scholarly journals Computational QSAR model combined molecular descriptors and fingerprints to predict HDAC1 inhibitors

2018 ◽  
Vol 34 ◽  
pp. 52-58 ◽  
Author(s):  
Jingsheng Shi ◽  
Guanglei Zhao ◽  
Yibing Wei
Keyword(s):  
Histone Deacetylases ◽  
Computational Models ◽  
Nearest Neighbor ◽  
Dynamic Balance ◽  
Regulation Of Gene Expression ◽  
Hdac Inhibitors ◽  
Qsar Model ◽  
Support Vector ◽  
C4.5 Decision Tree ◽  
Fold Cross Validation

The dynamic balance between acetylation and deacetylation of histones plays a crucial role in the epigenetic regulation of gene expression. It is equilibrated by two families of enzymes: histone acetyltransferases and histone deacetylases (HDACs). HDACs repress transcription by regulating the conformation of the higher-order chromatin structure. HDAC inhibitors have recently become a class of chemical agents for potential treatment of the abnormal chromatin remodeling process involved in certain cancers. In this study, we constructed a large dataset to predict the activity value of HDAC1 inhibitors. Each compound was represented with seven fingerprints, and computational models were subsequently developed to predict HDAC1 inhibitors via five machine learning methods. These methods include naïve Bayes, κ-nearest neighbor, C4.5 decision tree, random forest, and support vector machine (SVM) algorithms. The best predicting model was CDK fingerprint with SVM, which exhibited an accuracy of 0.89. This model also performed best in five-fold cross-validation. Some representative substructure alerts responsible for HDAC1 inhibitors were identified by using MoSS in KNIME, which could facilitate the identification of HDAC1 inhibitors.

scholarly journals Food Bioactive HDAC Inhibitors in the Epigenetic Regulation of Heart Failure

Nutrients ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1120 ◽  
Author(s):  
Levi Evans ◽  
Bradley Ferguson
Keyword(s):  
Gene Expression ◽  
Heart Failure ◽  
Signaling Pathways ◽  
Intracellular Signaling ◽  
Histone Deacetylases ◽  
Regulation Of Gene Expression ◽  
Hdac Inhibitors ◽  
Drug Efficacy ◽  
Histone Deacetylation ◽  
Epigenetic Modifiers

Approximately 5.7 million U.S. adults have been diagnosed with heart failure (HF). More concerning is that one in nine U.S. deaths included HF as a contributing cause. Current HF drugs (e.g., β-blockers, ACEi) target intracellular signaling cascades downstream of cell surface receptors to prevent cardiac pump dysfunction. However, these drugs fail to target other redundant intracellular signaling pathways and, therefore, limit drug efficacy. As such, it has been postulated that compounds designed to target shared downstream mediators of these signaling pathways would be more efficacious for the treatment of HF. Histone deacetylation has been linked as a key pathogenetic element for the development of HF. Lysine residues undergo diverse and reversible post-translational modifications that include acetylation and have historically been studied as epigenetic modifiers of histone tails within chromatin that provide an important mechanism for regulating gene expression. Of recent, bioactive compounds within our diet have been linked to the regulation of gene expression, in part, through regulation of the epi-genome. It has been reported that food bioactives regulate histone acetylation via direct regulation of writer (histone acetyl transferases, HATs) and eraser (histone deacetylases, HDACs) proteins. Therefore, bioactive food compounds offer unique therapeutic strategies as epigenetic modifiers of heart failure. This review will highlight food bio-actives as modifiers of histone deacetylase activity in the heart.

Comparative analysis of breast cancer detection in mammograms and thermograms

2015 ◽  
Vol 60 (1) ◽  
Author(s):  
Marina Milosevic ◽  
Dragan Jankovic ◽  
Aleksandar Peulic
Keyword(s):  
Nearest Neighbor ◽  
Region Of Interest ◽  
Texture Features ◽  
Classification Performance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Characteristic Analysis ◽  
Analysis Society ◽  
Fold Cross Validation ◽  
Neighbor Classifier

AbstractIn this paper, we present a system based on feature extraction techniques for detecting abnormal patterns in digital mammograms and thermograms. A comparative study of texture-analysis methods is performed for three image groups: mammograms from the Mammographic Image Analysis Society mammographic database; digital mammograms from the local database; and thermography images of the breast. Also, we present a procedure for the automatic separation of the breast region from the mammograms. Computed features based on gray-level co-occurrence matrices are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 texture features are extracted from the region of interest. The ability of feature set in differentiating abnormal from normal tissue is investigated using a support vector machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. To evaluate the classification performance, five-fold cross-validation method and receiver operating characteristic analysis was performed.

scholarly journals Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter

2019 ◽  
Vol 6 (2) ◽  
pp. 226-235
Author(s):  
Muhammad Rangga Aziz Nasution ◽  
Mardhiya Hayaty
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Fold Cross Validation

Salah satu cabang ilmu komputer yaitu pembelajaran mesin (machine learning) menjadi tren dalam beberapa waktu terakhir. Pembelajaran mesin bekerja dengan memanfaatkan data dan algoritma untuk membuat model dengan pola dari kumpulan data tersebut. Selain itu, pembelajaran mesin juga mempelajari bagaimama model yang telah dibuat dapat memprediksi keluaran (output) berdasarkan pola yang ada. Terdapat dua jenis metode pembelajaran mesin yang dapat digunakan untuk analisis sentimen:  supervised learning dan unsupervised learning. Penelitian ini akan membandingkan dua algoritma klasifikasi yang termasuk dari supervised learning: algoritma K-Nearest Neighbor dan Support Vector Machine, dengan cara membuat model dari masing-masing algoritma dengan objek teks sentimen. Perbandingan dilakukan untuk mengetahui algoritma mana lebih baik dalam segi akurasi dan waktu proses. Hasil pada perhitungan akurasi menunjukkan bahwa metode Support Vector Machine lebih unggul dengan nilai 89,70% tanpa K-Fold Cross Validation dan 88,76% dengan K-Fold Cross Validation. Sedangkan pada perhitungan waktu proses metode K-Nearest Neighbor lebih unggul dengan waktu proses 0.0160s tanpa K-Fold Cross Validation dan 0.1505s dengan K-Fold Cross Validation.

Zinc Dependent Histone Deacetylase Inhibitors in Cancer Therapeutics: Recent Update

2020 ◽  
Vol 26 (40) ◽  
pp. 7212-7280 ◽  
Author(s):  
Faria Sultana ◽  
Kesari Lakshmi Manasa ◽  
Siddiq Pasha Shaik ◽  
Srinivasa Reddy Bonam ◽  
Ahmed Kamal
Keyword(s):  
Gene Expression ◽  
Drug Therapy ◽  
Histone Deacetylases ◽  
Histone Deacetylase Inhibitors ◽  
Regulation Of Gene Expression ◽  
Hdac Inhibitors ◽  
Cancer Therapeutics ◽  
Rational Drug Design ◽  
Combination Drug Therapy ◽  
Combination Drug

Background: Histone deacetylases (HDAC) are an important class of enzymes that play a pivotal role in epigenetic regulation of gene expression that modifies the terminal of core histones leading to remodelling of chromatin topology and thereby controlling gene expression. HDAC inhibitors (HDACi) counter this action and can result in hyperacetylation of histones, thereby inducing an array of cellular consequences such as activation of apoptotic pathways, generation of reactive oxygen species (ROS), cell cycle arrest and autophagy. Hence, there is a growing interest in the potential clinical use of HDAC inhibitors as a new class of targeted cancer therapeutics. Methodology and Result: Several research articles spanning between 2016 and 2017 were reviewed in this article and presently offer critical insights into the important strategies such as structure-based rational drug design, multi-parameter lead optimization methodologies, relevant SAR studies and biology of various class of HDAC inhibitors, such as hydroxamic acids, benzamides, cyclic peptides, aliphatic acids, summarising the clinical trials and results of various combination drug therapy till date. Conclusion: This review will provide a platform to the synthetic chemists and biologists to cater the needs of both molecular targeted therapy and combination drug therapy to design and synthesize safe and selective HDAC inhibitors in cancer therapeutics.

scholarly journals Histone deacetylases (HDACs): characterization of the classical HDAC family

2003 ◽  
Vol 370 (3) ◽  
pp. 737-749 ◽  
Author(s):  
Annemieke J.M. de RUIJTER ◽  
Albert H. van GENNIP ◽  
Huib N. CARON ◽  
Stephan KEMP ◽  
André B.P. van KUILENBURG
Keyword(s):  
Gene Expression ◽  
Histone Deacetylases ◽  
Regulation Of Gene Expression ◽  
Hdac Inhibitors ◽  
Building Blocks ◽  
Post Translational Modification ◽  
Comprehensive Overview ◽  
Almost All ◽  
Enzyme Complexes

Transcriptional regulation in eukaryotes occurs within a chromatin setting, and is strongly influenced by the post-translational modification of histones, the building blocks of chromatin, such as methylation, phosphorylation and acetylation. Acetylation is probably the best understood of these modifications: hyperacetylation leads to an increase in the expression of particular genes, and hypoacetylation has the opposite effect. Many studies have identified several large, multisubunit enzyme complexes that are responsible for the targeted deacetylation of histones. The aim of this review is to give a comprehensive overview of the structure, function and tissue distribution of members of the classical histone deacetylase (HDAC) family, in order to gain insight into the regulation of gene expression through HDAC activity. SAGE (serial analysis of gene expression) data show that HDACs are generally expressed in almost all tissues investigated. Surprisingly, no major differences were observed between the expression pattern in normal and malignant tissues. However, significant variation in HDAC expression was observed within tissue types. HDAC inhibitors have been shown to induce specific changes in gene expression and to influence a variety of other processes, including growth arrest, differentiation, cytotoxicity and induction of apoptosis. This challenging field has generated many fascinating results which will ultimately lead to a better understanding of the mechanism of gene transcription as a whole.

scholarly journals Bayes Classifier dan Support Vector Machine dalam Klasifikasi Judul Karya Akhir Mahasiswa Program Studi PTIK UNJ

2019 ◽  
Vol 3 (1) ◽  
pp. 54-62
Author(s):  
Razi Aziz Syahputro ◽  
Widodo ◽  
Hamidillah Ajie
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Vector Space Model ◽  
Support Vector ◽  
Bayes Classifier ◽  
K Nearest Neighbor ◽  
Space Model ◽  
Fold Cross Validation

Penelitian ini dilatarbelakangi dengan dibutuhkannya sistem pengklasifikasian untuk memudahkan pihak Jurusan Teknik Elektro khususnya Program Studi PTIK untuk mengklasifikasikan judul skripsi berdasarkan peminatan. Sebelum sistem dibuat diperlukan pertimbangan dari beberapa algoritma klasifikasi yang ada, maka dari itu penelitian ini memilih 3 algoritma dari 10 algoritma terbaik menurut ICDM tahun 2006. Klasifikasi terhadap dokumen teks pendek seperti judul skripsi mahasiswa memiliki kesulitan tersendiri daripada dokumen teks panjang karena semakin sedikit kata semakin sulit diklasifikasi. Sehingga tujuan dari penelitian ini adalah untuk mengetahui algoritma yang paling efektif untuk mengklasifikasi judul skripsi. Penelitian ini terdiri dari beberapa tahap yaitu pengumpulan data, pengelompokan data melalui angket oleh dosen ahli, pre-processing text, pembobotan kata menggunakan vector space model dan tf-idf, evaluasi dengan k-fold cross validation, klasifikasi menggunakan k-nearest neighbor, naïve bayes classifier, dan support vector machine, dan analisis dengan confusion matrix. Percobaan dilakukan dengan menggunakan 266 data judul skripsi mahasiswa PTIK UNJ dari angkatan 2010-2013, dengan data terakhir berasal dari sidang skripsi pada semester 105(semester ganjil 2016/2017). Hasil dari klasifikasi menggunakan algoritma tersebut didapatkan algoritma yang paling efisien yaitu support vector machine dengan akurasi 82% dari 10 kali percobaan.

PseKNC and Adaboost-Based Method for DNA-Binding Proteins Recognition

2021 ◽  
pp. 2150022
Author(s):  
Lina Yang ◽  
Xiangyu Li ◽  
Ting Shu ◽  
Patrick Wang ◽  
Xichun Li
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Nearest Neighbor ◽  
Dna Recombination ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Jackknife Test ◽  
Optimal Feature ◽  
Fold Cross Validation

DNA-binding proteins are an essential part of the DNA. It also an integral component during life processes of various organisms, for instance, DNA recombination, replication, and so on. Recognition of such proteins helps medical researchers pinpoint the cause of disease. Traditional techniques of identifying DNA-binding proteins are expensive and time-consuming. Machine learning methods can identify these proteins quickly and efficiently. However, the accuracies of the existing related methods were not high enough. In this paper, we propose a framework to identify DNA-binding proteins. The proposed framework first uses PseKNC (ps), MomoKGap (mo), and MomoDiKGap (md) methods to combine three algorithms to extract features. Further, we apply Adaboost weight ranking to select optimal feature subsets from the above three types of features. Based on the selected features, three algorithms (k-nearest neighbor (knn), Support Vector Machine (SVM), and Random Forest (RF)) are applied to classify it. Finally, three predictors for identifying DNA-binding proteins are established, including [Formula: see text], [Formula: see text], [Formula: see text]. We utilize benchmark and independent datasets to train and evaluate the proposed framework. Three tests are performed, including Jackknife test, 10-fold cross-validation and independent test. Among them, the accuracy of ps+md is the highest. We named the model with the best result as psmdDBPs and applied it to identify DNA-binding proteins.

scholarly journals Machine Learning Models Combined with Virtual Screening and Molecular Docking to Predict Human Topoisomerase I Inhibitors

Molecules ◽  
2019 ◽  
Vol 24 (11) ◽  
pp. 2107 ◽  
Author(s):  
Bingke Li ◽  
Xiaokang Kang ◽  
Dan Zhao ◽  
Yurong Zou ◽  
Xudong Huang ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Topoisomerase I ◽  
Nearest Neighbor ◽  
Binding Energies ◽  
Support Vector ◽  
Features Selection ◽  
K Nearest Neighbor ◽  
Autodock Vina ◽  
Relative Probability ◽  
C4.5 Decision Tree

In this work, random forest (RF), support vector machine, k-nearest neighbor and C4.5 decision tree, were used to establish classification models for predicting whether an unknown molecule is an inhibitor of human topoisomerase I (Top1) protein. All these models have achieved satisfactory results, with total prediction accuracies from 89.70% to 97.12%. Through comparative analysis, it can be found that the RF model has the best forecasting effect. The parameters were further optimized to generate the best-performing RF model. At the same time, features selection was implemented to choose properties most relevant to the inhibition of Top1 from 189 molecular descriptors through a special RF procedure. Subsequently, a ligand-based virtual screening was performed from the Maybridge database by the optimal RF model and 596 hits were picked out. Then, 67 molecules with relative probability scores over 0.7 were selected based on the screening results. Next, the 67 molecules above were docked to Top1 using AutoDock Vina. Finally, six top-ranked molecules with binding energies less than −10.0 kcal/mol were screened out and a common backbone, which is entirely different from that of existing Top1 inhibitors reported in the literature, was found.

scholarly journals In Search of Selectivity: Design, Synthesis, and Biological Evaluation of New Classes of HDAC Inhibitors

Proceedings ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 63
Author(s):  
L. M. Viranga Tillekeratne ◽  
Ayad Ayad Al-Hamashi ◽  
Samkeliso Dlamini ◽  
William Taylor ◽  
Radhika Koranne ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Sequences ◽  
Metal Binding ◽  
Histone Deacetylases ◽  
Regulation Of Gene Expression ◽  
Hdac Inhibitors ◽  
Biological Evaluation ◽  
Design Synthesis ◽  
Design And Synthesis ◽  
Epigenetic Modulation

Epigenetic regulation of gene expression without changing DNA sequences is a promising strategy in developing therapeutic agents for human diseases, especially cancer. Histone deacetylases (HDACs) are a family of enzymes involved in epigenetic modulation of gene expression by chromatin remodeling. Deacetylation of the lysine side chains of histones by HDAC proteins renders DNA transcriptionally inactive, resulting in the inhibition of expression of tumor suppressor genes, leading to tumorigenesis and tumor progression. Therefore, inhibiting HDAC enzymes has become an attractive strategy to modulate gene expression as a strategy for developing anticancer drugs. There are currently four FDA-approved cancer drugs in clinical use, and many more are in different stages of clinical trials. However, their clinical utility is limited due to undesirable side effects, mainly attributed to their lack of selectivity and the presence of a hydroxamic acid moiety as the metal-binding group. There are eighteen different isoforms of HDACs belonging to four classes that have been identified in humans. A major challenge in HDAC inhibitor development is how to make them selective for these HDAC isoforms and classes. We report the design and synthesis of new classes of HDAC inhibitors and the evaluation of their anticancer activity and selectivity.

scholarly journals Computer-Driven Development of an in Silico Tool for Finding Selective Histone Deacetylase 1 Inhibitors

Molecules ◽  
2020 ◽  
Vol 25 (8) ◽  
pp. 1952 ◽  
Author(s):  
Hajar Sirous ◽  
Giuseppe Campiani ◽  
Simone Brogi ◽  
Vincenzo Calderone ◽  
Giulia Chemi
Keyword(s):  
In Silico ◽  
Histone Deacetylases ◽  
Predictive Power ◽  
Hdac Inhibitors ◽  
3D Qsar ◽  
Qsar Model ◽  
Large Set ◽  
Roc Curve Analysis ◽  
Histone Deacetylase 1 ◽  
Highly Active

Histone deacetylases (HDACs) are a class of epigenetic modulators overexpressed in numerous types of cancers. Consequently, HDAC inhibitors (HDACIs) have emerged as promising antineoplastic agents. Unfortunately, the most developed HDACIs suffer from poor selectivity towards a specific isoform, limiting their clinical applicability. Among the isoforms, HDAC1 represents a crucial target for designing selective HDACIs, being aberrantly expressed in several malignancies. Accordingly, the development of a predictive in silico tool employing a large set of HDACIs (aminophenylbenzamide derivatives) is herein presented for the first time. Software Phase was used to derive a 3D-QSAR model, employing as alignment rule a common-features pharmacophore built on 20 highly active/selective HDAC1 inhibitors. The 3D-QSAR model was generated using 370 benzamide-based HDACIs, which yielded an excellent correlation coefficient value (R2 = 0.958) and a satisfactory predictive power (Q2 = 0.822; Q2F3 = 0.894). The model was validated (r2ext_ts = 0.794) using an external test set (113 compounds not used for generating the model), and by employing a decoys set and the receiver-operating characteristic (ROC) curve analysis, evaluating the Güner–Henry score (GH) and the enrichment factor (EF). The results confirmed a satisfactory predictive power of the 3D-QSAR model. This latter represents a useful filtering tool for screening large chemical databases, finding novel derivatives with improved HDAC1 inhibitory activity.

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