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Treatment of COVID-19 that is based on plants could be a more cost-effective therapy against the disease. Flavonoids, a group of compounds that have been observed to have various effects, including antiviral activity, were chosen as the candidate molecule for treatment of COVID-19. Kalanchoe Pinnata is one of the plants containing flavonoids that has been demonstrated to have antiviral activity. The structure of ACE2 and various flavonoids were retrieved and cleaned from unnecessary residues. The ACE2 structure was subjected to molecular docking in order to analyze the binding affinity. Following that, the ADME properties of each flavonoid were analyzed accordingly. The QSAR analysis was also performed for each type of flavonoid. Lastly, molecular dynamics simulation was conducted. All of the tested compounds were able to bind to human ACE2 and SARS-CoV-2 Spike protein, but were unable to compete with them as the binding affinity of the compounds to the protein were lower compared to ACE2-Spike interaction. The ADME and toxicity analysis showed that most of the ligands were able to be absorbed by the GI tract, but have low bioavailability. The compounds also cause no major toxicity effects and were able to be sufficiently distributed to the body. Molecular dynamics analysis also revealed that among the compounds, quercetin and rutin were able to interact with ACE2 and Spike protein stably. The QSAR analysis showed that friedelin, kaempferol, quercetin, and rutin are mostly non-toxic, but the high Cramer values indicate that there are no initial safety impressions for these molecules and could cause toxicity. In conclusion, quercetin and rutin have potential to be a candidate for COVID-19 drug development based on the in-silico predictions results obtained. Friedelin and Narcissin whose affinity to the proteins were relatively stronger but had unstable interactions from molecular dynamics simulation results, may also be a potential COVID-19 treatment with further investigation. However, further research is required to assess the effectiveness and also specially to measure the toxicity of the compounds.

Context: COVID-19 was caused by the spread and transmission of SARS-CoV-2 at the end of 2019 until now. The problem comes when antiviral drugs have not yet been found and patients infected with SARS-CoV-2 can trigger a cytokine storm condition due to the effects of viral replication. Indonesia has various kinds of medicinal plants, such as Sonchus arvensis L., which are used as medicinal plants. Aims: To analyze the activity of the inhibitor as SARS-CoV-2 antiviral agents from n-hexane fractions of S. arvensis leaves. Methods: The sample was collected from GC-MS analysis, PubChem, and Protein Databank database, then drug-likeness identification using Lipinski Rule of Five server and bioactive prediction of bioactive compounds as inhibitor activity was conducted by Molinspiration server. Furthermore, the docking simulation was performed using PyRx 0.9.9 software to determine the binding activity, molecular interaction by Discovery Studio software to identify position and interaction type, 3D molecular visualization by PyMol 2.5. software, and dynamic by CABS-flex 2.0 server to predict interaction stability. Results: α-Amyrin and β-amyrin from n-hexane fractions of S. arvensis leaves had activity as SARS-CoV-2 inhibitors through interactions on helicase, RdRp, Mpro, and RBD-Spike, both compounds had more negative binding affinity than control drug and can produce stable chemical bond interactions in the ligand-protein complexes. However, the results were merely computational, so they must be validated through an in vivo and in vitro research approach. Conclusions: Sonchus arvensis L. leaves were predicted to have SARS-CoV-2 antiviral through inhibitor activity by α-amyrin and β-amyrin.

Triple Negative Breast Cancer (TNBC) is considered as the most dangerous breast cancer type in women and the most difficult to be medicated due to its unresponsive nature towards drug treatments. However, as transcriptomics studies are becoming more advanced, the research on non-coding (nc)RNAs like miRNA and siRNA is being considered as a more feasible approach to deal with TNBC. The mir-135b, a newly found miRNA that has a role in the molecular mechanism of TNBC, is studied mainly for its function as a possible biomarker and a drug candidate. The molecular interaction and structures are still unknown and determining them is the objective of this research. This research utilizes the RNAComposer webservice for 3D RNA structure prediction and the AutoDockTool for molecular docking to determine their interaction. The result shows satisfactory illustration of mir-135b and its siRNA structures with feasible bindings between them. It is shown that the bindings are strong enough to maintain silencing of the gene. However, the molecular docking result shows reversible interaction based on the binding energy meaning its biomarker potential requires combination with other special drug delivery systems. It could be inferred that the siRNAs have potential to be developed in the wet laboratory settings.

Background: Numerous types of research on malaria were done over a long period of time but there are still some unknowns. However, it is globally known that malaria is caused by the Plasmodium parasite, mainly and most lethally by Plasmodium falciparum. The purpose of this research is to understand the structure and function of three uncharacterized P. falciparum proteins (PF3D7_1468000, PF3D7_1147400, PF3D7_1351100) using bioinformatic methods in hopes to learn more about malaria. Methods: The three uncharacterized P. falciparum proteins were inserted into Phyre2 for knowing the protein homology, InterPro, and SUPERFAMILY hidden Markov models for understanding the domain annotation, scanprosite for knowing the post-translational modification, Ramachandran plot for protein validation, and Yasara for visualizing the protein. Results: According to the Phyre2 results, the third protein showed the highest confidence and coverage level of 100%, followed by the second protein, and the lowest was the first protein. Interpro and SUPERFAMILY results identified the first protein as WD40 repeat superfamily, the second protein as Cytochrome C subunit II-like, and the third protein as CXXC motif. Scanprosite revealed all sequences possessing protein domains in which the first protein has three protein domains, the second protein has one protein domain, and the third protein has two protein domains. According to the Ramachandran plot, the first and second protein generally has an α-helix structure while the third protein has an overall β-sheet structure, which differs to some extent from the protein structure visualization. The three protein visualizations exhibited secondary structures and more than 50 amino acid residues for each protein. Conclusion: This research concluded that the second and third uncharacterized proteins (PF3D7_1147400, PF3D7_1351100) could be promising antimalarial drug targets leading to the P. falciparum parasite death.

Context: Dengue viruses (DENVs) are the cause of dengue disease, which is one of the most frequent diseases caused by mosquito-borne viral infections. Currently, no specific treatment is available for dengue. Aims: To identify the most promising inhibitors of dengue virus 2 (DENV2) envelope protein of DENV2 envelope protein from flavonoids compounds through computational methods. Methods: Structures of 54 flavonoids were collected, then the compounds were screened based on Lipinski’s rules, and there were only 34 compounds that passed the screening. Then QSAR analysis was performed, followed by molecular docking analysis, ADMET evaluation, and molecular dynamics simulations to assess the stability of the protein. Results: Based on the QSAR analysis, only 32 compounds were subjected to molecular docking analysis. Silymarin had the highest docking score, while juglanin had the lowest ACE score compared to positive controls. The ADMET evaluation showed silymarin and juglanin had good absorption and could not penetrate the blood-brain barrier. In contrast to silymarin which had negative results for the Ames test, carcinogenicity, skin sensitization, and eye irritation, juglanin was positive for Ames test and skin sensitization. Even though the molecular dynamic simulation of both ligands with DENV2 envelope protein showed unstable confirmation, it did not necessarily mean that the ligands cannot be used as inhibitors since the molecular docking results provide evidence of the ligands binding to the DENV2 envelope protein. Conclusions: Based on the favorable results of QSAR analysis, molecular docking, and ADMET evaluation, juglanin and silymarin were chosen as the candidate with the most potential for DENV2 envelope protein inhibitors. However, further analyses such as in vitro and in vivo analyses are necessary to validate the result of this study.

Hyperhidrosis is a non-fatal condition in which the patient would express excessive sweat. There are a lot of treatments already available including anticholinergic drugs which would inhibit the M3 human receptors. The purpose of this study is to look for potential alternative herbal treatment that is effective against hyperhidrosis with molecular docking using PyRx, visualization, molecular dynamics with CABSFLEX2, ADME and toxicity analysis using SwissADME and AMBIT respectively. The result of this study showed that all 41 ligands from Datura metel have better binding affinity than the control of acetylcholine. Tropine is considered as the best option out of the 41 ligands as it has low binding affinity, can be synthesized, pass the lipinski test, and has similar toxicity results with aluminum chloride which is the common topical treatment of hyperhidrosis. However, based on the molecular dynamics, all ligands have a very fluctuating RMSF value, including tropine that has RMSF between 0 to 14. It means that tropine could be investigate further as a drug lead.

Background: Myelofibrosis is a type of malignancy that is mainly caused by JAK2 gene mutation. This study employed an in silico approach to analyse the binding affinity of five JAK2 inhibitors that were already FDA-approved in order to determine the most effective JAK2 inhibitor to suppress the overstimulating JAK-STATs caused by the mutation Methods: The 3D structure of JAK2 protein was built using the Swiss-Prot Expas and loaded into PyMol to remove the solvent and add the hydrogen atom. On the other hand, five JAK inhibitors including Ruxolitinib, Fedratinib, Momelotinib, Pacritinib, and Ilginatinib (NS-018) were retrieved in 3D conformer from the PubChem NCBI database. The ligands were minimized and combined with the JAK2 protein using PyRx, as well as to be visualized using BIOVE and Castp. Results: The molecular docking analysis showed that Fedratinib has the most negative binding affinity followed by Pacritinib, Ruxolitinib, Ilginitinib, and Momelotinib. By comparing BIOVIA and Castp visualisation results, all JAK2 inhibitors bind to the JAK2 protein Pocket 1 Chain A, whereas Ruxolitinib and Momelotinib have an additional binding to JAK2 protein Pocket 1 Chain B. Conclusion: Fedratinib is the most potent JAK2 inhibitor based on the binding affinity it possesses. Further pharmacology research is required to analyse further the efficacy and safety of the Fedratinib.

Leprosy is an infectious disease caused by Mycobacterium leprae that mainly affects the skin, peripheral nerve, mucosa of the upper respiratory tract, and eyes. There is no vaccine designed specifically to prevent leprosy. The most common vaccine strategy is Bacille Calmette-Guérin (BCG), however its efficacy is highly variable between studies. Current study utilized a computational method to predict antigenic epitopes from Mycobacterium leprae for peptide vaccine development. Molecular docking of top predicted peptides from 6 antigenic B-cell and 3 CTL epitopes were analyzed. These predicted antigenic epitopes might potentially be target peptides for future leprosy vaccines.

Tilapia (Oreochromis niloticus) is an important fisheries commodity. Scientific efforts have been done to increase its quality. One of them is staging a premium diet such as a fat-enriched diet. The transcriptomics approach is able to provide the signatures of the diet outcomes by observing the micro(mi)RNA signature in transcriptional regulation. Hence, it was found that the availability of mir-122 is essential in the regulation of a high-fat diet in tilapia. However, this transcriptomics signature is lacking structural annotations and the complete interaction annotations with its silencing(si)RNA. RNAcentral website was navigated for the latest annotation of mir-122 from tilapia and other species as a comparison. MEGA X was employed to comprehend the miRNA evolutionary repertoire. The RNA secondary structure prediction tools from the Vienna RNA package and the RNA tertiary structure prediction tools from simRNA and modeRNA are secured with default parameters. The HNADOCK tools were leveraged to observe the interaction between mir-122 and its siRNA. The post-processing was conducted with the Chimera visualization tool. The secondary and tertiary structure of the mir-122 and its siRNA could be elucidated, docked, and visualized. In this end, further effort to develop a comprehensive molecular breeding tool could be secured with the structural annotation information.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the novel coronavirus disease 19 (COVID19) pandemic. Despite drugs had been deployed through intense research, none has been proven to cure the infection. Flavonoids, a natural substance, have been reported to exhibit various pharmaceutical and medical properties. The aim of this research is to discover lead compounds for covid-19 from flavonoid compounds through in silico study. Through computational examination, the potential of flavonoid compounds against SARS-CoV-2 has been examined and it shows that some flavonoids have potential against SARS-CoV-2.

The recent outbreak of SARS-CoV-2 across the globe and the absence of a specific cure against the disease lead the scientific community to investigate some alternative indigenous treatments. SARS-CoV-2 is the virus responsible for the coronavirus ailment 2019 (COVID-19). This virus has 4 auxiliary proteins namely the S (spike), E (envelope), M (membrane), and N (nucleocapsid) proteins. The main proteases and RNA dependent RNA polymerase are also essential structures by which the virus replicates and survives. Each of these proteins are structures of the virus that are potential targets for drugs which are leads in the drug discovery process of any drug for the virus. Currently available treatments are not specific to the disease and therefore carry unwanted adverse effects that can be highly dangerous and sometimes fatal. Many of these treatments are supplementary in nature or based on repurposed drugs from other viral outbreaks. Alternatives of conventional drugs are required to control the spread and severity of the disease. Allicin, curcumin and their derivatives have been researched for their antiviral property and shown to have good binding affinity towards SARS-CoV-2 structures essential in their survival, especially the main proteases and RNA dependent RNA polymerases. The structural bioinformatics tools have elicited methods to predict the bioactivity of the natural product-based compounds. Apart from the beneficial medication that they offer, natural products carry along other advantages for the current pandemic situation in terms of supply, logistics, and affordability.

BackgroundTuberculosis is one of the deadliest disease caused byMycobacterium tuberculosis. Its treatment still becomes a burden for many countries including Indonesia. Drug resistance is one of the problems in TB treatment. However, a development in the molecular field through Whole-genome sequencing (WGS) can be used as a solution in detecting mutations associated with TB- drugs. This investigation intended to implement this data for supporting the scientific community in deeply understanding any TB epidemiology and evolution in Papua along with detecting any mutations in genes associated with TB-Drugs.ResultA whole-genome sequencing was performed on the random samples from TB Referral Laboratory in Papua utilizing MiSeq 600 cycle Reagent Kit (V3). Furthermore, TBProfiler was used for genome analysis, RAST Server was employed for annotation, while Gview server was applied for BLAST genome mapping and a Microscope server was implemented for Regions of Genomic Plasticity (RGP). The largest genome ofM. tuberculosisobtained was at the size of 4,396,040 bp with subsystems number at 309 and the number of coding sequences at 4326. One sample (TB751) contained one RGP. The drug resistance analysis revealed that several mutations associated with TB-drug resistance existed. In details, mutations ofrpoBgene which were identified as S450L, D435Y, H445Y, L430P, and Q432K had caused the reduced effectiveness of rifampicin; while the mutases inkatG(S315T),kasA(312S),inhA(I21V), and Rv1482c-fabG1(C-15 T) genes had contributed to the resistance in isoniazid. In streptomycin, the resistance was triggered by the mutations inrpsL(K43R) andrrs(A514C, A514T) genes, and, in Amikacin, its resistance was led by mutations inrrs(A514C) gene. Additionally, in Ethambutol and Pyrazinamide, their reduced effectiveness was provoked byembBgene mutases (M306L, M306V, D1024N) andpncA(W119R).ConclusionsThe results from whole-genome sequencing of TB clinical sample in Papua, Indonesia could contribute to the surveillance of TB-drug resistance. In the drug resistance profile, there were 15 Multi Drugs Resistance (MDR) samples. However, Extensively Drug-resistant (XDR) samples have not been found, but samples were resistant to only Amikacin, a second-line drug.

Known as the causal factor of the recent global COVID-19 pandemic, any SARS-CoV-2 is not the only coronavirus that has constituted a menace to society and taken thousands of human lives. Two previous pandemics were also led by coronaviruses; such as the MERS-CoV in 2012 and another SARS-CoV in 2002. Only five months into existence, SARS-CoV-2 transmitted infection to approximately 75 million people and it has led to more than 1.5 million of deaths all over the world. Unfortunately, there has not been any specific treatment yet for COVID-19 yet and its control is purely empirical. The examinations of the parity between SARS-CoV-2 and SARS-CoV, in terms of their genomics, origin, epidemiology, and pathogenesis, suggested that we may use the previous data of MERS-CoV or SARS-CoV as a guideline for uncovering the effective approach to strive against SARS-CoV-2. Various studies have reported the positive effects of numerous phytochemical compounds against SARS-CoV and MERS-CoV. Interestingly, this idea has been emplyed for SARS-CoV-2, and in silico screening of phytochemical compounds has been performed for identifying the potential candidates for COVID-19 treatment. Curcumin is an example of a natural compound which was demonstrated as potent candidate contrary to SARS-CoV-2 protease derived from the in silico studies. Herein, the occurrence of SARS-CoV-2 and the aplication of alternative medicines for treating coronavirus diseases are briefly reviewed.

The e-Bintech 5.0 online workshop was held to sharpen the concept of village 5.0 that is a multidisciplinary acculturation-constellation. In this workshop, multiplatform frameworks were successfully formulated as a basic guideline for village developments towards the glory of Indonesia. Pentahelix platform synergy is needed (academics, business, government, community, media) supported by relevant stakeholders for fostering a futuristic village towards the creation of Indonesia 5.0, especially in terms of ease of investment, simplification of bureaucracy and administration. Key Words: edubioneurolitechnopreneurship; Indonesia 5.0; pentahelix; village 5.0 Abstrak : Lokakarya daring e-Bintech 5.0 diadakan untuk menajamkan konsep desa 5.0 yang merupakan akulturasi-konstelasi multidisipliner. Dalam lokakarya ini, berhasil dirumuskan multiplatform-frameworks sebagai pedoman dasar pengembangan-pembangunan desa menuju kejayaan Indonesia. Diperlukan sinergi platform pentahelix (akademisi, bisnis, pemerintah, komunitas, media) didukung stakeholders terkait untuk pembinaan desa futuristik menuju terciptanya Indonesia 5.0, terutama dalam hal kemudahan investasi, simplifikasi birokrasi dan administrasi.

Leprosy persists to be a health problem in Indonesia, especially in the provinces of North Maluku, West Papua and Papua. Early diagnosis and complete treatment with multidrug therapy (MDT) remain the key strategy for reducing the disease burden. One of the major components of MDT is rifampicin which in certain cases in several countries, M. leprae resistance to this drug issue has been reported albeit only a few. This research aimed to detect and analyze polymorphism in M. leprae rpoB gene that was isolated from leprosy patients in three provinces: North Maluku Province, West Papua Province and Papua Province, Indonesia. The identification of mutations in the M. leprae rpoB gene was carried out by aligning the results of DNA sequencing with the reference strain. The 3D structure of rpoB was derived using the Swiss Model. The T450A, S456L, and H451Y variants of RNA Polymerase B subunits were constructed using FoldX based on the wild-type structure. The structures were repaired, and protein stability was evaluated using foldX under the Yasara viewer. The QC of the rpoB M. leprae homology models was conducted with Ramachandran Plot modeling using PROCHECK. The difference in binding affinity between native protein and T450A, S456L, and H45I variants were analyzed using molecular docking. rpoB gene of M. leprae contains a mutation found in nucleotide of 1348 bp. The mutation triggered the conversion of the amino acid Threonine to Alanine in the amino acid to 450 rpoB subunit B. The structure of 3D RNA Polymerase Subunit B was constructed using rpoB Mycobacterium tuberculosis with PDB code 5UH5 as template. According to Ramachandran Plot, the percentage of residues in the most favored regions are 91.9%, and there was no significant number of residues in the disallowed regions. The results of molecular docking showed that the T450A variant had the same binding affinity with the native protein which was -8.9 kcal. Binding affinity on the S456L and H451Y variants increased by -7.3 kcal and -8.2 kcal, respectively. According to Molecular Docking analysis, T450A variant did not affect the energy binding between RNA polymerase and rifampicin.

Acquired immune deficiency syndrome (AIDS) has been identified from US patients since 1981. AIDS is caused by infection with the human immunodeficiency virus type 1 (HIV-1) which is a retrovirus. HIV-1 gp120 can be recognized by the immune system because it is located outside the virion. The conserved region is identified in gp120, and it is recognized by an immune cell which then initiates specific immune responses, viral mutation escape, and increase vaccine protection coverage, a benefit derived from the conserved region-based vaccine design. However, previous researchers have little knowledge on this conserved region as a target for vaccine design. This paper explains how the conserved region of gp120 HIV-1 is a major target for vaccine design through a bioinformatics approach. The conserved region from gp120 was explored as a vaccine design target with a bioinformatics tool that consists of B-cell epitope mapping, vaccine properties, molecular docking, and dynamic simulation. The peptide vaccine candidate of B5 with the gp120 HIV-1 conserved region was found to provoke B-cell activation through a direct pathway, produce specific antibody, and increase protection from multi-strain viral infection.

In order to function properly the human body requires adequate hydration due to 70% of the human body being built up by water with lots of chemical reactions involved in order to work optimally throughout the day. This paper presents an idea to make a water intake app based on human excrement surveillance. It might be a solution to intensify people's ability to become self-conscious when drinking less water by surveying the excreted substance. Currently, available software that measures one indicator which is between urine and feces detector on how much water should the user drink. But actually, both excrement indicators are needed to detect users' drinking amount. If one of these indicators shows a bad result, it could lead to water intoxication or hydration. The application was created using Python to give feedback regarding the user's water intake based on the condition of their excreted substance.

The Rafflesiaceae family consists of three genera of parasitic plants – Rafflesia, Rhizanthes, and Sapria – with purported ethnobotanical and ethnomedicinal properties. In this study, the inhibitory properties of 21 characterized metabolites associated with Rafflesia and Sapria were tested against eight proteins linked to human diseases – including seven pathogenic-associated HMGCR, VEGFR2, acetylcholinesterase, NMT, H1N1 neuraminidase, GSK3-β, and estrogen receptor α, and one plant-pathogenic associated Colletotrichum chitin deacetylase. Each metabolite was tested using drug-likeness screening, screening metabolite activity, and molecular docking to eight diseases and microbial physiological processes. Hydrogen bonds and hydrophobic interactions between metabolite ligands and protein residues were characterized. Molecular dynamics were also assessed to analyze the stability of the protein-ligand interaction. Our results indicate that the gallotannins and flavonol phenolics from Rafflesia and Sapria display high inhibitory potential against disease proteins. All metabolite-protein pairs displayed stable fluctuations. However, some compounds disobeyed LRO5 drug-likeness and displayed moderate bioavailability and synthetic accessibility, so an improved drug delivery method is required. All 21 metabolites are available in other popular edible plants (mainly tea and certain berries) and could be used to create artificially mixed metabolite-based medicine to prevent the exploitation and endangerment of wild Rafflesia and Sapria populations. Our activity likelihood screening and molecular docking data indicate that Rafflesia and Sapria metabolites possess considerable potential as anti-cholesterol, respiratory antiviral, wound-healing, and antifungal properties. To protect Rafflesiaceae plants in the wild, metabolites can be assessed from other plant sources and combined as an artificial herbal mix.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a newly emerging virus well known as the major cause of the worldwide pandemic due to Coronavirus Disease 2019 (COVID-19). Major breakthroughs in the Next Generation Sequencing (NGS) field were elucidated following the first release of a full-length SARS-CoV-2 genome on the 10 January 2020, with the hope of turning the table against the worsening pandemic situation. Previous studies in respiratory virus characterization require mapping of raw sequences to the human genome in the downstream bioinformatics pipeline as part of metagenomic principles. Illumina, as the major player in the NGS arena, took action by releasing guidelines for improved enrichment kits called the Respiratory Virus Oligo Panel (RVOP) based on a hybridization capture method capable of capturing targeted respiratory viruses, including SARS-CoV-2; therefore, allowing a direct map of raw sequences data to SARS-CoV-2 genome in downstream bioinformatics pipeline. Consequently, two bioinformatics pipelines emerged with no previous studies benchmarking the pipelines. This study focuses on gaining insight and understanding of target enrichment workflow by Illumina through the utilization of different bioinformatics pipelines named as ‘Fast Pipeline’ and ‘Normal Pipeline’ to SARS-CoV-2 strains isolated from Yogyakarta and Central Java, Indonesia. Overall, both pipelines work well in the characterization of SARS-CoV-2 samples, including in the identification of major studied nucleotide substitutions and amino acid mutations. A higher number of reads mapped to the SARS-CoV-2 genome in Fast Pipeline and merely were discovered as a contributing factor in a higher number of coverage depth and identified variations (SNPs, insertion, and deletion). Fast Pipeline ultimately works well in a situation where time is a critical factor. On the other hand, Normal Pipeline would require a longer time as it mapped reads to the human genome. Certain limitations were identified in terms of pipeline algorithm, whereas it is highly recommended in future studies to design a pipeline in an integrated framework, for instance, by using NextFlow, a workflow framework to combine all scripts into one fully integrated pipeline.

SARS-CoV-2 virus, as the causal agent for the COVID-19 pandemic, remains an enigma in the bioinformatics sense. Current efforts in drug and vaccine design in primarily targeting general devised protein domain while overlooking the specific features in the proteomics repertoire. However, the NCBI Conserved Domain Database (CDD) could annotate the specific features that are indispensable for a more advanced drug and vaccine design. In this regard, the annotation efforts were initiated with CDD database, and visualized with the 3D Protein Visualizer of Cn3D. The exsistence of the ATP and ADP binding protein with respected domains were found to be a very potential target for drug design. It is recommended that nucleoside inhibitor that could mimick the ATP molecule could serve as a potential drug lead agains SARS-CoV-2.

Triple-negative breast cancer is one of the deadliest diseases for women, according to the World Health Organization. The most common drugs used and in development for this disease are based upon the proteomics approach, but this so far has not accounted for the whole repertoire of human genome expression. However, a novel approach is currently under development to model the molecular interaction between miR-145 and the 3' untranslated region (UTR) of ARF6 mRNA. This approach should eventually be useful for transcriptomics-based drug development. The utilized methods are molecular-docking and dynamics based, using open-source software. It was found that there was a fine-grained molecular interaction between miR-145 and the 3' UTR of ARF6 mRNA. It is concluded that the information from the interaction could be utilized as the basis for drug development.

One of the primary contributors to the mortality of women is breast cancer. Several approaches are used to cure it, but recurrence occurs in 79% of the cases because the underlying mechanism of the protein molecules is not carefully ex-amined. The goal of this research was to use machine-learning tools is to elucidate conserved regions and to obtain functional annotations of breast-cancer-related proteins. The sequences of five breast-cancer-related proteins (BRCA2, BCAR1, BCAR3, BCAR4, and BRMS1) and their annotations were retrieved from the UniProt and TCGA databases, respectively. Conserved regions were extracted using CLUSTALX. We constructed a phylogenetic tree using the MEGA 7.0. SUPERFAMILY database to obtain fine-grained domain annotation. The tree revealed that the BRCA2 and BCAR4 protein sequences are located in a clade, which indicates that they have overlapping functions. Several protein domains were identified, including the SH2 and Ras GEF domains in BCAR3, the SH3 domain in BCAR1, and the BRCA2 helical domain, the nucleic-acid-binding protein, and tower domain. We found that no protein domains could be annotated for BCAR4 or BRMS1, which may indicate the presence of a disordered protein state. We suggest that each protein has distinct functionalities that are complementary in regulating the progression of breast cancer, although further study is necessary for confirmation. This protein-domain annotation project could be leveraged by the complete integration of mapping with respect to gene and disease ontology. This type of leverage is vital for obtaining biochemical insights regarding breast cancer.

The SARS-CoV-2 or COVID-19 pandemic has reached a new height with an unprecedented infection rate and mortality post-world war II history. However, there is no particular designed drug for COVID-19 up to this point. Thus, there exist three strategies for COVID-19 drug design; drug repurposing option, herbal medicine development, and transcriptomics-based drug lead. As the most underutilized option, transcriptomics-based drug lead could be leveraged to deal with SARS-CoV-2 infection. One of the main methods to block the SARS-CoV-2 infection is to inhibit the RNA polymerase enzyme that is responsible to the viral replication. In this regard, the objective of the strategy is to design the anti-sense siRNA drug and lead to inhibit the mRNA of the RNA Polymerase Enzyme (RdRp) gene that encodes the viral RNA Polymerase of the SARS-CoV-2. The Computer-Aided Drug Design (CADD)-based method was leveraged with sequence retrieval of 24 RdRp gene sequences, multiple sequence alignment, phylogenetic tree reconstruction, 2D/3D RNA structure predictions, and RNA-RNA docking. Both the RNAalifold conserved structure from the RdRp genes and the RNAfold structure of the siRNA for blocking the conserved structure are negative or less than 0 kcal/mol. The predicted RNA docking occurred with the best RMSD score of 22.53 Å, which is beyond the accepted threshold of 10-20 Å. Based on the findings, the 2D/3D structures of both the siRNA and mRNA could be elucidated, and the docking between them is feasible. However, this finding should be elucidated in the wet laboratory setting for the final lead validation.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel coronavirus and the primary causative agent of coronavirus disease 2019 (COVID-19), first occurred in China and rapidly spread worldwide. The government of the Republic of Indonesia confirmed its first two cases of COVID-19 in March 2020. COVID-19 is a serious illness with no efficacious antiviral medication or approved vaccine currently available. Therefore, there is a need to investigate the genome of SARS-CoV-2. In this study, we characterized SARS-CoV-2 spike glycoprotein genes from Indonesia to investigate their genetic composition and variability. Overall, ten SARS-CoV-2 spike glycoprotein gene sequences retrieved from GenBank (National Center for Biotechnology Information, USA) and the GISAID EpiCoV database (Germany) were compared. We analyzed nucleotide variants and amino acid changes using Molecular Evolutionary Genetics Analysis (MEGA) X and analyzed gene similarity using the LALIGN web server. Interestingly, we revealed several specific mutation sites, however, there were no significant changes in the genetic composition of SARS-CoV-2 spike glycoprotein genes, when compared to the Wuhan-Hu-1 isolate from China. However, this is a preliminary study and we recommend that molecular epidemiology and surveillance programs against COVID-19 in Indonesia be improved.

Ethanol and nicotine are two common substances that are often linked to complications in alcoholic smokers. The high number of the co-consumptions in alcoholic smokers suggested a possible interaction between ethanol and nicotine in the central nervous system and a potential similar mechanism of action. Both ethanol and nicotine are shown to bind with neuronal nicotinic acetylcholine receptors (nAChRs), a ligand gated cation channel specifically targeted by the endogenous acetylcholine. Ethanol has a much less specific binding capability to modulate the receptors, however, emerging reports indicates that ethanol can interact with nAChRs both directly and indirectly. This study focuses on the analysis of ethanol binding sites with nAChRs using molecular docking techniques obtained from the Protein Data Bank. The obtained data showed a possible binding site for ethanol in nAChRs, however, upon validation, result is not substantial. Nevertheless, the obtained data should be useful for future reference for the basis of ethanol interactions with the human nAChRs proteins.

Cervical cancer is a type of cancer characterized by abnormal cell growth in the cervical area. One of the main events that happen during tumorigenesis is the inactivation or degradation of the genome guardian, P53. Under normal circumstances, P53 is regulated by MDM2 protein. MDM2 can transfer ubiquitin to the transactivation domain of P53, targeting it for degradation by proteasomes. However, in the presence of HPV, HPV-E6 can utilize cellular E6AP to perform P53 degradation through the ubiquitin ligase mechanism. We validated both interactions through molecular docking in ClusPro. We also checked their normalized expression level in The Cancer Genomic Atlas (TCGA) using TCGA-Assembler. We found out both interactions are highly likely due to the spontaneity of the reaction indicated by the low free energy. MDM2 is overexpressed in cervical cancer cells, but E6AP expression is relatively constant. This indicates that the MDM2-mediated pathway is still sustained in cervical cancer cells. But since E6AP-mediated pathway is finally activated due to the presence of HPV-E6, both pathways may happen simultaneously. Further research is needed to confirm both pathways existence in cervical cancer cells and how they may coincide and affect each other.

One approach for solving the problem of antibiotic resistance and bacterial persistence in biofilms is treatment with metals, including silver in the form of silver nanoparticles (AgNPs). Green synthesis is an environmentally friendly method to synthesize nanoparticles with a broad spectrum of unique properties that depend on the plant extracts used. AgNPs with antibacterial and antibiofilm effects were obtained using green synthesis from plant extracts of

The prediction of protein subcellular localization (SCL) has been a long-running challenge in bioinformatics. Protein SCL is crucial for a protein to exercise its functions properly. The reliance of protein localization on signaling peptides and the information available in gene ontology (GO) databases makes it possible to use computational approaches to predict protein SCL. SCL methods can be classified as either sequence-based or annotation-based. Machine learning algorithms and classifiers are used in protein SCL prediction tools. This review presents a list of protein SCL predictors published in the last 5 years.

The protein domain is still considered one of the most critical evolutionary unit in the cellular, molecular mechanism. Hence, providing the quantitative measurements of the domain contents of proteins would be essential to give insights on the dynamics of the cell’s biochemical machinery. Bioinformatics, as a quantitative science, has successfully provided several approaches to comprehend the domain contents and their dynamics. It was done mainly with supervised learning approach. In eukaryote domain, there is the tendency of transcription factor domain avoidance in the higher organism, and co-occurrence in a single cell has shed light on the complexity of domain functionality. It is widely assumed that protein domain tends to avoid each other as the organism gained more sophisticated molecular features. However, more sample organisms should be provided to obtain better insight on the domain co-occurrence in the cells. This systematic review was conducted by searching literature in Google Scholars and PubMed. To this end, automatic pipelines should be provided by software packages such as DOMOSAIC with the help of the R-based scripting to uphold the statistical significance. The latest updates on this topic are covering annotation on the orphan domain in Drosophila, and architecture plasticity in the eukaryote.

Basic Local Alignment Sequencing Tool (BLAST) is a bioinformatics tool used for analyzing nucleotide sequences with regards to their similarity. BLAST can be found online on biological databases such as the National Center for Biotechnology Information (NCBI) and other such repositories. The mechanism of BLAST allows the target sequence to be compared with other sequences to find regions of local similarity, and thus, a comparability quotient that determines the resemblance between the sequences is created. Due to the open-platform nature of the online databanks, several sequences can be accepted with little to no interjections regarding the quality of sequence submitted. An example of unclean nucleotide sequences can be based on the number of non-template nucleotides, denoted as “N,” present within the sequence. Here we develop a self-established nucleotide sequence reading program known as “DNAChecker,” which helps identify the quality of the target sequence and therefore proposes the effectiveness of the BLAST result. DNAChecker is an inbuilt, program that runs on Python 3.4 and was implemented in the United States Agency for International Development (USAID) project conducted in Indonesia International Institute for Life Sciences. Although DNAChecker has proven to be useful, it has a lot of room for improvements, such as having a more objectively accurate means of differentiating between good and bad sequences.

Cancer is still an epidemiological disease in Indonesia. Drug development against cancer still relies to pharmacological laboratories and natural chemicals, which could have side effects. Cancer drug development has entered the stage of molecular biology, where the interaction of ligand chemical structure with receptor protein can be studied with high accuracy. Various chemical compounds, ranging from synthetic, semi-synthetic, to natural materials, developed for the purpose to fight one of the most dangerous diseases. In the context of the development of herbal-based drugs, there has been found heaps of natural compounds, curated and annotated, in various databases belonging to China, Taiwan, Indonesia, Japan, and several other countries. However, problems arise when choosing the best bioactive compounds to develop against cancer. Complexity arises because the metabolic pathway of cancer is very diverse, depending on the type and phase of cancer. Therefore, in this systematic review, we developed a machine learning approach to screen for these bioactive compounds, then took the best candidates for molecular simulation operations that would be tested for validity in wet experiments. Thus, the automation of the candidate drug development process for cancer could be achieved with great significance. It is known that the most effective and efficient machine learning method was Naive Bayes, but the best in processing large amounts of compound data was classfier SVM. The future of complex bioactive compounds data could be secured by employing deep learning method. Keywords: machine learning, drug development, natural material compounds, metabolic pathways, cancer

LncRNAs (Long noncoding RNAs) are novel group of ncRNAs and has been discovered to be pervasively transcripted in the genome, characterized as endogenous cellular RNAs consist of more than 200 nucleotides. They are ordered in view of function, transcript length, relation with protein-coding genes and other functional DNA elements, and subcellular localization. Theranostics is a novel study in medicine that combines specific targeted biomolecules based upon molecular-based test. As novel finding in the field of molecular medicine, lncRNA is indispensable tools in theranostics based medicine that allows specific targeting of molecular pathway for diagnostics and therapeutics. LncRNAs may execute as signals, decoys, guides, and scaffolds in their natural capacities. LncRNA expression is controlled by transcriptional and epigenetic factors and processes. LncRNAs also relate detracting biological programs. Here we reviewed lncRNAs in disorders/diseasest horoughly based on CONDBITs perspectives, i.e.: cardiology, oncology, neurology and neuroscience, dermatology, the biology of molecular and bioinformatics, immunology, and technologies (related with “-omics”; transcriptomics and “nano”; nanotechnology). It was narrated the lncRNA biomarkers that abundant in cardiovascular, neurodegenerative, dermatology, and immunology perspective. However, as cancer is the most widely studied disease, more biomarkers are available for this particular case. There are abundant cancer-associated lncRNAs. The most frequent learned lncRNA molecules in cancer are HOTAIR, MALAT1, LincRNA-p21, H19, GAS5, ANRIL, MEG3, XIST, HULC. LncRNAs in cancer diagnosis and monitoring, e.g.: H19 and AA174084 (gastric), HULC (hepatocellular), PCA3 (prostate). Prognostic lncRNAs, e.g.: HOTAIR and NKILA (breast), MEG3 (meningioma), NBAT-1 (neuroblastoma), SCHLAP1 (prostate). LncRNAs predicting therapeutic responsiveness, e.g.: CCAT1 (colorectal), HOTAIR (ovarian). Thus, it is concluded that the CONDBIT perspective is useful to describe the encouraging outlook of this transcriptomics-based medicinal approach.

Diagnosis is a crucial step to identify the disease that experienced by the patient. Diagnosis includes information gathering, integration, and interpretation. However, diagnosis process is not an easy task. Diagnostic accuracy is depending on the experience and cognitive ability of diagnosticians. The new algorithm called deep learning that is developed by simulating the human visual mechanism has been implemented in medical diagnostics. One of the diseases that can be diagnosed by using deep learning algorithm is the breast cancer. Several studies showed that deep learning algorithm can be used for detecting and classifying lesions, detecting mitosis, and predicting specific gene status. In this review article, 16 research journals were reviewed and discussed. The limitations of each algorithm are provided. All of the journals showed that deep learning algorithm has high diagnostics accuracy in assisting the professional diagnosticians to determine diagnosis outcome accordingly.

COVID-19 pandemic has no immediate ending in sight, and any significant increasing cases were observed worldwide. Currently, there are only two main strategies for developing COVID-19 drugs that predominantly use a proteomics-based approach, which are drug repurposing and herbal medicine strategies. However, a third strategy has existed, called small interfering RNA or siRNA, which is based on the transcriptomics approach. In the case of SARS-CoV-2 infection, it is expected to perform by silencing the viral gene, which brings the surface glycoprotein (S) gene responsible for SARS-CoV-2 viral attachment to the ACE2 receptor on the human host cell. This third approach applies a molecular simulation method comprising data retrieval, multiple sequence alignment, phylogenetic tree depiction, 2D/3D structure prediction, and RNA-RNA molecular docking. The expected results are the prediction of 2D and 3D structures of both siRNA and mRNA silenced S genes along with a complex as the result of a docking method formed by those silenced genes. An Insilco chemical interaction study was performed in testing siRNA and mRNA complex’s stability with the confirmation result of a stable complex which is expected to be formed before mRNA reaches the ribosome for the translation process. Thus, siRNA from the S gene could be considered a candidate for the COVID-19 therapeutic agent.

Recently, the world is facing the outbreaks of severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 and the number of infected patients is increasing every day. Researchers are doing their best to find the most effective treatment to tackle this deathly virus. Several approaches had been proposed to be tested in the lab for the efficacy but none of them are qualified to be use as the treatment of the COVID-19. Therefore, the aim of this study is to design a vaccine based on epitope, which were obtained from the nucleocapsid phosphoprotein (N protein). In addition, 38 samples of SARS-CoV-2 Isolates were being retrieved from the GISAID Database and NCBI GenBank. Later on, these samples will be used for checking the evolutionary relationship of the SARS-CoV-2 and also, to determine whether this nucleocapsid proteins are well-conserved (less or even no mutations occur at all) and whether there was any evolutionary relationship between the recent coronavirus with the previous coronavirus by conducting the phylogenetic analysis. Then, we wanted to see the molecular interaction between the human BCR/FAB receptor with the predicted peptides through the molecular docking process. All of the peptides were generated by the IEDB analysis tools and have already been tested for the antigenicity, so the one that was being docked are the peptide that has antigen properties. Based on the analysis that had been done, we would like to recommend the PEP1 as an epitope-based peptide vaccine candidate to deal with the SARS-CoV-2 outbreaks.

Water as one of the main sources of life, holds an important aspect to public health and safety. Up until now there have been many concerns about water pollution especially in developing countries. Heavy polluted water that is not treated well could cause many concerning diseases that can lead to deaths. Contaminants that came from chemical, physical, and biological compounds are commonly found in the water sources. Gram negative bacteria (GNB) has been shown to develop multiple drugs and antibiotics resistant, making the infection more fatal. This has become a major concern on public health especially in water treatment as it becomes more challenging. Our study investigates human coagulation factor II that is responsible for blood clotting as a possible method for water treatment against GNB. By investigating the coagulation protein interaction with several bacterial lipopolysaccharides proteins and calculating the binding affinity of the interaction, the results show factor II has a lower binding affinity compared to previously studied factor VII. This shows possibilities of factor II to hydrolyse several gram-negative bacteria to act as a potential treatment against these GNBs.