DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion

An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm that predicts ACPs by fusing multi-view features based on dual-channel deep neural network ensemble model. In the model, one channel used the convolutional neural network CNN to automatically extract the potential spatial features of a sequence. Another channel was used to process and extract more effective features from handcrafted features. Additionally, an effective feature fusion method was explored for the mutual fusion of different features. Finally, we adopted the neural network to predict ACPs based on the fusion features. The performance comparisons across the single and fusion features showed that the fusion of multi-view features could effectively improve the model’s predictive ability. Among these, the fusion of the features extracted by the CNN and composition of k-spaced amino acid group pairs achieved the best performance. To further validate the performance of our model, we compared it with other existing methods using two independent test sets. The results showed that our model’s area under curve was 0.90, which was higher than that of the other existing methods on the first test set and higher than most of the other existing methods on the second test set. The source code and datasets are available at https://github.com/wame-ng/DLFF-ACP.

A comparative study of intravenous hydration and amnioinfusion for IUGR associated with oligohydramnios in pregnant women and fetomaternal outcome

Background: Ultrasound assessment of amniotic fluid has significant implication in obstetric care and it has become an integral and important component of pregnancy assessment.Methods: A prospective study done in all pregnant women (n=30) who had been diagnosed with oligohydromnios (with AFI<8 by Phelan’s method) by ultrasonography will be attending in obstetric gynecology department SMS Medical College, Jaipur will be selected according to inclusion or exclusion criteria (as per sample size) after written informed consent.Results: Higher incidence of preterm delivery in the i.v. infusion group as compared to the amino acid group and difference was significant (p value <0.05). In amnioinfusion group 3 cases (20.0%) had LSCS and in i.v. infusion group 6 cases (40.0%) had delivered by LSCS. The distribution of delivery mode did not differ significantly across two intervention groups (p value >0.05). Significantly higher proportion of cases from amino acid group had larger birth weight and significantly higher proportion of cases from i.v. infusion group had smaller birth weight (p value <0.001).Conclusions: This study points towards the use of intravenous hydration and amnioinfusion in increasing the liquor in oligohydramnios associated with IUGR and proves useful in reducing perinatal morbidity and mortality.

prPred: A Predictor to Identify Plant Resistance Proteins by Incorporating k-Spaced Amino Acid (Group) Pairs

To infect plants successfully, pathogens adopt various strategies to overcome their physical and chemical barriers and interfere with the plant immune system. Plants deploy a large number of resistance (R) proteins to detect invading pathogens. The R proteins are encoded by resistance genes that contain cell surface-localized receptors and intracellular receptors. In this study, a new plant R protein predictor called prPred was developed based on a support vector machine (SVM), which can accurately distinguish plant R proteins from other proteins. Experimental results showed that the accuracy, precision, sensitivity, specificity, F1-score, MCC, and AUC of prPred were 0.935, 1.000, 0.806, 1.000, 0.893, 0.857, and 0.948, respectively, on an independent test set. Moreover, the predictor integrated the HMMscan search tool and Phobius to identify protein domain families and transmembrane protein regions to differentiate subclasses of R proteins. prPred is available at https://github.com/Wangys-prog/prPred. The tool requires a valid Python installation and is run from the command line.

Glycotoxins Relationship with Body Mass Index (BMI)

Glycotoxins termed as advanced glycation endproducts (AGEs) are made endogenously and exogenously. Maillard reaction is a chemical non-enzymatic process, between sugars and a free amino acid group of nucleic acids, proteins, and lipids which happens exogenously when handled with high temperature for a prolonged period and in a dry environment, resulting in glycotoxins (AGEs) formations. This reaction is responsible for the taste, color, and smell of food. Glycotoxins derived from food are absorbed in the intestine and store in cells and tissues. Also, glycotoxins are derived exogenously from smoking. While endogenous glycotoxins are generated by normal body physiology. Obesity and insulin resistance may induce glycotoxins (AGEs) accumulation. The objective of this study was to evaluate the relationship between glycotoxins (AGEs) and obesity. 66 infertile females who were undergoing intracytoplasmic sperm injection ICSI. These females were classified into three groups according to their body mass index. On the day of ova pick up the collect serum and follicular fluid for subsequent measurement of glycotoxins (AGEs) by ELISA kits. there was a significant difference in the concentration of glycotoxins in follicular fluid and serum between the three ICSI groups of patients. As well as there was a significant difference in the concentration of glycotoxins (AGEs) in follicular fluid and serum in the same ICSI groups of patients. there is a significant positive relation between glycotoxins (AGEs) and increase BMI.

Spectroscopic Study of Interaction of Bare, Citrate Capped and BSA Capped Fe3O4 Nanoparticles with Hemoglobin

Background: Fe3O4nanoparticles have great potential in different biomedical applications. Study of interaction of bare, and capped Fe3O4 nanoparticles with common blood proteins is a field of interest for understanding the underlying mechanism and biocompatibility. Objective: This work is aimed at studying the nature of binding of bare, citrate functionalised and bovine serum albumin coated Fe3O4 nanoparticles (Fe3O4NPs, CFe3O4NPs and BFe3O4NPs) with human hemoglobin (Hb), their instantaneous effect on amino acid group, heme group and secondary structure of Hb. Methods: Nanoparticles were prepared by chemical route and characterised by TEM, XRD and UV-Visible and FTIR spectroscopy. UV-Visible absorbance and fluorescence emission/excitation spectroscopy and circular dichroism were done to study the interaction of nanoparticles with Hb. Results: UV-Visible absorbance spectroscopy shows no blue or red shift of absorption peaks; Benesi-Hildebrand curves for amino acid band and soret band of Hb absorbance spectrum are straight lines with positive intercepts; apparent binding constants and Gibbs free energy change are within moderate level; they are larger for amino acid band in presence of CFe3O4NPs,larger for soret band in presence of Fe3O4NPs,and noticeably small for both bands in presence of BFe3O4NPs.Fluorescence emission/excitation spectra shows no noticeable shift of emission/excitation peak position of Hb in presence of the three nanoparticles. Multiple peak fitting, done for the L-peak of the excitation spectrum of Hb, shows major increase in the percentage of peak area of Tyr in presence of CFe3O4NPs.Circular dichroism measurement shows that CFe3O4NPs, Fe3O4NPs and BFe3O4NPs have reduced the α-helix content of Hb in decreasing order. Conclusion: Ground state complex formation of human hemoglobin with the studied nanoparticles with 1:1 stoichiometric ratio is suggested. Moreover, it is suggested thatCFe3O4NPsmay have stronger interaction with amino acid group while bare Fe3O4NPs may have stronger interaction with the heme group of Hb. Hindering of the energy transfer from tyrosine to tryptophan of Hb in presence of CFe3O4NPs is suggested. CFe3O4NPsmay also have some effect on the secondary structure of Hb as indicated through reduction of the α-helix content. BFe3O4NPshave shown very weak interaction with Hb in UV-Visible absorbance spectroscopy, fluorescence emission/excitation spectroscopy and circular dichroism experiment.

dagLogo: An R/Bioconductor package for identifying and visualizing differential amino acid group usage in proteomics data

Sequence logos have been widely used as graphical representations of conserved nucleic acid and protein motifs. Due to the complexity of the amino acid (AA) alphabet, rich post-translational modification, and diverse subcellular localization of proteins, few versatile tools are available for effective identification and visualization of protein motifs. In addition, various reduced AA alphabets based on physicochemical, structural, or functional properties have been valuable in the study of protein alignment, folding, structure prediction, and evolution. However, there is lack of tools for applying reduced AA alphabets to the identification and visualization of statistically significant motifs. To fill this gap, we developed an R/Bioconductor package dagLogo, which has several advantages over existing tools. First, dagLogo allows various formats for input sets and provides comprehensive options to build optimal background models. It implements different reduced AA alphabets to group AAs of similar properties. Furthermore, dagLogo provides statistical and visual solutions for differential AA (or AA group) usage analysis of both large and small data sets. Case studies showed that dagLogo can better identify and visualize conserved protein sequence patterns from different types of inputs and can potentially reveal the biological patterns that could be missed by other logo generators.

Thyroxine binding to type III iodothyronine deiodinase

Iodothyronine deiodinases (Dios) are important selenoproteins that control the concentration of the active thyroid hormone (TH) triiodothyronine through regioselective deiodination. The X-ray structure of a truncated monomer of Type III Dio (Dio3), which deiodinates TH inner rings through a selenocysteine (Sec) residue, revealed a thioredoxin-fold catalytic domain supplemented with an unstructured Ω-loop. Loop dynamics are driven by interactions of the conserved Trp207 with solvent in multi-microsecond molecular dynamics simulations of the Dio3 thioredoxin(Trx)-fold domain. Hydrogen bonding interactions of Glu200 with residues conserved across the Dio family anchor the loop’s N-terminus to the active site Ser-Cys-Thr-Sec sequence. A key long-lived loop conformation coincides with the opening of a cryptic pocket that accommodates thyroxine (T4) through an I⋯Se halogen bond to Sec170 and the amino acid group with a polar cleft. The Dio3-T4 complex is stabilized by an I⋯O halogen bond between an outer ring iodine and Asp211, consistent with Dio3 selectivity for inner ring deiodination. Non-conservation of residues, such as Asp211, in other Dio types in the flexible portion of the loop sequence suggests a mechanism for regioselectivity through Dio type-specific loop conformations. Cys168 is proposed to attack the selenenyl iodide intermediate to regenerate Dio3 based upon structural comparison with related Trx-fold proteins.

dagLogo: an R/Bioconductor Package for Identifying and Visualizing Differential Amino Acid Group Usage in Proteomics data

Sequence logos have been widely used as graphical representations of conserved nucleic acid and protein motifs. Due to the complexity of the amino acid (AA) alphabet, rich post-translational modification, and diverse subcellular localization of proteins, few versatile tools are available for effective identification and visualization of protein motifs. In addition, various reduced AA alphabets based on physicochemical, structural, or functional properties have been valuable in the study of protein alignment, folding, structure prediction, and evolution. However, there is lack of tools for applying reduced AA alphabets to the identification and visualization of statistically significant motifs. To fill this gap, we developed an R/Bioconductor package dagLogo, which has several advantages over existing tools. First, dagLogo allows various formats for input sets and provides comprehensive options to build optimal background models. It implements different reduced AA alphabets to group AAs of similar properties. Furthermore, dagLogo provides statistical and visual solutions for differential AA (or AA group) usage analysis of both large and small data sets. Case studies showed that dagLogo can better identify and visualize conserved protein sequence patterns from different types of inputs and can potentially reveal the biological patterns that could be missed by other logo generators.

Comparative Analysis of Human Coronaviruses Focusing on Nucleotide Variability and Synonymous Codon Usage Pattern

Prevailing pandemic across the world due to SARSCoV-2 drawing great attention towards discovering its evolutionary origin. We perform an exploratory study to understand the variability of the whole coding region of possible proximal evolutionary neighbours of SARSCoV-2. We consider seven (07) human coronavirus strains from six different species as a candidate for our study.First, we observe a good variability of nucleotides across candidate strains. We did not find a significant variation of GC content across the strains for codon position first and second. However, we interestingly see huge variability of GC-content in codon position 3rd (GC3), and pairwise mean GC-content (SARSCoV, MERSCoV), and (SARSCoV-2, hCoV229E) are quite closer. While observing the relative abundance of dinucleotide feature, we find a shared typical genetic pattern, i.e., high usage of GC and CT nucleotide pair at the first two positions (P12) of codons and the last two positions (P23) of codons, respectively. We also observe a low abundance of CG pair that might help in their evolution bio-process. Secondly, Considering RSCU score, we find a substantial similarity for mild class coronaviruses, i.e., hCoVOC43, hCoVHKU1, and hCoVNL63 based on their codon hit with high RSCU value (≥ 1.5), and minim number of codons hit (count-9) is observed for MERSCoV. We see seven codons ATT, ACT, TCT, CCT, GTT, GCT and GGT with high RSCU value, which are common in all seven strains. These codons are mostly from Aliphatic and Hydroxyl amino acid group. A phylogenetic tree built using RSCU feature reveals proximity among hCoVOC43 and hCoV229E (mild). Thirdly, we perform linear regression analysis among GC content in different codon position and ENC value. We observe a strong correlation (significant p-value) between GC2 and GC3 for SARSCoV-2, hCoV229E and hCoVNL63, and between GC1 and GC3 for hCoV229E, hCoVNL63, SARSCoV. We believe that our findings will help in understanding the mechanism of human coronavirus.

PROSPECT: A web server for predicting protein histidine phosphorylation sites

Background: Phosphorylation of histidine residues plays crucial roles in signaling pathways and cell metabolism in prokaryotes such as bacteria. While evidence has emerged that protein histidine phosphorylation also occurs in more complex organisms, its role in mammalian cells has remained largely uncharted. Thus, it is highly desirable to develop computational tools that are able to identify histidine phosphorylation sites. Result: Here, we introduce PROSPECT that enables fast and accurate prediction of proteome-wide histidine phosphorylation substrates and sites. Our tool is based on a hybrid method that integrates the outputs of two convolutional neural network (CNN)-based classifiers and a random forest-based classifier. Three features, including the one-of-K coding, enhanced grouped amino acids content (EGAAC) and composition of k-spaced amino acid group pairs (CKSAAGP) encoding, were taken as the input to three classifiers, respectively. Our results show that it is able to accurately predict histidine phosphorylation sites from sequence information. Our PROSPECT web server is user-friendly and publicly available at http://PROSPECT.erc.monash.edu/ . Conclusions: PROSPECT is superior than other pHis predictors in both the running speed and prediction accuracy and we anticipate that the PROSPECT webserver will become a popular tool for identifying the pHis sites in bacteria.