is compared with traditional cross-validated accuracies and blind-tested on a dataset of G Switch proteins, obtaining accuracies of ∼81 %. The model demonstrates better results when compared to several techniques in the literature. A comparative case study of the worst performing chain identifies hydrogen bond contacts that lead to Coil ⇔ Sheet misclassifications. Overall, mispredicted Coil residues have a higher propensity to participate in backbone hydrogen bonding than correctly predicted Coils.ConclusionsThe implications of these findings are: (i) the choice of training proteins is important in preserving the generalization of a classifier to predict new sequences accurately and (ii) SSP techniques sensitive in distinguishing between backbone hydrogen bonding and side-chain or water-mediated hydrogen bonding might be needed in the reduction of Coil ⇔ Sheet misclassifications.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1209-0) contains supplementary material, which is available to authorized users.

Hyperinsulinaemic hypoglycaemia (HH) is a biochemical finding of low blood glucose levels due to the dysregulation of insulin secretion from pancreatic β-cells. Under normal physiological conditions, glucose metabolism is coupled to β-cell insulin secretion so that blood glucose levels are maintained within the physiological range of 3.5–5.5 mmol/L. However, in HH this coupling of glucose metabolism to insulin secretion is perturbed so that insulin secretion becomes unregulated. HH typically occurs in the neonatal, infancy and childhood periods and can be due to many different causes. Adults can also present with HH but the causes in adults tend to be different. Somatostatin (SST) is a peptide hormone that is released by the delta cells (δ-cells) in the pancreas. It binds to G protein-coupled SST receptors to regulate a variety of location-specific and selective functions such as hormone inhibition, neurotransmission and cell proliferation. SST plays a potent role in the regulation of both insulin and glucagon secretion in response to changes in glucose levels by negative feedback mechanism. The half-life of SST is only 1–3 min due to quick degradation by peptidases in plasma and tissues. Thus, a direct continuous intravenous or subcutaneous infusion is required to achieve the therapeutic effect. These limitations prompted the discovery of SST analogues such as octreotide and lanreotide, which have longer half-lives and therefore can be administered as injections. SST analogues are used to treat different forms of HH in children and adults and therapeutic effect is achieved by suppressing insulin secretion from pancreatic β-cells by complex mechanisms. These treatments are associated with several side effects, especially in the newborn period, with necrotizing enterocolitis being the most serious side effect and hence SS analogues should be used with extreme caution in this age group.

Aims/Introduction Corneal confocal microscopy is a rapid, non‐invasive ophthalmic technique to identify subclinical neuropathy. The aim of this study was to quantify corneal nerve morphology in children with type 1 diabetes mellitus compared with age‐matched healthy controls using corneal confocal microscopy. Materials and Methods A total of 20 participants with type 1 diabetes mellitus (age 14 ± 2 years, diabetes duration 4.08 ± 2.91 years, glycated hemoglobin 9.3 ± 2.1%) without retinopathy or microalbuminuria and 20 healthy controls were recruited from outpatient clinics. Corneal confocal microscopy was undertaken, and corneal nerve fiber density ( n /mm 2 ), corneal nerve branch density ( n /mm 2 ), corneal nerve fiber length (mm/mm 2 ), corneal nerve fiber tortuosity and inferior whorl length (mm/mm 2 ) were quantified manually. Results Corneal nerve fiber density (22.73 ± 8.84 vs 32.92 ± 8.59; P < 0.001), corneal nerve branch density (26.19 ± 14.64 vs 47.34 ± 20.01; P < 0.001), corneal nerve fiber length (13.26 ± 4.06 vs 19.52 ± 4.54; P < 0.001) and inferior whorl length (15.50 ± 5.48 vs 23.42 ± 3.94; P < 0.0001) were significantly lower, whereas corneal nerve fiber tortuosity (14.88 ± 5.28 vs 13.52 ± 3.01; P = 0.323) did not differ between children with type 1 diabetes mellitus and controls. Glycated hemoglobin correlated with corneal nerve fiber tortuosity ( P < 0.006) and aspartate aminotransferase correlated with corneal nerve fiber density ( P = 0.039), corneal nerve branch density ( P = 0.003) and corneal nerve fiber length ( P = 0.037). Conclusion Corneal confocal microscopy identifies significant subclinical corneal nerve loss, especially in the inferior whorl of children with type 1 diabetes mellitus without retinopathy or microalbuminuria.