Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

In silico study reveals unconventional interactions between MDC1 of DDR and Beclin-1 of autophagy

DNA damage response (DDR) and autophagy are concerned with maintaining cellular homeostasis and dysregulation of these two pathways lead to pathologic conditions including tumorigenesis. Autophagy is activated as a protective mechanism during DDR which is indicative of their functional cooperativity but the molecular mechanism leading to the convergence of these two pathways during genotoxic stress remains elusive. In this study, through in silico analysis, we have shown an interaction between the Mediator of DNA damage checkpoint 1 (MDC1), an important DDR-associated protein, and Beclin-1, an autophagy inducer. MDC1 is an adaptor or scaffold protein known to regulate DDR, apoptosis, and cell cycle progression. While, Beclin-1 is involved in autophagosome nucleation and exhibits affinity for binding to Fork-head-associated domain (FHA) containing proteins. The FHA domain is commonly conserved in DDR-related proteins including MDC1. Through molecular docking, we have predicted the modeled complex between the MDC1 FHA domain and the Beclin-1 Coiled coil domain (CCD). The docking complex was modeled using ClusPro2.0, based on the crystal structure for the dimerized MDC1 FHA domain and Beclin-1 CCD. The complex stability and binding affinities were assessed using a Ramachandran plot, MD simulation, MM/GBSA, and PRODIGY webserver. Finally, the hot-spot residues at the interface were determined using computational alanine scanning by the DrugScorePPI webserver. Our analysis unveils significant interaction between MDC1 and Beclin-1, involving hydrogen bonds, non-bonded contacts, and salt bridges and indicates MDC1 possibly recruits Beclin-1 to the DSBs, as a consequence of which Beclin-1 is able to modulate DDR.

Identification of coastal pesticide pollutants as potent inhibitors of Bacillus pasteurii urease mediated calcium carbonate precipitation: a computational approach

Microbially induced calcite precipitation (MICP) through urease enzyme has attained a lot of recognition in various fields of civil engineering and geotechnology for stabilizing the strength of soil and various concrete materials. The activity of urease has been found to be affected by various factors like temperature, substrate concentrations, pH of the medium, presence of inhibitors, etc. Through this study, the outcome of the interaction of pesticides (commonly found in Indian coastal regions) on Bacillus pasteurii urease, a major organism reported for MICP studies has been investigated in silico. The results from the study revealed that the enzyme has higher interactions of -4.1, -3.2, and -3.4 kJ/mol with common pesticides like dichloro diphenyl dichloro ethane(DDD), dichloro diphenyl trichloroe thane (DDT), and methyl parathion of organochlorides and organophosphates class. From the molecular dynamics simulation analysis, complex 1 (DDD -receptor) has been found to have the highest and more compact structure followed by methyl parathion -receptor. Prime MM-GBSA analysis also revealed the highest binding energy of -27.8 kcal/mol with the protein and DDD. Thus, it can be inferred from the current study that pesticides, particularly, DDD, DDT, and methyl parathion present in the coastal areas may have an impact on urease. This interaction can result in the inhibition of the urease activity of B. pasteurii, thus preventing the biomineralization process. This study would be the first report on the computational approach to understanding the interaction of prominent pesticides on the coastal region and B. pasteurii urease.Communicated by Ramaswamy H. Sarma.

Identification of Potential TMPRSS2 Inhibitors for COVID-19 Treatment in Chinese Medicine by Computational Approaches and Surface Plasmon Resonance Technology

BACKGROUND

Coronavirus disease-19 (COVID-19) pneumonia continues to spread in the entire globe with limited medication available. In this study, the active compounds in Chinese medicine (CM) recipes targeting the transmembrane serine protease 2 (TMPRSS2) protein for the treatment of COVID-19 were explored.

METHODS

The conformational structure of TMPRSS2 protein (TMPS2) was built through homology modeling. A training set covering TMPS2 inhibitors and decoy molecules was docked to TMPS2, and their docking poses were re-scored with scoring schemes. A receiver operating characteristic (ROC) curve was applied to select the best scoring function. Virtual screening of the candidate compounds (CCDs) in the six highly effective CM recipes against TMPS2 was conducted based on the validated docking protocol. The potential CCDs after docking were subject to molecular dynamics (MD) simulations and surface plasmon resonance (SPR) experiment.

RESULTS

A training set of 65 molecules were docked with modeled TMPS2 and LigScore2 with the highest area under the curve, AUC, value (0.886) after ROC analysis selected to best differentiate inhibitors from decoys. A total of 421 CCDs in the six recipes were successfully docked into TMPS2, and the top 16 CCDs with LigScore2 higher than the cutoff (4.995) were screened out. MD simulations revealed a stable binding between these CCDs and TMPS2 due to the negative binding free energy. Lastly, SPR experiments validated the direct combination of narirutin, saikosaponin B1, and rutin with TMPS2.

CONCLUSIONS

Specific active compounds including narirutin, saikosaponin B1, and rutin in CM recipes potentially target and inhibit TMPS2, probably exerting a therapeutic effect on COVID-19.

Homology modeling, docking and molecular dynamics studies of some secondary metabolites of actinomycetes as biocontrol agents against the 3HNR enzyme of the phytopathogenic fungus Alternaria alternata

Early blight of tomatoes is a common disease caused by the phytopathogenic fungi Alternaria, in particular the species A. alternata. This disease causes significant losses in the tomato harvest. The enzyme 1,3,8-trihydroxynaphthalene reductase (3HNR) is a key enzyme involved in the production of melanin, that plays a crucial role in the process of fungi invasion. This enzyme is the target of some chemical fungicides, but the problem of resistance against these molecules requires the search for new molecules that are both effective and environment-friendly. Actinomycetes represent an important source of secondary metabolites with antimicrobial activity. Thus, in this study 110 secondary metabolites of actinomycetes were subjected to an in silico screening of their antifungal activity as possible inhibitors of the 3HNR of A. alternata. For this reason, the 3D structure of this enzyme was modeled. Then, a molecular docking study of the secondary actinomycetal metabolites was carried out within the catalytic site of the enzyme. Indole-3-carboxylic acid, Streptokordin, 3-Phenylpropionic acid, Phenylacetate, and 8-Hydroxyquinoline have shown the most promising results with binding energies of -6.1 kcal/mol, -6.1 kcal/mol, -5.4 kcal/mol, -5.3 kcal/mol, and -5.0 kcal/mol, respectively. These metabolites have also shown satisfactory results for drug-likeness and ADMET analysis. The interaction stability of the Streptokordin, Indole-3-carboxylic acid, Phenylacetate, and 8-Hydroxyquinoline within the catalytic site of 3HNR was confirmed by the results of the MD simulation and MM-PBSA analyzes. With their favorable interactive and pharmacokinetic characteristics, these metabolites may be potential antifungal molecules against A. alternata, and good candidates for further studies.Communicated by Ramaswamy H. Sarma.

Biological, toxicological and molecular docking evaluations of isoxazoline-thiazolidine-2,4-dione analogues as new class of anti-hyperglycemic agents

In this work, three isoxazoline-thiazolidine-2,4-dione derivatives were synthesized and characterized by FT-IR, ¹H-NMR, ¹³C-NMR and ESI-MS spectrometry. All compounds have been investigated for their α-amylase and α-glucosidase inhibitory activities. In vitro enzymatic evaluation revealed that all compounds were inhibitory potent against α-glucosidase with IC₅₀ values varied from 40.67 ± 1.81 to 92.54 ± 0.43 µM, and α-amylase with IC₅₀ in the range of 07.01 ± 0.02 to 75.10 ± 1.06 µM. One of the tested compounds were found to be more potent inhibitor compared to other compounds and standard drug Acarbose (IC_{50 glucosidase}= 97.12 ± 0.35 µM and IC_{50 amylase}= 2.97 ± 0.01 μM). All compounds were then evaluated for their acute toxicity in vivo and shown their safety at a high dose with LD > 2000mg/kg BW. A cell-based toxicity evaluation was performed to determine the safety of compounds on liver cells, using the MTT assay against HepG2 cells, and the results shown that all compounds have non-toxic impact against cell viability and proliferation compared to reference drug (Pioglitazone). Furthermore, the molecular homology analysis, SAR and the molecular binding properties of compound with the active site of α-amylase and α-glucosidase were confirmed through computational analysis. This study has identified the inhibitory potential of a new class of synthesized isoxazoline-thiazolidine-2,4-dione derivatives in controlling both hyperglycemia and type 2 diabetes mellitus without any hepatic toxicity.Communicated by Ramaswamy H. Sarma.

Mechanism Research of Platelet Core Marker Prediction and Molecular Recognition in Cardiovascular Events

BACKGROUND

Thrombosis triggered by platelet activation plays a vital role in the pathogenesis of cardiovascular and cerebrovascular diseases.

OBJECTIVE

This study aims to find platelet combined biomarkers for cardiovascular diseases and investigate the possibility of Concanavalin A (ConA) acting on platelets as a new pharmacological target.

METHODS

High-throughput Technology and bioinformatics analysis were combined and groups of microarray chip gene expression profiles for acute myocardial infarction (AMI) and sickle cell disease (SCD) were obtained using GEO database screening. R language limma package was used to obtain differentially expressed genes (DEGs). GO, KEGG, and other databases were utilized to perform the enrichment analysis of DEGs' functions, pathways, etc. PPI network was constructed using STRING database and Cytoscape software, and MCC algorithm was used to obtain the 200 core genes of the two groups of DEGs. Core targets were confirmed by constructing an intersection area screening. A type of molecular probe, ConA, was molecularly docked with the above core targets on the Zdock, HEX, and 3D-DOCK servers.

RESULTS

We found six core markers, CD34, SOCS2, ABL1, MTOR, VEGFA, and SMURF1, which were simultaneously related to both diseases, and the docking effect showed that VEGFA is the best-performing.

CONCLUSION

VEGFA is most likely to reduce its expression by binding to ConA, which could affect the downstream regulation of the PI3K/Akt signaling pathway during platelet activation. Some other core targets also have the opportunity to interact with ConA to affect platelet-activated thrombosis and trigger changes in cardiovascular events.

Secondary structure prediction of protein based on multi scale convolutional attention neural networks

To fully extract the local and long-range information of amino acid sequences and enhance the effective information, this research proposes a secondary structure prediction model of protein based on a multi-scale convolutional attentional neural network. The model uses a multi-channel multi-scale parallel architecture to extract amino acid structure features of different granularity according to the window size. The reconstructed feature maps are obtained via multiple convolutional attention blocks. Then, the reconstructed feature map is fused with the input feature map to obtain the enhanced feature map. Finally, the enhanced feature map is fed to the Softmax classifier for prediction. While the traditional cross-entropy loss cannot effectively solve the problem of non-equilibrium training samples, a modified correlated cross-entropy loss function may alleviate this problem. After numerous comparison and ablation experiments, it is verified that the improved model can indeed effectively extract amino acid sequence feature information, alleviate overfitting, and thus improve the overall prediction accuracy.

Investigation of machine learning techniques on proteomics: A comprehensive survey

Proteomics is the extensive investigation of proteins which has empowered the recognizable proof of consistently expanding quantities of protein. Proteins are necessary part of living life form, with numerous capacities. The proteome is the complete arrangement of proteins that are created or altered by a life form or framework of the organism. Proteome fluctuates with time and unambiguous prerequisites, or stresses, that a cell or organism experiences. Proteomics is an interdisciplinary area that has derived from the hereditary data of different genome ventures. Much proteomics information is gathered with the assistance of high throughput techniques, for example, mass spectrometry and microarray. It would regularly take weeks or months to analyze the information and perform examinations by hand. Therefore, scholars and scientific experts are teaming up with computer science researchers and mathematicians to make projects and pipeline to computationally examine the protein information. Utilizing bioinformatics procedures, scientists are prepared to do quicker investigation and protein information storing. The goal of this paper is to brief about the review of machine learning procedures and its application in the field of proteomics.

Nonparametric collective spectral density estimation with an application to clustering the brain signals

In this paper, we develop a method for the simultaneous estimation of spectral density functions (SDFs) for a collection of stationary time series that share some common features. Due to the similarities among the SDFs, the log-SDF can be represented using a common set of basis functions. The basis shared by the collection of the log-SDFs is estimated as a low-dimensional manifold of a large space spanned by a prespecified rich basis. A collective estimation approach pools information and borrows strength across the SDFs to achieve better estimation efficiency. Moreover, each estimated spectral density has a concise representation using the coefficients of the basis expansion, and these coefficients can be used for visualization, clustering, and classification purposes. The Whittle pseudo-maximum likelihood approach is used to fit the model and an alternating blockwise Newton-type algorithm is developed for the computation. A web-based shiny App found at "https://ncsde.shinyapps.io/NCSDE" is developed for visualization, training, and learning the SDFs collectively using the proposed technique. Finally, we apply our method to cluster similar brain signals recorded by the for identifying synchronized brain regions according to their spectral densities.

Prediction of Protein Backbone Torsion Angles Using Deep Residual Inception Neural Networks

Prediction of protein backbone torsion angles (Psi and Phi) can provide important information for protein structure prediction and sequence alignment. Existing methods for Psi-Phi angle prediction have significant room for improvement. In this paper, a new deep residual inception network architecture, called DeepRIN, is proposed for the prediction of Psi-Phi angles. The input to DeepRIN is a feature matrix representing a composition of physico-chemical properties of amino acids, a 20-dimensional position-specific substitution matrix (PSSM) generated by PSI-BLAST, a 30-dimensional hidden Markov Model sequence profile generated by HHBlits, and predicted eight-state secondary structure features. DeepRIN is designed based on inception networks and residual networks that have performed well on image classification and text recognition. The architecture of DeepRIN enables effective encoding of local and global interatcions between amino acids in a protein sequence to achieve accruacte prediction. Extensive experimental results show that DeepRIN outperformed the best existing tools significantly. Compared to the recently released state-of-the-art tool, SPIDER3, DeepRIN reduced the Psi angle prediction error by more than 5 degrees and the Phi angle prediction error by more than 2 degrees on average. The executable tool of DeepRIN is available for download at http://dslsrv8.cs.missouri.edu/~cf797/MUFoldAngle/.