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"ABSTRACT

Malaria is one of problematic infectious diseases worldwide. The absence of an effective vaccine and the spread of drug resistant strains of Plasmodium clearly indicate the necessity for the deveploment of new chemotherapeutic agents. Recent method being developed is searching a new drug of antimalarial using in silica screening, or also know as virtual screening. One of enzyme target that important for growth of the malaria parasite is P/asmodium /a/ciparum Enoyl' Acyl Canier Protein Reductase (PfENR). Inhibition of this enzyme cause the fatty acid biosynthesis type ll will be tem1inated. In this research, in silica screening was performed using GOLD softwa,<;_ to find inhibitor candidates of PfENR by using I igands from the natural compound database of Medicinal Plants in Indonesia. On the GOLD software moleculer docking experiments were perfom1ed between ligands and macromolecule target PfENR. This target that has been optimized with residue removal and charges addition. Ligand is expected to be the PfENR inhibitors. Based on the results obtained from the in silico screening there were S inhibitor candidates which expected to be developed as an antimalarials. These compounds \\"ere Kacmpferol 3-rhamnosyl-(1-3)-rhamnosyl- (1-6)­glucoside, Cyanidin 3.5-di-(6-1mlonylglucoside), 8-Hydroxyapigenin 8-(2",4"­disulfatoglucuronidc). Epigallocmechin 3.5.-di-O-gallate, a··;d Querceti.r1 3.4'-dimethyl ether 7-alpha-L- Arabinofuranosyl-(1-6)-glucoside with the GoldScore ranged from 80,63 to I 00,4 I."

"Drug discovery is all about finding small molecules that interact in a desired way with larger molecules, namely proteins and other macromolecules in the human body. If the three-dimensional structures of both the small and large molecule are known, their interaction can be tested by computer simulation with a reasonable degree of accuracy. Alternatively, if active ligands are already available, molecular similarity searches can be used to find new molecules. This virtual screening can even be applied to compounds that have yet to be synthesized, as opposed to "real" screening that requires cost- and labor-intensive laboratory testing with previously synthesized drug compounds. Unique in its focus on the end user, this is a real "how to" book that does not presuppose prior experience in virtual screening or a background in computational chemistry. It is both a desktop reference and practical guide to virtual screening applications in drug discovery, offering a comprehensive and up-to-date overview. Clearly divided into four major sections, the first provides a detailed description of the methods required for and applied in virtual screening, while the second discusses the most important challenges in order to improve the impact and success of this technique. The third and fourth, practical parts contain practical guidelines and several case studies covering the most important scenarios for new drug discovery, accompanied by general guidelines for the entire workflow of virtual screening studies. Throughout the text, medicinal chemists from academia, as well as from large and small pharmaceutical companies report on their experience and pass on priceless practical advice on how to make best use of these powerful methods."

"Diabetes merupakan salah satu penyebab kematian terbesar di dunia. Salah satu obat untuk menangani diabetes adalah penghambat DPP-4. Obat penghambat DPP-4 berpotensi menjadi obat diabetes aksi panjang, menyelesaikan masalah tatalaksana diabetes terkait ketidakpatuhan pasien dalam mengonsumsi obat. Namun, belum ada obat penghambat DPP-4 aksi panjang dengan hasil memuaskan. Obat penghambat DPP-4 juga masih memiliki masalah berupa efek samping pankreatitis dan nyeri sendi. Oleh karena itu, penelitian ini bertujuan menemukan kandidat penghambat DPP-4. Penapisan virtual dilakukan dengan model HKSA pembelajaran mesin yang dilatih dengan data aktivitas DPP-4 menggunakan pustaka PyCaret. Hasilnya didapatkan tiga model yaitu model klasifikasi biner dan multiclass untuk menentukan aktivitas molekul dan model regresi untuk memprediksi tingkat aktivitas. Model dievaluasi dengan membandingkan kualitas model dan hasil dengan penelitian Hermansyah et al. Hasil penelitian menunjukkan bahwa model klasifikasi biner yang dibuat memiliki kualitas yang lebih baik, sementara model regresi sedikit lebih rendah. Perbedaan ini disebabkan oleh variasi data dan pendekatan penggunaan model. Kesepuluh senyawa yang diidentifikasi berbeda dengan hasil Hermansyah. Nilai pIC50 tertinggi dari penelitian ini adalah 8,44, sedangkan Hermansyah mencapai 9,21. Namun, validitas model dapat dipertanggungjawabkan dengan hasil analisis SHAP yang menunjukkan peran substruktur farmakofor obat dalam keputusan model.

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Diabetes is one of the biggest causes of death in the world. DPP-4 inhibitor is a drug to treat diabetes. DPP-4 inhibitors can potentially be long-acting diabetes drugs, solving diabetes management problems related to patient non-compliance with taking medication. However, there are no long-acting DPP-4 inhibitors with satisfactory results. DPP-4 inhibitors also still have problems in the form of side effects of pancreatitis and joint pain. Therefore, this study aims to find candidate DPP-4 inhibitors. Virtual screening was performed with machine learning QSAR model trained with DPP-4 activity data using PyCaret library. Three models were obtained: binary and multiclass classification models to determine molecular activity and regression models to predict activity levels. The model was evaluated by comparing the quality and results with Hermansyah et al’s research. The results showed that binary classification model had better quality, while regression model was slightly lower. This difference is caused by variations in data and approaches to using the model. Ten compounds identified were different from Hermansyah's results. The highest pIC50 value from this study was 8.44, while Hermansyah reached 9.21. However, validity of the model is justified by SHAP analysis results which shows the role of drug pharmacophore substructure in model decisions."

"ABSTRAKVirus dengue merupakan masalah kesehatan yang dihadapi oleh dunia, terutama negara-negara tropis dan subtropics seperti Asia, Afrrika dan Amerika. Berdasarakan data yang dikumpulkan oleh WHO terdapat sekitar 50-100 juta kasus infeksi dengue diseluruh dunia setiap tahunnya, akan tetapi belum terdapat vaksin maupun antivirus yang mampu mencegah dan mengobati penyakit ini. Penelitian ini dilakukan untuk memanfaatkan bahan alam untuk menginhibisi aktivitas enzimatis dari sisi aktif NS5 Methyltransferase yang berperan dalam mensintesis cap-RNA virus dengue. Senyawa inhibitor yang digunakan adalah senyawa bahan alam yang diunduh dari pangkalan data UNPD sebanyak 229.000. Metode insilico yang digunakan adalah metode penampisan (virtual screening) yang dikemudian dilakukan penambatan molekul (molecular docking) terhadap inhibitor pada sisi aktif protein target. Didapatkan sebanyak 3 senyawa inhibitor terbaik yang telah melalui tahap uji farmakologi untuk dapat dijadikan sebagai kandidat obat.

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ABSTRACTDengue virus is a health problem faced by the world, especially tropical countries and subtropics such as Asia, Africa and America. Based on data collected by WHO there are around 50-100 million cases of dengue infection throughout the world each year, but there are no vaccines or antiviral agents that are able to prevent and treat this disease. This study was conducted to utilize natural materials to feed enzymatic activity from the active side of NS5 Methyltransferase which plays a role in synthesizing dengue virus RNA. The inhibitor compounds used were natural material compounds downloaded from UNPD database totaling 229,000. The insilico method used is a method of screening (virtual screening) which is carried out by molecular tethering (molecular docking) to the inhibitor on the active side of the target protein. The best 3 inhibitor compounds were obtained which had gone through the pharmacological test stage to be used as drug candidates."

"ABSTRAKKanker adalah suatu kondisi yang ditandai dengan adanya pertumbuhan abnormal dari sel-sel tubuh yang tidak terkontrol dan mampu mempengaruhi sel normal lainnya. Saat ini banyak dilakukan penelitian untuk mencari senyawa-senyawa baru yang berpotensi sebagai antikanker. Salah satu cara yang digunakan untuk mendukung analisis ini adalah dengan metode in silico. Selain itu, metode ini juga mendukung green chemistry yang cukup diminati akhir-akhir ini. Dalam penelitian ini, diteliti sepuluh senyawa dari basis data herbal hasil Virtual Screening yang memiliki aktivitas sebagai inhibitor enzim α-Glukosidase manusia. Model tiga dimensi (3D) enzim dikonstruksi berdasarkan struktur kristal α-glukosidase S. solphataricus (MalA) dan sub-unit N-terminal Maltase Glukoamilase manusia (NtMGAM). Penambatan sepuluh senyawa yang akan diuji; yakni 6-Deoxoteasterone, Diosgenin, Withangulatin A, Withanolide, Lanosterol, Cassiamin C, Asiatic Acid, Isoarborinol, Yamogenin, dan Lantic Acid, ditambatkan menggunakan AutoDock 4.2 dan hasilnya menunjukkan nilai ΔG secara berturut-turut yakni -9,09; -8,76; -8,73; -8,66; -8,65; -8,65; -8,64; -8,59; -8,48; dan -8,45 kkal/mol. Analisis kemudian dilanjutkan dengan melakukan simulasi diamika molekuler selama 2 nanodetik menggunakan Amber 11. Sebagai kontrol positif, digunakan senyawa Castanospermine dan 1,6-Epi-Cyclophellitol. Hasil analisis menunjukkan bahwa secara umum, pada kompleks senyawa ligan dan makromolekul ada interaksi yang kuat dan stabil pada residu Asp587, Asp511, Asp 398, Trp 274, dan Phe 620.

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ABSTRACT

Cancer is a condition that characterized by the abnormal growth of cells that are not controlled and capable to affect normal cells. Nowadays, there's a lot of research to find new compounds that have the potential as an anticancer. One of the ways to support this analysis is the in silico. In addition, this method also supports green chemistry that considerable interest lately. This study will investigated ten compounds from Herbal Database that have been researched before through Virtual Screening, that have the activity as an inhibitor of α-glucosidase. Three-dimensional (3D)'s model was constructed by the crystal structure of the enzyme α-glucosidase S. solphataricus (mala) and sub-units of N-terminal human maltase Glucoamylase (NtMGAM). 6-Deoxoteasterone, Diosgenin, Withangulatin A, Withanolide, lanosterol, Cassiamin C, Asiatic Acid, Isoarborinol, Yamogenin, and Lantic Acid was tethered using Autodock 4.2 and the results show the value of ΔG are -9.09; -8.76; -8.73; -8.66; -8.65; -8.65; -8.64; -8.59; -8.48; and -8.45 kcal/mol. The analysis then continued by performing simulation on mollecular dynamics for 2 nanoseconds using Amber 11. Castanospermine and 1,6-Epi-Cyclophellitol was used as the positive control. The analysis showed that in general the complex of ligand and macromolecule, that there is a strong and stable interaction at residues Asp587, Asp511, Asp 398, Trp 274 and Phe 620."

"SIRT1 merupakan salah satu dari tujuh sirtuin manusia SIRT1-7 yang termasuk dalam HDAC kelas III. Sejumlah penelitian tentang SIRT1 telah banyak dibuktikan berperan dalam regulasi metabolisme seluler serta sering dihubungkan dengan pathogenesis penyakit seperti kanker dan penyakit nuerodegeneratif. Untuk menemukan kandidat obat yang baik beberapa menggunakan metode in silico sebagai tool yang cepat dalam menganalisis aktifitas biologis obat secara virtual.Metode in silico dalam penelitian ini dimulai dari penapisan virtual, penambatan molekul dan simulasi dinamika molekuler yang menggunakan database herbal Indonesia untuk menemukan senyawa kandidat yang berpotensi sebagai inhibitor SIRT1.Hasilnya diperoleh ada enam senyawa kandidat dari database Herbal Indonesia yang memiliki potensi sebagai inhibitor SIRT1 yaitu 5-oxocoronaridine, 3-oxocoronaridine, 5-hydroxy-6-oxocoronaridine, dregamine, isovoacristine dan tabernaemontanine.Hasil penambatan molekul senyawa kandidat terhadap dua makromolekul SIRT1 PDB ID: 4I5I dan 4ZZI menunjukkan nilai pengikatan energi bebas senyawa kandidat mendekati dan lebih tinggi dari senyawa ligand co-kristal. Dari analisis simulasi dinamika molekuler diperoleh energi bebas MMPBSA di atas -21 kkal/ mol sedangkan occupancy ikatan hidrogen residu Ile347 dan Asp348 diatas 80 .

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SIRT1 is one of seven human sirtuins SIRT1 7 are included in class III of HDAC. A number studies of SIRT1 has been widely demonstrated a role in the regulation of cellular metabolism and linked to pathogenesis of diseases such as cancer and neurodegeneratif diseases. To find a good drug candidates could using in silico methods as a quick tool in analyzing the biological activity of drugs virtually.In silico methods in this research started from a virtual screening, docking and molecular dynamics simulations that use Indonesian herbal database to find potential candidate compounds as SIRT1 inhibitor.The result was obtained there are six candidates compound of Indonesian Herbal database that has potential as SIRT1 inhibitor that is 5 oxocoronaridine, 3 oxocoronaridine, 5 hydroxy 6 oxocoronaridine, dregamine, isovoacristine and tabernaemontanine.Docking results shown that molecule candidate compounds against two of macromolecules SIRT1 PDB ID 4I5I and 4ZZI have value of the candidate compound binding free energy approach and higher than the co crystal ligands. From the analysis of molecular dynamics simulations obtained free energy MMPBSA about 21 kcal mol while occupancy hydrogen bonding of residues Ile347 and Asp348 about 80 ."

"ABSTRACT

Histone Deacetylase (HDAC) enzymes in the human body play an important role in the transcriptional regulation of gene expression. In the last decade, HDAC inhibitors and activators have been explored and have become known as therapeutic agents for many diseases such as osteodystrophy, neurogenerative disorders, cardiomyopathy, cancer, and diabetes. In recent years, the development of HDAC inhibitors or activators to obtain new potent lead compounds has been conducted using in vitro, in vivo, and in silico methods. Some HDAC family inhibitors and activators have been discovered. But some compounds have limitations such as not selectively binding to one of the HDAC variants. Methods: At present, through bioinformation, HDAC family sequences have been revealed, and some in silico methods such as molecular modelling (homology modelling and pharmacophore modelling), virtual screening, and molecular dynamics are widely used to find and develop new potent and selective compounds. Results: The main utilization of molecular modelling in this work is intended to complete the HDAC structure that partially lacks data regarding its amino acid monomer. Virtual screening methods are helpful in finding the best binding affinity of the test compounds. By molecular dynamic simulation, the temperature, time, and pressure can be adjusted to analyze the hydrogen bond. Conclusion: Combining these in silico approaches will be a more effective and efficient solution in finding new lead compounds for HDAC drug discovery research in the future."

"ABSTRACTVirtual screening (VS) is a computational technique used in drug discovery. Virtual Screening process usually works by identifying structures that are most likely to bind the target of drug. Virtual screening is usually based on compound similarity or database docking. Thus, the identification for drugcompounds based on structure classification still remain as a challenging task. The purpose of this research is to find a new approach for ligand-based virtual screening using machine learning technique. In this paper, theclassification has been done by using Deep Belief Networks (DBN) method. The data from Nicotinamide Adenine Dinucleotide (NAD) protein target family were used for training and testing the model. This research used four protein target classes from literature and two protein target classes from DUD-E docking website. Feature were obtained from molecular fingerprint descriptor. The experiments result show that DBN method outperform the existing pharmacophore approach."

"Kanker kolorektal adalah kanker yang terletak pada rektum atau kolon dengan sel-sel yang berkembang dengan tidak terkendali. Obat kanker untuk kanker kolorektal memiliki beberapa kelemahan seperti kurangnya spesifitas dan dapat terjadinya resistensi, sehingga perlu dikembangkan terapi target untuk mengurangi kelemahan tersebut. Berbagai penelitian dilakukan untuk merancang obat yang dapat mentarget protein yang berkaitan dengan perkembangan kanker kolorektal, salah satunya adalah Matrix Metalloproteinase-9. Penghambatan dari protein tersebut memungkinkan untuk menghambat penyebaran kanker, sehingga pencarian senyawa penghambat Matrix Metalloproteinase-9 menarik untuk dilakukan. Penelitian dapat dilakukan dengan memanfaatkan metode penapisan virtual. Tujuan dari penelitian ini adalah mendapatkan 10 senyawa kandidat dengan potensi menjadi inhibitor Matrix Metalloproteinase-9. Penapisan virtual dapat digunakan untuk melakukan pencarian senyawa kandidat inhibitor dari basis data HerbalDB. Berdasarkan hasil penapisan didapatkan 10 peringkat senyawa terbaik berdasarkan energi ikatan bebas dan pose pengikatan yaitu Cassiamin C (-11,1 kkal/mol), Sikloartokarpesin (-10,9 kkal/mol), Prunin 6”-p-kumarat (-10,9 kkal/mol), Isookaninrhamnosida (-10,5 kkal/mol), 5,7,3',4'-tetrahidroksiflavanon 7-alfa-l-arabinofuranosil-(1-6)-glukosida (-10,3 kkal/mol), Kuwanon T (-10,3 kkal/mol), Boesenbergin B (-10,2 kkal/mol), Sianidin 3-arabinosida (-10,2 kkal/mol), Morusin (-10,2 kkal/mol), dan Dehidropipernonalin (-10,1 kkal/mol). Hasil tersebut menunjukkan senyawa memiliki potensi untuk menghambat Matrix Metalloproteinase-9

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Colorectal Cancer is located on the colon or the rectum of the patient with uncontrolled cell growth. Cancer drugs for colorectal cancer have several weaknesses such as lack of specificity, so it is necessary to develop targeted therapies to reduce these weaknesses. Various research were done to design a drug with purpose of targeting the protein in which is related to the growth of colorectal cancer, one of them being Matrix Metalloproteinase-9. Inhibition of Matrix Metalloproteinase-9 possibly inhibits the spread of colorectal cancer, therefore a research to find candidates is appealing. Research can be done by using the virtual screening method. The purpose of this study is to obtain 10 candidate compounds with the potential to inhibit Matrix Metalloproteinase-9. Virtual screening method is used to search for candidate compounds from HerbalDB database. Based on the screening results, the best compound rankings based on the free bond energy and binding pose were obtained, namely Cassiamin C (-11,1 kkal/mol), Cycloartocarpesin (-10,9 kkal/mol), Prunin 6”-p-coumarate (-10,9 kkal/mol), Isookaninrhamnoside (-10,5 kkal/mol), 5,7,3',4'-tetrahydroxyflavanone 7-alpha-l-arabinofuranosyl-(1-6)-glucoside (-10,3 kkal/mol), Kuwanon T (-10,3 kkal/mol), Boesenbergin B (-10,2 kkal/mol), Cyanidin 3-arabinoside (-10,2 kkal/mol), Morusin (-10,2 kkal/mol), dan Dehydropipernonaline (-10,1 kkal/mol). The results indicate potential candidates to inhibit Matrix Metalloproteinase-9."