DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options.

The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underlying data, the addition of experimental structural information, the inclusion of new data download options, and an upgraded graphical interface. DescribePROT currently covers 19 structural and functional descriptors for proteins in 273 reference proteomes generated by 11 accurate and complementary predictive tools. Users can search our resource in multiple ways, interact with the data using the graphical interface, and download data at various scales including individual proteins, entire proteomes, and whole database. The annotations in DescribePROT are useful for a broad spectrum of studies that include investigations of protein structure and function, development and validation of predictive tools, and to support efforts in understanding molecular underpinnings of diseases and development of therapeutics. DescribePROT can be freely accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

Synthesis, Kinetics and Computational Explorations of 4-Phenylpiperazine Bearing N-(Aryl)-3-substituted-benzamides as Auspicious Tyrosinase Inhibitors.

In the aimed research study, a new series of N-(aryl)-3-[(4-phenyl-1-piperazinyl)methyl]benzamides was synthesized, which was envisaged as tyrosinase inhibitor. The structures of these newly designed molecules were verified by IR, 1H-NMR, 13C-NMR, EI-MS and CHN analysis data. These molecules were screened against tyrosinase and their inhibitory activity explored that these 3-substituted-benzamides exhibit good to excellent potential, comparative to the standard. The Kinetics mechanism was investigated through Lineweaver-Burk plots which depicted that molecules inhibited this enzyme in a competitive mode. Moreover, molecular docking was also performed to determine the binding interaction of all synthesized molecules (ligands) with the active site of tyrosinase enzyme and the results showed that most of the ligands exhibited efficient binding energy values. Therefore, it is anticipated that these molecules might serve as auspicious therapeutic scaffolds for treatment of the tyrosinase associated skin disorders.

Design and synthesis of thiadiazole-oxadiazole-acetamide derivatives: Elastase inhibition, cytotoxicity, kinetic mechanism, and computational studies.

Considering the biological significance of 1,3,4-thiadiazole/oxadiazole heterocyclic scaffolds, a novel series of 1,3,4-thiadiazole-1,3,4-oxadiazole-acetamide derivatives (7a-j) was designed and synthesized using molecular hybridization. The inhibitory effects of the target compounds on elastase were evaluated, and all of these molecules were found to be potent inhibitors compared to the standard reference oleanolic acid. Compound 7f exhibited the excellent inhibitory activity (IC50 = 0.06 ± 0.02 µM), which is 214-fold more active than oleanolic acid (IC50 = 12.84 ± 0.45 µM). Kinetic analysis was also performed on the most potent compound (7f) to determine the mode of binding with the target enzyme, and it was discovered that 7f inhibits the enzyme in a competitive manner. Furthermore, the MTT assay method was used to assess their toxicity on the viability of B16F10 melanoma cell lines, and all compounds did not display any toxic effect on the cells even at high concentrations. The molecular docking studies of all compounds also justified with their good docking score and among them, compound 7f had a good conformational state with hydrogen bond interactions within the receptor binding pocket, which is consistent with the experimental inhibition studies.

DescribePROT: database of amino acid-level protein structure and function predictions.

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

Nature's Own Pharmacy: Mushroom-Based Chemical Scaffolds and Their Therapeutic Implications.

Mushrooms are new potential sources of valuable medicines, long neglected because of difficulties experienced in their cultivation. There is a large variety of medicinal mushrooms which possess significant therapeutic properties and are used as medications for various diseases because they contain several novel highly bioactive components. Medicinal mushrooms can be identified based on their morphology, size, mass, and the color of the stalk, cap and spore, and attachment to the stalk. Medicinal mushrooms possess a variety of important biological activities and are used as antioxidants, hepatoprotectors, anticancer, antidiabetic, anti-inflammatory, antiaging, antiviral, antiparasitic, and antimicrobial agents, among others. This review provides a basic overview of the chemical scaffolds present in mushrooms and their therapeutic implications in the human body.

Investigation of Flavonoid Scaffolds as DAX1 Inhibitors against Ewing Sarcoma through Pharmacoinformatic and Dynamic Simulation Studies.

Dosage-sensitive sex reversal, adrenal hypoplasia critical region, on chromosome X, gene 1 (DAX1) is an orphan nuclear receptor encoded by the NR0B1 gene. The functional study showed that DAX1 is a physiologically significant target for EWS/FLI1-mediated oncogenesis, particularly Ewing Sarcoma (ES). In this study, a three-dimensional DAX1 structure was modeled by employing a homology modeling approach. Furthermore, the network analysis of genes involved in Ewing Sarcoma was also carried out to evaluate the association of DAX1 and other genes with ES. Moreover, a molecular docking study was carried out to check the binding profile of screened flavonoid compounds against DAX1. Therefore, 132 flavonoids were docked in the predicted active binding pocket of DAX1. Moreover, the pharmacogenomics analysis was performed for the top ten docked compounds to evaluate the ES-related gene clusters. As a result, the five best flavonoid-docked complexes were selected and further evaluated by Molecular Dynamics (MD) simulation studies at 100 ns. The MD simulation trajectories were evaluated by generating RMSD, hydrogen bond plot analysis, and interaction energy graphs. Our results demonstrate that flavonoids showed interactive profiles in the active region of DAX1 and can be used as potential therapeutic agents against DAX1-mediated augmentation of ES after in-vitro and in-vivo evaluations.

Tyrosinase Inhibitors Naturally Present in Plants and Synthetic Modifications of These Natural Products as Anti-Melanogenic Agents: A Review.

Tyrosinase is a key enzyme target to design new chemical ligands against melanogenesis. In the current review, different chemical derivatives are explored which have been used as anti-melanogenic compounds. These are different chemical compounds naturally present in plants and semi-synthetic and synthetic compounds inspired by these natural products, such as kojic acid produced by several species of fungi; arbutin-a glycosylated hydroquinone extracted from the bearberry plant; vanillin-a phenolic aldehyde extracted from the vanilla bean, etc. After enzyme inhibition screening, various chemical compounds showed different therapeutic effects as tyrosinase inhibitors with different values of the inhibition constant and IC50. We show how appropriately designed scaffolds inspired by the structures of natural compounds are used to develop novel synthetic inhibitors. We review the results of numerous studies, which could lead to the development of effective anti-tyrosinase agents with increased efficiency and safety in the near future, with many applications in the food, pharmaceutical and cosmetics industries.

Exploration of Flavonoids as Lead Compounds against Ewing Sarcoma through Molecular Docking, Pharmacogenomics Analysis, and Molecular Dynamics Simulations.

Ewing sarcoma (ES) is a highly malignant carcinoma prevalent in children and most frequent in the second decade of life. It mostly occurs due to t(11;22) (q24;q12) translocation. This translocation encodes the oncogenic fusion protein EWS/FLI (Friend leukemia integration 1 transcription factor), which acts as an aberrant transcription factor to deregulate target genes essential for cancer. Traditionally, flavonoids from plants have been investigated against viral and cancerous diseases and have shown some promising results to combat these disorders. In the current study, representative flavonoid compounds from various subclasses are selected and used to disrupt the RNA-binding motif of EWS, which is required for EWS/FLI fusion. By blocking the RNA-binding motif of EWS, it might be possible to combat ES. Therefore, molecular docking experiments validated the binding interaction patterns and structural behaviors of screened flavonoid compounds within the active region of the Ewing sarcoma protein (EWS). Furthermore, pharmacogenomics analysis was used to investigate potential drug interactions with Ewing sarcoma-associated genes. Finally, molecular dynamics simulations were used to investigate the stability of the best selected docked complexes. Taken together, daidzein, kaempferol, and genistein exhibited a result comparable to ifosfamide in the proposed in silico study and can be further analyzed as possible candidate compounds in biological in vitro studies against ES.

Therapeutic Implications of microRNAs in Depressive Disorders: A Review.

MicroRNAs are hidden players in complex psychophysical phenomena such as depression and anxiety related disorders though the activation and deactivation of multiple proteins in signaling cascades. Depression is classified as a mood disorder and described as feelings of sadness, loss, or anger that interfere with a person's everyday activities. In this review, we have focused on exploration of the significant role of miRNAs in depression by affecting associated target proteins (cellular and synaptic) and their signaling pathways which can be controlled by the attachment of miRNAs at transcriptional and translational levels. Moreover, miRNAs have potential role as biomarkers and may help to cure depression through involvement and interactions with multiple pharmacological and physiological therapies. Taken together, miRNAs might be considered as promising novel therapy targets themselves and may interfere with currently available antidepressant treatments.

Ultrasonic-Assisted Synthesis of Benzofuran Appended Oxadiazole Molecules as Tyrosinase Inhibitors: Mechanistic Approach through Enzyme Inhibition, Molecular Docking, Chemoinformatics, ADMET and Drug-Likeness Studies.

Furan-oxadiazole structural hybrids belong to the most promising and biologically active classes of oxygen and nitrogen containing five member heterocycles which have expanded therapeutic scope and potential in the fields of pharmacology, medicinal chemistry and pharmaceutics. A novel series 5a-j of benzofuran-oxadiazole molecules incorporating S-alkylated amide linkage have been synthesized using ultrasonic irradiation and screened for bacterial tyrosinase inhibition activity. Most of the synthesized furan-oxadiazole structural motifs exhibited significant tyrosinase inhibition activity in the micromolar range, with one of the derivatives being more potent than the standard drug ascorbic acid. Among the tested compounds, the scaffold 5a displayed more tyrosinase inhibition efficacy IC50 (11 ± 0.25 µM) than the ascorbic acid IC50 (11.5 ± 0.1 µM). Compounds 5b, 5c and 5d efficiently inhibited bacterial tyrosinase with IC50 values in the range of 12.4 ± 0.0-15.5 ± 0.0 µM. The 2-fluorophenylacetamide containing furan-oxadiazole compound 5a may be considered as a potential lead for tyrosinase inhibition with lesser side effects as a skin whitening and malignant melanoma anticancer agent.

Hematopoietic Stem and Progenitor Cells (HSPCs) and Hematopoietic Microenvironment: Molecular and Bioinformatic Studies of the Zebrafish Models.

Hematopoietic stem cells (HSCs) reside in a specialized microenvironment in a peculiar anatomic location which regulates the maintenance of stem cells and controls its functions. Recent scientific progress in experimental technologies have enabled the specific detection of epigenetic factors responsible for the maintenance and quiescence of the hematopoietic niche, which has improved our knowledge of regulatory mechanisms. The aberrant role of RNA-binding proteins and their impact on the disruption of stem cell biology have been reported by a number of recent studies. Despite recent modernization in hematopoietic microenvironment research avenues, our comprehension of the signaling mechanisms and interactive pathways responsible for integration of the hematopoietic niche is still limited. In the past few decades, zebrafish usage with regards to exploratory studies of the hematopoietic niche has expanded our knowledge for deeper understanding of novel cellular interactions. This review provides an update on the functional roles of different genetic and epigenetic factors and molecular signaling events at different sections of the hematopoietic microenvironment. The explorations of different molecular approaches and interventions of latest web-based tools being used are also outlined. This will help us to get more mechanistic insights and develop therapeutic options for the malignancies.

Addressing Noise and Estimating Uncertainty in Biomedical Data through the Exploration of Chemical Space.

Noise is a basic ingredient in data, since observed data are always contaminated by unwanted deviations, i.e., noise, which, in the case of overdetermined systems (with more data than model parameters), cause the corresponding linear system of equations to have an imperfect solution. In addition, in the case of highly underdetermined parameterization, noise can be absorbed by the model, generating spurious solutions. This is a very undesirable situation that might lead to incorrect conclusions. We presented mathematical formalism based on the inverse problem theory combined with artificial intelligence methodologies to perform an enhanced sampling of noisy biomedical data to improve the finding of meaningful solutions. Random sampling methods fail for high-dimensional biomedical problems. Sampling methods such as smart model parameterizations, forward surrogates, and parallel computing are better suited for such problems. We applied these methods to several important biomedical problems, such as phenotype prediction and a problem related to predicting the effects of protein mutations, i.e., if a given single residue mutation is neutral or deleterious, causing a disease. We also applied these methods to de novo drug discovery and drug repositioning (repurposing) through the enhanced exploration of huge chemical space. The purpose of these novel methods that address the problem of noise and uncertainty in biomedical data is to find new therapeutic solutions, perform drug repurposing, and accelerate and optimize drug discovery, thus reestablishing homeostasis. Finding the right target, the right compound, and the right patient are the three bottlenecks to running successful clinical trials from the correct analysis of preclinical models. Artificial intelligence can provide a solution to these problems, considering that the character of the data restricts the quality of the prediction, as in any modeling procedure in data analysis. The use of simple and plain methodologies is crucial to tackling these important and challenging problems, particularly drug repositioning/repurposing in rare diseases.

Potent Alkaline Phosphatase Inhibitors, Pyrazolo-Oxothiazolidines: Synthesis, Biological Evaluation, Molecular Docking, and Kinetic Studies.

To develop new alkaline phosphatase inhibitors (ALP), a series of pyrazolo-oxothiazolidine derivatives were synthesized and biologically assessed, and the results showed that all of the synthesized compounds significantly inhibited ALP. Specifically, compound 7g displayed the strongest inhibitory activity (IC50 = 0.045 ± 0.004 µM), which is 116-fold more active than monopotassium phosphate (IC50 = 5.242 ± 0.472 µM) as a standard reference. The most potent compound among the series (7g) was checked for its mode of binding with the enzyme and shown as non-competitively binding with the target enzyme. The antioxidant activity of these compounds was examined to investigate the radical scavenging effect. Moreover, the MTT assay method was performed to evaluate their toxic effects on the viability of MG-63 human osteosarcoma cells, and all compounds have no toxic effect on the cells at 4 µM. Computational research was also conducted to examine the binding affinity of the ligands with alkaline phosphatase, and the results revealed that all compounds showed good binding energy values within the active site of the target. Therefore, these novel pyrazolo-oxothiazolidine derivatives might be employed as promising pharmacophores for potent and selective alkaline phosphatase inhibitors.

Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review.

Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.

Biological and Cheminformatics Studies of Newly Designed Triazole Based Derivatives as Potent Inhibitors against Mushroom Tyrosinase.

A series of nine novel 1,2,4-triazole based compounds were synthesized through a multistep reaction pathway and their structures were scrutinized by using spectral methods such as FTIR, LC-MS, 1H NMR, and 13C NMR. The synthesized derivatives were screened for inhibitory activity against the mushroom tyrosinase and we found that all the synthesized compounds demonstrated decent inhibitory activity against tyrosinase. However, among the series of compounds, N-(4-fluorophenyl)-2-(5-(2-fluorophenyl)-4-(4-fluorophenyl)-4H-1,2,4-triazol-3-ylthio) acetamide exhibited more prominent activity when accompanied with the standard drug kojic acid. Furthermore, the molecular docking studies identified the interaction profile of all synthesized derivatives at the active site of tyrosinase. Based on these results, N-(4-fluorophenyl)-2-(5-(2-fluorophenyl)-4-(4-fluorophenyl)-4H-1,2,4-triazol-3-ylthio) acetamide could be used as a novel scaffold to design some new drugs against melanogenesis.

Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning.

We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer's Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced. Our results suggest that common pathways for Alzheimer's disease and MCI are mainly related to viral mRNA translation, influenza viral RNA transcription and replication, gene expression, mitochondrial translation, and metabolism, with these results being highly consistent regardless of the comparative methods. The cross-validated predictive accuracies achieved for the LOAD and MCI discriminations were 84% and 81.5%, respectively. The difference between LOAD and MCI could not be clearly established (74% accuracy). The most discriminatory genes of the LOAD-MCI discrimination are associated with proteasome mediated degradation and G-protein signaling. Based on these findings we have also performed drug repositioning using Dr. Insight package, proposing the following different typologies of drugs: isoquinoline alkaloids, antitumor antibiotics, phosphoinositide 3-kinase PI3K, autophagy inhibitors, antagonists of the muscarinic acetylcholine receptor and histone deacetylase inhibitors. We believe that the potential clinical relevance of these findings should be further investigated and confirmed with other independent studies.

Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques.

We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presented in this paper corresponds to the category of template-based modeling. The algorithm performs a preselection of protein templates before constructing a lower dimensional subspace via a regularized LDA. The protein coordinates in the reduced spaced are sampled using a highly explorative optimization algorithm, regressive-regressive PSO (RR-PSO). The obtained structure is then projected onto a reduced space via singular value decomposition and further optimized via RR-PSO to carry out a structure refinement. The final structures are similar to those predicted by best structure prediction tools, such as Rossetta and Zhang servers. The main advantage of our methodology is that alleviates the ill-posed character of protein structure prediction problems related to high dimensional optimization. It is also capable of sampling a wide range of conformational space due to the application of a regularized linear discriminant analysis, which allows us to expand the differences over a reduced basis set.

Robust Sampling of Defective Pathways in Multiple Myeloma.

We present the analysis of defective pathways in multiple myeloma (MM) using two recently developed sampling algorithms of the biological pathways: The Fisher's ratio sampler, and the holdout sampler. We performed the retrospective analyses of different gene expression datasets concerning different aspects of the disease, such as the existing difference between bone marrow stromal cells in MM and healthy controls (HC), the gene expression profiling of CD34+ cells in MM and HC, the difference between hyperdiploid and non-hyperdiploid myelomas, and the prediction of the chromosome 13 deletion, to provide a deeper insight into the molecular mechanisms involved in the disease. Our analysis has shown the importance of different altered pathways related to glycosylation, infectious disease, immune system response, different aspects of metabolism, DNA repair, protein recycling and regulation of the transcription of genes involved in the differentiation of myeloid cells. The main difference in genetic pathways between hyperdiploid and non-hyperdiploid myelomas are related to infectious disease, immune system response and protein recycling. Our work provides new insights on the genetic pathways involved in this complex disease and proposes novel targets for future therapies.

Entropy, Fluctuations, and Disordered Proteins.

Entropy should directly reflect the extent of disorder in proteins. By clustering structurally related proteins and studying the multiple-sequence-alignment of the sequences of these clusters, we were able to link between sequence, structure, and disorder information. We introduced several parameters as measures of fluctuations at a given MSA site and used these as representative of the sequence and structure entropy at that site. In general, we found a tendency for negative correlations between disorder and structure, and significant positive correlations between disorder and the fluctuations in the system. We also found evidence for residue-type conservation for those residues proximate to potentially disordered sites. Mutation at the disorder site itself appear to be allowed. In addition, we found positive correlation for disorder and accessible surface area, validating that disordered residues occur in exposed regions of proteins. Finally, we also found that fluctuations in the dihedral angles at the original mutated residue and disorder are positively correlated while dihedral angle fluctuations in spatially proximal residues are negatively correlated with disorder. Our results seem to indicate permissible variability in the disordered site, but greater rigidity in the parts of the protein with which the disordered site interacts. This is another indication that disordered residues are involved in protein function.