scholarly journals Triggering Amino Acid Detection by Atomistic Resolved Tunneling Current Signals in Graphene Nanoribbon Devices for Peptide Sequencing

2020 ◽  
Author(s):  
Tommaso Civitarese ◽  
Giuseppe Zollo
Keyword(s):  
Amino Acid ◽  
Tunneling Current ◽  
Peptide Sequencing ◽  
Graphene Nanoribbon ◽  
Amino Acid Detection

Electron Tunneling Current in an n-p-n Bipolar Transistor Based on Armchair Graphene Nanoribbon by Using Airy-Wavefunction Approach

2015 ◽  
Vol 1112 ◽  
pp. 80-84
Author(s):  
Fatimah A. Noor ◽  
Rifky Syariati ◽  
Endi Suhendi ◽  
Mikrajuddin Abdullah ◽  
Khairurrijal
Keyword(s):  
Electron Tunneling ◽  
Tunneling Current ◽  
Band Gap Energy ◽  
Graphene Nanoribbon ◽  
Bipolar Transistor ◽  
Electron Effective Mass ◽  
Collector Voltage ◽  
Armchair Graphene Nanoribbon ◽  
Armchair Graphene ◽  
Emitter Voltage

We have developed a model of the tunneling current in n-p-n bipolar transistor based on armchair graphene nanoribbon (AGNR). Airy-wavefunction approach is employed to obtain electron transmittance, and the obtained transmittance is then used to obtain the tunneling current. The tunneling current is calculated for various variables such as base-emitter voltage, base-current voltage, and AGNR width. It is found that the tunneling current increases with increasing the base-emitter voltage or the base-collector voltage. This result is due to the lowered barrier height of the base region caused by the increase in the base-emitter voltage or the base-collector voltage. In addition, the tunneling current density increases with the width for narrow AGNR and, on the other hand, it decreases for wide AGNR. This finding might be due to the contributions of the band gap energy and the electron effective mass of AGNR which are inversely proportional to the AGNR width.

scholarly journals Modeling of Electron Tunneling Current in a p-n Junction Based on Strained Armchair Graphene Nanoribbon

2014 ◽  
Vol 4 (4) ◽  
pp. 259-262
Author(s):  
Rifky Syariati ◽  
◽  
Endi Suhendi ◽  
Fatimah A. Noor ◽  
Mikrajuddin Abdullah ◽  
...  
Keyword(s):  
Electron Tunneling ◽  
Tunneling Current ◽  
Graphene Nanoribbon ◽  
Armchair Graphene Nanoribbon ◽  
Armchair Graphene

scholarly journals Evolutionary Algorithms for Improving De Novo Peptide Sequencing

2021 ◽  
Author(s):  
◽  
Samaneh Azari
Keyword(s):  
Amino Acid ◽  
De Novo ◽  
Peptide Identification ◽  
Peptide Sequencing ◽  
De Novo Sequencing ◽  
Amino Acid Sequences ◽  
Full Length ◽  
Multi Objective ◽  
De Novo Peptide Sequencing ◽  
De Novo Peptide

<p>De novo peptide sequencing algorithms have been developed for peptide identification in proteomics from tandem mass spectra (MS/MS), which can be used to identify and discover novel peptides and proteins that do not have a database available. Despite improvements in MS instrumentation and de novo sequencing methods, a significant number of CID MS/MS spectra still remain unassigned with the current algorithms, often leading to low confidence of peptide assignments to the spectra. Moreover, current algorithms often fail to construct the completely matched sequences, and produce partial matches. Therefore, identification of full-length peptides remains challenging. Another major challenge is the existence of noise in MS/MS spectra which makes the data highly imbalanced. Also missing peaks, caused by incomplete MS fragmentation makes it more difficult to infer a full-length peptide sequence. In addition, the large search space of all possible amino acid sequences for each spectrum leads to a high false discovery rate. This thesis focuses on improving the performance of current methods by developing new algorithms corresponding to three steps of preprocessing, sequence optimisation and post-processing using machine learning for more comprehensive interrogation of MS/MS datasets. From the machine learning point of view, the three steps can be addressed by solving different tasks such as classification, optimisation, and symbolic regression. Since Evolutionary Algorithms (EAs), as effective global search techniques, have shown promising results in solving these problems, this thesis investigates the capability of EAs in improving the de novo peptide sequencing. In the preprocessing step, this thesis proposes an effective GP-based method for classification of signal and noise peaks in highly imbalanced MS/MS spectra with the purpose of having a positive influence on the reliability of the peptide identification. The results show that the proposed algorithm is the most stable classification method across various noise ratios, outperforming six other benchmark classification algorithms. The experimental results show a significant improvement in high confidence peptide assignments to MS/MS spectra when the data is preprocessed by the proposed GP method. Moreover, the first multi-objective GP approach for classification of peaks in MS/MS data, aiming at maximising the accuracy of the minority class (signal peaks) and the accuracy of the majority class (noise peaks) is also proposed in this thesis. The results show that the multi-objective GP method outperforms the single objective GP algorithm and a popular multi-objective approach in terms of retaining more signal peaks and removing more noise peaks. The multi-objective GP approach significantly improved the reliability of peptide identification. This thesis proposes a GA-based method to solve the complex optimisation task of de novo peptide sequencing, aiming at constructing full-length sequences. The proposed GA method benefits the GA capability of searching a large search space of potential amino acid sequences to find the most likely full-length sequence. The experimental results show that the proposed method outperforms the most commonly used de novo sequencing method at both amino acid level and peptide level. This thesis also proposes a novel method for re-scoring and re-ranking the peptide spectrum matches (PSMs) from the result of de novo peptide sequencing, aiming at minimising the false discovery rate as a post-processing approach. The proposed GP method evolves the computer programs to perform regression and classification simultaneously in order to generate an effective scoring function for finding the correct PSMs from many incorrect ones. The results show that the new GP-based PSM scoring function significantly improves the identification of full-length peptides when it is used to post-process the de novo sequencing results.</p>

scholarly journals Electron Spectrum and Tunneling Current of the Toroidal and Helical Graphene Nanoribbon-Quantum Dots Contact

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Mikhail B. Belonenko ◽  
Nikolay G. Lebedev ◽  
Alexander V. Zhukov ◽  
Natalia N. Yanyushkina
Keyword(s):  
Quantum Dots ◽  
Dirac Equation ◽  
Density Of States ◽  
Electron Spectrum ◽  
Tunneling Current ◽  
Graphene Nanoribbon ◽  
Curved Space ◽  
Long Wave ◽  
Current Voltage ◽  
Current Voltage Characteristics

We study the electron spectrum and the density of states of long-wave electrons in the curved graphene nanoribbon based on the Dirac equation in a curved space-time. The current-voltage characteristics for the contact of nanoribbon-quantum dot have been revealed. We also analyze the dependence of the specimen properties on its geometry.

Biosensors for d-Amino Acid Detection

2011 ◽  
pp. 313-324 ◽  
Author(s):  
Silvia Sacchi ◽  
Elena Rosini ◽  
Laura Caldinelli ◽  
Loredano Pollegioni
Keyword(s):  
Amino Acid ◽  
Amino Acid Detection

scholarly journals Involvement of Sialoadhesin in Entry of Porcine Reproductive and Respiratory Syndrome Virus into Porcine Alveolar Macrophages

2003 ◽  
Vol 77 (15) ◽  
pp. 8207-8215 ◽  
Author(s):  
Nathalie Vanderheijden ◽  
Peter L. Delputte ◽  
Herman W. Favoreel ◽  
Joël Vandekerckhove ◽  
Jozef Van Damme ◽  
...  
Keyword(s):  
Amino Acid ◽  
Viral Entry ◽  
Alveolar Macrophages ◽  
Amino Acid Identity ◽  
Peptide Sequencing ◽  
Respiratory Syndrome Virus ◽  
Vesicle Membrane ◽  
Virus Particles ◽  
Internal Peptide ◽  
Peptide Data

ABSTRACT Porcine reproductive and respiratory syndrome virus (PRRSV) shows a very restricted tropism for cells of the monocyte/macrophage lineage. It enters cells via receptor-mediated endocytosis. A monoclonal antibody (MAb) that is able to block PRRSV infection of porcine alveolar macrophages (PAM) and that recognizes a 210-kDa protein (p210) was described previously (MAb41D3) (X. Duan, H. Nauwynck, H. Favoreel, and M. Pensaert, J. Virol. 72:4520-4523, 1998). In the present study, the p210 protein was purified from PAM by immunoaffinity using MAb41D3 and was subjected to internal peptide sequencing after tryptic digestion. Amino acid sequence identities ranging from 56 to 91% with mouse sialoadhesin, a macrophage-restricted receptor, were obtained with four p210 peptides. Using these peptide data, the full p210 cDNA sequence (5,193 bp) was subsequently determined. It shared 69 and 78% amino acid identity, respectively, with mouse and human sialoadhesins. Swine (PK-15) cells resistant to viral entry were transfected with the cloned p210 cDNA and inoculated with European or American PRRSV strains. Internalized virus particles were detected only in PK-15 cells expressing the recombinant sialoadhesin, demonstrating that this glycoprotein mediated uptake of both types of strains. However, nucleocapsid disintegration, like that observed in infected Marc-145 cells as a result of virus uncoating after fusion of the virus with the endocytic vesicle membrane, was not observed, suggesting a block in the fusion process. The ability of porcine sialoadhesin to mediate endocytosis was demonstrated by specific internalization of MAb41D3 into PAM. Altogether, these results show that sialoadhesin is involved in the entry process of PRRSV in PAM.

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