Abstract: The coronavirus disease 2019 (COVID-19) is a pandemic that has severely posed substantial health challenges and claimed millions of lives. Though vaccines have been produced to stem the spread of this disease, the death rate remains high since drugs used for treatment have therapeutic challenges. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease, has a slew of potential therapeutic targets. Among them is the furin protease, which has a cleavage site on the virus's spike protein. The cleavage site facilitates the entry of the virus into human cells via cell-cell fusion. This critical involvement of furin in the disease pathogenicity has made it a viable therapeutic strategy against the virus. This study employs the consensus docking approach using HYBRID and AutoDock Vina to virtually screen a pre-filtered library of 3942 natural product compounds of African origin against the human furin protease (PDB: 4RYD). Twenty of these compounds were selected as hits after meeting molecular docking cut-off of - 7 kcal.mol(-1), pose alignment inspection, and having favorable furin-ligand interactions. An area under the curve (AUC) value of 0.72 was computed from the receiver operator characteristic (ROC) curve, and Boltzmann-enhanced discrimination of the ROC curve (BEDROC) value of 0.65 showed that AutoDock Vina was a reasonable tool for selecting actives for this target. Seven of these hits were proposed as potential leads having had bonding interactions with catalytic triad residues Ser368, His194, and Asp153, and other essential residues in the active site with plausible binding free energies between - 189 and - 95 kJ/mol from the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations as well as favorable ADME/Tox properties. The molecules were also predicted as antiviral, anti-inflammatory, membrane permeability inhibitors, RNA synthesis inhibitors, cytoprotective, and hepatoprotective with probable activity (Pa) above 0.5 and probable inactivity values below 0.1. Some of them also have anti-influenza activity. Influenza virus has many similarities with SARS-CoV-2 in their mode of entry into human cells as both are facilitated by the furin protease. Pinobanksin 3-(E)-caffeate, one of the potential leads is a propolis compound. Propolis compounds have shown inhibitory effects against ACE2, TMPRSS2, and PAK1 signaling pathways of SARS-CoV-2 in previous studies. Likewise, quercitrin is structurally similar to isoquercetin, which is currently in clinical trials as possible medication for COVID-19. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-02056-1.

Abstract: Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction and virtual screening can accelerate polymer design by prioritizing candidates expected to have favorable properties. However, in contrast to organic molecules, polymers are often not well-defined single structures but an ensemble of similar molecules, which poses unique challenges to traditional chemical representations and machine learning approaches. Here, we introduce a graph representation of molecular ensembles and an associated graph neural network architecture that is tailored to polymer property prediction. We demonstrate that this approach captures critical features of polymeric materials, like chain architecture, monomer stoichiometry, and degree of polymerization, and achieves superior accuracy to off-the-shelf cheminformatics methodologies. While doing so, we built a dataset of simulated electron affinity and ionization potential values for >40k polymers with varying monomer composition, stoichiometry, and chain architecture, which may be used in the development of other tailored machine learning approaches. The dataset and machine learning models presented in this work pave the path toward new classes of algorithms for polymer informatics and, more broadly, introduce a framework for the modeling of molecular ensembles.

Abstract: The establishment of photosynthetic protein complexes during chloroplast development requires the influx of a large number of chloroplast proteins that are encoded by the nuclear genome, which is critical for cytosol and chloroplast protein homeostasis and chloroplast development. However, the mechanisms regulating this process are still not well-understood in higher plants. Here, we report the isolation and characterization of the pale green Arabidopsis pga1-1 mutant, which is defective in chloroplast development and chloroplast protein accumulation. Using genetic and biochemical evidence, we reveal that PGA1 encodes AtFtsH12, a chloroplast envelope-localized protein of the FtsH family proteins. We determined a G703R mutation in the GAD motif of the conserved ATPase domain renders the pga1-1 a viable hypomorphic allele of the essential gene AtFtsH12. In de-etiolation assays, we showed that the accumulation of photosynthetic proteins and the expression of photosynthetic genes were impaired in pga1-1. Using the FNRctp-GFP and pTAC2-GFP reporters, we demonstrated that AtFtsH12 was required for the accumulation of chloroplast proteins in vivo. Interestingly, we identified an increase in expression of the mutant AtFtsH12 gene in pga1-1, suggesting a feedback regulation. Moreover, we found that cytosolic and chloroplast proteostasis responses were triggered in pga1-1. Together, taking advantage of the novel pga1-1 mutant, we demonstrate the function of AtFtsH12 in chloroplast protein homeostasis and chloroplast development.

Abstract: BACKGROUND: The main focus of in silico drug repurposing, which is a promising area for using artificial intelligence in drug discovery, is the prediction of drug-disease relationships. Although many computational models have been proposed recently, it is still difficult to reliably predict drug-disease associations from a variety of sources of data. RESULTS: In order to identify potential drug-disease associations, this paper introduces a novel end-to-end model called Graph convolution network based on a multimodal attention mechanism (GCMM). In particular, GCMM incorporates known drug-disease relations, drug-drug chemical similarity, drug-drug therapeutic similarity, disease-disease semantic similarity, and disease-disease target-based similarity into a heterogeneous network. A Graph Convolution Network encoder is used to learn how diseases and drugs are embedded in various perspectives. Additionally, GCMM can enhance performance by applying a multimodal attention layer to assign various levels of value to various features and the inputting of multi-source information. CONCLUSION: 5 fold cross-validation evaluations show that the GCMM outperforms four recently proposed deep-learning models on the majority of the criteria. It shows that GCMM can predict drug-disease relationships reliably and suggests improvement in the desired metrics. Hyper-parameter analysis and exploratory ablation experiments are also provided to demonstrate the necessity of each module of the model and the highest possible level of prediction performance. Additionally, a case study on Alzheimer's disease (AD). Four of the five medications indicated by GCMM to have the highest potential correlation coefficient with AD have been demonstrated through literature or experimental research, demonstrating the viability of GCMM. All of these results imply that GCMM can provide a strong and effective tool for drug development and repositioning.

Abstract: The therapeutic potential of venom-derived peptides, such as bioactive peptides (BAPs), is determined by specificity, stability, and pharmacokinetics properties. BAPs, including anti-infective or antimicrobial peptides (AMPs) and cell-penetrating peptides (CPPs), share several physicochemical characteristics and are potential alternatives to antibiotic-based therapies and drug delivery systems, respectively. This study used in silico methods to predict AMPs and CPPs derived from natterins from the venomous fish Thalassophryne nattereri. Fifty-seven BAPs (19 AMPs, 8 CPPs, and 30 AMPs/CPPs) were identified using the web servers CAMP, AMPA, AmpGram, C2Pred, and CellPPD. The physicochemical properties were analyzed using ProtParam, PepCalc, and DispHred tools. The membrane-binding potential and cellular location of each peptide were analyzed using the Boman index by APD3, and TMHMM web servers. All CPPs and two AMPs showed high membrane-binding potential. Fifty-four peptides were located in the plasma membrane. Peptide immunogenicity, toxicity, allergenicity, and ADMET parameters were evaluated using several web servers. Sixteen antiviral peptides and 37 anticancer peptides were predicted using the web servers Meta-iAVP and ACPred. Secondary structures and helical wheel projections were predicted using the PEP-FOLD3 and Heliquest web servers. Fifteen peptides are potential lead compounds and were selected to be further synthesized and tested experimentally in vitro to validate the in silico screening. The use of computer-aided design for predicting peptide structure and activity is fast and cost-effective and facilitates the design of potent therapeutic peptides. The results demonstrate that toxins form a natural biotechnological platform in drug discovery, and the presence of CPP and AMP sequences in toxin families opens new possibilities in toxin biochemistry research.

Abstract: Long-term use of urate-lowering therapies (ULT) may reduce inflammaging and thus prevent cognitive decline during aging. This article examined the association between long-term use of ULT and cognitive decline among community-dwelling older adults with spontaneous memory complaints. We performed a secondary observational analysis using data of 1673 participants >/= 70 years old from the Multidomain Alzheimer Preventive Trial (MAPT Study), a randomized controlled trial assessing the effect of a multidomain intervention, the administration of polyunsaturated fatty acids (PUFA), both, or placebo on cognitive decline. We compared cognitive decline during the 5-year follow-up between three groups according to ULT (i.e. allopurinol and febuxostat) use: participants treated with ULT during at least 75% of the study period (PT >/= 75; n = 51), less than 75% (PT < 75; n = 31), and non-treated participants (PNT; n = 1591). Cognitive function (measured by a composite score) was assessed at baseline, 6 months and every year for 5 years. Linear mixed models were performed and results were adjusted for age, sex, body mass index (BMI), diagnosis of arterial hypertension or diabetes, baseline composite cognitive score, and MAPT intervention groups. After the 5-year follow-up, only non-treated participants presented a significant decline in the cognitive composite score (mean change - 0.173, 95%CI - 0.212 to - 0.135; p < 0.0001). However, there were no differences in change of the composite cognitive score between groups (adjusted between-group difference for PT >/= 75 vs. PNT: 0.144, 95%CI - 0.075 to 0.363, p = 0.196; PT < 75 vs. PNT: 0.103, 95%CI - 0.148 to 0.353, p = 0.421). Use of ULT was not associated with reduced cognitive decline over a 5-year follow-up among community-dwelling older adults at risk of dementia.

Abstract: Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CLtot) and steady-state Vd (Vdss) (CLtot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes.

Abstract: The SARS-CoV-2 proteases M(pro) and PL(pro) are important targets for the development of antivirals against COVID-19. The functional group 1,2,4-thiadiazole has been indicated to inhibit cysteinyl proteases, such as papain and cathepsins. Of note, the 1,2,4-thiadiazole moiety is found in a new class of cephalosporin FDA-approved antibiotics: ceftaroline fosamil, ceftobiprole, and ceftobiprole medocaril. Here we investigated the interaction of these new antibiotics and their main metabolites with the SARS-CoV-2 proteases by molecular docking, molecular dynamics (MD), and density functional theory (DFT) calculations. Our results indicated the PL(pro) enzyme as a better in silico target for the new antibacterial cephalosporins. The results with ceftaroline fosamil and the dephosphorylate metabolite compounds should be tested as potential inhibitor of PL(pro), M(pro), and SARS-CoV-2 replication in vitro. In addition, the data here reported can help in the design of new potential drugs against COVID-19 by exploiting the S atom reactivity in the 1,2,4-thiadiazole moiety. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-02036-5.

Abstract: Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., 'drug treats disease', 'gene interacts with gene'). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.

Abstract: The main characteristic feature of diabetes mellitus is the disturbance of carbohydrate, lipid, and protein metabolism, which results in insulin insufficiency and can also lead to insulin resistance. Both the acute and chronic diabetic cases are increasing at an exponential rate, which is also flagged by the World Health Organization (WHO) and the International Diabetes Federation (IDF). Treatment of diabetes mellitus with synthetic drugs often fails to provide desired results and limits its use to symptomatic treatment only. This has resulted in the exploration of alternative medicine, of which herbal treatment is gaining popularity these days. Owing to their safety benefits, treatment compliance, and ability to exhibit effects without disturbing internal homeostasis, research in the field of herbal and ayurvedic treatments has gained importance. Medicinal phytoconstituents include micronutrients, amino acids, proteins, mucilage, critical oils, triterpenoids, saponins, carotenoids, alkaloids, flavonoids, phenolic acids, tannins, and coumarins, which play a dynamic function in the prevention and treatment of diabetes mellitus. Alkaloids found in medicinal plants represent an intriguing potential for the inception of novel approaches to diabetes mellitus therapies. Thus, this review article highlights detailed information on alkaloidal phytoconstituents, which includes sources and structures of alkaloids along with the associated mechanism involved in the management of diabetes mellitus. From the available literature and data presented, it can be concluded that these compounds hold tremendous potential for use as monotherapies or in combination with current treatments, which can result in the development of better efficacy and safety profiles.