RCSB Protein Data Bank: Enabling biomedical research and drug discovery

Novel Biomimetic Human TLR2-Derived Peptides for Potential Targeting of Lipoteichoic Acid: An In Silico Assessment

Antimicrobial resistance is one of the most significant threats to health and economy around the globe and has been compounded by the emergence of COVID-19, raising important consequences for antimicrobial resistance development. Contrary to conventional targeting approaches, the use of biomimetic application via nanoparticles for enhanced cellular targeting, cell penetration and localized antibiotic delivery has been highlighted as a superior approach to identify novel targeting ligands for combatting antimicrobial resistance. Gram-positive bacterial cell walls contain lipoteichoic acid (LTA), which binds specifically to Toll-like receptor 2 (TLR2) on human macrophages. This phenomenon has the potential to be exploited for the design of biomimetic peptides for antibacterial application. In this study, we have derived peptides from sequences present in human TLR2 that bind to LTA with high affinity. In silico approaches including molecular modelling, molecular docking, molecular dynamics, and thermodynamics have enabled the identification of these crucial binding amino acids, the design of four novel biomimetic TLR2-derived peptides and their LTA binding potential. The outcomes of this study have revealed that one of these novel peptides binds to LTA more strongly and stably than the other three peptides and has the potential to enhance LTA targeting and bacterial cell penetration.

Functional Classification of Super-Large Families of Enzymes Based on Substrate Binding Pocket Residues for Biocatalysis and Enzyme Engineering Applications

Large enzyme families such as the groups of zinc-dependent alcohol dehydrogenases (ADHs), long chain alcohol oxidases (AOxs) or amine dehydrogenases (AmDHs) with, sometimes, more than one million sequences in the non-redundant protein database and hundreds of experimentally characterized enzymes are excellent cases for protein engineering efforts aimed at refining and modifying substrate specificity. Yet, the backside of this wealth of information is that it becomes technically difficult to rationally select optimal sequence targets as well as sequence positions for mutagenesis studies. In all three cases, we approach the problem by starting with a group of experimentally well studied family members (including those with available 3D structures) and creating a structure-guided multiple sequence alignment and a modified phylogenetic tree (aka binding site tree) based just on a selection of potential substrate binding residue positions derived from experimental information (not from the full-length sequence alignment). Hereupon, the remaining, mostly uncharacterized enzyme sequences can be mapped; as a trend, sequence grouping in the tree branches follows substrate specificity. We show that this information can be used in the target selection for protein engineering work to narrow down to single suitable sequences and just a few relevant candidate positions for directed evolution towards activity for desired organic compound substrates. We also demonstrate how to find the closest thermophile example in the dataset if the engineering is aimed at achieving most robust enzymes.

Combining High-Throughput Synthesis and High-Throughput Protein Crystallography for Accelerated Hit Identification

Protein crystallography (PX) is widely used to drive advanced stages of drug optimization or to discover medicinal chemistry starting points by fragment soaking. However, recent progress in PX could allow for a more integrated role into early drug discovery. Here, we demonstrate for the first time the interplay of high throughput synthesis and high throughput PX. We describe a practical multicomponent reaction approach to acrylamides and -esters from diverse building blocks suitable for mmol scale synthesis on 96-well format and on a high-throughput nanoscale format in a highly automated fashion. High-throughput PX of our libraries efficiently yielded potent covalent inhibitors of the main protease of the COVID-19 causing agent, SARS-CoV-2. Our results demonstrate, that the marriage of in situ HT synthesis of (covalent) libraires and HT PX has the potential to accelerate hit finding and to provide meaningful strategies for medicinal chemistry projects.

Direct proof of soft knock-on mechanism of ion permeation in a voltage gated sodium channel

Sodium channels selectively conduct Na⁺ ions across cellular membrane with extraordinary efficiency, which is essential for initiating action potentials. However, how Na⁺ ions permeate the ionic channels remains obscure and ambiguous. With more than 40 conductance events from microsecond molecular dynamics simulation, the soft knock-on ion permeation mediated by water molecules was observed and confirmed by the free energy profile and electrostatic potential calculation in this study. During the soft knock-on process, the change of average distance between four oxygen atoms in Glu177-Glu177 plays a very important role for the permeation of Na⁺ ion. Exploration of the ionic conductance mechanism could provide a guideline for designing ion channel targeted drug.

Biomimetic pH/lipase dual responsive vitamin-based solid lipid nanoparticles for on-demand delivery of vancomycin

In this study, ascorbyl tocopherol succinate (ATS) was designed, synthesized and characterized via FT-IR, HR-MS, H¹ NMR and C¹³ NMR, to simultaneously confer biomimetic and dual responsive properties of an antibiotic nanosystem to enhance their antibacterial efficacy and reduce antimicrobial resistance. Therefore, an in silico-aided design (to mimic the natural substrate of bacterial lipase) was employed to demonstrate the binding potential of ATS to lipase (-32.93 kcal/mol binding free energy (ΔG_bind) and bacterial efflux pumps blocking potential (NorA ΔG_bind: -37.10 kcal/mol, NorB ΔG_bind: -34.46 kcal/mol). ATS bound stronger to lipase than the natural substrate (35 times lower Kd value). The vancomycin loaded solid lipid nanoparticles (VM-ATS-SLN) had a hydrodynamic diameter, zeta potential, polydispersity index and entrapment efficiency of 106.9 ± 1.4 nm, -16.5 ± 0.93 mV, 0.11 ± 0.012 and 61.9 ± 1.31%, respectively. In vitro biocompatibility studies revealed VM-ATS-SLN biosafety and non-haemolytic activity. Significant enhancement in VM release was achieved in response to acidified pH and lipase enzyme, compared to controls. VM-ATS-SLN showed enhanced sustained in vitro antibacterial activity for 5 days, 2-fold greater MRSA biofilm growth inhibition and 3.44-fold reduction in bacterial burden in skin infected mice model compared to bare VM. Therefore, ATS shows potential as a novel multifunctional adjuvant for effective and targeted delivery of antibiotics.

Network Pharmacology-Based Analysis of Gegenqinlian Decoction Regulating Intestinal Microbial Activity for the Treatment of Diarrhea

Gegenqinlian decoction (GD) has been extensively used for the treatment of diarrhea with intestinal dampness-heat syndrome (IDHS) with a satisfying therapeutic effect. The purpose of this study is to clarify the active ingredients and mechanism of GD in the treatment of diarrhea with IDHS. The TCMSP database was used to screen out the active ingredients of the four Chinese herbal medicines in GD, and the targets of the active ingredients were predicted. We selected the targets related to diarrhea through the DisGeNET database, then used the NCBI database to screen out related targets of lactase and sucrase, and constructed the visual network to search for the active ingredients of GD in the treatment of diarrhea and related mechanisms of the targets. Combined with network pharmacology, we screened out 146 active ingredients in GD corresponding to 252 ingredient targets, combined with 328 disease targets in diarrhea, and obtained 12 lactase targets and 11 sucrase targets. The key active ingredients involved quercetin, formononetin, β-sitosterol kaempferol, and wogonin. Furthermore, molecular docking showed that these five potential active ingredients had good affinities with the core targets PTGS2. The active ingredients in GD (such as quercetin, formononetin, and β-sitosterol) may increase the microbial activity of the intestinal mucosa of mice and reduce the microbial activity of the intestinal contents through multiple targets, thereby achieving the effect of treating diarrhea.

CNS anti-depressant, anxiolytic and analgesic effects of Ganoderma applanatum (mushroom) along with ligand-receptor binding screening provide new insights: Multi-disciplinary approaches

This research was designed to evaluate the CNS depressant, anxiolytic, and analgesic action of aqueous and ethanol extract of Ganoderma applanatum, a valuable medicinal fungus used in multiple disorders belongs to Ganodermataceae family. Two extracts of G. applanatum were prepared using distilled water and ethanol as solvents and named AEGA and EEGA. Open field method, rotarod method, tail suspension method, and hole cross method were utilized for the CNS depressant action. In contrast, elevated plus-maze test and hole board method were utilized for the anxiolytic action. For determining the analgesic potential, acetic acid-induced writhing test, hot plate method, and tail immersion test were used. Besides, molecular docking has been implemented by using Discovery studio 2020, UCSF Chimera and PyRx autodock vina. At both doses (200 and 400 mg/kg) of AEGA and EEGA showed significant CNS depressant effect (p < 0.05 to 0.001) against all four tests used for CNS depressant activity. Both doses of AEGA and EEGA exhibited important anxiolytic activity effect (p < 0.05 to 0.001)against the EPM and hole board test. Both doses of AEGA and EEGA also exhibited a potential analgesic effect (p < 0.05 to 0.001) against all three tests used for analgesic action. In addition, in the molecular docking the compounds obtained the scores of −5.2 to −12.8 kcal/mol. Ganoapplanin, sphaeropsidin D and cytosporone C showed the best binding affinity to the selected recptors. It can be concluded that AEGA and EEGA have potential CNS depressant, anxiolytic, and analgesic action, which can be used as a natural antidepressant, anxiolytic, and analgesic source.

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The mushroom extracts were found to possess dose-dependent potentiality in antidepressant and anxiolytic test on mice model.

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The mushroom extracts revealed significant inhibition in pain.

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The mushroom extract is non-toxic evident from acute toxicity study.

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Ganoderma applanatum can be a prominent source of CNS depressant, anxiety and pain management.

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Ganoderma applanatum is a bracket fungus with a cosmopolitan distribution.

Regulations, Open Data and Healthcare Innovation: A Case of MSK-IMPACT and Its Implications for Better Cancer Care

Simple Summary

The advancement in both science and technology has contributed to the development of novel diagnostic technologies; such technologies enable medical practitioners to diagnose diseases that could not be previously detected. However, in order to translate new technologies into practical applications, various types of challenges need to be overcome. To address these challenges, including those in clinical management and regulatory science, healthcare policies have been constantly implemented to promote the practical application of outcomes generated by healthcare innovation. This study conducted comparative analyses of three tumor profiling tests approved by the U.S. Food and Drug Administration (FDA) in 2017, hypothesizing that the FDA’s regulatory reforms, early application of new technologies to both research and clinical settings, and open data accumulated as a result of large-scale research programs have promoted new drug development in oncology. The study then discussed the implications potentially suggested by the outcomes and challenges of the three tests.

Abstract

This study investigated a case of Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT), a tumor profiling test approved by the U.S. Food and Drug Administration (FDA) in 2017, to examine what factors would contribute to healthcare innovation. First, we set the following three parameters to observe cases: (i) the FDA regulatory reforms, (ii) early application of new technologies, such as next-generation sequencing (NGS), to both research and clinical settings, and (iii) accumulation of open data. Then, we performed a comparative analysis of MSK-IMPACT with FoundationOne CDx and Oncomine Dx Target Test, both of which were FDA-approved tumor profiling tests launched in 2017. As a result, we found that MSK-IMPACT secures neutrality as a non-profit organization, achieves the active incorporation of basic research results, and performs superiorly in clinical operations, such as patient enrollment. On the contrary, we confirmed that FoundationOne CDx was the most prominent case in terms of the number of new drugs and expanded indications approved in which the FDA’s expedited approval programs were considerably utilized. Consequently, to uncover the full potential of MSK-IMPACT, it is suggested that more intersectoral collaborative activities between various healthcare stakeholders, in particular, pharmaceutical companies, for driving clinical development must be carried out based on an organizational framework that facilitates collaboration.

The interplay of SARS-CoV-2 evolution and constraints imposed by the structure and functionality of its proteins

The unprecedented pace of the sequencing of the SARS-CoV-2 virus genomes provides us with unique information about the genetic changes in a single pathogen during ongoing pandemic. By the analysis of close to 200,000 genomes we show that the patterns of the SARS-CoV-2 virus mutations along its genome are closely correlated with the structural and functional features of the encoded proteins. Requirements of foldability of proteins' 3D structures and the conservation of their key functional regions, such as protein-protein interaction interfaces, are the dominant factors driving evolutionary selection in protein-coding genes. At the same time, avoidance of the host immunity leads to the abundance of mutations in other regions, resulting in high variability of the missense mutation rate along the genome. "Unexplained" peaks and valleys in the mutation rate provide hints on function for yet uncharacterized genomic regions and specific protein structural and functional features they code for. Some of these observations have immediate practical implications for the selection of target regions for PCR-based COVID-19 tests and for evaluating the risk of mutations in epitopes targeted by specific antibodies and vaccine design strategies.

The NanoInformatics Knowledge Commons: Capturing spatial and temporal nanomaterial transformations in diverse systems

The empirical necessity for integrating informatics throughout the experimental process has become a focal point of the nano-community as we work in parallel to converge efforts for making nano-data reproducible and accessible. The NanoInformatics Knowledge Commons (NIKC) Database was designed to capture the complex relationship between nanomaterials and their environments over time in the concept of an 'Instance'. Our Instance Organizational Structure (IOS) was built to record metadata on nanomaterial transformations in an organizational structure permitting readily accessible data for broader scientific inquiry. By transforming published and on-going data into the IOS we are able to tell the full transformational journey of a nanomaterial within its experimental life cycle. The IOS structure has prepared curated data to be fully analyzed to uncover relationships between observable phenomenon and medium or nanomaterial characteristics. Essential to building the NIKC database and associated applications was incorporating the researcher's needs into every level of development. We started by centering the research question, the query, and the necessary data needed to support the question and query. The process used to create nanoinformatic tools informs usability and analytical capability. In this paper we present the NIKC database, our developmental process, and its curated contents. We also present the Collaboration Tool which was built to foster building new collaboration teams. Through these efforts we aim to: 1) elucidate the general principles that determine nanomaterial behavior in the environment; 2) identify metadata necessary to predict exposure potential and bio-uptake; and 3) identify key characterization assays that predict outcomes of interest.

Molecular dynamics simulations and analysis for bioinformatics undergraduate students

A computational biochemistry laboratory, fitted for bioinformatics students, is presented. The molecular dynamics package GROMACS is used to prepare and simulate a solvated protein. Students analyze the trajectory with different available tools (GROMACS and VMD) to probe the structural stability of the protein during the simulation. Students are also required to make use of Python libraries and write their own code to probe non-covalent interactions between the amino acid side chains. Based on these results, students characterize the system in a qualitatively approach but also assess the importance of each specific interaction through time. This work mobilizes biochemical concepts and programming skills, fostering critical thinking and group work and developing presenting skills.

Network-based analysis of virulence factors for uncovering Aeromonas veronii pathogenesis

BACKGROUND

Aeromonas veronii is a bacterial pathogen in aquaculture, which produces virulence factors to enable it colonize and evade host immune defense. Given that experimental verification of virulence factors is time-consuming and laborious, few virulence factors have been characterized. Moreover, most studies have only focused on single virulence factors, resulting in biased interpretation of the pathogenesis of A. veronii.

RESULTS

In this study, a PPI network at genome-wide scale for A. veronii was first constructed followed by prediction and mapping of virulence factors on the network. When topological characteristics were analyzed, the virulence factors had higher degree and betweenness centrality than other proteins in the network. In particular, the virulence factors tended to interact with each other and were enriched in two network modules. One of the modules mainly consisted of histidine kinases, response regulators, diguanylate cyclases and phosphodiesterases, which play important roles in two-component regulatory systems and the synthesis and degradation of cyclic-diGMP. Construction of the interspecies PPI network between A. veronii and its host Oreochromis niloticus revealed that the virulence factors interacted with homologous proteins in the host. Finally, the structures and interacting sites of the virulence factors during interaction with host proteins were predicted.

CONCLUSIONS

The findings here indicate that the virulence factors probably regulate the virulence of A. veronii by involving in signal transduction pathway and manipulate host biological processes by mimicking and binding competitively to host proteins. Our results give more insight into the pathogenesis of A. veronii and provides important information for designing targeted antibacterial drugs.

Discovery of Novel Allosteric Modulators Targeting an Extra-Helical Binding Site of GLP-1R Using Structure- and Ligand-Based Virtual Screening

Allosteric modulators have emerged with many potential pharmacological advantages as they do not compete the binding of agonist or antagonist to the orthosteric sites but ultimately affect downstream signaling. To identify allosteric modulators targeting an extra-helical binding site of the glucagon-like peptide-1 receptor (GLP-1R) within the membrane environment, the following two computational approaches were applied: structure-based virtual screening with consideration of lipid contacts and ligand-based virtual screening with the maintenance of specific allosteric pocket residue interactions. Verified by radiolabeled ligand binding and cAMP accumulation experiments, two negative allosteric modulators and seven positive allosteric modulators were discovered using structure-based and ligand-based virtual screening methods, respectively. The computational approach presented here could possibly be used to discover allosteric modulators of other G protein-coupled receptors.

Completeness and Consistency in Structural Domain Classifications

Domain classifications are a useful resource for computational analysis of the protein structure, but elements of their composition are often opaque to potential users. We perform a comparative analysis of our classification ECOD against the SCOPe, SCOP2, and CATH domain classifications with respect to their constituent domain boundaries and hierarchal organization. The coverage of these domain classifications with respect to ECOD and to the PDB was assessed by structure and by sequence. We also conducted domain pair analysis to determine broad differences in hierarchy between domains shared by ECOD and other classifications. Finally, we present domains from the major facilitator superfamily (MFS) of transporter proteins and provide evidence that supports their split into domains and for multiple conformations within these families. We find that the ECOD and CATH provide the most extensive structural coverage of the PDB. ECOD and SCOPe have the most consistent domain boundary conditions, whereas CATH and SCOP2 both differ significantly.

Design, synthesis and in-vitro evaluation of fluorinated triazoles as multi-target directed ligands for Alzheimer disease

Alzheimer disease is multi-factorial and inflammation plays a major role in the disease progression and severity. Metals and reactive oxygen species (ROS) are the key mediators for inflammatory conditions associated with Alzheimer's. Along multi-factorial nature, major challenge for developing new drug is the ability of the molecule to cross blood brain barrier (BBB). We have designed and synthesized multi-target directed hexafluorocarbinol containing triazoles to inhibit Amyloid β aggregation and simultaneously chelate the excess metals present in the extracellular space and scavenge the ROS thus reduce the inflammatory condition. From the screened compound library, compound 1c found to be potent and safe. It has demonstrated inhibition of Amyloid β aggregation (IC₅₀ of 4.6 μM) through selective binding with Amyloid β at the nucleation site (evidenced from the molecular docking). It also chelate metals (Cu⁺², Zn⁺² and Fe⁺³) and scavenges ROS significantly. Due to the presence of hexafluorocarbinol moiety in the molecule it may assist to permeate BBB and improve the pharmacokinetic properties. The in-vitro results of compound 1c indicate the promiscuity for the development of hexafluorocarbinol containing triazoles amide scaffold as multi-target directed therapy against Alzheimer disease.

A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19

In this study we have developed a method based on Flux Balance Analysis to identify human metabolic enzymes which can be targeted for therapeutic intervention against COVID-19. A literature search was carried out in order to identify suitable inhibitors of these enzymes, which were confirmed by docking calculations. In total, 10 targets and 12 bioactive molecules have been predicted. Among the most promising molecules we identified Triacsin C, which inhibits ACSL3, and which has been shown to be very effective against different viruses, including positive-sense single-stranded RNA viruses. Similarly, we also identified the drug Celgosivir, which has been successfully tested in cells infected with different types of viruses such as Dengue, Zika, Hepatitis C and Influenza. Finally, other drugs targeting enzymes of lipid metabolism, carbohydrate metabolism or protein palmitoylation (such as Propylthiouracil, 2-Bromopalmitate, Lipofermata, Tunicamycin, Benzyl Isothiocyanate, Tipifarnib and Lonafarnib) are also proposed.

RCSB Protein Data Bank: Architectural Advances Towards Integrated Searching and Efficient Access to Macromolecular Structure Data from the PDB Archive

The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) serves many millions of unique users worldwide by delivering experimentally-determined 3D structures of biomolecules integrated with >40 external data resources via RCSB.org, application programming interfaces (APIs), and FTP downloads. Herein, we present the architectural redesign of RCSB PDB data delivery services that build on existing PDBx/mmCIF data schemas. New data access APIs (data.rcsb.org) enable efficient delivery of all PDB archive data. A novel GraphQL-based API provides flexible, declarative data retrieval along with a simple-to-use REST API. A powerful new search system (search.rcsb.org) seamlessly integrates heterogeneous types of searches across the PDB archive. Searches may combine text attributes, protein or nucleic acid sequences, small-molecule chemical descriptors, 3D macromolecular shapes, and sequence motifs. The new RCSB.org architecture adheres to the FAIR Principles, empowering users to address a wide array of research problems in fundamental biology, biomedicine, biotechnology, bioengineering, and bioenergy.

Integrated bioinformatic analysis reveals the underlying molecular mechanism of and potential drugs for pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a devastating cardiovascular disease without a clear mechanism or drugs for treatment. Therefore, it is crucial to reveal the underlying molecular mechanism and identify potential drugs for PAH. In this study, we first integrated three human lung tissue datasets (GSE113439, GSE53408, GSE117261) from GEO. A total of 151 differentially expressed genes (DEGs) were screened, followed by KEGG and GO enrichment analyses and PPI network construction. Five hub genes (CSF3R, NT5E, ANGPT2, FGF7, and CXCL9) were identified by Cytoscape (Cytohubba). GSEA and GSVA were performed for each hub gene to uncover the potential mechanism. Moreover, to repurpose known and therapeutic drugs, the CMap database was retrieved, and nine candidate compounds (lypressin, ruxolitinib, triclabendazole, L-BSO, tiaprofenic acid, AT-9283, QL-X-138, huperzine-a, and L-741742) with a high level of confidence were obtained. Then ruxolitinib was selected to perform molecular docking simulations with ANGPT2, FGF7, NT5E, CSF3R, JAK1, JAK2, JAK3, TYK2. A certain concentration of ruxolitinib could inhibit the proliferation and migration of rat pulmonary artery smooth muscle cells (rPASMCs) in vitro. Together, these analyses principally identified CSF3R, NT5E, ANGPT2, FGF7 and CXCL9 as candidate biomarkers of PAH, and ruxolitinib might exert promising therapeutic action for PAH.

Application of beta and gamma carbonic anhydrase sequences as tools for identification of bacterial contamination in the whole genome sequence of inbred Wuzhishan minipig (Sus scrofa) annotated in databases

Sus scrofa or pig was domesticated thousands of years ago. Through various indigenous breeds, different phenotypes were produced such as Chinese inbred miniature minipig or Wuzhishan pig (WZSP), which is broadly used in the life and medical sciences. The whole genome of WZSP was sequenced in 2012. Through a bioinformatics study of pig carbonic anhydrase (CA) sequences, we detected some β- and γ-class CAs among the WZSP CAs annotated in databases, while β- or γ-CAs had not previously been described in vertebrates. This finding urged us to analyze the quality of whole genome sequence of WZSP for the possible bacterial contamination. In this study, we used bioinformatics methods and web tools such as UniProt, European Bioinformatics Institute, National Center for Biotechnology Information, Ensembl Genome Browser, Ensembl Bacteria, RSCB PDB and Pseudomonas Genome Database. Our analysis defined that pig has 12 classical α-CAs and 3 CA-related proteins. Meanwhile, it was approved that the detected CAs in WZSP are categorized in the β- and γ-CA families, which belong to Pseudomonas spp. and Acinetobacter spp. The protein structure study revealed that the identified β-CA sequence from WZSP belongs to Pseudomonas aeruginosa with PDB ID: 5JJ8, and the identified γ-CA sequence from WZSP belongs to P. aeruginosa with PDB ID: 3PMO. Bioinformatics and computational methods accompanied with bacterial-specific markers, such as 16S rRNA and β- and γ-class CA sequences, can be used to identify bacterial contamination in mammalian DNA samples.

Network Pharmacology-Based Systematic Analysis of Molecular Mechanisms of Dingji Fumai Decoction for Ventricular Arrhythmia

Background

Dingji Fumai Decoction (DFD), a traditional herbal mixture, has been widely used to ventricular arrhythmia (VA) in clinical practice in China. However, research on the bioactive components and underlying mechanisms of DFD in VA is still scarce.

Methods

Components of DFD were collected from TCMSP, ETCM, and literature. The chemical structures of each component were obtained from PubChem. Next, SwissADME and SwissTargetPrediction were applied for compounds screening and targets prediction of DFD; meanwhile, targets of VA were collected from DrugBank and Online Mendelian Inheritance in Man (OMIM). Then, the H-C-T-D network and the protein-protein interaction (PPI) network were constructed based on the data obtained above. CytoNCA was utilized to filter hub genes and VarElect was used to analyze the relationship between genes and diseases. At last, Metascape was employed for systematic analysis on the potential targets of herbals against VA, and AutoDock was applied for molecular docking to verify the results.

Results

A total of 434 components were collected, 168 of which were qualified, and there were 28 shared targets between DFD and VA. Three function modules of DFD were found from the PPI network. Further systematic analysis of shared genes and function modules explained the potential mechanism of DFD in the treatment of VA; molecular docking has verified the interactions.

Conclusions

DFD could be employed for VA through mechanisms, including complex interactions between related components and targets, as predicted by network pharmacology and molecular docking. This work confirmed that DFD could apply to the treatment of VA and promoted the explanation of DFD for VA in the molecular mechanisms.

The whole is greater than its parts: ensembling improves protein contact prediction

The prediction of amino acid contacts from protein sequence is an important problem, as protein contacts are a vital step towards the prediction of folded protein structures. We propose that a powerful concept from deep learning, called ensembling, can increase the accuracy of protein contact predictions by combining the outputs of different neural network models. We show that ensembling the predictions made by different groups at the recent Critical Assessment of Protein Structure Prediction (CASP13) outperforms all individual groups. Further, we show that contacts derived from the distance predictions of three additional deep neural networks-AlphaFold, trRosetta, and ProSPr-can be substantially improved by ensembling all three networks. We also show that ensembling these recent deep neural networks with the best CASP13 group creates a superior contact prediction tool. Finally, we demonstrate that two ensembled networks can successfully differentiate between the folds of two highly homologous sequences. In order to build further on these findings, we propose the creation of a better protein contact benchmark set and additional open-source contact prediction methods.

Recent Developments in New Therapeutic Agents against Alzheimer and Parkinson Diseases: In-Silico Approaches

Neurodegenerative diseases (ND), including Alzheimer's (AD) and Parkinson's Disease (PD), are becoming increasingly more common and are recognized as a social problem in modern societies. These disorders are characterized by a progressive neurodegeneration and are considered one of the main causes of disability and mortality worldwide. Currently, there is no existing cure for AD nor PD and the clinically used drugs aim only at symptomatic relief, and are not capable of stopping neurodegeneration. Over the last years, several drug candidates reached clinical trials phases, but they were suspended, mainly because of the unsatisfactory pharmacological benefits. Recently, the number of compounds developed using in silico approaches has been increasing at a promising rate, mainly evaluating the affinity for several macromolecular targets and applying filters to exclude compounds with potentially unfavorable pharmacokinetics. Thus, in this review, an overview of the current therapeutics in use for these two ND, the main targets in drug development, and the primary studies published in the last five years that used in silico approaches to design novel drug candidates for AD and PD treatment will be presented. In addition, future perspectives for the treatment of these ND will also be briefly discussed.

Insights into Glucose-6-phosphate Allosteric Activation of β-Glucosidase A

Second-generation ethanol production involves the use of agricultural and forestry waste as feedstock, being an alternative to the first-generation technology as it relies on low-cost abundant residues and does not affect food agriculture. However, the success of second-generation biorefineries relies on energetically efficient processes and effective enzyme cocktails to convert cellulose into fermentable sugars. β-glucosidases catalyze the last step on the enzymatic hydrolysis of cellulose; however, they are often inhibited by glucose. Previous studies demonstrated that glucose-6-phosphate (G6P) is a positive allosteric modulator of Bacillus polymyxa β-glucosidase A, improving enzymatic efficiency, providing thermoresistance, and imparting glucose tolerance. However, the precise molecular details of G6P-β-glucosidase A interactions have not yet been described so far. We investigated the molecular details of G6P binding into B. polymyxa β-glucosidase A through in silico docking using the site identification by ligand competitive saturation technology followed by site-directed mutagenesis studies, from which an allosteric binding site for G6P was identified. In addition, a mechanistic shift toward the transglycosylation reaction as opposed to hydrolysis was observed in the presence of G6P, suggesting a new role of G6P allosteric modulation of the catalytic activity of β-glucosidase A.

Molecular modeling for potential cathepsin L inhibitor identification as new anti-photoaging agents from tropical medicinal plants

Ultraviolet exposure can cause photoaging toward the human skin which is begun by the inflammation on the exposure area, also resulting in activation of a degradative enzyme cathepsin L. This enzyme is one of the interesting novel therapeutic targets for antiaging agents. Three plants, named Kleinhovia hospita, Aleurites moluccana, and Centella asiatica, are well-known in the tropical region as anti-inflammatory herbs. The aims of this study were to predict the antiaging activity of the 31 compounds from these plants via inhibition of cathepsin L. All compounds were minimized their energies and then used in molecular docking. After that, molecular dynamics (MD) simulation was employed for the 5 candidate ligands and the positive control; schinol. Interaction analysis results of the pre-MD and post-MD simulation structures were obtained. Furthermore, a toxicity test was performed using ADMET Predictor 7.1. Based on the molecular docking and the MD simulation results, kleinhospitine A, β-amyrin, and castiliferol exhibited lower binding free energy than schinol (-27.0925, -28.6813, -26.0037 kcal/mol) and also had interactions with the S´ region binding site. The toxicity test indicated that β-amyrin is the most potential candidate since it exhibited the lowest binding energy and the high safety level.

Computational drug repurposing study elucidating simultaneous inhibition of entry and replication of novel corona virus by Grazoprevir

Outcomes of various clinical studies for the coronavirus disease 2019 (COVID-19) treatment indicated that the drug acts via inhibition of multiple pathways (targets) is likely to be more successful and promising. Keeping this hypothesis intact, the present study describes for the first-time, Grazoprevir, an FDA approved anti-viral drug primarily approved for Hepatitis C Virus (HCV), mediated multiple pathway control via synergistic inhibition of viral entry targeting host cell Angiotensin-Converting Enzyme 2 (ACE-2)/transmembrane serine protease 2 (TMPRSS2) and viral replication targeting RNA-dependent RNA polymerase (RdRP). Molecular modeling followed by in-depth structural analysis clearly demonstrated that Grazoprevir interacts with the key residues of these targets. Futher, Molecular Dynamics (MD) simulations showed stability and burial of key residues after the complex formation. Finally, Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) analysis identified the governing force of drug-receptor interactions and stability. Thus, we believe that Grazoprevir could be an effective therapeutics for the treatment of the COVID-19 pandemic with a promise of unlikely drug resistance owing to multiple inhibitions of eukaryotic and viral proteins, thus warrants further clinical studies.

Profiling the structural determinants of aminoketone derivatives as hNET and hDAT reuptake inhibitors by field-based QSAR based on molecular docking

BACKGROUND

Bupropion, one of the dual norepinephrine and dopamine reuptake inhibitors (NDRIs), is an aminoketone derivative performed effect in improving cognitive function for depression. However, its therapeutic effect is unsatisfactory due to poor clinical response, and there are only few derivatives in pre-clinical settings.

OBJECTIVE

This work attempted to elucidate the essential structural features for the activity and designed a series of novel derivatives with good inhibitive ability, pharmacokinetic and medicinal chemistry properties.

METHODS

The field-based QSAR of aminoketone derivatives of two targets were established based on docking poses, and the essential structural properties for designing novel compounds were supplied by comparing contour maps.

RESULTS

The selected two models performed good predictability and reliability with R2 of 0.8479 and 0.8040 for training set, Q2 of 0.7352 and 0.6266 for test set respectively, and the designed 29 novel derivatives performed no less than the highest active compound with good ADME/T pharmacokinetic properties and medicinal chemistry friendliness.

CONCLUSIONS

Bulky groups in R1, bulky groups with weak hydrophobicity in R3, and potent hydrophobic substituted group with electronegative in R2 from contour maps provided important insights for assessing and designing 29 novel NDRIs, which were considered as candidates for cognitive dysfunction with depression or other related neurodegenerative disorders.

Crystal structures of the EVE-HNH endonuclease VcaM4I in the presence and absence of DNA

Many modification-dependent restriction endonucleases (MDREs) are fusions of a PUA superfamily modification sensor domain and a nuclease catalytic domain. EVE domains belong to the PUA superfamily, and are present in MDREs in combination with HNH nuclease domains. Here, we present a biochemical characterization of the EVE-HNH endonuclease VcaM4I and crystal structures of the protein alone, with EVE domain bound to either 5mC modified dsDNA or to 5mC/5hmC containing ssDNA. The EVE domain is moderately specific for 5mC/5hmC containing DNA according to EMSA experiments. It flips the modified nucleotide, to accommodate it in a hydrophobic pocket of the enzyme, primarily formed by P24, W82 and Y130 residues. In the crystallized conformation, the EVE domain and linker helix between the two domains block DNA binding to the catalytic domain. Removal of the EVE domain and inter-domain linker, but not of the EVE domain alone converts VcaM4I into a non-specific toxic nuclease. The role of the key residues in the EVE and HNH domains of VcaM4I is confirmed by digestion and restriction assays with the enzyme variants that differ from the wild-type by changes to the base binding pocket or to the catalytic residues.

Molecular storytelling for online structural biology outreach and education

Knowledge about the structure and function of biomolecules continues to grow exponentially, enabling us to "see" structural snapshots of biomolecular interactions and functional assemblies. At PDB-101, the educational portal of the RCSB Protein Data Bank, we have taken a storytelling approach to make this body of knowledge accessible and comprehensible to a wide community of students, educators, and the general public. For over 20 years, the Molecule of the Month series has utilized a traditional illustrated storytelling approach that is regularly adapted for classroom instruction. Similar visual and interactive storytelling approaches are used to present topical subjects at PDB-101 and full curricular materials and case studies for building a detailed narrative around topics of particular interest. This emphasis on storytelling led to the Video Challenge for High School students, now in its 8th year. In this Article, we will present some of the lessons we have learned for teaching and communicating structural biology using the PDB archive of biomolecular structures.

Microtubule Destabilizing Sulfonamides as an Alternative to Taxane-Based Chemotherapy

Pan-Gyn cancers entail 1 in 5 cancer cases worldwide, breast cancer being the most commonly diagnosed and responsible for most cancer deaths in women. The high incidence and mortality of these malignancies, together with the handicaps of taxanes-first-line treatments-turn the development of alternative therapeutics into an urgency. Taxanes exhibit low water solubility that require formulations that involve side effects. These drugs are often associated with dose-limiting toxicities and with the appearance of multi-drug resistance (MDR). Here, we propose targeting tubulin with compounds directed to the colchicine site, as their smaller size offer pharmacokinetic advantages and make them less prone to MDR efflux. We have prepared 52 new Microtubule Destabilizing Sulfonamides (MDS) that mostly avoid MDR-mediated resistance and with improved aqueous solubility. The most potent compounds, N-methyl-N-(3,4,5-trimethoxyphenyl-4-methylaminobenzenesulfonamide 38, N-methyl-N-(3,4,5-trimethoxyphenyl-4-methoxy-3-aminobenzenesulfonamide 42, and N-benzyl-N-(3,4,5-trimethoxyphenyl-4-methoxy-3-aminobenzenesulfonamide 45 show nanomolar antiproliferative potencies against ovarian, breast, and cervix carcinoma cells, similar or even better than paclitaxel. Compounds behave as tubulin-binding agents, causing an evident disruption of the microtubule network, in vitro Tubulin Polymerization Inhibition (TPI), and mitotic catastrophe followed by apoptosis. Our results suggest that these novel MDS may be promising alternatives to taxane-based chemotherapy in chemoresistant Pan-Gyn cancers.

REDCRAFT: A computational platform using residual dipolar coupling NMR data for determining structures of perdeuterated proteins in solution

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the three primary experimental means of characterizing macromolecular structures, including protein structures. Structure determination by solution NMR spectroscopy has traditionally relied heavily on distance restraints derived from nuclear Overhauser effect (NOE) measurements. While structure determination of proteins from NOE-based restraints is well understood and broadly used, structure determination from Residual Dipolar Couplings (RDCs) is relatively less well developed. Here, we describe the new features of the protein structure modeling program REDCRAFT and focus on the new Adaptive Decimation (AD) feature. The AD plays a critical role in improving the robustness of REDCRAFT to missing or noisy data, while allowing structure determination of larger proteins from less data. In this report we demonstrate the successful application of REDCRAFT in structure determination of proteins ranging in size from 50 to 145 residues using experimentally collected data, and of larger proteins (145 to 573 residues) using simulated RDC data. In both cases, REDCRAFT uses only RDC data that can be collected from perdeuterated proteins. Finally, we compare the accuracy of structure determination from RDCs alone with traditional NOE-based methods for the structurally novel PF.2048.1 protein. The RDC-based structure of PF.2048.1 exhibited 1.0 Å BB-RMSD with respect to a high-quality NOE-based structure. Although optimal strategies would include using RDC data together with chemical shift, NOE, and other NMR data, these studies provide proof-of-principle for robust structure determination of largely-perdeuterated proteins from RDC data alone using REDCRAFT.

Residual Dipolar Couplings have the potential to improve the accuracy and reduce the time needed to characterize protein structures. In addition, RDC data have been demonstrated to concurrently elucidate structure of proteins, provide assignment of resonances, and characterize the internal dynamics of proteins. Given all the advantages associated with the study of proteins from RDC data, based on the statistics provided by the Protein Databank (PDB), surprisingly only 124 proteins (out of nearly 150,000 proteins) have utilized RDCs as part of their structure determination. Even a smaller subset of these proteins (approximately 7) have utilized RDCs as the primary source of data for structure determination. One key factor in the use of RDCs is the challenging computational and analytical aspects of this source of data. In this report, we demonstrate the success of the REDCRAFT software package in structure determination of proteins using RDC data that can be collected from small and large proteins in a routine fashion. REDCRAFT accomplishes the challenging task of structure determination from RDCs by introducing a unique search and optimization technique that is both robust and computationally tractable. Structure determination from routinely collectable RDC data using REDCRAFT can complement existing methods to provide faster and more accurate studies of larger and more complex protein structures by NMR spectroscopy in solution state.

Chloroquine and Hydroxychloroquine Interact Differently with ACE2 Domains Reported to Bind with the Coronavirus Spike Protein: Mediation by ACE2 Polymorphism

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection inducing coronavirus disease 2019 (COVID-19) is still an ongoing challenge. To date, more than 95.4 million have been infected and more than two million deaths have been officially reported by the WHO. Angiotensin-converting enzyme (ACE) plays a key role in the disease pathogenesis. In this computational study, seventeen coding variants were found to be important for ACE2 binding with the coronavirus spike protein. The frequencies of these allele variants range from 3.88 × 10^-3 to 5.47 × 10^-6 for rs4646116 (K26R) and rs1238146879 (P426A), respectively. Chloroquine (CQ) and its metabolite hydroxychloroquine (HCQ) are mainly used to prevent and treat malaria and rheumatic diseases. They are also used in several countries to treat SARS-CoV-2 infection inducing COVID-19. Both CQ and HCQ were found to interact differently with the various ACE2 domains reported to bind with coronavirus spike protein. A molecular docking approach revealed that intermolecular interactions of both CQ and HCQ exhibited mediation by ACE2 polymorphism. Further explorations of the relationship and the interactions between ACE2 polymorphism and CQ/HCQ would certainly help to better understand the COVID-19 management strategies, particularly their use in the absence of specific vaccines or drugs.

jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population

In the Tohoku Medical Megabank project, genome and omics analyses of participants in two cohort studies were performed. A part of the data is available at the Japanese Multi Omics Reference Panel (jMorp; https://jmorp.megabank.tohoku.ac.jp) as a web-based database, as reported in our previous manuscript published in Nucleic Acid Research in 2018. At that time, jMorp mainly consisted of metabolome data; however, now genome, methylome, and transcriptome data have been integrated in addition to the enhancement of the number of samples for the metabolome data. For genomic data, jMorp provides a Japanese reference sequence obtained using de novo assembly of sequences from three Japanese individuals and allele frequencies obtained using whole-genome sequencing of 8,380 Japanese individuals. In addition, the omics data include methylome and transcriptome data from ∼300 samples and distribution of concentrations of more than 755 metabolites obtained using high-throughput nuclear magnetic resonance and high-sensitivity mass spectrometry. In summary, jMorp now provides four different kinds of omics data (genome, methylome, transcriptome, and metabolome), with a user-friendly web interface. This will be a useful scientific data resource on the general population for the discovery of disease biomarkers and personalized disease prevention and early diagnosis.

RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including >660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with >40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic

The pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is spreading all over the world. Medical health care systems are in urgent need to diagnose this pandemic with the support of new emerging technologies like artificial intelligence (AI), internet of things (IoT) and Big Data System. In this dichotomy study, we divide our research in two ways-firstly, the review of literature is carried out on databases of Elsevier, Google Scholar, Scopus, PubMed and Wiley Online using keywords Coronavirus, Covid-19, artificial intelligence on Covid-19, Coronavirus 2019 and collected the latest information about Covid-19. Possible applications are identified from the same to enhance the future research. We have found various databases, websites and dashboards working on real time extraction of Covid-19 data. This will be conducive for future research to easily locate the available information. Secondly, we designed a nested ensemble model using deep learning methods based on long short term memory (LSTM). Proposed Deep-LSTM ensemble model is evaluated on intensive care Covid-19 confirmed and death cases of India with different classification metrics such as accuracy, precision, recall, f-measure and mean absolute percentage error. Medical healthcare facilities are boosted with the intervention of AI as it can mimic human intelligence. Contactless treatment is possible only with the help of AI assisted automated health care systems. Furthermore, remote location self treatment is one of the key benefits provided by AI based systems.

Dynamic Structural Biology Experiments at XFEL or Synchrotron Sources

Macromolecular crystallography (MX) leverages the methods of physics and the language of chemistry to reveal fundamental insights into biology. Often beautifully artistic images present MX results to support profound functional hypotheses that are vital to entire life science research community. Over the past several decades, synchrotrons around the world have been the workhorses for X-ray diffraction data collection at many highly automated beamlines. The newest tools include X-ray-free electron lasers (XFELs) located at facilities in the USA, Japan, Korea, Switzerland, and Germany that deliver about nine orders of magnitude higher brightness in discrete femtosecond long pulses. At each of these facilities, new serial femtosecond crystallography (SFX) strategies exploit slurries of micron-size crystals by rapidly delivering individual crystals into the XFEL X-ray interaction region, from which one diffraction pattern is collected per crystal before it is destroyed by the intense X-ray pulse. Relatively simple adaptions to SFX methods produce time-resolved data collection strategies wherein reactions are triggered by visible light illumination or by chemical diffusion/mixing. Thus, XFELs provide new opportunities for high temporal and spatial resolution studies of systems engaged in function at physiological temperature. In this chapter, we summarize various issues related to microcrystal slurry preparation, sample delivery into the X-ray interaction region, and some emerging strategies for time-resolved SFX data collection.

Impact of structural biologists and the Protein Data Bank on small-molecule drug discovery and development

The Protein Data Bank (PDB) is an international core data resource central to fundamental biology, biomedicine, bioenergy, and biotechnology/bioengineering. Now celebrating its 50th anniversary, the PDB houses >175,000 experimentally determined atomic structures of proteins, nucleic acids, and their complexes with one another and small molecules and drugs. The importance of three-dimensional (3D) biostructure information for research and education obtains from the intimate link between molecular form and function evident throughout biology. Among the most prolific consumers of PDB data are biomedical researchers, who rely on the open access resource as the authoritative source of well-validated, expertly curated biostructures. This review recounts how the PDB grew from just seven protein structures to contain more than 49,000 structures of human proteins that have proven critical for understanding their roles in human health and disease. It then describes how these structures are used in academe and industry to validate drug targets, assess target druggability, characterize how tool compounds and other small-molecules bind to drug targets, guide medicinal chemistry optimization of binding affinity and selectivity, and overcome challenges during preclinical drug development. Three case studies drawn from oncology exemplify how structural biologists and open access to PDB structures impacted recent regulatory approvals of antineoplastic drugs.

Conclusions

This chapter presents the usage of data science, which further helps in exploring the global pandemic COVID-19. This disease suppresses an overwhelming burden, not only to healthcare systems but to the world's economy too. In this era of techniques and technologies, it is believed that data science can better utilize scarce healthcare resources. In this chapter, we provide an introduction of data science and its applications, which helps in combating different aspects of COVID-19. Publicly available datasets related to disease are used as community resources. Different kinds of datasets are used to analyze various aspects of pandemic at different scales. These different kinds of datasets can be audio, video, textual, speech, and sensor data. More than hundreds of research articles are also studied to prepare a bibliometric study. Apart from grabbing all the advantages from datasets, this paper highlights a few challenges, such as surety of correct data, need of multidisciplinary collaboration, new data modality, security issues, and availability of data.

Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic

Three-dimensional structures of SARS-CoV-2 and other coronaviral proteins archived in the Protein Data Bank were used to analyze viral proteome evolution during the first six months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48,000 viral proteome sequences showed how each one of the 29 viral study proteins have undergone amino acid changes. Structural models computed for every unique sequence variant revealed that most substitutions map to protein surfaces and boundary layers with a minority affecting hydrophobic cores. Conservative changes were observed more frequently in cores versus boundary layers/surfaces. Active sites and protein-protein interfaces showed modest numbers of substitutions. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for six drug discovery targets and four structural proteins comprising the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and functional interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.

Increased usability, algorithmic improvements and incorporation of data mining for structure calculation of proteins with REDCRAFT software package

Background

Traditional approaches to elucidation of protein structures by Nuclear Magnetic Resonance spectroscopy (NMR) rely on distance restraints also known as Nuclear Overhauser effects (NOEs). The use of NOEs as the primary source of structure determination by NMR spectroscopy is time consuming and expensive. Residual Dipolar Couplings (RDCs) have become an alternate approach for structure calculation by NMR spectroscopy. In previous works, the software package REDCRAFT has been presented as a means of harnessing the information containing in RDCs for structure calculation of proteins. However, to meet its full potential, several improvements to REDCRAFT must be made.

Results

In this work, we present improvements to REDCRAFT that include increased usability, better interoperability, and a more robust core algorithm. We have demonstrated the impact of the improved core algorithm in the successful folding of the protein 1A1Z with as high as ±4 Hz of added error. The REDCRAFT computed structure from the highly corrupted data exhibited less than 1.0 Å with respect to the X-ray structure. We have also demonstrated the interoperability of REDCRAFT in a few instances including with PDBMine to reduce the amount of required data in successful folding of proteins to unprecedented levels. Here we have demonstrated the successful folding of the protein 1D3Z (to within 2.4 Å of the X-ray structure) using only N-H RDCs from one alignment medium.

Conclusions

The additional GUI features of REDCRAFT combined with the NEF compliance have significantly increased the flexibility and usability of this software package. The improvements of the core algorithm have substantially improved the robustness of REDCRAFT in utilizing less experimental data both in quality and quantity.

Identification of potential inhibitors of Zika virus NS5 RNA-dependent RNA polymerase through virtual screening and molecular dynamic simulations

Zika virus (ZIKV) is one of the mosquito borne flavivirus with several outbreaks in past few years in tropical and subtropical regions. The non-structural proteins of flaviviruses are suitable active targets for inhibitory drugs due to their role in pathogenicity. In ZIKV, the non-structural protein 5 (NS5) RNA-Dependent RNA polymerase replicates its genome. Here we have performed virtual screening to identify suitable ligands that can potentially halt the ZIKV NS5 RNA dependent RNA polymerase (RdRp). During this process, we searched and screened a library of ligands against ZIKV NS5 RdRp. The selected ligands with significant binding energy and ligand-receptor interactions were further processed. Among the selected docked conformations, top five was further optimized at atomic level using molecular dynamic simulations followed by binding free energy calculations. The interactions of ligands with the target structure of ZIKV RdRp revealed that they form strong bonds within the active sites of the receptor molecule. The efficacy of these drugs against ZIKV can be further analyzed through in-vitro and in-vivo studies.

High-performance macromolecular data delivery and visualization for the web

Biomacromolecular structural data make up a vital and crucial scientific resource that has grown not only in terms of its amount but also in its size and complexity. Furthermore, these data are accompanied by large and increasing amounts of experimental data. Additionally, the macromolecular data are enriched with value-added annotations describing their biological, physicochemical and structural properties. Today, the scientific community requires fast and fully interactive web visualization to exploit this complex structural information. This article provides a survey of the available cutting-edge web services that address this challenge. Specifically, it focuses on data-delivery problems, discusses the visualization of a single structure, including experimental data and annotations, and concludes with a focus on the results of molecular-dynamics simulations and the visualization of structural ensembles.

Mechanism of Action of Bu-Fei-Yi-Shen Formula in Treating Chronic Obstructive Pulmonary Disease Based on Network Pharmacology Analysis and Molecular Docking Validation

Objective

To explore the mechanism of action of Bu-Fei-Yi-Shen formula (BFYSF) in treating chronic obstructive pulmonary disease (COPD) based on network pharmacology analysis and molecular docking validation.

Methods

First of all, the pharmacologically active ingredients and corresponding targets in BFYSF were mined by the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the analysis platform, and literature review. Subsequently, the COPD-related targets (including the pathogenic targets and known therapeutic targets) were identified through the TTD, CTD, DisGeNet, and GeneCards databases. Thereafter, Cytoscape was employed to construct the candidate component-target network of BFYSF in the treatment of COPD. Moreover, the cytoHubba plug-in was utilized to calculate the topological parameters of nodes in the network; then, the core components and core targets of BFYSF in the treatment of COPD were extracted according to the degree value (greater than or equal to the median degree values for all nodes in the network) to construct the core network. Further, the Autodock vina software was adopted for molecular docking study on the core active ingredients and core targets, so as to verify the above-mentioned network pharmacology analysis results. Finally, the Omicshare database was applied in enrichment analysis of the biological functions of core targets and the involved signaling pathways.

Results

In the core component-target network of BFYSF in treating COPD, there were 30 active ingredients and 37 core targets. Enrichment analysis suggested that these 37 core targets were mainly involved in the regulation of biological functions, such as response to biological and chemical stimuli, multiple cellular life processes, immunity, and metabolism. Besides, multiple pathways, including IL-17, Toll-like receptor (TLR), TNF, and HIF-1, played certain roles in the effect of BFYSF on treating COPD.

Conclusion

BFYSF can treat COPD through the multicomponent, multitarget, and multipathway synergistic network, which provides basic data for intensively exploring the mechanism of action of BFYSF in treating COPD.

A ligand-based computational drug repurposing pipeline using KNIME and Programmatic Data Access: case studies for rare diseases and COVID-19

Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces (APIs). The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage of flexibility, re-usability, and transparency. Here, we present a strategy for performing ligand-based in silico drug repurposing with the analytics platform KNIME. We demonstrate the usefulness of the developed workflow on the basis of two different use cases: a rare disease (here: Glucose Transporter Type 1 (GLUT-1) deficiency), and a new disease (here: COVID 19). The workflow includes a targeted download of data through web services, data curation, detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited data set of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of data for GLUT-1 deficiency syndrome and COVID-19 are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.

Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach

BACKGROUND

Novel coronavirus disease 2019 (COVID-19) is taking a huge toll on public health. Along with the non-therapeutic preventive measurements, scientific efforts are currently focused, mainly, on the development of vaccines and pharmacological treatment with existing drugs. Summarizing evidences from scientific literatures on the discovery of treatment plan of COVID-19 under a platform would help the scientific community to explore the opportunities in a systematic fashion.

OBJECTIVE

The aim of this study is to explore the potential drugs and biomedical entities related to coronavirus related diseases, including COVID-19, that are mentioned on scientific literature through an automated computational approach.

METHODS

We mined the information from publicly available scientific literature and related public resources. Six topic-specific dictionaries, including human genes, human miRNAs, diseases, Protein Databank, drugs, and drug side effects, were integrated to mine all scientific evidence related to COVID-19. We employed an automated literature mining and labeling system through a novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis, and deep learning. We also applied the concept of cosine similarity to confidently infer the associations between diseases and genes.

RESULTS

Based on the literature mining, we identified 1805 diseases, 2454 drugs, 1910 genes that are related to coronavirus related diseases including COVID-19. Integrating the extracted information, we developed the first knowledgebase platform dedicated to COVID-19, which highlights potential list of drugs and related biomedical entities. For COVID-19, we highlighted multiple case studies on existing drugs along with a confidence score for their applicability in the treatment plan. Based on our computational method, we found Remdesivir, Statins, Dexamethasone, and Ivermectin could be considered as potential effective drugs to improve clinical status and lower mortality in patients hospitalized with COVID-19. We also found that Hydroxychloroquine could not be considered as an effective drug for COVID-19. The resulting knowledgebase is made available as an open source tool, named COVID-19Base.

CONCLUSIONS

Proper investigation of the mined biomedical entities along with the identified interactions among those would help the research community to discover possible ways for the therapeutic treatment of COVID-19.

Using collections of structural models to predict changes of binding affinity caused by mutations in protein-protein interactions

Protein-protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state-of-the-art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state-of-art methods.

ProteinsPlus: interactive analysis of protein-ligand binding interfaces

Due to the increasing amount of publicly available protein structures searching, enriching and investigating these data still poses a challenging task. The ProteinsPlus web service (https://proteins.plus) offers a broad range of tools addressing these challenges. The web interface to the tool collection focusing on protein–ligand interactions has been geared towards easy and intuitive access to a large variety of functionality for life scientists. Since our last publication, the ProteinsPlus web service has been extended by additional services as well as it has undergone substantial infrastructural improvements. A keyword search functionality was added on the start page of ProteinsPlus enabling users to work on structures without knowing their PDB code. The tool collection has been augmented by three tools: StructureProfiler validates ligands and active sites using selection criteria of well-established protein–ligand benchmark data sets, WarPP places water molecules in the ligand binding sites of a protein, and METALizer calculates, predicts and scores coordination geometries of metal ions based on surrounding complex atoms. Additionally, all tools provided by ProteinsPlus are available through a REST service enabling the automated integration in structure processing and modeling pipelines.

Molecular docking between human TMPRSS2 and SARS-CoV-2 spike protein: conformation and intermolecular interactions

Entry of SARS-CoV-2, etiological agent of COVID-19, in the host cell is driven by the interaction of its spike protein with human ACE2 receptor and a serine protease, TMPRSS2. Although complex between SARS-CoV-2 spike protein and ACE2 has been structurally resolved, the molecular details of the SARS-CoV-2 and TMPRSS2 complex are still elusive. TMPRSS2 is responsible for priming of the viral spike protein that entails cleavage of the spike protein at two potential sites, Arg685/Ser686 and Arg815/Ser816. The present study aims to investigate the conformational attributes of the molecular complex between TMPRSS2 and SARS-CoV-2 spike protein, in order to discern the finer details of the priming of viral spike protein. Briefly, full length structural model of TMPRSS2 was developed and docked against the resolved structure of SARS-CoV-2 spike protein with directional restraints of both cleavage sites. The docking simulations showed that TMPRSS2 interacts with the two different loops of SARS-CoV-2 spike protein, each containing different cleavage sites. Key functional residues of TMPRSS2 (His296, Ser441 and Ser460) were found to interact with immediate flanking residues of cleavage sites of SARS-CoV-2 spike protein. Compared to the N-terminal cleavage site (Arg685/Ser686), TMPRSS2 region that interact with C-terminal cleavage site (Arg815/Ser816) of the SARS-CoV-2 spike protein was predicted as relatively more druggable. In summary, the present study provides structural characteristics of molecular complex between human TMPRSS2 and SARS-CoV-2 spike protein and points to the candidate drug targets that could further be exploited to direct structure base drug designing.

RCSB Protein Data Bank tools for 3D structure-guided cancer research: human papillomavirus (HPV) case study

Atomic-level three-dimensional (3D) structure data for biological macromolecules often prove critical to dissecting and understanding the precise mechanisms of action of cancer-related proteins and their diverse roles in oncogenic transformation, proliferation, and metastasis. They are also used extensively to identify potentially druggable targets and facilitate discovery and development of both small-molecule and biologic drugs that are today benefiting individuals diagnosed with cancer around the world. 3D structures of biomolecules (including proteins, DNA, RNA, and their complexes with one another, drugs, and other small molecules) are freely distributed by the open-access Protein Data Bank (PDB). This global data repository is used by millions of scientists and educators working in the areas of drug discovery, vaccine design, and biomedical and biotechnology research. The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides an integrated portal to the PDB archive that streamlines access for millions of worldwide PDB data consumers worldwide. Herein, we review online resources made available free of charge by the RCSB PDB to basic and applied researchers, healthcare providers, educators and their students, patients and their families, and the curious public. We exemplify the value of understanding cancer-related proteins in 3D with a case study focused on human papillomavirus.