Bioinformatics Tools for Data Processing and Prediction of Protein Function

Sandag, Green Arther; Taju, Semmy Wellem

doi:10.31154/cogito.v4i2.137.305-315

Cited by 2 publications

(3 citation statements)

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“…Berdasarkan metode yang diusulkan dari Gromiha dan Yabuki [5] dengan menggunakan neural network dalam mengklasifikasikan transport protein ke dalam tiga kelas utama yaitu Channels/pores, electrochemical, dan active transporters. Dalam penelitian sebelumnya juga, Ou telah menganalisis komposisi asam amino, komposisi pasangan residu dan sifat asam amino dalam tiga kelas dan enam family dengan menggunakan metode Position Specific Scoring Matrix (PSSM) dengan tingkat akurasi sebesar 78% [6][7][8]. Penelitian sebelumnya juga telah membuat tools untuk prediksi protein transport menggunakan PSSM [7].…”

Section: Pendahuluanunclassified

“…Dalam penelitian sebelumnya juga, Ou telah menganalisis komposisi asam amino, komposisi pasangan residu dan sifat asam amino dalam tiga kelas dan enam family dengan menggunakan metode Position Specific Scoring Matrix (PSSM) dengan tingkat akurasi sebesar 78% [6][7][8]. Penelitian sebelumnya juga telah membuat tools untuk prediksi protein transport menggunakan PSSM [7]. Melihat hasil yang didapat pada penelitian sebelumnya maka peneliti mengharapkan dan mengajukan metode klasifikasi fungsi protein dalam transport protein berdasarkan kelas protein transport dan dapat meningkatkan tingkat akurasi tersebut dengan menggunakan algoritma pembelajaran mesin.…”

Section: Pendahuluanunclassified

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Klasifikasi Fungsi Family Protein Transport Menggunakan Radial Basis Neural Network

Sandag

Kaunang

2019

CogITo Smart Journal

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AbstrakTransporter adalah protein transmembran yang penting dalam proses masuk dan keluarnya ion atau molekul sel di seluruh protein membran dan memainkan peran penting dalam mengenali sistem kekebalan tubuh dan transduser energi. Dalam beberapa tahun terakhir, penelitian sebelumnya telah dilakukan untuk menganalisis protein transport, terutama diskriminasi kelas dan familynya dalam memainkan peran penting dalam system control sel, mengangkut air, sinyal kimia dan listrik. Protein transport membrane cenderung membentuk system pompa dan channel span, serta span cell membrane. Oleh karena itu, membedakan kelas dan family transport protein adalah tugas penting dalam ilmu komputasi biologi dan diperlukan bagi para ahli biologi untuk mendapatkan pemahaman yang lebih baik tentang fungsi protein transport. Oleh karena itu, dalam penelitian ini, telah dilakukan pengembangan metode untuk mengidentifikasi fungsi kelas utama dan family protein transport menggunakan radial basis neural network. Peneliti telah mengalanisis karakteristik komposisi asam amino, komposisi residu pair pada protein transport. Metode dalam klasifikasi kelas protein transport untuk mengetahui fungsi protein transport peneliti menggunakan PSSM dengan metode quickRBF classifier memberikan hasil akurasi terbaik dibanding dengan metode yang lain. Hasil akurasi sebesar 84,84% untuk cross validation dan 80,71% untuk independent data, oleh karena itu maka motode yang peneliti usulkan dapat digunakan secara efektif untuk mengidentifikasi dan mendiskriminasi transporter ke dalam kelas protein transport dengan peningkatan 6-10 % dari penelitian yang sejenis. AbstractTransporters are important transmembrane proteins that involve the cellular entry and exit of ions or molecules throughout the membrane proteins and thereby play important roles in recognizing the immune system and energy transducers. In recent years, several studies have been conducted to analyze the transport proteins; especially the discrimination class of transporters and their subfamilies play crucial roles in cell control system, transporting water, chemical and electric signals. Membrane transport proteins tend to form an intricate system of pumps and channel span, and span cell membranes. Hence, discriminating the specific class of transporters and their subfamilies is an essential task in computational biology and necessary for biologists to gain better understanding about the function of transport proteins. Therefore, in this study, an attempt has been made to develop a method that used radial basis neural network to identify the function of transport proteins in major class and family. We have Cogito Smart Journal | VOL. 5 | NO.2 | Desember 2019 n 204Fakultas Ilmu Komputer | Universitas Klabat | CORIS | analyzed the charateristics of amino acid composition (AAC), dipeptide pair composition (DPC) in transport proteins, also we used PSSM with quickRBF classifier method give the best accuracy results compared to other methods. Accuracy results of 84.84% for cross validation and 80.71% for i...

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Section: Pendahuluanunclassified

Klasifikasi Fungsi Family Protein Transport Menggunakan Radial Basis Neural Network

Sandag

Kaunang

2019

CogITo Smart Journal

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“…Various proteins with specific biological roles have been found using in vitro and in silico methods as science and technology have advanced. For the determination of the function of a protein in silico, bioinformatics studies are used with great precision and validation [8].…”

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confidence: 99%

Isolation of Genes Encoding for Human Epidermal Growth Factor (hEGF) from Human Blood: In Vitro and In Silico Study

2023

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The 6.12 kDa human epidermal growth factor (hEGF) protein is expressed in the body and plays a role in cell proliferation and differentiation. This research aims to isolate the hEGF genes, determine the phylogenetic relationship between organisms based on the hEGF gene, construct the 3D structure of the hEGF protein, and identify the precise binding position between the hEGF protein and the tested Myosin-9. DNA was isolated from blood of healthy individuals followed by amplification using specified primers. DNA sequencing was utilized to identify the amplification results before generating the phylogenetic trees. The retrieved sequences were then modeled using the Swiss model and docking proteins in silico. In three samples, DNA was successfully amplified to 700 bp and the phylogenetic analysis revealed that the three hEGF gene samples belonged to the same clades. With a 30 % identity seq, aligned result sequences modeled using the Swiss model created proteins which were not homologous. The outcomes of this modeling were compared to the hEGF protein from the database, which had a sequence identity of greater than 90 %. The i-RMSD value of the target protein (5.4 +/- 0.4), the Van der Waals energy (-29.3 +/- 1.7), and the Z-score were determined by simulation (-1.7). These results indicate a potential interaction between the isolated EGF protein and the myosin-9 tested. HIGHLIGHTS Human Epidermal Growth Factor (hEGF) protein is a potential protein expressed in the body and plays a role in cell proliferation and differentiation Isolation of the EGF gene through human blood can be carried out to get data on protein size, phylogenetic analysis, and protein structure The results of in vitro study were modeled in silico to obtain the three-dimensional structure as well as intermolecular interactions of the protein Information beginning this study is used in follow-up research to predict EGF communications with other molecules GRAPHICAL ABSTRACT

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Bioinformatics Tools for Data Processing and Prediction of Protein Function

Cited by 2 publications

References 20 publications

Klasifikasi Fungsi Family Protein Transport Menggunakan Radial Basis Neural Network

Klasifikasi Fungsi Family Protein Transport Menggunakan Radial Basis Neural Network

Isolation of Genes Encoding for Human Epidermal Growth Factor (hEGF) from Human Blood: In Vitro and In Silico Study

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