scholarly journals Stabilizing DNA–Protein Co-Crystals via Intra-Crystal Chemical Ligation of the DNA

Crystals ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 49
Author(s):  
Abigail R. Ward ◽  
Sara Dmytriw ◽  
Ananya Vajapayajula ◽  
Christopher D. Snow
Keyword(s):  
Dna Binding ◽  
Structural Biology ◽  
Binding Proteins ◽  
Pure Water ◽  
Crosslinking Agent ◽  
Dna Binding Proteins ◽  
X Ray Diffraction ◽  
Chemical Ligation ◽  
X Ray ◽  
Dna Ligation

Protein and DNA co-crystals are most commonly prepared to reveal structural and functional details of DNA-binding proteins when subjected to X-ray diffraction. However, biomolecular crystals are notoriously unstable in solution conditions other than their native growth solution. To achieve greater application utility beyond structural biology, biomolecular crystals should be made robust against harsh conditions. To overcome this challenge, we optimized chemical DNA ligation within a co-crystal. Co-crystals from two distinct DNA-binding proteins underwent DNA ligation with the carbodiimide crosslinking agent 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) under various optimization conditions: 5′ vs. 3′ terminal phosphate, EDC concentration, EDC incubation time, and repeated EDC dose. This crosslinking and DNA ligation route did not destroy crystal diffraction. In fact, the ligation of DNA across the DNA–DNA junctions was clearly revealed via X-ray diffraction structure determination. Furthermore, crystal macrostructure was fortified. Neither the loss of counterions in pure water, nor incubation in blood serum, nor incubation at low pH (2.0 or 4.5) led to apparent crystal degradation. These findings motivate the use of crosslinked biomolecular co-crystals for purposes beyond structural biology, including biomedical applications.

PENGGUNAAN TiO2 PARTIKEL NANO HASIL SINTESIS BERBASIS AIR MENGGUNAKAN METODA SOL-GEL PADA BAHAN KAPAS SEBAGAI APLIKASI UNTUK TEKSTIL ANTI UV

Arena Tekstil ◽  
2013 ◽  
Vol 28 (1) ◽  
Author(s):  
Maya Komalasari ◽  
Bambang Sunendar
Keyword(s):  
Fourier Transform ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Sol Gel ◽  
Crosslinking Agent ◽  
X Ray Diffraction ◽  
Nano Tio2 ◽  
X Ray ◽  
Infra Red ◽  
Scanning Electron

Partikel nano TiO2 berbasis air dengan pH basa telah berhasil disintesis dengan menggunakan metode sol-gel dan diimobilisasi pada kain kapas dengan menggunakan kitosan sebagai zat pengikat silang. Sintesis dilakukan  dengan prekursor TiCl4 pada konsentrasi 0,3 M, 0,5 M dan 1 M, dan menggunakan templat kanji dengan proses kalsinasi pada suhu 500˚C selama 2 jam. Partikel nano TiO2 diaplikasikan ke kain kapas dengan metoda pad-dry-cure dan menggunakan kitosan sebagai crosslinking agent. Berdasarkan hasil Scanning Electron Microscope (SEM),diketahui bahwa morfologi partikel TiO2 berbentuk spherical dengan ukuran nano (kurang dari 100 nm). Karakterisasi X-Ray Diffraction (XRD) menunjukkan adanya tiga tipe struktur kristal utama, yaitu (100), (101) dan (102) dengan fasa kristal yang terbentuk adalah anatase dan rutile. Pada karakterisasi menggunakan SEM terhadap serbuk dari TiO2 yang telah diaplikasikan ke permukaan kain kapas, terlihat adanya imobilisasi partikel nano TiO2 melalui ikatan hidrogen silang dengan kitosan pada kain kapas. Hasil analisa tersebut kemudian dikonfirmasi dengan FTIR (Fourier Transform Infra Red) yang hasilnya memperlihatkan puncak serapan pada bilangan gelombang 3495 cm-1, 2546 cm-1, dan 511 cm-1,  yang masing-masing diasumsikan sebagai adanya vibrasi gugus fungsi O-H, N-H dan Ti-O-Ti. Hasil SEM menunjukkan pula bahwa kristal nano yang terbentuk diantaranya adalah fasa rutile , yang berdasarkan literatur terbukti dapatberfungsi sebagai anti UV.

DBP-PSSM: Combination of evolutionary profiles with the XGBoost algorithm to improve the identification of DNA-binding proteins

2020 ◽  
Vol 23 ◽  
Author(s):  
Yanping Zhang ◽  
Pengcheng Chen ◽  
Ya Gao ◽  
Jianwei Ni ◽  
Xiaosheng Wang
Keyword(s):  
Logistic Regression ◽  
Protein Structure ◽  
Dna Binding ◽  
Molecular Biology ◽  
Binding Proteins ◽  
Protein Sequences ◽  
Low Complexity ◽  
Dna Binding Proteins ◽  
Position Information ◽  
Position Representation

Aim and Objective:: Given the rapidly increasing number of molecular biology data available, computational methods of low complexity are necessary to infer protein structure, function, and evolution. Method:: In the work, we proposed a novel mthod, FermatS, which based on the global position information and local position representation from the curve and normalized moments of inertia, respectively, to extract features information of protein sequences. Furthermore, we use the generated features by FermatS method to analyze the similarity/dissimilarity of nine ND5 proteins and establish the prediction model of DNA-binding proteins based on logistic regression with 5-fold crossvalidation. Results:: In the similarity/dissimilarity analysis of nine ND5 proteins, the results are consistent with evolutionary theory. Moreover, this method can effectively predict the DNA-binding proteins in realistic situations. Conclusion:: The findings demonstrate that the proposed method is effective for comparing, recognizing and predicting protein sequences. The main code and datasets can download from https://github.com/GaoYa1122/FermatS.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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