Protein Level and Infantile Diarrhea in a Postweaning Piglet Model

Infantile diarrhea is a serious public health problem around worldwide and results in millions of deaths each year. The levels and sources of dietary protein are potential sources of diarrhea, but the relationship between the pathogenesis causes of infantile diarrhea and protein intake remains poorly understood. Many studies have indicated that the key to understanding the relationship between the protein in the diet and the postweaning diarrhea of piglets is to explore the influences of protein sources and levels on the mammalian digestion system. The current study was designed to control diarrhea control by choosing different protein levels in the diet and aimed at providing efficient regulatory measures for infantile diarrhea by controlling the protein levels in diets using a postweaning piglets model. To avoid influences from other protein sources, casein was used as the only protein source in this study. Fourteen piglets (7.98 ± 0.14 kg, weaned at 28 d) were randomly allotted to two dietary treatments: a control group (Cont, containing 17% casein) and a high protein group (HP, containing 30% casein). The experiment lasted for two weeks and all animals were free to eat and drink water ad libitum. The diarrhea score (1 = normal; 3 = watery diarrhea) and growth performance were recorded daily. The results showed that the piglets in HP group had persistent diarrhea during the whole study, while no diarrhea was noticed in the control groups. Also, the feed intake and body weights were reduced in the HP groups compared with the other group (P < 0.05). The diarrhea-related mRNA abundances were analyzed by real-time PCR; the results showed that HP treatment markedly decreased the expression of aquaporin (AQP, P < 0.05) and the tight junction protein (P<0.05), but increased inflammatory cytokines (P < 0.01) than those in control group. In addition, the Adenosine 5′-monophosphate (AMP)-activated protein kinase (AMPK) signaling pathway (P < 0.01) was inhibited in the HP group. Intestinal microbiota was tested by 16S sequencing, and we found that the HP group had a low diversity compared the other group. In conclusion, despite being highly digestible, a high casein diet induced postweaning diarrhea and reduced the growth performance of the postweaning piglets. Meanwhile, AQP, tight junction protein, and intestinal immune were compromised. Thus, the mechanism of how a highly digestible protein diet induces diarrhea might be associated with the AMPK signaling pathway and intestinal microbiome.

Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials

Geometric comparisons of binding sites and their electrostatic properties can identify subtle variations that select different binding partners and subtle similarities that accommodate similar partners. Because subtle features are central for explaining how proteins achieve specificity, algorithmic efficiency and geometric precision are central to algorithmic design. To address these concerns, this paper presents pClay, the first algorithm to perform parallel and arbitrarily precise comparisons of molecular surfaces and electrostatic isopotentials as geometric solids. pClay was presented at the 2019 Workshop on Algorithms in Bioinformatics (WABI 2019) and is described in expanded detail here, especially with regard to the comparison of electrostatic isopotentials. Earlier methods have generally used parallelism to enhance computational throughput, pClay is the first algorithm to use parallelism to make arbitrarily high precision comparisons practical. It is also the first method to demonstrate that high precision comparisons of geometric solids can yield more precise structural inferences than algorithms that use existing standards of precision. One advantage of added precision is that statistical models can be trained with more accurate data. Using structural data from an existing method, a model of steric variations between binding cavities can overlook 53% of authentic steric influences on specificity, whereas a model trained with data from pClay overlooks none. Our results also demonstrate the parallel performance of pClay on both workstation CPUs and a 61-core Xeon Phi. While slower on one core, additional processor cores rapidly outpaced single core performance and existing methods. Based on these results, it is clear that pClay has applications in the automatic explanation of binding mechanisms and in the rational design of protein binding preferences.

Antitumor Effects of N-Butylidenephthalide Encapsulated in Lipopolyplexs in Colorectal Cancer Cells

Colorectal cancer (CRC) is the third most common type of cancer and the second most common cause of cancer-related death in the world. N-Butylidenephthalide (BP), a natural compound, inhibits several cancers, such as hepatoma, brain tumor and colon cancer. However, due to the unstable structure, the activity of BP is quickly lost after dissolution in an aqueous solution. A polycationic liposomal polyethylenimine and polyethylene glycol complex (LPPC), a new drug carrier, encapsulates both hydrophobic and hydrophilic compounds, maintains the activity of the compound, and increases uptake of cancer cells. The purpose of this study is to investigate the antitumor effects and protection of BP encapsulated in LPPC in CRC cells. The LPPC encapsulation protected BP activity, increased the cytotoxicity of BP and enhanced cell uptake through clathrin-mediated endocytosis. Moreover, the BP/LPPC-regulated the expression of the p21 protein and cell cycle-related proteins (CDK4, Cyclin B1 and Cyclin D1), resulting in an increase in the population of cells in the G₀/G₁ and subG₁ phases. BP/LPPC induced cell apoptosis by activating the extrinsic (Fas, Fas-L and Caspase-8) and intrinsic (Bax and Caspase-9) apoptosis pathways. Additionally, BP/LPPC combined with 5-FU synergistically inhibited the growth of HT-29 cells. In conclusion, LPPC enhanced the antitumor activity and cellular uptake of BP, and the BP/LPPC complex induced cell cycle arrest and apoptosis, thereby causing death. These findings suggest the putative use of BP/LPPC as an adjuvant cytotoxic agent for colorectal cancer.

Future avenues for Alzheimer's disease detection and therapy: liquid biopsy, intracellular signaling modulation, systems pharmacology drug discovery

When Alzheimer's disease (AD) disease-modifying therapies will be available, global healthcare systems will be challenged by a large-scale demand for clinical and biological screening. Validation and qualification of globally accessible, minimally-invasive, and time-, cost-saving blood-based biomarkers need to be advanced. Novel pathophysiological mechanisms (and related candidate biomarkers) - including neuroinflammation pathways (TREM2 and YKL-40), axonal degeneration (neurofilament light chain protein), synaptic dysfunction (neurogranin, synaptotagmin, α-synuclein, and SNAP-25) - may be integrated into an expanding pathophysiological and biomarker matrix and, ultimately, integrated into a comprehensive blood-based liquid biopsy, aligned with the evolving ATN + classification system and the precision medicine paradigm. Liquid biopsy-based diagnostic and therapeutic algorithms are increasingly employed in Oncology disease-modifying therapies and medical practice, showing an enormous potential for AD and other brain diseases as well. For AD and other neurodegenerative diseases, newly identified aberrant molecular pathways have been identified as suitable therapeutic targets and are currently investigated by academia/industry-led R&D programs, including the nerve-growth factor pathway in basal forebrain cholinergic neurons, the sigma1 receptor, and the GTPases of the Rho family. Evidence for a clinical long-term effect on cognitive function and brain health span of cholinergic compounds, drug candidates for repositioning programs, and non-pharmacological multidomain interventions (nutrition, cognitive training, and physical activity) is developing as well. Ultimately, novel pharmacological paradigms, such as quantitative systems pharmacology-based integrative/explorative approaches, are gaining momentum to optimize drug discovery and accomplish effective pathway-based strategies for precision medicine. This article is part of the special issue on 'The Quest for Disease-Modifying Therapies for Neurodegenerative Disorders'.

Special Issue on Advances in Deep Learning

Nowadays, deep learning is the fastest growing research field in machine learning and has a tremendous impact on a plethora of daily life applications, ranging from security and surveillance to autonomous driving, automatic indexing and retrieval of media content, text analysis, speech recognition, automatic translation, and many others [...]

Bacterial association and comparison between lung and intestine in rats

The association between lung and intestine has already been reported, but the differences in community structures or functions between lung and intestine bacteria yet need to explore. To explore the differences in community structures or functions, the lung tissues and fecal contents in rats were collected and analyzed through 16S rRNA sequencing. It was found that intestine bacteria was more abundant and diverse than lung bacteria. In intestine bacteria, Firmicutes and Bacteroides were identified as major phyla while Lactobacillus was among the most abundant genus. However, in lung the major identified phylum was Proteobacteria and genus Pseudomonas was most prominent genus. On the other hand, in contrast the lung bacteria was more concentrated in cytoskeleton and function in energy production and conversion. While, intestine bacteria were enriched in RNA processing, modification chromatin structure, dynamics and amino acid metabolism. The study provides the basis for understanding the relationships between lung and intestine bacteria.

MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer

OBJECTIVES

This study aimed to evaluate the efficiency of imaging features and texture analysis (TA) based on baseline rectal MRI for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) and tumor recurrence in patients with locally advanced rectal cancer (LARC).

METHODS

Consecutive patients with LARC who underwent rectal MRI between January 2014 and December 2015 and surgical resection after completing nCRT were retrospectively enrolled. Imaging features were analyzed, and TA parameters were extracted from the tumor volume of interest (VOI) from baseline rectal MRI. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the optimal TA parameter cutoff values to stratify the patients. Logistic and Cox regression analyses were performed to assess the efficacy of each imaging feature and texture parameter in predicting tumor response and disease-free survival.

RESULTS

In total, 78 consecutive patients were enrolled. In the logistic regression, good treatment response was associated with lower tumor location (OR = 13.284, p = 0.012), low Conv_Min (OR = 0.300, p = 0.013) and high Conv_Std (OR = 3.174, p = 0.016), Shape_Sphericity (OR = 3.170, p = 0.015), and Shape_Compacity (OR = 2.779, p = 0.032). In the Cox regression, a greater risk of tumor recurrence was related to higher cT stage (HR = 5.374, p = 0.044), pelvic side wall lymph node positivity (HR = 2.721, p = 0.013), and gray-level run length matrix_long-run low gray-level emphasis (HR = 2.268, p = 0.046).

CONCLUSIONS

Imaging features and TA based on baseline rectal MRI could be valuable for predicting the treatment response to nCRT for rectal cancer and tumor recurrence.

KEY POINTS

• Imaging features and texture parameters of T2-weighted MR images of rectal cancer can help to predict treatment response and the risk for tumor recurrence. • Tumor location as well as conventional and shape indices of texture features can help to predict treatment response for rectal cancer. • Clinical T stage, positive pelvic side wall lymph nodes, and the high-order texture parameter, GLRLM_LRLGE, can help to predict tumor recurrence for rectal cancer.

Component-Based Design and Assembly of Heuristic Multiple Sequence Alignment Algorithms

In recent years, there has been an explosive increase in the amount of bioinformatics data produced, but data are not information. The purpose of bioinformatics research is to obtain information with biological significance from large amounts of data. Multiple sequence alignment is widely used in sequence homology detection, protein secondary and tertiary structure prediction, phylogenetic tree analysis, and other fields. Existing research mainly focuses on the specific steps of the algorithm or on specific problems, and there is a lack of high-level abstract domain algorithm frameworks. As a result, multiple sequence alignment algorithms are complex, redundant, and difficult to understand, and it is not easy for users to select the appropriate algorithm, which may lead to computing errors. Here, through in-depth study and analysis of the heuristic multiple sequence alignment algorithm (HMSAA) domain, a domain-feature model and an interactive model of HMSAA components have been established according to the generative programming method. With the support of the PAR (partition and recur) platform, the HMSAA algorithm component library is formalized and a specific alignment algorithm is assembled, thus improving the reliability of algorithm assembly. This work provides a valuable theoretical reference for the applications of other biological sequence analysis algorithms.

PETRA: Drug Engineering via Rigidity Analysis

Rational drug design aims to develop pharmaceutical agents that impart maximal therapeutic benefits via their interaction with their intended biological targets. In the past several decades, advances in computational tools that inform wet-lab techniques have aided the development of a wide variety of new medicines with high efficacies. Nonetheless, drug development remains a time and cost intensive process. In this work, we have developed a computational pipeline for assessing how individual atoms contribute to a ligand’s effect on the structural stability of a biological target. Our approach takes as input a protein-ligand resolved PDB structure file and systematically generates all possible ligand variants. We assess how the atomic-level edits to the ligand alter the drug’s effect via a graph theoretic rigidity analysis approach. We demonstrate, via four case studies of common drugs, the utility of our pipeline and corroborate our analyses with known biophysical properties of the medicines, as reported in the literature.

Synthesis, Molecular Docking, and Preliminary Evaluation of 2-(1,2,3-Triazoyl)benzaldehydes As Multifunctional Agents for the Treatment of Alzheimer's Disease

We described here our results on the use of thiourea as a ligand in the copper catalysed azide-alkyne cycloaddition (CuAAC) of 2-azidobenzaldehyde with alkynes. Reactions were performed reacting 2-azidobenzaldehyde with a range of terminal alkynes using 10 mol % of copper iodide as a catalyst, 20 mol % of thiourea as a ligand, triethylamine as base, DMSO as solvent at 100 °C under nitrogen atmosphere. The corresponding 2-(1H-1,2,3-triazoyl)-benzaldehydes (2-TBH) were obtained in moderated to excellent yields and according our experiments, the use of thiourea decreases the formation of side products. The obtained compounds were screened for their binding affinity with multiple therapeutic targets of AD by molecular docking: β-secretase (BACE), glycogen synthase kinase (GSK-3β) and acetylcholinesterase (AChE). The three compounds with highest affinity, 5 a (2-(4-phenyl-1H-1,2,3-triazol-1-yl)benzaldehyde), 5 b (2-(4-(p-tolyl)-1H-1,2,3-triazol-1-yl)benzaldehyde), and 5 d (2-(4-(4-(tert-butyl)phenyl)-1H-1,2,3-triazol-1-yl)benzaldehyde) were selected and evaluated on its antioxidant effect, in view of select the most promising one to perform the in vivo validation. Due the antioxidant potential ally to the affinity with BACE, GSK-3β and AChE, compound 5 b was evaluated in a mouse model of AD induced by intracerebroventricular injection of streptozotocin (STZ). Our results indicate that 5 b (1 mg/kg) treatment during 20 days is able to reverse the cognitive and memory impairment induced by STZ trough the modulation of AChE activity, amyloid cascade and GSK-3β expression.

Modelling Surface Roughness in the Function of Torque When Drilling

Given the application of a multiple regression and artificial neural networks (ANNs), this paper describes development of models for predicting surface roughness, linking an arithmetic mean deviation of a surface roughness to a torque as an input variable, in the process of drilling enhancement steel EN 42CrMo4, thermally treated to the hardness level of 28 HRC, using cruciform blade twist drills made of high speed steel with hardness level of 64–68 HRC. The model was developed using process parameters (nominal diameters of twist drills, speed, feed, and angle of installation of work pieces) as input variables varied at three levels by Taguchi design of experiment and measured experimental data for a torque and arithmetic mean deviation of a surface roughness for different values of flank wear of twist drills. The comparative analysis of the models results and the experimental data, acquired for the inputs at the moment when a wear span reaches a limit value corresponding to a moment of the drills blunting, demonstrates that the neural network model gives better results than the results obtained in the application of multiple linear and nonlinear regression models.

Encapsulated n-Butylidenephthalide Efficiently Crosses the Blood-Brain Barrier and Suppresses Growth of Glioblastoma

Background

n-Butylidenephthalide (BP) has anti-tumor effects on glioblastoma. However, the limitation of BP for clinical application is its unstable structure. A polycationic liposomal polyethylenimine (PEI) and polyethylene glycol (PEG) complex (LPPC) has been developed to encapsulate BP for drug structure protection. The purpose of this study was to investigate the anti-cancer effects of the BP/LPPC complex on glioblastoma in vitro and in vivo.

Methods

DBTRG-05MG tumor bearing xenograft mice were treated with BP and BP/LPPC and then their tumor sizes, survival, drug biodistribution were measured. RG2 tumor bearing F344 rats also treated with BP and BP/LPPC and then their tumor sizes by magnetic resonance imaging for evaluation blood–brain barrier (BBB) across and drug therapeutic effects. After treated with BP/LPPC in vitro, cell uptake, cell cycle and apoptotic regulators were analyzed for evaluation the therapeutic mechanism.

Results

In athymic mice, BP/LPPC could efficiently suppress tumor growth and prolong survival. In F334 rats, BP/LPPC crossed the BBB and led to tumor shrinkage. BP/LPPC promoted cell cycle arrest at the G₀/G₁ phase and triggered the extrinsic and intrinsic cell apoptosis pathways resulting cell death. BP/LPPC also efficiently suppressed VEGF, VEGFR1, VEGFR2, MMP2 and MMP9 expression.

Conclusion

BP/LPPC was rapidly and efficiently transported to the tumor area across the BBB and induced cell apoptosis, anti-angiogenetic and anti-metastatic effects in vitro and in vivo.

South African Abietane Diterpenoids and Their Analogs as Potential Antimalarials: Novel Insights from Hybrid Computational Approaches

The hemoglobin degradation process in Plasmodium parasites is vital for nutrient acquisition required for their growth and proliferation. In P. falciparum, falcipains (FP-2 and FP-3) are the major hemoglobinases, and remain attractive antimalarial drug targets. Other Plasmodium species also possess highly homologous proteins to FP-2 and FP-3. Although several inhibitors have been designed against these proteins, none has been commercialized due to associated toxicity on human cathepsins (Cat-K, Cat-L and Cat-S). Despite the two enzyme groups sharing a common structural fold and catalytic mechanism, distinct active site variations have been identified, and can be exploited for drug development. Here, we utilize in silico approaches to screen 628 compounds from the South African natural sources to identify potential hits that can selectively inhibit the plasmodial proteases. Using docking studies, seven abietane diterpenoids, binding strongly to the plasmodial proteases, and three additional analogs from PubChem were identified. Important residues involved in ligand stabilization were identified for all potential hits through binding pose analysis and their energetic contribution determined by binding free energy calculations. The identified compounds present important scaffolds that could be further developed as plasmodial protease inhibitors. Previous laboratory assays showed the effect of the seven diterpenoids as antimalarials. Here, for the first time, we demonstrate that their possible mechanism of action could be by interacting with falcipains and their plasmodial homologs. Dynamic residue network (DRN) analysis on the plasmodial proteases identified functionally important residues, including a region with high betweenness centrality, which had previously been proposed as a potential allosteric site in FP-2.

Perspectives towards antiviral drug discovery against Ebola virus

Ebola virus disease (EVD), caused by Ebola viruses, resulted in more than 11 500 deaths according to a recent 2018 WHO report. With mortality rates up to 90%, it is nowadays one of the most deadly infectious diseases. However, no Food and Drug Administration‐approved Ebola drugs or vaccines are available yet with the mainstay of therapy being supportive care. The high fatality rate and absence of effective treatment or vaccination make Ebola virus a category‐A biothreat pathogen. Fortunately, a series of investigational countermeasures have been developed to control and prevent this global threat. This review summarizes the recent therapeutic advances and ongoing research progress from research and development to clinical trials in the development of small‐molecule antiviral drugs, small‐interference RNA molecules, phosphorodiamidate morpholino oligomers, full‐length monoclonal antibodies, and vaccines. Moreover, difficulties are highlighted in the search for effective countermeasures against EVD with additional focus on the interplay between available in silico prediction methods and their evidenced potential in antiviral drug discovery.

Prediction of Surface Roughness of 304 Stainless Steel and Multi-Objective Optimization of Cutting Parameters Based on GA-GBRT

Establishing and controlling the prediction model of a machined surface quality is known as the basis for sustainable manufacturing. An ensemble learning algorithm—the gradient boosting regression tree—is incorporated into the surface roughness modeling. In order to address the problem of a high time cost and tendency to fall into a local optimum solution when the grid search and conjugate gradient method is adopted to obtain the super-parameters of the ensemble learning algorithm, a genetic algorithm is employed to search for the optimal super-parameters in the training process, and a genetic-gradient boosting regression tree (GA-GBRT) algorithm is developed. A fitting goodness of fit is taken as the fitness function value of the genetic algorithm and combined with k-fold cross-validation, as such, the initial model parameters of the gradient boosting regression tree are optimized. Compared to the optimized artificial neural network (ANN) and support vector regression (SVR) and combined with the cutting experiment of 304 stainless steel with a micro-groove tool, a genetic algorithm multi-objective optimization model with the highest cutting efficiency and a supreme surface quality was constructed by applying the GA-GBRT model. The response relationship reveals the non-linear interaction that occurs between the cutting parameters and the surface roughness of 304 stainless steel that is machined by the micro-groove tool. As indicated by the results obtained from the multi-objective optimization, the cutting efficiency can be enhanced by increasing the cutting speed and depth within a small range of surface quality variations. The GA-GBRT model is validated to be reliable in making a prediction of the surface roughness and optimizing the cutting parameters with turning and milling data.

GPU-Accelerated GLRLM Algorithm for Feature Extraction of MRI

The gray level run length matrix (GLRLM) whose entries are statistics recording distribution and relationship of images pixels is a widely used method for extracting statistical features for medical images, e.g., magnetic resonance (MR) images. Recently these features are usually employed in some artificial neural networks to identify and distinguish texture patterns. But GLRLM construction and features extraction are tedious and computationally intensive while the images are too big with high resolution, or there are too many small or intermediate Regions of Interest (ROI) to process in a single image, which makes the preprocess a time consuming stage. Hence, it is of great importance to accelerate the procedure which is nowadays possible with the rapid development of massively parallel Graphics Processing Unit, i.e. the GPU computing technology. In this article, we propose a new paradigm based on mature parallel primitives for generating GLRLMs and extracting multiple features for many ROIs simultaneously in a single image. Experiments show that such a paradigm is easy to implement and offers an acceleration over 5 fold increase in speed than an optimized serial counterpart.

Elevator Fault Detection Using Profile Extraction and Deep Autoencoder Feature Extraction for Acceleration and Magnetic Signals

In this paper, we propose a new algorithm for data extraction from time-series data, and furthermore automatic calculation of highly informative deep features to be used in fault detection. In data extraction, elevator start and stop events are extracted from sensor data including both acceleration and magnetic signals. In addition, a generic deep autoencoder model is also developed for automated feature extraction from the extracted profiles. After this, extracted deep features are classified with random forest algorithm for fault detection. Sensor data are labelled as healthy and faulty based on the maintenance actions recorded. The remaining healthy data are used for validation of the model to prove its efficacy in terms of avoiding false positives. We have achieved above 90% accuracy in fault detection along with avoiding false positives based on new extracted deep features, which outperforms results using existing features. Existing features are also classified with random forest to compare results. Our developed algorithm provides better results due to the new deep features extracted from the dataset when compared to existing features. This research will help various predictive maintenance systems to detect false alarms, which will in turn reduce unnecessary visits of service technicians to installation sites.

Bioassay Directed Isolation, Biological Evaluation and in Silico Studies of New Isolates from Pteris cretica L

Members of genus Pteris have their established role in the traditional herbal medicine system. In the pursuit to identify its biologically active constituents, the specie Pteris cretica L. (P. cretica) was selected for the bioassay-guided isolation. Two new maleates (F9 and CB18) were identified from the chloroform extract and the structures of the isolates were elucidated through their spectroscopic data. The putative targets, that potentially interact with both of these isolates, were identified through reverse docking by using in silico tools PharmMapper and ReverseScreen3D. On the basis of reverse docking results, both isolates were screened for their antioxidant, acetylcholinesterase (AChE) inhibition, α-glucosidase (GluE) inhibition and antibacterial activities. Both isolates depicted moderate potential for the selected activities. Furthermore, docking studies of both isolates were also studied to investigate the binding mode with respective targets followed by molecular dynamics simulations and binding free energies. Thereby, the current study embodies the poly-pharmacological potential of P. cretica.

GMSA: A Data Sharing System for Multiple Sequence Alignment Across Multiple Users

Background:

In recent years, the rapid growth of biological datasets in Bioinformatics has made the computation of Multiple Sequence Alignment (MSA) become extremely slow. Using the GPU to accelerate MSA has shown to be an effective approach. Moreover, there is a trend that many bioinformatic researchers or institutes setup a shared server for remote users to submit MSA jobs via provided web-pages or tools.

Objective:

Given the fact that different MSA jobs submitted by users often process similar datasets, there can be an opportunity for users to share their computation results between each other, which can avoid the redundant computation and thereby reduce the overall computing time. Furthermore, in the heterogeneous CPU/GPU platform, many existing applications assign their computation on GPU devices only, which leads to a waste of the CPU resources. Co-run computation can increase the utilization of computing resources on both CPUs and GPUs by dispatching workloads onto them simultaneously.

Methods:

In this paper, we propose an efficient MSA system called GMSA for multi-users on shared heterogeneous CPU/GPU platforms. To accelerate the computation of jobs from multiple users, data sharing is considered in GMSA due to the fact that different MSA jobs often have a percentage of the same data and tasks. Additionally, we also propose a scheduling strategy based on the similarity in datasets or tasks between MSA jobs. Furthermore, co-run computation model is adopted to take full use of both CPUs and GPUs.

Results:

We use four protein datasets which were redesigned according to different similarity. We compare GMSA with ClustalW and CUDA-ClustalW in multiple users scenarios. Experiments results showed that GMSA can achieve a speedup of up to 32X.

Conclusion:

GMSA is a system designed for accelerating the computation of MSA jobs with shared input datasets on heterogeneous CPU/GPU platforms. In this system, a strategy was proposed and implemented to find the common datasets among jobs submitted by multiple users, and a scheduling algorithm is presented based on it. To utilize the overall resource of both CPU and GPU, GMSA employs the co-run computation model. Results showed that it can speed up the total computation of jobs efficiently.

Computer Design of Low-Molecular-Weight Inhibitors of Coagulation Factors

The review discusses main approaches to searching for new low-molecular-weight inhibitors of coagulation factors IIa, Xa, IXa, and XIa and the results of such studies conducted from 2015 to 2018. For each of these factors, several inhibitors with IC₅₀ < 10 nM have been found, some of which are now tested in clinical trials. However, none of the identified inhibitors meets the requirements for an "ideal" anticoagulant, so further studies are required.

Integration of PEG 400 into a self-nanoemulsifying drug delivery system improves drug loading capacity and nasal mucosa permeability and prolongs the survival of rats with malignant brain tumors

Introduction: Kolliphor^® EL (K-EL) is among the most useful surfactants in the preparation of emulsions. However, it is associated with low hydrophobic drug loading in the resulting emulsified formulation.

Methods: In this study, a formulation for intranasal administration of butylidenephthalide (Bdph), a candidate drug against glioblastoma (GBM), was prepared. Physical characteristics of the formulation such as particle size, zeta potential, conductivity, and viscosity were assessed, as well as its cytotoxicity and permeability, in order to optimize the formulation and improve its drug loading capacity.

Results: The optimized formulation involved the integration of polyethylene glycol 400 (PEG 400) in K-EL to encapsulate Bdph dissolved in dimethyl sulfoxide (DMSO), and it exhibited higher drug loading capacity and drug solubility in water than the old formulation, which did not contain PEG 400. Incorporation of PEG 400 as a co-surfactant increased Bdph loading capacity to up to 50% (v/v), even in formulations using Kolliphor^® HS 15 (K-HS15) as a surfactant, which is less compatible with Bdph than K-EL. The optimized Bdph formulation presented 5- and 2.5-fold higher permeability and cytotoxicity, respectively, in human GBM than stock Bdph. This could be attributed to the high drug loading capacity and the high polarity index due to DMSO, which increases the compatibility between the drug and the cell. Rats bearing a brain glioma treated with 160 mg/kg intranasal emulsified Bdph had a mean survival of 37 days, which is the same survival time achieved by treatment with 320 mg/kg stock Bdph. This implies that the optimized emulsified formulation required only half the Bdph dose to achieve an efficacy similar to that of stock Bdph in the treatment of animals with malignant brain tumor.

In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors

The Crimean-Congo Hemorrhagic Fever virus (CCHFV) is a segmented negative single-stranded RNA virus (-ssRNA) which causes severe hemorrhagic fever in humans with a mortality rate of ~50%. To date, no vaccine has been approved. Treatment is limited to supportive care with few investigational drugs in practice. Previous studies have identified viral RNA dependent RNA Polymerase (RdRp) as a potential drug target due to its significant role in viral replication and transcription. Since no crystal structure is available yet, we report the structural elucidation of CCHFV-RdRp by in-depth homology modeling. Even with low sequence identity, the generated model suggests a similar overall structure as previously reported RdRps. More specifically, the model suggests the presence of structural/functional conserved RdRp motifs for polymerase function, the configuration of uniform spatial arrangement of core RdRp sub-domains, and predicted positively charged entry/exit tunnels, as seen in sNSV polymerases. Extensive pharmacophore modeling based on per-residue energy contribution with investigational drugs allowed the concise mapping of pharmacophoric features and identified potential hits. The combination of pharmacophoric features with interaction energy analysis revealed functionally important residues in the conserved motifs together with in silico predicted common inhibitory binding modes with highly potent reference compounds.

Computational investigations of gram-negative bacteria phosphopantetheine adenylyltransferase inhibitors using 3D-QSAR, molecular docking and molecular dynamic simulations

Phosphopantetheine adenylyltransferase (PPAT) has been recognized as a promising target to develop novel antimicrobial agents, which is a hexameric enzyme that catalyzes the penultimate step in coenzyme A biosynthesis. In this work, molecular modeling study was performed with a series of PPAT inhibitors using molecular docking, three-dimensional qualitative structure-activity relationship (3D-QSAR) and molecular dynamic (MD) simulations to reveal the structural determinants for their bioactivities. Molecular docking study was applied to understand the binding mode of PPAT with its inhibitors. Subsequently, 3D-QSAR model was constructed to find the features required for different substituents on the scaffolds. For the best comparative molecular field analysis (CoMFA) model, the Q² and R² values of which were calculated as 0.702 and 0.989, while they were calculated as 0.767 and 0.983 for the best comparative molecular similarity index analysis model. The statistical data verified the significance and accuracy of our 3D-QSAR models. Furthermore, MD simulations were carried out to evaluate the stability of the receptor-ligand contacts in physiological conditions, and the results were consistent with molecular docking studies and 3D-QSAR contour map analysis. Binding free energy was calculated with molecular mechanics generalized born surface area approach, the result of which coincided well with bioactivities and demonstrated that van der Waals accounted for the largest portion. Overall, our study provided a valuable insight for further research work on the recognition of potent PPAT inhibitors.Communicated by Ramaswamy H. Sarma.

Structure Based Design and Molecular Docking Studies for Phosphorylated Tau Inhibitors in Alzheimer's Disease

The purpose of our study is to identify phosphorylated tau (p-tau) inhibitors. P-tau has recently received great interest as a potential drug target in Alzheimer’s disease (AD). The continuous failure of Aβ-targeted therapeutics recommends an alternative drug target to treat AD. There is increasing evidence and growing awareness of tau, which plays a central role in AD pathophysiology, including tangles formation, abnormal activation of phosphatases/kinases, leading p-tau aggregation in AD neurons. In the present study, we performed computational pharmacophore models, molecular docking, and simulation studies for p-tau in order to identify hyperphosphorylated sites. We found multiple serine sites that altered the R1/R2 repeats flanking sequences in the tau protein, affecting the microtubule binding ability of tau. The ligand molecules exhibited the p-O ester scaffolds with inhibitory and/or blocking actions against serine residues of p-tau. Our molecular docking results revealed five ligands that showed high docking scores and optimal protein-ligand interactions of p-tau. These five ligands showed the best pharmacokinetic and physicochemical properties, including good absorption, distribution, metabolism, and excretion (ADME) and admetSAR toxicity tests. The p-tau pharmacophore based drug discovery models provide the comprehensive and rapid drug interventions in AD, and tauopathies are expected to be the prospective future therapeutic approach in AD.

Two-Phase PFAC Algorithm for Multiple Patterns Matching on CUDA GPUs

The rapid advancement of high speed networks has resulted in a significantly increasing number of network packets per second nowadays, implying network intrusion detection systems (NIDSs) need to accelerate the inspection of packet content to protect the computer systems from attacks. On average, the pattern matching process in a NIDS consumes approximately 70% of the overall processing time. The conventional Aho–Corasick (AC) algorithm, adopting a finite state machine to identify attack patterns in NIDSs, is too slow to meet the requirement of high speed networks. In view of this, several studies have used the features of a graphics processing unit (GPU) to improve the core searching process of the AC algorithm. For instance, parallel failureless Aho-Corasick (PFAC) algorithm improves the process of pattern matching effectively by removing backward branches in the original finite state machine created using the AC algorithm. In this way, boundary detection can be avoided totally if we allocate an individual thread to each byte of an input stream to identify any pattern starting at the thread’s starting position. However, through analysis, we found that this algorithm experiences a serious load imbalance problem. Therefore, this paper proposes a two-phase PFAC algorithm to address the problem. A threshold is predefined to divide execution into two phases, and the failureless finite state machine is also decoupled into two parts accordingly. In the first phase, every thread identifies patterns by running the tiny part of the decoupled failureless finite state machine that are stored in fast shared memory. In the second phase, all the threads requiring further searching in a same block are regrouped into a few warps for less branch divergence. According to experimental results, the proposed algorithm shows a performance improvement of 50% compared to the PFAC algorithm.

Specific drug delivery efficiently induced human breast tumor regression using a lipoplex by non-covalent association with anti-tumor antibodies

Background

A cationic liposome-PEG-PEI complex (LPPC) was employed as a carrier for achieving targeted delivery of drug to human epidermal growth factor receptor-2 (HER2/neu)-expressing breast cancer cells. LPPC can be easily loaded with an anti-tumor drug and non-covalently associated with an anti-tumor antibody such as Herceptin that is clinically used to rapidly form immunoparticles within 1 h.

Results

Drug-loaded LPPC have an average size about 250 nm and a zeta potential of about 40 mV. Herceptin was complexed onto surface of the LPPC to form the drug/LPPC/Herceptin complexes. The size of curcumin/LPPC/Herceptin complexes were 280 nm and the zeta potentials were about 23 mV. Targeting ability of this delivery system was demonstrated through specific binding on surface of cells and IVIS images in vivo, which showed specific binding in HER2-positive SKBR3 cells as compared to HER2-negative Hs578T cells. Only the drug/LPPC/Herceptin complexes displayed dramatically increased the cytotoxic activity in cancer cells. Both in vitro and in vivo results indicated that Herceptin adsorbed on LPPC directed the immunocomplex towards HER2/neu-positive cells but not HER2/neu-negative cells. The complexes with either component (curcumin or doxorubicin) used in the LPPC-delivery system provided a better therapeutic efficacy compared to the drug treatment alone and other treatment groups, including clinical dosages of Herceptin and LipoDox, in a xenografted model.

Conclusions

LPPC displays important clinical implications by easily introducing a specific targeting characteristic to drugs utilized for breast cancer therapy.

Electronic supplementary material

The online version of this article (10.1186/s12951-019-0457-3) contains supplementary material, which is available to authorized users.

Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations-whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico⁻chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties.

Studying the effects of haplotype partitioning methods on the RA-associated genomic results from the North American Rheumatoid Arthritis Consortium (NARAC) dataset

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Haplotype blocks methods plays a complementary role to the single-SNP approaches.

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CIT, FGT, SSLD, and single-SNP methods should be applied to discover the markers.

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Selection of the method used for the association has an impact on the biomarkers.

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SSLD method detected more significant SNPs than CIT, FGT, and single-SNP methods.

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The 383 SNPs discovered by all methods are significantly associated with RA.

The human genome, which includes thousands of genes, represents a big data challenge. Rheumatoid arthritis (RA) is a complex autoimmune disease with a genetic basis. Many single-nucleotide polymorphism (SNP) association methods partition a genome into haplotype blocks. The aim of this genome wide association study (GWAS) was to select the most appropriate haplotype block partitioning method for the North American Rheumatoid Arthritis Consortium (NARAC) dataset. The methods used for the NARAC dataset were the individual SNP approach and the following haplotype block methods: the four-gamete test (FGT), confidence interval test (CIT), and solid spine of linkage disequilibrium (SSLD). The measured parameters that reflect the strength of the association between the biomarker and RA were the P-value after Bonferroni correction and other parameters used to compare the output of each haplotype block method. This work presents a comparison among the individual SNP approach and the three haplotype block methods to select the method that can detect all the significant SNPs when applied alone. The GWAS results from the NARAC dataset obtained with the different methods are presented. The individual SNP, CIT, FGT, and SSLD methods detected 541, 1516, 1551, and 1831 RA-associated SNPs respectively, and the individual SNP, FGT, CIT, and SSLD methods detected 65, 156, 159, and 450 significant SNPs respectively, that were not detected by the other methods. Three hundred eighty-three SNPs were discovered by the haplotype block methods and the individual SNP approach, while 1021 SNPs were discovered by all three haplotype block methods. The 383 SNPs detected by all the methods are promising candidates for studying RA susceptibility. A hybrid technique involving all four methods should be applied to detect the significant SNPs associated with RA in the NARAC dataset, but the SSLD method may be preferred because of its advantages when only one method was used.

eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates

BACKGROUND

The efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined. Nonetheless, recent advances in high-throughput experimental techniques and computational modeling promise reductions in the costs and development times required to bring new drugs to market. The prediction of toxicity of drug candidates is one of the important components of modern drug discovery.

RESULTS

In this work, we describe eToxPred, a new approach to reliably estimate the toxicity and synthetic accessibility of small organic compounds. eToxPred employs machine learning algorithms trained on molecular fingerprints to evaluate drug candidates. The performance is assessed against multiple datasets containing known drugs, potentially hazardous chemicals, natural products, and synthetic bioactive compounds. Encouragingly, eToxPred predicts the synthetic accessibility with the mean square error of only 4% and the toxicity with the accuracy of as high as 72%.

CONCLUSIONS

eToxPred can be incorporated into protocols to construct custom libraries for virtual screening in order to filter out those drug candidates that are potentially toxic or would be difficult to synthesize. It is freely available as a stand-alone software at https://github.com/pulimeng/etoxpred .

Scalable Extraction of Big Macromolecular Data in Azure Data Lake Environment

Calculation of structural features of proteins, nucleic acids, and nucleic acid-protein complexes on the basis of their geometries and studying various interactions within these macromolecules, for which high-resolution structures are stored in Protein Data Bank (PDB), require parsing and extraction of suitable data stored in text files. To perform these operations on large scale in the face of the growing amount of macromolecular data in public repositories, we propose to perform them in the distributed environment of Azure Data Lake and scale the calculations on the Cloud. In this paper, we present dedicated data extractors for PDB files that can be used in various types of calculations performed over protein and nucleic acids structures in the Azure Data Lake. Results of our tests show that the Cloud storage space occupied by the macromolecular data can be successfully reduced by using compression of PDB files without significant loss of data processing efficiency. Moreover, our experiments show that the performed calculations can be significantly accelerated when using large sequential files for storing macromolecular data and by parallelizing the calculations and data extractions that precede them. Finally, the paper shows how all the calculations can be performed in a declarative way in U-SQL scripts for Data Lake Analytics.

Lead Optimization Studies Towards Finding NS2B/NS3 Protease Targetspecific Inhibitors as Potential Anti-dengue Drug-like Compounds

BACKGROUND

Dengue Fever is a major threatening global health issue caused by a mosquito-borne pathogen. Even though some anti-viral drugs are now available to reduce the disease severity. Still, there is a need of better drug compound to combat with dengue fever. The NS2B/NS3 protease is a major therapeutic drug target for Insilco drug discovery.

MATERIALS & METHODS

Previously, we have performed a pharmacophore features based virtual screening studies, which has led to the identification of ZINC92615064 compound as a potent NS2B/NS3 protease inhibitor and demonstrated its potential to act as anti-dengue drug-like compound using computational approaches. In this present study, the identified lead compound ZINC92615064 has been made to undergo scaffold hopping based novel library generation, and the resulted novel library of compounds has been virtually screened on to NS2B/NS3 protease towards identifying novel proprietary scaffold of compound which is acting as a potent inhibitor for the given drug target of NS2B/NS3.

RESULT & CONCLUSION

A total of 16,847 novel designed compounds library was generated using the scaffold hopping technology based on the structure of the lead compound ZINC92615064. Out of which, compound design no. 3718 has shown the best binding potential with a predicted IC50 value of 417.13 nM along with a permissible range of ADMET properties based on its descriptor values. This NS2B/NS3 protease in complex with compound 3718 was subjected to a rigorous molecular dynamic simulation study to further validate this complex thermodynamic stability, along with the aim to reveal the underlying molecular level interactions and potential mode of action.

Molegro Virtual Docker for Docking

Molegro Virtual Docker is a protein-ligand docking simulation program that allows us to carry out docking simulations in a fully integrated computational package. MVD has been successfully applied to hundreds of different proteins, with docking performance similar to other docking programs such as AutoDock4 and AutoDock Vina. The program MVD has four search algorithms and four native scoring functions. Considering that we may have water molecules or not in the docking simulations, we have a total of 32 docking protocols. The integration of the programs SAnDReS ( https://github.com/azevedolab/sandres ) and MVD opens the possibility to carry out a detailed statistical analysis of docking results, which adds to the native capabilities of the program MVD. In this chapter, we describe a tutorial to carry out docking simulations with MVD and how to perform a statistical analysis of the docking results with the program SAnDReS. To illustrate the integration of both programs, we describe the redocking simulation focused the cyclin-dependent kinase 2 in complex with a competitive inhibitor.

Comparative study for haplotype block partitioning methods - Evidence from chromosome 6 of the North American Rheumatoid Arthritis Consortium (NARAC) dataset

Haplotype-based methods compete with "one-SNP-at-a-time" approaches on being preferred for association studies. Chromosome 6 contains most of the known genetic biomarkers for rheumatoid arthritis (RA) disease. Therefore, chromosome 6 serves as a benchmark for the haplotype methods testing. The aim of this study is to test the North American Rheumatoid Arthritis Consortium (NARAC) dataset to find out if haplotype block methods or single-locus approaches alone can sufficiently provide the significant single nucleotide polymorphisms (SNPs) associated with RA. In addition, could we be satisfied with only one method of the haplotype block methods for partitioning chromosome 6 of the NARAC dataset? In the NARAC dataset, chromosome 6 comprises 35,574 SNPs for 2,062 individuals (868 cases, 1,194 controls). Individual SNP approach and three haplotype block methods were applied to the NARAC dataset to identify the RA biomarkers. We employed three haplotype partitioning methods which are confidence interval test (CIT), four gamete test (FGT), and solid spine of linkage disequilibrium (SSLD). P-values after stringent Bonferroni correction for multiple testing were measured to assess the strength of association between the genetic variants and RA susceptibility. Moreover, the block size (in base pairs (bp) and number of SNPs included), number of blocks, percentage of uncovered SNPs by the block method, percentage of significant blocks from the total number of blocks, number of significant haplotypes and SNPs were used to compare among the three haplotype block methods. Individual SNP, CIT, FGT, and SSLD methods detected 432, 1,086, 1,099, and 1,322 associated SNPs, respectively. Each method identified significant SNPs that were not detected by any other method (Individual SNP: 12, FGT: 37, CIT: 55, and SSLD: 189 SNPs). 916 SNPs were discovered by all the three haplotype block methods. 367 SNPs were discovered by the haplotype block methods and the individual SNP approach. The P-values of these 367 SNPs were lower than those of the SNPs uniquely detected by only one method. The 367 SNPs detected by all the methods represent promising candidates for RA susceptibility. They should be further investigated for the European population. A hybrid technique including the four methods should be applied to detect the significant SNPs associated with RA for chromosome 6 of the NARAC dataset. Moreover, SSLD method may be preferred for its favored benefits in case of selecting only one method.

Advances in distributed computing with modern drug discovery

INTRODUCTION

Computational chemistry dramatically accelerates the drug discovery process and high-performance computing (HPC) can be used to speed up the most expensive calculations. Supporting a local HPC infrastructure is both costly and time-consuming, and, therefore, many research groups are moving from in-house solutions to remote-distributed computing platforms. Areas covered: The authors focus on the use of distributed technologies, solutions, and infrastructures to gain access to HPC capabilities, software tools, and datasets to run the complex simulations required in computational drug discovery (CDD). Expert opinion: The use of computational tools can decrease the time to market of new drugs. HPC has a crucial role in handling the complex algorithms and large volumes of data required to achieve specificity and avoid undesirable side-effects. Distributed computing environments have clear advantages over in-house solutions in terms of cost and sustainability. The use of infrastructures relying on virtualization reduces set-up costs. Distributed computing resources can be difficult to access, although web-based solutions are becoming increasingly available. There is a trade-off between cost-effectiveness and accessibility in using on-demand computing resources rather than free/academic resources. Graphics processing unit computing, with its outstanding parallel computing power, is becoming increasingly important.

Antibiotic prescribing practices in secondary and tertiary hospitals in Shaanxi province, western China, 2013-2015

Introduction

The irrational use of antibiotics is a major driver of antimicrobial resistance. This study aimed to explore how antibiotics were used in secondary and tertiary hospitals in Shaanxi Province, western China from 2013 to 2015.

Method

A retrospective study was conducted with a sample of 16 hospitals in Shaanxi Province (2 tertiary and 14 secondary hospitals; 8 public and 8 private hospitals) using a stratified random sampling method. All of the macro data from these hospitals from 2013 to 2015 were analyzed. All collected data were double-entered and analyzed using Excel 2007.

Results

The percentage of injectable antibiotic prescriptions was 26.6% of all of the antibiotic prescriptions in the secondary hospitals and 14.2% in the tertiary hospitals. Injectable antibiotic prescriptions in private tertiary hospitals (enter %) were more than two times that of public tertiary hospitals (enter %). In both tertiary and secondary hospitals, the percentage of antibiotic prescriptions for outpatients, emergency patients and inpatients were within the scope of the national standards, while the intensity of antibiotic use was higher than the national standard of 40 DDD/100 bed-days. The prophylactic antibiotic use rate in clean surgery was 40.4% in tertiary hospitals and 60.7% in secondary hospitals, which were both higher than the national standard of 30%. The preventive use rate of antibiotics in private tertiary hospitals (55.00%) was more than two times that of public tertiary hospitals (25.90%), and the rate was also higher in private secondary hospitals (61.50%) than in public secondary hospitals (59.70%).

Conclusions

Substantial antibiotic abuse occurred in the sample Chinese hospitals, especially in secondary hospitals. The government should continue to strengthen the administration of antimicrobial use in hospitals. At the same time, medical professional training and interventions for physicians should be conducted to fundamentally reduce the irrational use of antibiotics.

Antitumor Effect of n-Butylidenephthalide Encapsulated on B16/F10 Melanoma Cells In Vitro with a Polycationic Liposome Containing PEI and Polyethylene Glycol Complex

Advanced melanoma can metastasize to distal organs from the skin and yield an aggressive disease and poor prognosis even after treatment with chemotherapeutic agents. The compound n-Butylidenephthalide (BP) is isolated from Angelica sinensis, which is used to treat anemia and gynecological dysfunction in traditional Chinese medicine. Studies have indicated that BP can inhibit cancers, including brain, lung, prostate, liver, and colon cancers. However, because BP is a natural hydrophobic compound, it is quickly metabolized by the liver within 24 h, and thus has limited potential for development in cancer therapy. This study investigated the anticancer mechanisms of BP through encapsulation with a novel polycationic liposome containing polyethylenimine (PEI) and polyethylene glycol complex (LPPC) in melanoma cells. The results demonstrated that BP/LPPC had higher cytotoxicity than BP alone and induced cell cycle arrest at the G₀/G₁ phase in B16/F10 melanoma cells. The BP/LPPC-treated cell indicated an increase in subG₁ percentage and TUNEL positive apoptotic morphology through induction of extrinsic and intrinsic apoptosis pathways. The combination of BP and LPPC and clinical drug 5-Fluorouracil had a greater synergistic inhibition effect than did a single drug. Moreover, LPPC encapsulation improved the uptake of BP values through enhancement of cell endocytosis and maintained BP cytotoxicity activity within 24 h. In conclusion, BP/LPPC can inhibit growth of melanoma cells and induce cell arrest and apoptosis, indicating that BP/LPPC has great potential for development of melanoma therapy agents.

The application of Hadoop in structural bioinformatics

The paper reviews the use of the Hadoop platform in structural bioinformatics applications. For structural bioinformatics, Hadoop provides a new framework to analyse large fractions of the Protein Data Bank that is key for high-throughput studies of, for example, protein-ligand docking, clustering of protein-ligand complexes and structural alignment. Specifically we review in the literature a number of implementations using Hadoop of high-throughput analyses and their scalability. We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms. The scalability exhibits a variable behaviour in comparison with other batch schedulers, particularly as direct comparisons on the same platform are generally not available. Direct comparisons of Hadoop with batch schedulers are absent in the literature but we note there is some evidence that Message Passing Interface implementations scale better than Hadoop. A significant barrier to the use of the Hadoop ecosystem is the difficulty of the interface and configuration of a resource to use Hadoop. This will improve over time as interfaces to Hadoop, e.g. Spark improve, usage of cloud platforms (e.g. Azure and Amazon Web Services (AWS)) increases and standardised approaches such as Workflow Languages (i.e. Workflow Definition Language, Common Workflow Language and Nextflow) are taken up.

ParStream-seq: An improved method of handling next generation sequence data

The exponential growth of next generation sequencing (NGS) data has put forward the challenge for its storage as well as its efficient and faster analysis. Storing the entire amount of data for a particular experiment and its alignment to the reference genome is an essential step for any quantitative analysis of NGS data. Here, we introduce streaming access technique 'ParStream-seq' that splits the bulk sequence data, accessed from a remote repository into short manageable packets followed by executing their alignment process in parallel in each of the compute core. The optimal packet size with fixed number of reads is determined in the stream that maximizes system utilization. Result shows a reduction in the execution time and improvement in the memory footprint. Overall, this streaming access technique provides means to overcome the hurdle of storing the entire volume of sequence data corresponding to a particular experiment, prior to its analysis.

Characterization and Genomic Analyses of Pseudomonas aeruginosa Podovirus TC6: Establishment of Genus Pa11virus

Phages have attracted a renewed interest as alternative to chemical antibiotics. Although the number of phages is 10-fold higher than that of bacteria, the number of genomically characterized phages is far less than that of bacteria. In this study, phage TC6, a novel lytic virus of Pseudomonas aeruginosa, was isolated and characterized. TC6 consists of an icosahedral head with a diameter of approximately 54 nm and a short tail with a length of about 17 nm, which are characteristics of the family Podoviridae. TC6 can lyse 86 out of 233 clinically isolated P. aeruginosa strains, thus showing application potentials for phage therapy. The linear double-stranded genomic DNA of TC6 consisted of 49796 base pairs and was predicted to contain 71 protein-coding genes. A total of 11 TC6 structural proteins were identified by mass spectrometry. Comparative analysis revealed that the P. aeruginosa phages TC6, O4, PA11, and IME180 shared high similarity at DNA sequence and proteome levels, among which PA11 was the first phage discovered and published. Meanwhile, these phages contain 54 core genes and have very close phylogenetic relationships, which distinguish them from other known phage genera. We therefore proposed that these four phages can be classified as Pa11virus, comprising a new phage genus of Podoviridae that infects Pseudomonas spp. The results of this work promoted our understanding of phage biology, classification, and diversity.

An update on the discovery and development of selective heat shock protein inhibitors as anti-cancer therapy

INTRODUCTION

Over the years, not a single HSP inhibitor has progressed into the post-market phase of drug development despite the success recorded in various pre-clinical and clinical studies. The inability of existing drugs to specifically target oncogenic HSPs has majorly accounted for these setbacks. Recent combinatorial strategies that incorporated computer-aided drug design (CADD) techniques are geared towards the development of highly specific HSP inhibitors with increased activities and minimal toxicities. Areas covered: In this review, strategic therapeutic approaches that have recently aided the development of selective HSP inhibitors were highlighted. Also, the significant contributions of CADD techniques over the years were discussed in detail. This article further describes promising computational paradigms and their applications towards the discovery of highly specific inhibitors of oncogenic HSPs. Expert opinion: The recent shift towards highly selective and specific HSP inhibition has shown great promise as evidenced by the development of paralog/isoform-selective HSP drugs. It could be further augmented with computer-aided drug design strategies, which incorporate reliable methods that would greatly enhance the design and optimization of novel inhibitors with improved activities and minimal toxicities.

Computational screening, ensemble docking and pharmacophore analysis of potential gefitinib analogues against epidermal growth factor receptor

The observable mutated isoforms of epidermal growth factor receptor (EGFR) are important considerable therapeutic benchmarks in moderating the non-small cell lung cancer (NSCLC). Recently, quinazoline-based ATP competitive inhibitors have been developed against the EGFR; however, these imply the mutation probabilities, which contribute to the discovery of high probable novel inhibitors for EGFR mutants. Therefore, SAR-based bioactivity analysis, molecular docking and computational toxicogenomics approaches were performed to identify and evaluate new analogs of gefitinib against the ligand-binding domain of the EGFR double-mutated model. From the diverse groups of molecular clustering and molecular screening strategies, top high-binding gefitinib-analogues were identified and studied against EGFR core cavity through three-phase ensemble docking approach. Resulted high possible leads showed good binding orientations than gefitinib (positive control) thus they were subjected to pharmacophore analysis that possesses possible molecular assets to tight binding with EGFR domain. Residues Ser720, Arg841 and Trp880 were observed as novel hot spots and involved in H-bonds, pi-stacking and π-cation interactions that contribute additional electrostatic potency to sustain stability and complexity of protein-ligand complexes, thus they have ability to profoundly adopted by pharmacophoric features. Furthermore, lead molecules have an inhibition percent probability, anticancer potency, toxic impacts, flexible pharmacokinetics, potential gene-chemical interactions towards EGFR were revealed by computational systems biology tools. Our multiple screening strategies confirmed that the druggable sub-pocket was crucial to strong EGFR-ligand binding. The essential pharmacophoric features of ligands provided viewpoints for new inhibitors envisaging, and predicted scaffolds could used as anticancer agents against selected EGFR mutated isoforms.

Anti-Tumor and Radiosensitization Effects of N-Butylidenephthalide on Human Breast Cancer Cells

N-Butylidenephthalide (BP), which is extracted from a traditional Chinese medicine, Radix Angelica Sinensis (danggui), displays antitumor activity against various cancer cell lines. The purpose of this study was to investigate the cytotoxic and radiosensitizing effect of BP and the underlying mechanism of action in human breast cancer cells. BP induces apoptosis in breast cancer cells, which was revealed by the TUNEL assay; the activation of caspase-9 and PARP was detected by western blot. In addition, BP-induced G2/M arrest was examined by flow cytometry and the expression levels of the G2/M regulatory protein were detected by western blot. BP also suppresses the migration and invasion of breast cancer cells, which was tested by wound healing and the matrigel invasion assay; the involvement of EMT-related gene expressions was detected by real-time PCR. Furthermore, BP enhanced the radiosensitivity of breast cancer cells, which was measured by the colony formation assay and comet assay, where the foci of γ-H2AX after radiation significantly increased in BP pretreated cells and was evidenced by immunocytochemistry staining and western blot. The homologous recombination (HR) repair protein Rad51 was down-regulated after BP pretreatment. These results indicate that BP might be a potential chemotherapeutic and radiosensitizing agent for breast cancer therapy.

Exploring the interaction between Salvia miltiorrhiza and α-glucosidase: insights from computational analysis and experimental studies

α-Glucosidase has emerged as an important target for type 2 diabetes mellitus. Salvia miltiorrhiza is a widely used traditional Chinese medicine. The interaction between the chemicals of S. miltiorrhiza and α-glucosidase are still not clear, and need to be deeply investigated. Herein, an integrated approach consisting of computational analysis and experimental studies was employed to illustrate the interactions between S. miltiorrhiza and α-glucosidase. Molecular docking simulations were performed to reveal the proposed binding characteristics of the chemicals identified in S. miltiorrhiza on the basis of the total docking scores and key molecular determinants for binding. The affinities of 13 representative compounds from the medicinal herb to α-glucosidase were predicted and then confirmed by enzyme inhibitory assay in vitro. The obtained results suggested that two compounds including salvianolic acid C and salvianolic acid A in S. miltiorrhiza showed potent α-glucosidase inhibitory activity with IC₅₀ values of 4.31 and 19.29 μM, respectively. The active inhibitor, salvianolic acid C, exerted a mixed-competitive inhibition mode when binding to α-glucosidase. Such findings could be helpful to efficiently discover bioactive molecules from complex natural products, which suggests the usefulness of the integrated approach for this scenario.

An integrated approach was used to explore the interaction between Salvia miltiorrhiza and α-glucosidase.

A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks

Wireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and cause errors when making a final decision. Data fusion is well suited to reduce the influence of actuator-based noise and improve automation accuracy. A key step is to identify the sensor nodes disturbed by actuator noise and reduce their degree of participation in the data fusion results. A smoothing value is introduced and a searching method based on Prim's algorithm is designed to help obtain stable sensing data. A voting mechanism with dynamic weights is then proposed to obtain the data fusion result. The dynamic weighting process can sharply reduce the influence of actuator noise in data fusion and gradually condition the data to normal levels over time. To shorten the data fusion time in large networks, an acceleration method with prediction is also presented to reduce the data collection time. A real-time system is implemented on STMicroelectronics STM32F103 and NORDIC nRF24L01 platforms and the experimental results verify the improvement provided by these new algorithms.