Characterization of Protein Complexes and Subcomplexes in Protein-Protein Interaction Databases

The identification and characterization of protein complexes implicated in protein-protein interaction data are crucial to the understanding of the molecular events under normal and abnormal physiological conditions. This paper provides a novel characterization of subcomplexes in protein interaction databases, stressing definition and representation issues, quantification, biological validation, network metrics, motifs, modularity, and gene ontology (GO) terms. The paper introduces the concept of “nested group” as a way to represent subcomplexes and estimates that around 15% of those nested group with the higher Jaccard index may be a result of data artifacts in protein interaction databases, while a number of them can be found in biologically important modular structures or dynamic structures. We also found that network centralities, enrichment in essential proteins, GO terms related to regulation, imperfect 5-clique motifs, and higher GO homogeneity can be used to identify proteins in nested complexes

Global and temporal COVID-19 risk evaluation

The COVID-19 pandemic has caused unprecedented crisis across the world, with many countries struggling with the pandemic. In order to understand how each country is impacted by the virus and assess the risk on a global scale we present a regression based analysis using two pre-existing indexes, namely the Inform and Infectious Disease Vulnerability Index, in conjunction with the number of elderly living in the population. Further we introduce a temporal layer in our modeling by incorporating the stringency level employed by each country over a period of 6 time intervals. Our results show that the indexes and level of stringency are not ideally suited for explaining variation in COVID-19 risk, however the ratio of elderly in the population is a stand out indicator in terms of its predictive power for mortality risk. In conclusion, we discuss how such modeling approaches can assist public health policy

Protein-protein interaction based on pairwise similarity

<p>Abstract</p> <p>Background</p> <p>Protein-protein interaction (PPI) is essential to most biological processes. Abnormal interactions may have implications in a number of neurological syndromes. Given that the association and dissociation of protein molecules is crucial, computational tools capable of effectively identifying PPI are desirable. In this paper, we propose a simple yet effective method to detect PPI based on pairwise similarity and using only the primary structure of the protein. The PPI based on Pairwise Similarity (PPI-PS) method consists of a representation of each protein sequence by a vector of pairwise similarities against large subsequences of amino acids created by a shifting window which passes over concatenated protein training sequences. Each coordinate of this vector is typically the E-value of the Smith-Waterman score. These vectors are then used to compute the kernel matrix which will be exploited in conjunction with support vector machines.</p> <p>Results</p> <p>To assess the ability of the proposed method to recognize the difference between "<it>interacted</it>" and "<it>non-interacted</it>" proteins pairs, we applied it on different datasets from the available yeast <it>saccharomyces cerevisiae </it>protein interaction. The proposed method achieved reasonable improvement over the existing state-of-the-art methods for PPI prediction.</p> <p>Conclusion</p> <p>Pairwise similarity score provides a relevant measure of similarity between protein sequences. This similarity incorporates biological knowledge about proteins and it is extremely powerful when combined with support vector machine to predict PPI.</p

Decision Tree model for automatic Sterilizied Water Feeding system in Poultry Farm based on Internet of Things

The automatic sterilized drinking water supply system for chickens is designed to facilitate the activities of farmers in the process of providing drinking water to chickens and to facilitate the process of sterilizing the water. The process of sterilizing water with UV light must be done properly in order to get good results. In this study, there were five HC-SR04 ultrasonic sensors connected to the ESP8266 microcontroller. The ESP8266 microcontroller is embedded with the decision tree method as an output decision maker based on the calculation of the C4.5 algorithm. There are three processes, namely the process of determining the dataset, forming a decision tree and forming a rule. From the results of several tests that have been carried out, it is known that the reading error values of each ultrasonic sensor are 3.63%, 2.09%, 1.54%, 3.39% and 3.38%, respectively. Then in testing the rules that have been generated from the decision tree method with a total of 10 test data, an accuracy of 100% is obtained. Average system execution time is 0.98 seconds

Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme

Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study

Insight into eco-friendly fabrication of silver nanoparticles by Pseudomonas aeruginosa and its potential impacts

Although green synthesis of nanoparticles (NPs) has replaced conventional physicochemical methods owing to eco-friendly and cost effective nature but molecular mechanism is not known completely. Elucidation of the mechanism is needed to enhance the production of control size synthesis and for understanding the biomineralization process. Here we report the facile, extracellular biosynthesis of silver nanoparticles (AgNPs) by Pseudomonas aeruginosa JP1 through nitrate reductase mediated mechanism. AgNO3 was reduced to AgNPs by cell filtrate exposure. UV-visible spectrum of the reaction mixture depicted reduction of ionic silver (Ag+) to atomic silver (Ag0) by a progressive upsurge in surface plasmon resonance (SPR) band range 435-450 nm. X-ray diffraction analysis showed the 2θ values at 38.08°, 44.52°, 64.42° and 77.44° confirming the crystalline nature and mean diameter [6.5-27.88nm (Ave = 13.44 nm)] of AgNPs. Transmission electron microscopy analysis demonstrated the spherical AgNPs with size range 5-45 nm. Stabilizing proteins and rhamnolipids were recognized by Fourier transform infrared spectroscopy. Nitrate reductase was purified and characterized (molecular weight 65 kDa and specific activity = 5.6 U/mg). To probe the plausible mechanism purified enzyme was retreated with AgNO3. Characteristic SPR bands range (435-450 nm) and Particle-induced x-ray emission results also confirmed the synthesis of AgNPs (59679.5 ppm) in solution. These results demonstrated that, nitrate reductase as a principal reducing agent in the mechanistic pathway of AgNPs synthesis, which leads to the understanding of metal transformation and biomineralization processes for controlling the biogeochemical cycles of silver and other heavy metals

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision

BackgroundHypoxia is a potentially life-threatening condition that can be seen in pneumonia patients.ObjectiveWe aimed to develop and test an automatic assessment of lung impairment in COVID-19 associated pneumonia with machine learning regression models that predict markers of respiratory and cardiovascular functioning from radiograms and lung CT.Materials and MethodsWe enrolled a total of 605 COVID-19 cases admitted to Al Ain Hospital from 24 February to 1 July 2020 into the study. The inclusion criteria were as follows: age ≥ 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We designed a CNN-based regression model to predict systemic oxygenation markers from lung CT and 2D diagnostic images of the chest. The 2D images generated by averaging CT scans were analogous to the frontal and lateral view radiograms. The functional (heart and breath rate, blood pressure) and biochemical findings (SpO2, HCO3-, K+, Na+, anion gap, C-reactive protein) served as ground truth.ResultsRadiologic findings in the lungs of COVID-19 patients provide reliable assessments of functional status with clinical utility. If fed to ML models, the sagittal view radiograms reflect dyspnea more accurately than the coronal view radiograms due to the smaller size and the lower model complexity. Mean absolute error of the models trained on single-projection radiograms was approximately 11÷12% and it dropped by 0.5÷1% if both projections were used (11.97 ± 9.23 vs. 11.43 ± 7.51%; p = 0.70). Thus, the ML regression models based on 2D images acquired in multiple planes had slightly better performance. The data blending approach was as efficient as the voting regression technique: 10.90 ± 6.72 vs. 11.96 ± 8.30%, p = 0.94. The models trained on 3D images were more accurate than those on 2D: 8.27 ± 4.13 and 11.75 ± 8.26%, p = 0.14 before lung extraction; 10.66 ± 5.83 and 7.94 ± 4.13%, p = 0.18 after the extraction. The lung extraction boosts 3D model performance unsubstantially (from 8.27 ± 4.13 to 7.94 ± 4.13%; p = 0.82). However, none of the differences between 3D and 2D were statistically significant.ConclusionThe constructed ML algorithms can serve as models of structure-function association and pathophysiologic changes in COVID-19. The algorithms can improve risk evaluation and disease management especially after oxygen therapy that changes functional findings. Thus, the structural assessment of acute lung injury speaks of disease severity

A Combination of Compositional Index and Genetic Algorithm for Predicting Transmembrane Helical Segments

Transmembrane helix (TMH) topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method. Availability: The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm