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Ade Arsianti
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INDONESIA
Indonesian Journal of Medical Chemistry and Bioinformatics
Published by Universitas Indonesia
ISSN : -     EISSN : 29633818     DOI : https://doi.org/10.7454/ijmcb
Core Subject : Science,
Chemistry Computer Science & IT
The Indonesian Journal of Medical Chemistry and Bioinformatics (IJMCB) provides a forum for disseminating information on both the theory and the application of in silico, in vitro, and in vivo methods in the analysis and design of molecules, phytochemistry, medicinal chemistry and bioinformatics. Indonesian Journal of Medical Chemistry and Bioinformatics was published by Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia. This peer-reviewed academic open access journal has its first publish in in August 2022 and formerly publish every March and August. The scope of the journal encompasses papers which report new and original research and applications in the following areas: 1. Phytochemical and Medicinal chemistry (identification of targets, design, synthesis and evaluation of biological target) 2. Bioinformatics (genomic profiling, mutation analysis) 3. Molecular modeling (pharmacophore, molecular docking, molecular dynamic simulation) 4. Protein Modeling 5. Network Pharmacology and protein-protein interaction 6. Genomic 7. Metagenomics
Arjuna Subject : Kedokteran - Kedokteran (Lain-Lain)
Articles 36 Documents
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Innovation in Bioinformatics: Recent tools, Database and Involvement of Artificial Intelligence Ahmed, Aziz; Shuaib, Mohd; Banga, Abdulbasid; Ahmad, Rizwan
Indonesian Journal of Medical Chemistry and Bioinformatics
Publisher : UI Scholars Hub

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Abstract

Bioinformatics has evolved in recent years into a crucial subject and a well-liked research area that is interconnected with many approaches and disciplines. The capacity of bioinformatics and its approaches to tackle challenging biological problems and promote research and development. There are various tools and database which are used in bioinformatics. AI is the capacity of a computational system to carry out various activities associated with intellectual beings and as a computer system's imitation of human intelligence processes. The bioinformatics applications with artificial intelligence have the capacity to annotate the data in the direction of logical conclusions. By combining AI and bioinformatics molecular dynamic simulations, molecular docking studies, annotations of biological sequences, computational drug design, and gene prediction can be analyzed effectively. The structural bioinformatics tools with artificial intelligence (AI) are effective approaches for designing novel active chemicals to treat neurological diseases and cancer. Immunoinformatics, vaccinology, health informatics, medical informatics, medical science, and pharmaceutical sciences are just a few of the health sciences that have benefited greatly from advances in AI and bioinformatics. Future developments in omics and other fields are predicted to generate large amounts of data quickly, and bioinformatics will be essential in managing, analyzing, and discovering new uses for this data. Bioinformatics will be crucial in saving time and costs by applying AI to examine the massive data sets. Additionally, it will hasten biological discoveries, particularly those related to health, biomedical research, and robotic surgery.
Prothrombin Time (PT), Activated Partial Thromboplastin Time (APTT), Fibrinogen, and D-dimer in Coronavirus Disease 2019 Outcome Atmaja, Fredy Wirya; Adiyanti, Sri Suryo; Kristanty, Diyah; Dwira, Surya; Kusmardi, Kusmardi
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 1
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COVID-19, caused by SARS-CoV-2 has been reported to be associated with coagulopathy and DIC. This study aimed to investigate the profiles and differences of PT, APTT, fibrinogen, and D-dimer in COVID- 19 outcome. This retrospective cohort was conducted at Central Laboratory Clinical Pathology Department of dr. Cipto Mangunkusumo Hospital from July – December 2020. Demographic, clinical, and laboratory data were extracted from EHR and compared between poor and good outcome. Ninety-seven subjects were confirmed positive COVID-19, 45 of whom (46.4%) were in poor outcome group, while 52 subjects (53.6%) were in good outcome group. Median of PT 11.0” (9.7-28.3), APTT 38.4” (23.9-121), fibrinogen 484.8 mg/dL (51.2-940.9), and D-dimer 1,800 µg/L (190-35,200). Longer PT, APTT, and higher D-dimer (p < 0.05), while lower fibrinogen (p > 0.05) was found in poor outcome group. There were significant differences of PT, APTT and D-dimer in COVID-19 outcome.
Molecular Insights into Propylthiouracil as a Thyroid Peroxidase Inhibitor: A Computational Study Approach Suryandari, Dwi Anita; Yunaini, Luluk; Sunaryo, Hadi; Istiadi, Khaerunissa Anbar; Pratomo, Irandi Putra
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 1
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Abstract

Thyroid peroxidase (TPO) is a crucial enzyme in the biosynthesis of thyroid hormones, catalyzing the iodination of tyrosine residues in thyroglobulin and the coupling of iodotyrosines to form thyroxine (T4) and triiodothyronine (T3). Propylthiouracil (PTU) is an antithyroid drug commonly used to manage hyperthyroidism by inhibiting TPO. Understanding the molecular interactions between TPO and PTU can provide insights into the inhibitory mechanisms and guide the design of more effective antithyroid medications. Objective: This study aims to elucidate the binding interactions between TPO and PTU through molecular docking, providing a detailed understanding of how PTU inhibits TPO activity. Methods: The three-dimensional structure of TPO was obtained from Prosite and modelling by swissmodel and prepared for docking. The structure of PTU was optimized, and molecular docking was performed using AutoDock. The binding affinity, binding poses, and key interactions between TPO and PTU were analyzed. Visualization of the docking results was performed using PyMOL to identify critical residues involved in PTU binding. Results: The docking analysis revealed that PTU binds effectively to the active site of TPO with a binding affinity of -5.45 kcal/mol. The interaction involves coordination with the heme group and several key residues, including His239, which coordinates the heme, and Ser314, which forms hydrogen bonds with PTU. Additionally, hydrophobic interactions with residues Phe241 and Ile399 stabilize the binding of PTU in the active site. Conclusion: The docking study highlights the significant interactions between PTU and TPO, elucidating the molecular basis of TPO inhibition by PTU. The binding affinity and key interactions identified in this study provide a foundation for the design of more potent antithyroid drugs.
Metabolite Biomarker Discovery for Lung Cancer Using Machine Learning Fajarido, Ariski; Erlina, Linda; Tedjo, Aryo; Fadilah, Fadilah; Arozal, Wawaimuli
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 1
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Abstract

Lung cancer is the leading cause of cancer death worldwide. About 2.1 million lung cancer patients were diagnosed in 2018, accounting for about 11.6% of all newly diagnosed cancer cases. For lung cancer, blood is the first choice as a source of screening biomarker candidates. Blood biomarkers provide a snapshot of the patient's entire body, including the primary tumor, metastatic disease, immune response, and peritumoral stroma. However, sputum sampling, bronchial lavage or aspiration, exhaled breath (EB), and airway epithelial sampling represent unique samples for lung cancer and other airway cancers as potential sources for alternative biomarkers. Metabolites are products of cell metabolism that are unique biomarkers in a disease. In this article, we aim to find metabolite biomarkers using machine learning. Metabolite data were obtained from Metabolomic workbench, while detection and identification were performed in silico. From 82 samples, controls and cancers, we found 158 metabolites and analyzed them. From the analysis, we found 3 metabolites that play an important role in lung cancer and found 1 metabolite that is the most influential. From there we found that glutamic acid is one of the best biomarker candidates we provide for detecting lung cancer. However, this simulation still needs to be improved in order to find other biomarkers that can provide a better detection of lung cancer
The Prospect of Indonesian Herbal as An Alternative Treatment for Covid-19 Patient: A Literature-Based Study SUNARYO, HADI; Hidayati, Wahyu
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 1
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Coronavirus disease 2019 (COVID-19) is a new disease caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease has several symptoms from mild to severe and could lead to death for comorbidities and the elderly. Therefore, the infections and mortality cases keep growing day by day, moreover many authorities are trying to suppress it by applying several health protocols, including medicines. Several studies were conducted on drug discovery from plants to cure the disease. The article aimed to do a narrative review about the prospect of JAMU becoming an alternative medicine to cure COVID-19 by mapping the literature deposited on PubMed which reported medicinal plants as an alternative medicine for COVID-19 by using a text mining program. There are approximately 30,191 articles on PUBMED related to COVID-19 and medicine. Medicinal plants with antiviral and anti-inflammatory activities are the best plants for COVID-19. JAMU, an Indonesian traditional medicine, has an outstanding possibility to be applied in the COVID-19 strategy to recover patients and prevent infections.
In Silico Modelling and Docking Simulation of EGFR-Targeted Diphtheria Toxin Chimera with Various Targeting Moieties Afif Naufal, Muhammad, BMed; Ansell Susanto, Benedictus, BMed; Gunawan, Talitha Dinda, BMed; Ridha Lukman, Azhar, BMed; Rizqina, Alifa Rahma, BMed; Misbahul Fuad, Muhammad, BMed
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 1
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Introduction: Cancer is a major etiology of death worldwide due to high mortality and suboptimal medicine. However, an emerging field, targeted therapy enabled a more selective and effective therapeutic action. This article aims to analyze in-silico the hypothetical targeted therapy agent that is combinations of conjugate consisting of EGFR targeting moieties and diphtheria toxin (DT-390). Method: Our novel peptide is a conjugate of a novel EGFR targeting peptide and DT-390, forming a chimera. The tertiary structure was predicted using AlphaFold 2.0. The best IDDT scoring and stereochemistry profiles were utilized. The HADDOCK2.4 webserver modelled the docking between our model and EGFR dimers, limited to its active residues. Gibbs free energy analysis, dissociation constants, and interfacial contacts are the primary outcomes measured. Results: The confidence of the models ranged from moderate to high. The model conjugated with native hEGF (ΔG -14 kcal/mol) provided the best confidence compared to our novel peptide (ΔG -12.8 kcal/mol). Higher valences of peptides were found to have better confidences (hEGF ΔG -19.3 kcal/mol; EGFR de novo ΔG -14.3 kcal/mol). Our findings correspond to an in vitro study by Qi et al that concludes a bivalent hEGF is more effective than monovalent. However, the linker used also displays considerable bonding to the target. This may be from the linker’s considerable flexibility that allows it to accidentally interact with EGFR active residues. It is to be noted that the interactions formed were nonspecific and therefore unlikely to cause off-target effects. Conclusion: Our novel EGFR targeting peptide is effective in increasing selectivity of DT-390 to EGFR active residues. Our study does not consider the structural changes that might occur due to erroneous binding to other receptors. Further docking and molecular dynamics studies are important to further develop this novel system as a targeted therapy agent.
Pathogenesis, Diagnosis, and Examination of Corynebacterium diphtheriae Ningsih, Ika; Safrullah, Muhammad Iqbal
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 2
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Corynebacterium diphtheriae is a rod-shaped bacterium, Gram-positive (purple), growth requires the presence of oxygen or can live with oxygen or without oxygen, nonmotile, non-capsular, non-sporing, catalase positive. Most species ferment carbohydrates such as fructose, galactose, glucose, maltose, and mannose and produce an exotoxin called diphtheria toxin (DT) which can cause diphtheria, a respiratory infection characterized by sore throat and the production of a thick layer / gray pseudomembrane and generally affects children aged 15 years and under and is very vulnerable in people who are not immunized and in low immune systems. Diphtheria is a dangerous and life-threatening disease if not detected early, so clinical diagnosis must be made immediately. Therefore, clinical diagnosis methods must be supported by laboratory examinations to detect the bacteria. Examinations that can be performed for the diagnosis of bacteria of the genus Corynebacterium include culture examination on growth media, toxicity test/toxin identification, serology test, histology examination and imaging test, biomarker test, and PCR (Polymerase Chain-Reaction) test.
In Silico Analysis of CD40 Mutations and Their Implications for Quinoline-benzoic acid derivatives Based Therapy in Graves' Disease Yunaini, Luluk; Kristanty, Diyah; Sari, Puji; Dwira, Surya; Suryandari, Dwi Anita; Bustami, Arleni
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 2
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Graves' disease is an autoimmune disorder in which the CD40-CD154 interaction plays a critical role in T-cell activation. In this study, in silico methods were employed to analyze the binding interactions of quinoline-benzoic acid derivatives (NSB, FSB, and NQB) with the CD40 receptor and to investigate the implications of specific CD40 mutations for drug efficacy. In this reseach conducted by molecular simulation approach with molecular docking Results Mutation analysis of CD40 identified alterations in key residues, such as R203C, which may impact ligand-independent activation and downstream TRAF binding, crucial for signal transduction. These findings highlight the therapeutic potential of quinoline-benzoic acid derivatives for targeting CD40 in Graves' disease, particularly in the context of receptor mutations. The integration of molecular docking, mutation analysis, and pharmacokinetic profiling provides a comprehensive framework for designing effective CD40-targeted therapies.
Phytochemistry, Antioxidant and Cytotoxic Activities of Hibiscus (Hibiscus Rosa-Sinensis) Extract on MCF-7 Breast Cancer Cells Arsianti, Ade; Suhaima, Isma Zahira; Gill, Steven; Fajrin, Ajeng Megawati; Nadapdap, Lince Dameria; Azizah, Norma Nur; Tanimoto, Hiroki; Hirota, Shun
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 2
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Background: Breast cancer is the most common type of cancer in women with a very high mortality rate. This cancer is caused by uncontrolled growth of breast cells. Treatments for this malignancy are surgery, chemotherapy, and radiotherapy, however those methods can cause adverse effects and quite expensive. Complementary and alternative medicines (CAMs) are also used to support those main treatments, one of them is herbal medicine. Hibiscus rosa-sinensis is known to have various phytochemical components with antioxidant, antibacterial, anticancer, and analgesic activities. This study is aimed to determine the phytochemical composition, antioxidant activity and cytotoxicity of Hibiscus rosa-sinensis extract towards MCF-7 breast cancer cells. Method: Dried Hibiscus rosa-sinensis was milled to a powder, subsequently extracted by multilevel maceration method using n-hexane, ethyl acetate and ethanol as solvents. Phytochemical components were analyzed by phytochemistry test and thin layer chromatography (TLC). Antioxidant activity was determined using DPPH method, whereas cytotoxic activity towards MCF-7 breast cancer cells was evaluated by MTT assay. Results: Hibiscus rosa-sinensis were proved to contain triterpenoids in all extracts, alkaloids in n-hexane and ethyl acetate extracts, flavonoids and tannins in ethyl acetate and ethanol extracts, and steroids in n-hexane extract. TLC analysis showed n-hexane extract contains 8 phytochemical compounds, ethyl acetate contains 6 compounds, and ethanol extract contains 2 phytochemical compounds. Antioxidant activity of Hibiscus rosa-sinensis extracts towards DPPH free radicals were highly active with IC50 value of 1.56 µg/mL for ethyl acetate extract and 42.30 µg/mL for ethanol extract. Cytotoxicity of ethyl acetate extract of Hibiscus rosa-sinensis towards MCF-7 breast cancer cells was moderately active with IC50 value of 79.37 µg/mL. IC50 value of n-hexane and ethanol extracts were 125.23 µg/mL and 210.77 µg/mL, respectively, in which both were categorized in weakly active. Conclusion: Hibiscus rosa-sinensis contains several phytochemical components which showed highly active antioxidant activity towards DPPH free radicals and moderate-to-weak cytotoxic activity towards MCF-7 breast cancer cells.
Analysis of Differentially Expressed Genes (DEG) and Upstream Regulator Proteins Indicates That Inhibition of Transforming Growth Factor Beta 1 (TGFB1) is A Potential Target for Acne Inversa Veranita, Weri; Fauziah, Siva; Nurbaya, Siti
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 2
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Acne inversa (AI) is a chronic inflammatory skin disease characterized by painful nodules, abscesses, and scarring, primarily in intertriginous areas. This study aims to identify potential therapeutic targets for managing acne inversa based on the analysis of differentially expressed genes (DEG). The expression targets of these genes were then validated for their potential as biomarkers, and upstream regulator proteins (URPs) were identified from the resulting DEG. DEG analysis on the GEO dataset GSE122592 (acne inversa vs. healthy donor skin) revealed five DEG that can serve as biomarkers for acne inversa, with a sensitivity and specificity of (100%). These DEG—IL10, GZMB, FASLG, PRF1, and HLA-DPB10—are genes associated with autoimmune thyroiditis (AIT). AIT has previously been significantly linked to acne vulgaris. URP analysis indicates that inhibition of Transforming Growth Factor Beta 1 (TGFB1) is a therapeutic target that could be used to downregulate these five DEGs, returning their expression to healthy skin levels.

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