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Andrey Lisitsa

Researcher at Russian Academy

Publications -  100
Citations -  1622

Andrey Lisitsa is an academic researcher from Russian Academy. The author has contributed to research in topics: Proteome & Human proteome project. The author has an hindex of 20, co-authored 92 publications receiving 1377 citations.

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The Size of the Human Proteome: The Width and Depth.

TL;DR: In this article, meta-analysis of neXtProt knowledge base is proposed for theoretical prediction of the number of different proteoforms that arise from alternative splicing (AS), single amino acid polymorphisms (SAPs), and posttranslational modifications (PTMs).
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AFM fishing nanotechnology is the way to reverse the Avogadro number in proteomics.

TL;DR: In cases where the fishing becomes irreversible, its combination with an AFM detector enables the registration of single protein molecules, and that opens up a way to lower the CSL down to the reverse Avogadro number.
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Peripherally applied synthetic peptide isoAsp7-Aβ(1-42) triggers cerebral β-amyloidosis.

TL;DR: This article showed that repetitive intravenous administration of 100μg of synthetic peptide corresponding to isoAsp7-containing Aβ(1-42), an abundant age-dependent Aβ isoform present both in the pathological brain and in synthetic Aβ preparations, robustly accelerates formation of classic dense-core congophilic amyloid plaques in the brain of β-amyloid precursor protein transgenic mice.
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Biospecific irreversible fishing coupled with atomic force microscopy for detection of extremely low‐abundant proteins

TL;DR: The proposed method, which combines biospecific fishing with AFM, allowed us to attain DL values of 10−11 M under reversible binding conditions and 10−16‽M under irreversible binding conditions.
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PPLine: An Automated Pipeline for SNP, SAP, and Splice Variant Detection in the Context of Proteogenomics

TL;DR: PPLine is developed, a Python-based proteogenomic pipeline providing automated single-amino-acid polymorphism, indel, and alternative-spliced-variants discovery based on raw transcriptome and exome sequence data, single-nucleotide polymorphism (SNP) annotation and filtration, and the prediction of proteotypic peptides.