Correction to: Drug repurposing using computational methods to identify therapeutic options for COVID-19.

[This corrects the article DOI: 10.1007/s40200-020-00546-9.].

Corrigendum to ?Repurposing FDA-approved drugs to fight COVID-19 using in silico methods: Targeting SARS-CoV-2 RdRp enzyme and host cell receptors (ACE2, CD147) through virtual screening and molecular dynamic simulationsË® [Inform. Med. Unlocked 23 (2021) 1-9/100541].

[This corrects the article DOI: 10.1016/j.imu.2021.100541.].

Integration of Machine Learning and Structural Analysis for Predicting Peptide Antibiofilm Effects: Advancements in Drug Discovery for Biofilm-Related Infections.

Background: The rise of antibiotic resistance has become a major concern, signaling the end of the golden age of antibiotics. Bacterial biofilms, which exhibit high resistance to antibiotics, significantly contribute to the emergence of antibiotic resistance. Therefore, there is an urgent need to discover new therapeutic agents with specific characteristics to effectively combat biofilm-related infections. Studies have shown the promising potential of peptides as antimicrobial agents. Objectives: This study aimed to establish a cost-effective and streamlined computational method for predicting the antibiofilm effects of peptides. This method can assist in addressing the intricate challenge of designing peptides with strong antibiofilm properties, a task that can be both challenging and costly. Methods: A positive library, consisting of peptide sequences with antibiofilm activity exceeding 50%, was assembled, along with a negative library containing quorum-sensing peptides. For each peptide sequence, feature vectors were calculated, while considering the primary structure, the order of amino acids, their physicochemical properties, and their distributions. Multiple supervised learning algorithms were used to classify peptides with significant antibiofilm effects for subsequent experimental evaluations. Results: The computational approach exhibited high accuracy in predicting the antibiofilm effects of peptides, with accuracy, precision, Matthew's correlation coefficient (MCC), and F1 score of 99%, 99%, 0.97, and 0.99, respectively. The performance level of this computational approach was comparable to that of previous methods. This study introduced a novel approach by combining the feature space with high antibiofilm activity. Conclusions: In this study, a reliable and cost-effective method was developed for predicting the antibiofilm effects of peptides using a computational approach. This approach allows for the identification of peptide sequences with substantial antibiofilm activities for further experimental investigations. Accessible source codes and raw data of this study can be found online (hiABF), providing easy access and enabling future updates.

Insights from cyanobacterial genomic and transcriptomic analyses into adaptation strategies in terrestrial environments.

Phylogenomic analysis of Nostocsp. MG11, a terrestrial cyanobacterium, and some terrestrial and freshwater Nostoc strains showed that the terrestrial strains grouped together in a distinctive clade, which reveals the effect of habitat on shaping Nostoc genomes. Terrestrial strains showed larger genomes and had higher predicted CDS contents than freshwater strains. Comparative genomic analysis demonstrated that genome expansion in the terrestrial Nostoc is supported by an increase in copy number of the core genes and acquisition of shared genes. Transcriptomic profiling analysis under desiccation stress revealed that Nostoc sp. MG11 protected its cell by induction of catalase, proteases, sucrose synthase, trehalose biosynthesis and maltodextrin utilization genes and maintained its normal metabolism during this condition by up-regulation of genes related to phycobilisomes and light reactions of photosynthesis, CO2 fixation and protein metabolism. These results provide insights into the strategies related to survival and adaptation of Nostoc strains to terrestrial environments.

In silico evidence for prednisone and progesterone efficacy in recurrent implantation failure treatment.

Increased expression and activation of tumor necrosis factor-α (TNF-α) could lead to recurrent implantation failure (RIF). Therefore, TNF-α inhibition may be a strategic way to enhance the implantation rate in women with RIF. Nowadays, monoclonal antibodies are considered an effective therapeutic method for TNF-α inhibition. Unfortunately, monoclonal antibody treatments have several disadvantages. Thus, the design of small molecules capable of inhibiting TNF-α has become critical in recent years. In silico drug repurposing of FDA-approved drugs for TNF-α inhibition was used in this study. PyRx tools were employed for virtual screening. Additionally, the free energy of binding, the number of hydrogen bonds, and the number of drug contacts with the protein were calculated using the molecular dynamics (MD) simulation method. Virtual screening results reveal that 17 of 2471 FDA-approved drugs benefited from favorable binding energy with TNF-α (delta G < - 10 kcal/mol). Two of the 17 drugs, progesterone and prednisone, were the most frequently used without adverse effects during pregnancy. As a result, MD simulation was used to investigate these two drugs further. According to the MD simulation results, prednisone appears to have a higher affinity for TNF-α than progesterone, and consequently, the prednisone complex stability is higher. For the first time, this study examined the possible role of prednisone and progesterone in inhibiting TNF-α using in silico methods.

A Comparative Taxonomic Profile of Microbial Polyethylene and Hydrocarbon-Degrading Communities in Diverse Environments.

BACKGROUND: Polyethylene (PE) is one of the most abundant plastic wastes which accumulates in marine and terrestrial environments. As microbial degradation has been a promising approach for the bioremediation of polluted environments, identification of the microbial community profile where these pollutants accumulate, has recently been in focus. OBJECTIVE: We have investigated the taxonomic and functional characteristics of polyethylene- degrading microorganisms in a plastic waste recycling site in Tehran, Iran. MATERIALS AND METHODS: We have analyzed and compared a 16S rRNA dataset from this study with 15 datasets from 4 diverse plastic and oil polluted habitats to identify and evaluate bacterial communities involved in bioremediation. RESULTS: Our findings reveal that Proteobacteria, Actinobacteria, Acidobacteria and Cloroflexi were the dominant phyla and Actinobacteria, Alphaproteobacteria, Gammaproteobacteria and Acidimicrobia were dominant classes in these samples. The most dominant Kegg Orthology associated with PE bioremediation in these samples are related to peroxidases, alcohol dehydrogenases, monooxygenases and dioxygenases. CONCLUSIONS: Long-term presence of contaminants in soil could lead to changes in bacterial phyla abundance, resulting in metabolic adaptations to optimize biological activity and waste management in a diverse group of bacteria.

Repurposing FDA-approved drugs to fight COVID-19 using in silico methods: Targeting SARS-CoV-2 RdRp enzyme and host cell receptors (ACE2, CD147) through virtual screening and molecular dynamic simulations.

BACKGROUND: Different approaches have been proved effective for combating the COVID-19 pandemic. Accordingly, in silico drug repurposing strategy, has been highly regarded as an accurate computational tool to achieve fast and reliable results. Considering SARS-CoV-2's structural proteins and their interaction the host's cell-specific receptors, this study investigated a drug repurposing strategy aiming to screen compatible inhibitors of FDA-approved drugs against viral entry receptors (ACE2 and CD147) and integral enzyme of the viral polymerase (RdRp). METHODS: The study screened the FDA-approved drugs against ACE2, CD147, and RDRP by virtual screening and molecular dynamics (MD) simulation. RESULTS: The results of this study indicated that five drugs with ACE2, four drugs with RDRP, and seven drugs with CD147 achieved the most favorable free binding energy (ΔG < -10). This study selected these drugs for MD simulation investigation whose results demonstrated that ledipasvir with ACE2, estradiol benzoate with CD147, and vancomycin with RDRP represented the most favorable ΔG. Also, paritaprevir and vancomycin have good binding energy with both targets (ACE2 and RdRp). CONCLUSIONS: Ledipasvir, estradiol benzoate, and vancomycin and paritaprevir are potentially suitable candidates for further investigation as possible treatments of COVID-19 and novel drug development.

Drug repurposing using computational methods to identify therapeutic options for COVID-19.

PURPOSE: Recently, the world has been dealing with a new type of coronavirus called COVID-19 that in terms of symptoms is similar to the SARS coronavirus. Unfortunately, researchers could not find a registered therapy to treat the infection related to the virus yet. Regarding the fact that drug repurposing is a good strategy for epidemic viral infection, we applied the drug repurposing strategy using virtual screening to identify therapeutic options for COVID-19. For this purpose, five proteins of COVID-19 (3-chymotrypsin-like protease (3CLpro), Papain-Like protease (PLpro), cleavage site, HR1 and RBD in Spike protein) were selected as target proteins for drug repositioning. METHODS: First, five proteins of COVID-19 were built by homology modeling. Then FDA-approved drugs (2471 drugs) were screened against cleavage site and RBD in Spike protein via virtual screening. One hundred and twenty-eight FDA-approved drugs with the most favorable free-binding energy were attached to the cleavage site and RBD in Spike protein. Of these 128 drugs, 18 drugs have either been used currently as antiviral or have been reported to possess antiviral effects. Virtual screening was then performed for the 18 selected drugs with ACE2, 3CLpro and PLpro and HR1 and TMPRSS2. RESULTS: According to the results, glecaprevir, paritaprevir, simeprevir, ledipasvir, glycyrrhizic acid, TMC-310911, and hesperidin showed highly favorably free binding energies with all tested target proteins. CONCLUSION: The above-mentioned drugs can be regarded as candidates to treat COVID-19 infections, but further study on the efficiency of these drugs is also necessary.

In silico identification of new inhibitors for ßeta-2-glycoprotein I as a major antigen in antiphospholipid antibody syndrome.

Beta 2 glycoprotein I (ß2GPI) is a major antigen for autoantibodies present in antiphospholipid antibody syndrome (APS). ß2GPI is a single polypeptide with five repeated domains and different conformations. The activated J-shaped conformation of ß2GPI binds to negatively charged phospholipids in the membrane via the fifth domain and causes blood clotting reactions. We applied a drug repurposing strategy using virtual screening and molecular dynamics to find the best FDA drugs against the fifth domain of ß2GPI. In the first phase, FDA drugs that had the most favorable ΔG with the fifth domain of ß2GPI were selected by virtual screening. Among these drugs that had the most favorable ΔG, Vorapaxar and Antrafenine were selected for molecular dynamics (MD) simulation studies. MD simulation was performed to evaluate the stability of Vorapaxar and Antrafenine complexes and the effect of the two drugs on protein conformation. Also, MD simulation was done to investigate the effect of Antrafenine and Vorapaxar on the binding of ß2GPI to the platelet model membrane. According to the results, Vorapaxar and Antrafenine were bound to the protein with the favorable binding energy (Vorapaxar and Antrafenine binding energies are - 49.641 and - 38.803 kcal/mol, respectively). In this study, it was shown that unlike protein alone and protein in the Antrafenine complex, the protein in the Vorapaxar complex was completely separated from the model membrane after 350 ns. Moreover, Vorapaxar led to more changes in the activated J-shape of ß2GPI. Thus, Vorapaxar can be a suitable candidate for further investigations on the treatment of APS.

Reporting a Transcript from Iranian Viola Tricolor, Which May Encode a Novel Cyclotide-Like Precursor: Molecular and in silico Studies.

The cyclotides are the largest known family of cyclic proteins, which are found in several plant families including Violaceae. They are circular bioactive peptides consisting of 28-37 amino acids, which possess a cyclic cystine knot (CCK) motif and could be useful in biotechnology and drug design as scaffolds for peptide-based drugs. This study describes our finding of a potentially novel gene transcript from the petals of the Iranian Viola tricolor (V. tricolor) flowers. This study is based on the cDNA screening method employed for isolation of cyclotide precursor genes and in silico analysis. Our study resulted in the finding of a novel cyclotide-like precursor from V. tricolor, which is documented in the NCBI by GenBank accession number: KP065812. The in silico analysis revealed that there are lots of similar sequences in many other plant families and they all exhibit some different features from previously discovered cyclotide precursors. The differences occur particularly in the main cyclotide domain that exists without the usual CCK structure. All of these hypothetical precursors have a conserved ER-signal sequence, a Cysteine (C)-rich sequence forming two zinc finger motifs and a cyclotide-like region containing several conserved elements including two highly conserved C residues. In conclusion, using the cDNA screening method we found a potentially new cyclotide-like precursor gene and in silico studies revealed its significant characteristics that may open up a new research line on the distribution and evolution of cyclotides.