Classical molecular dynamics simulation identifies catechingallate as a promising antiviral polyphenol against MPOX palmitoylated surface protein.

Cumulative global prevalence of the emergent monkeypox (MPX) infection in the non-endemic countries has been professed as a global public health predicament. Lack of effective MPX-specific treatments sets the baseline for designing the current study. This research work uncovers the effective use of known antiviral polyphenols against MPX viral infection, and recognises their mode of interaction with the target F13 protein, that plays crucial role in formation of enveloped virions. Herein, we have employed state-of-the-art machine learning based AlphaFold2 to predict the three-dimensional structure of F13 followed by molecular docking and all-atoms molecular dynamics (MD) simulations to investigate the differential mode of F13-polyphenol interactions. Our extensive computational approach identifies six potent polyphenols Rutin, Epicatechingallate, Catechingallate, Quercitrin, Isoquecitrin and Hyperoside exhibiting higher binding affinity towards F13, buried inside a positively charged binding groove. Intermolecular contact analysis of the docked and MD simulated complexes divulges three important residues Asp134, Ser137 and Ser321 that are observed to be involved in ligand binding through hydrogen bonds. Our findings suggest that ligand binding induces minor conformational changes in F13 to affect the conformation of the binding site. Concomitantly, essential dynamics of the six-MD simulated complexes reveals Catechin gallate, a known antiviral agent as a promising polyphenol targeting F13 protein, dominated with a dense network of hydrophobic contacts. However, assessment of biological activities of these polyphenols need to be confirmed through in vitro and in vivo assays, which may pave the way for development of new novel antiviral drugs.

In silico screening of phytoconstituents as potential anti-inflammatory agents targeting NF-κB p65: an approach to promote burn wound healing.

Chronic burn wounds are frequently characterised by a prolonged and dysregulated inflammatory phase that is mediated by over-activation of NF-κB p65. Synthetic wound healing drugs used for treatment of inflammation are primarily associated with several shortcomings which reduce their therapeutic index. In this scenario, phytoconstituents that exhibit multifaceted biological activities including anti-inflammatory effects have emerged as a promising therapeutic alternative. However, identification and isolation of phytoconstituents from medicinal herbs is a cumbersome method that is linked to profound uncertainty. Hence, present study aimed to identify prospective phytoconstituents as inhibitors of RHD of NF-κB p65 by utilizing in silico approach. Virtual screening of 2821 phytoconstituents was performed against protein model. Out of 2821 phytoconstituents, 162 phytoconstituents displayed a higher binding affinity (≤ -8.0 kcal/mol). These 162 phytoconstituents were subjected to ADMET predictions, and 15 of them were found to satisfy Lipinski's rule of five and showed favorable pharmacokinetic properties. Among these 15 phytoconstituents, 5 phytoconstituents with high docking scores i.e. silibinin, bismurrayaquinone A, withafastuosin B, yuccagenin, (+)-catechin 3-gallate were selected for molecular dynamics (MD) simulation analysis. Results of MD simulation indicated that withafastuosin B, (+)-catechin 3-gallate and yuccagenin produced a compact and stable complex with protein without significant variations in conformation. Relative binding energy analysis of best hit molecules indicate that withafastuosin B, and (+)-catechin 3-gallate exhibit high binding affinity with target protein among other lead molecules. Findings of study suggest that these phytoconstituents could serve as promising anti-inflammatory agents for treatment of burn wounds by inhibiting the RHD of NF-κB p65.Communicated by Ramaswamy H. Sarma.

Identification of potential inhibitor against CTX-M-3 and CTX-M-15 proteins: an in silico and in vitro study.

Extended-spectrum beta-lactamase (ESBL) producing Enterobacteriaceae infection is a serious global threat. ESBLs target 3rd generation cephalosporin antibiotics, the most commonly prescribed medicine for gram-negative bacterial infections. As bacteria are prone to develop resistance against market-available ESBL inhibitors, finding a novel and effective inhibitor has become mandatory. Among ESBL, the worldwide reported two enzymes, CTX-M-15 and CTX-M-3, are selected for the present study. CTX-M-3 protein was modeled, and two thousand phyto-compounds were virtually screened against both proteins. After filtering through docking and pharmacokinetic properties, four phyto-compounds (catechin gallate, silibinin, luteolin, uvaol) were further selected for intermolecular contact analysis and molecular dynamics (MD) simulation. MD trajectory analysis results were compared, revealing that both catechin gallate and silibinin had a stabilizing effect against both proteins. Silibinin having the lowest docking score, also displayed the lowest MIC (128 µg/mL) against the bacterial strains. Silibinin was also reported to have synergistic activity with cefotaxime and proved to have bactericidal effect. Nitrocefin assay confirmed that silibinin could inhibit beta-lactamase enzyme only in living cells, unlike clavulanic acid. Thus the present study validated the CTX-M inhibitory activity of silibinin both in silico and in vitro and suggested its promotion for further studies as a potential lead. The present study adopted a protocol through the culmination of bioinformatics and microbiological analyses, which will help future researchers identify more potential leads and design new effective drugs.Communicated by Ramaswamy H. Sarma.

All-atoms molecular dynamics study to screen potent efflux pump inhibitors against KpnE protein of Klebsiella pneumoniae.

The Small Multidrug Resistance efflux pump protein KpnE, plays a pivotal role in multi-drug resistance in Klebsiella pneumoniae. Despite well-documented study of its close homolog, EmrE, from Escherichia coli, the mechanism of drug binding to KpnE remains obscure due to the absence of a high-resolution experimental structure. Herein, we exclusively elucidate its structure-function mechanism and report some of the potent inhibitors through drug repurposing. We used molecular dynamics simulation to develop a dimeric structure of KpnE and explore its dynamics in lipid-mimetic bilayers. Our study identified both semi-open and open conformations of KpnE, highlighting its importance in transport process. Electrostatic surface potential map suggests a considerable degree of similarity between KpnE and EmrE at the binding cleft, mostly occupied by negatively charged residues. We identify key amino acids Glu14, Trp63 and Tyr44, indispensable for ligand recognition. Molecular docking and binding free energy calculations recognizes potential inhibitors like acarbose, rutin and labetalol. Further validations are needed to confirm the therapeutic role of these compounds. Altogether, our membrane dynamics study uncovers the crucial charged patches, lipid-binding sites and flexible loop that could potentiate substrate recognition, transport mechanism and pave the way for development of novel inhibitors against K. pneumoniae.Communicated by Ramaswamy H. Sarma.

Illuminating the structural basis of human neurokinin 1 receptor (NK1R) antagonism through classical all-atoms molecular dynamics simulations.

Advances in structural biology have bestowed insights into the pleiotropic effects of neurokinin 1 receptors (NK1R) in diverse patho-physiological processes, thereby highlighting the potential therapeutic value of antagonists directed against NK1R. Herein, we investigate the mode of antagonist recognition to discern the obscure atomic facets germane for the function and molecular determinants of NK1R. To commence discernment of potent antagonists and the conformational changes in NK1R, induced upon antagonist binding, state-of-the-art classical all-atoms molecular dynamics (MD) simulations in lipid mimetic bilayers have been utilized. MD simulations of structural ensembles reveals the involvement of TM5 and TM6 in tight anchoring of antagonists through a network of interhelical hydrogen-bonds, while, the extracellular loop 2 (ECL2) governs the overall size and nature of the pocket, thereby modulating NK1R. Consistent comparison between experiments and MD simulation results discerns the predominant role of TM3, TM4, and TM6 in lipid-NK1R interaction. Correlation between hydrophobic index and helicity of TM domains elucidates their importance in maintaining the structural stability in addition to regulating NK1R antagonism. Taken together, we anticipate that our computational study marks a comprehensive structural basis of NK1R antagonism in lipid bilayers, which may facilitate designing of new therapeutics against associated diseases targeting human neurokinin receptors.

Reproducibility and Reliability of Computing Models in Segmentation and Volumetric Measurement of Brain.

Background: Segmentation and morphometric measurement of brain tissue and regions from non-invasive magnetic resonance images have clinical and research applications. Several software tools and models have been developed by different research groups which are increasingly used for segmentation and morphometric measurements. Variability in results has been observed in the imaging data processed with different neuroimaging pipelines which have increased the focus on standardization. Purpose: The availability of several tools and models for brain morphometry poses challenges as an analysis done on the same set of data using different sets of tools and pipelines may result in different results and interpretations and there is a need for understanding the reliability and accuracy of such models. Methods: T1-weighted (T1-w) brain volumes from the publicly available OASIS3 dataset have been analysed using recent versions of FreeSurfer, FSL-FAST, CAT12, and ANTs pipelines. grey matter (GM), white matter (WM), and estimated total intracranial volume (eTIV) have been extracted and compared for inter-method variability and accuracy. Results: All four methods are consistent and strongly reproducible in their measurement across subjects however there is a significant degree of variability between these methods. Conclusion: CAT12 and FreeSurfer methods have the highest degree of agreement in tissue class segmentation and are most reproducible compared to others.

Insilico generation of novel ligands for the inhibition of SARS-CoV-2 main protease (3CLpro) using deep learning.

The recent emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing the coronavirus disease (COVID-19) has become a global public health crisis, and a crucial need exists for rapid identification and development of novel therapeutic interventions. In this study, a recurrent neural network (RNN) is trained and optimized to produce novel ligands that could serve as potential inhibitors to the SARS-CoV-2 viral protease: 3 chymotrypsin-like protease (3CLpro). Structure-based virtual screening was performed through molecular docking, ADMET profiling, and predictions of various molecular properties were done to evaluate the toxicity and drug-likeness of the generated novel ligands. The properties of the generated ligands were also compared with current drugs under various phases of clinical trials to assess the efficacy of the novel ligands. Twenty novel ligands were selected that exhibited good drug-likeness properties, with most ligands conforming to Lipinski's rule of 5, high binding affinity (highest binding affinity: -9.4 kcal/mol), and promising ADMET profile. Additionally, the generated ligands complexed with 3CLpro were found to be stable based on the results of molecular dynamics simulation studies conducted over a 100 ns period. Overall, the findings offer a promising avenue for the rapid identification and development of effective therapeutic interventions to treat COVID-19.

Exploiting the potential of natural polyphenols as antivirals against monkeypox envelope protein F13 using machine learning and all-atoms MD simulations.

The re-emergence of monkeypox (MPX), in the era of COVID-19 pandemic is a new global menace. Regardless of its leniency, there are chances of MPX expediting severe health deterioration. The role of envelope protein, F13 as a critical component for production of extracellular viral particles makes it a crucial drug target. Polyphenols, exhibiting antiviral properties have been acclaimed as an effective alternative to the traditional treatment methods for management of viral diseases. To facilitate the development of potent MPX specific therapeutics, herein, we have employed state-of-the-art machine learning techniques to predict a highly accurate 3-dimensional structure of F13 as well as identify binding hotspots on the protein surface. Additionally, we have effectuated high-throughput virtual screening methodology on 57 potent natural polyphenols having antiviral activities followed by all-atoms molecular dynamics (MD) simulations, to substantiate the mode of interaction of F13 protein and polyphenol complexes. The structure-based virtual screening based on Glide SP, XP and MM/GBSA scores enables the selection of six potent polyphenols having higher binding affinity towards F13. Non-bonded contact analysis, of pre- and post- MD complexes propound the critical role of Glu143, Asp134, Asn345, Ser321 and Tyr320 residues in polyphenol recognition, which is well supported by per-residue decomposition analysis. Close-observation of the structural ensembles from MD suggests that the binding groove of F13 is mostly hydrophobic in nature. Taken together, this structure-based analysis from our study provides a lead on Myricetin, and Demethoxycurcumin, which may act as potent inhibitors of F13. In conclusion, our study provides new insights into the molecular recognition and dynamics of F13-polyphenol bound states, offering new promises for development of antivirals to combat monkeypox. However, further in vitro and in vivo experiments are necessary to validate these results.

Machine learning and classical MD simulation to identify inhibitors against the P37 envelope protein of monkeypox virus.

Monkeypox virus (MPXV) outbreak is a serious public health concern that requires international attention. P37 of MPXV plays a pivotal role in DNA replication and acts as one of the promising targets for antiviral drug design. In this study, we intent to screen potential analogs of existing FDA approved drugs of MPXV against P37 using state-of-the-art machine learning and computational biophysical techniques. AlphaFold2 guided all-atoms molecular dynamics simulations optimized P37 structure is used for molecular docking and binding free energy calculations. Similar to members of Phospholipase-D family , the predicted P37 structure also adopts a ß-α-ß-α-ß sandwich fold, harbouring strongly conserved HxKxxxxD motif. The binding pocket comprises of Tyr48, Lys86, His115, Lys117, Ser130, Asn132, Trp280, Asn240, His325, Lys327 and Tyr346 forming strong hydrogen bonds and dense hydrophobic contacts with the screened analogs and is surrounded by positively charged patches. Loops connecting the two domains and C-terminal region exhibit high degree of flexibility. In some structural ensembles, the partial disorderness in the C-terminal region is presumed to be due to its low confidence score, acquired during structure prediction. Transition from loop to ß-strands (244-254 aa) in P37-Cidofovir and its analog complexes advocates the need for further investigations. MD simulations support the accuracy of the molecular docking results, indicating the potential of analogs as potent binders of P37. Taken together, our results provide preferable understanding of molecular recognition and dynamics of ligand-bound states of P37, offering opportunities for development of new antivirals against MPXV. However, the need of in vitro and in vivo assays for confirmation of these results still persists.Communicated by Ramaswamy H. Sarma.

Identification of phytocompounds as newer antiviral drugs against COVID-19 through molecular docking and simulation based study.

COVID-19 pandemic has emerged as a global threat with its highly contagious and mutating nature. Several existing antiviral drugs has been worked on, without proper results and meanwhile the virus is mutating rapidly to create more infectious variant. In order to find some alternatives, phytocompounds can be opted as good one. In this study, three hundred phytocompounds were screened virtually against two viral proteins namely main protease and spike protein. Molecular docking and dynamic simulation study was used to find binding affinity, structural stability and flexibility of the complex. Pharmacokinetic properties were studied through ADMET analysis. To understand energy variation of the complex structure free energy landscape analysis was performed. Among three hundred phytocompounds virtual screening, three phytocompounds were selected for detailed molecular interaction analysis. Oleanderolide, Proceragenin A and Balsaminone A, showed strong binding affinity against both the target proteins and reflected conformational stability throughout the MD run. Oleanderolide, proceragenin A and balsaminone A has docking score -9.4 kcal/mol, -8.6 kcal/mol, and -8.1 kcal/mol respectively against main protease and same -8.3 kcal/mol docking score against spike protein. These three phytocompounds has high gastrointestinal absorption capacity. They were unexplored till now for their antiviral activity. Their promising in silico results suggests that they can be promoted in the long run for development of new antiviral drugs.

A Review of Publicly Available Automatic Brain Segmentation Methodologies, Machine Learning Models, Recent Advancements, and Their Comparison.

BACKGROUND: The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer's disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the "gold standard" is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods. PURPOSE: The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms. METHODS: The study focuses on the review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years. CONCLUSION: Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.

An insight into molecular interaction of PGIP with PG for banana cultivar.

PolyGalacturonase Inhibiting Proteins (PGIPs) are leucine rich repeat pathogenesis-related (PR) cell wall proteins, which interact and inhibit the PolyGalacturonase (PG), an enzyme secreted by the pathogen to degrade pectin. Interaction of PGIP with PG limits the vulnerability of PG by the activation of host defense response against pathogenic attack. Erwinia is gram-negative soft rot bacteria responsible for rhizome rot disease in banana and many other crop plants. The interaction of PG with PGIP is one of the crucial steps for plant-pathogen interaction. To study the molecular mechanism of PR proteins, we employed molecular modelling, protein-protein docking and molecular dynamics simulations of banana PGIP (bPGIP) with Erwinia carotovora PG (ecPG). Further, insilico site-directed mutagenesis was performed in Phaseolus vulgaris PGIP (pvPGIP2) to elucidate the interaction with ecPG. Docking and simulation studies divulge that binding of bPGIP and PvPGIP2 with active site residues of EcPG induces structural changes and thereby inhibit the enzyme. This study provides a unique insight into PG-PGIP interaction, which may help in the development of bacterial soft-rot resistant banana cultivars.

Searching haptens, carrier proteins, and anti-hapten antibodies.

Haptens are small molecules that are usually nonimmunogenic unless coupled to some carrier proteins. The generation of anti-hapten antibodies is important for the development of immunodiagnostics and therapeutics. Recently, our group has developed a database called HaptenDB, which provides comprehensive information about 1,087 haptens. In this chapter, we describe following web tools integrated in HaptenDB: (i) keyword search facility allows search on major fields, (ii) browsing service, to display all haptens, carrier proteins and antibodies, and (iii) structure similarity search, which allows the users to search their structure against hapten structures.

HaptenDB: a comprehensive database of haptens, carrier proteins and anti-hapten antibodies.

UNLABELLED: The key requirement for successful immunochemical assay is the availability of antibodies with high specificity and desired affinity. Small molecules, when used as haptens, are not immunogenic. However, on conjugating with carrier molecule they elicit antibody response. The production of anti-hapten antibodies of desired specificity largely depends on the hapten design (preserving greatly the chemical structure and spatial conformation of target compound), selection of the appropriate carrier protein and the conjugation method. This manuscript describes a curated database HaptenDB, where information is collected from published literature and web resources. The current version of the database has 2021 entries for 1087 haptens and 25 carrier proteins, where each entry provides comprehensive details about (1) nature of the hapten, (2) 2D and 3D structures of haptens, (3) carrier proteins, (4) coupling method, (5) method of anti-hapten antibody production, (6) assay method (used for characterization) and (7) specificities of antibodies. The current version of HaptenDB covers a wide array of haptens including pesticides, herbicides, insecticides, drugs, vitamins, steroids, hormones, toxins, dyes, explosives, etc. It provides internal and external links to various databases/resources to obtain further information about the nature of haptens, carriers and respective antibodies. For structure similarity comparison of haptens, the database also integrates tools like JME Editor and JMOL for sketching, displaying and manipulating hapten 2D/3D structures online. So the database would be of great help in identifying functional group(s) in smaller molecules using antibodies as well as for the development of immunodiagnostics/therapeutics by providing data and procedures available so far for the generation of specific or cross-reactive antibodies. AVAILABILITY: HaptenDB is available on http://www.imtech.res.in/raghava/haptendb/ and http://bioinformatics.uams.edu/raghava/haptendb/ (Mirror site).