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The aim of this work is to offer an alternative or complementary analytical tool to the time‐consuming and expensive methods commonly used for the recognition of animal species according to their hair. The paper introduces a simple and fast way for species differentiation of animal hairs called in‐sample digestion. A total of 10 European animal species, including cat, cow, common degu, dog, fallow deer, goat, horse, sika deer, rabbit, roe deer, and 17 different breeds of dogs were examined using specific tryptic cleavage directly in hair followed by matrix‐assisted laser desorption/ionization–time of flight mass spectrometry and liquid chromatography‐electrospray ionization quadrupole time of flight. Principal component analysis was used for the subsequent mass spectrometric data evaluation. This novel approach demonstrates the ability to distinguish among individual animal species, which is supported by finding characteristic m/z values obtained by the mass spectrometry for each animal species. The approach was successfully tested on two "blind" samples. On the other hand, the attempt to distinguish among hairs of different dog breeds has not been successful due to the very similar protein composition and their amino acid sequences. [ABSTRACT FROM AUTHOR]

As biological macromolecules, proteins are involved in important cellular functions ranging from DNA replication and biosynthesis to metabolic signalling and environmental sensing. Protein sequencing can help understand the relationship between protein function and structure, and provide key information for disease diagnosis and new drug design. Nanopore sensors are a novel technology to achieve the goal of label‐free and high‐throughput protein sequencing. In recent years, nanopore‐based biosensors have been widely used in the detection and analysis of biomolecules such as DNA, RNA, and proteins. At the same time, computer simulations can describe the transport of proteins through nanopores at the atomic level. This paper reviews the applications of nanopore sensors in protein sequencing over the past decade and the solutions to key problems from a computer simulation perspective, with the aim of pointing the way to the future of nanopore protein sequencing. [ABSTRACT FROM AUTHOR]

Since the large‐scale protein sequence data is available, applying deep neural networks to mine better features from the sequences becomes possible. Eukaryotic protein subcellular localization prediction which makes a contribution in many biology process, has used protein sequences in many automatic predicting methods. Moreover, gene ontology (GO) annotation has been shown to be helpful in improving the prediction accuracy of subcellular localization. However, experimentally annotated proteins are not always available. On the other hand, experimentally annotated proteins are available for certain species such as human, mouse, Arabidopsis thaliana, etc. It is highly motivated to perform deep learning of GO annotations on the available experimentally annotated proteins and to transfer it to subcellular localization prediction on other species. In this paper, we propose a deep protein subcellular localization predictor, consisting of a linear classifier and a deep feature extractor of convolution neural network (CNN). The deep CNN feature extractor is first shared and pre‐trained in a deep GO annotation predictor, and then is transferred to the subcellular localization predictor with fine‐tuning using protein localization samples. In this way, we have a deep protein subcellular localization predictor enhanced with transfer learning of GO annotation. The proposed method has good performances on the Swiss‐Prot datasets, when transfer learning using the protein samples both within and out species. Moreover, it outperforms the state‐of‐the‐art traditional methods on benchmark datasets. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]