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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]

Conditions including heating mode, time, temperature, pH, moisture and protein concentration, shear, and the presence of alcohols, chaotropic/reducing agents, enzymes, and/or salt influence amyloid fibril (AF) formation as they can affect the accessibility of amino acid sequences prone to aggregate. As some conditions applied on model protein resemble conditions in food processing unit operations, we here hypothesize that food processing can lead to formation of protein AFs with a compact cross β‐sheet structure. This paper reviews conditions and food constituents that affect amyloid fibrillation of egg and cereal proteins. While egg and cereal proteins often coexist in food products, their impact on each other's fibrillation remains unknown. Hen egg ovalbumin and lysozyme form AFs when subjected to moderate heating at acidic pH separately. AFs can also be formed at higher pH, especially in the presence of alcohols or chaotropic/reducing agents. Tryptic wheat gluten digests can form fibrillar structures at neutral pH and maize and rice proteins do so in aqueous ethanol or at acidic pH, respectively. [ABSTRACT FROM AUTHOR]

In this study, the authors studied the protein structure prediction problem by the two‐dimensional hydrophobic–polar model on triangular lattice. Particularly the non‐compact conformation was modelled to fold the amino acid sequence into a relatively larger triangular lattice, which is more biologically realistic and significant than the compact conformation. Then protein structure prediction problem was abstracted to match amino acids to lattice points. Mathematically, the problem was formulated as an integer programming and they transformed the biological problem into an optimisation problem. To solve this problem, classical particle swarm optimisation algorithm was extended by the single point adjustment strategy. Compared with square lattice, conformations on triangular lattice are more flexible in several benchmark examples. They further compared the authors' algorithm with hybrid of hill climbing and genetic algorithm. The results showed that their method was more effective in finding solution with lower energy and less running time. [ABSTRACT FROM AUTHOR]

The SARS‐CoV‐2 pandemic is maintained by the emergence of successive variants, highlighting the flexibility of the protein sequences of the virus. We show that experimentally determined intrinsically disordered regions (IDRs) are abundant in the SARS‐CoV‐2 viral proteins, making up to 28% of disorder content for the S1 subunit of spike and up to 51% for the nucleoprotein, with the vast majority of mutations occurring in the 13 major variants mapped to these IDRs. Strikingly, antigenic sites are enriched in IDRs, in the receptor‐binding domain (RBD) and in the N‐terminal domain (NTD), suggesting a key role of structural flexibility in the antigenicity of the SARS‐CoV‐2 protein surface. Mutations occurring in the S1 subunit and nucleoprotein (N) IDRs are critical for immune evasion and antibody escape, suggesting potential additional implications for vaccines and monoclonal therapeutic strategies. Overall, this suggests the presence of variable regions on S1 and N protein surfaces, which confer sequence and antigenic flexibility to the virus without altering its protein functions. [ABSTRACT FROM AUTHOR]

Despite its potential clinical relevance, the product of the DMWD (dystrophia myotonica, WD repeat containing) gene is a largely uncharacterized protein. The DMWD amino acid sequence is similar to that of WDR20, a known regulator of the USP12 and USP46 deubiquitinases (DUBs). Here, we apply a combination of in silico and experimental methods to investigate several aspects of DMWD biology. Molecular evolution and phylogenetic analyses reveal that WDR20 and DMWD, similar to USP12 and USP46, arose by duplication of a common ancestor during the whole genome duplication event in the vertebrate ancestor lineage. The analysis of public human gene expression datasets suggests that DMWD expression is positively correlated with USP12 expression in normal tissues and negatively correlated with WDR20 expression in tumors. Strikingly, a survey of the annotated interactome for DMWD and WDR20 reveals a largely nonoverlapping set of interactors for these proteins. Experimentally, we first confirmed that DMWD binds both USP12 and USP46 through direct coimmunoprecipitation of epitope‐tagged proteins. We found that DMWD and WDR20 share the same binding interface in USP12, suggesting that their interaction with the DUB may be mutually exclusive. Finally, we show that both DMWD and WDR20 promote USP12 enzymatic activity, but they differentially modulate the subcellular localization of the DUB. Altogether, our findings suggest a model whereby mutually exclusive binding of DMWD and WDR20 to USP12 may lead to formation of deubiquitinase complexes with distinct subcellular localization, potentially targeting different substrate repertoires. [ABSTRACT FROM AUTHOR]

Predicting protein structure from the amino acid sequence has been a challenge with theoretical and practical significance in biophysics. Despite the recent progresses elicited by improved inter‐residue contact prediction, contact‐based structure prediction has gradually reached the performance ceiling. New methods have been proposed to predict the inter‐residue distance, but unanimously by simplifying the real‐valued distance prediction into a multiclass classification problem. Here, a lightweight regression‐based distance prediction method is shown, which adopts the generative adversarial network to capture the delicate geometric relationship between residue pairs and thus could predict the continuous, real‐valued inter‐residue distance rapidly and satisfactorily. The predicted residue distance map allows quick structure modeling by the CNS suite, and the constructed models approach the same level of quality as the other state‐of‐the‐art protein structure prediction methods when tested on CASP13 targets. Moreover, this method can be used directly for the structure prediction of membrane proteins without transfer learning. [ABSTRACT FROM AUTHOR]

Quinoa protein has been paid more and more attention because of its nutritional properties and beneficial effects. With the development of bioinformatics, bioactive peptide database and computer‐assisted simulation provide an efficient and time‐saving method for the theoretical estimation of potential bioactivities of protein. Therefore, the potential of quinoa protein sequences for releasing bioactive peptides was evaluated using the BIOPEP database, which revealed that quinoa protein, especially globulin, is a potential source of peptides with dipeptidyl peptidase‐IV (DPP‐IV) and angiotensin‐I‐converting enzyme (ACE) inhibitory activities. Three plant proteases, namely papain, ficin, and stem bromelain, were employed for the in silico proteolysis of quinoa protein. Furthermore, four tripeptides (MAF, NMF, HPF, and MCG) were screened as novel promising bioactive peptides by PeptideRanker. The bioactivities of selected peptides were confirmed using chemical synthesis and in vitro assay. The present work suggests that quinoa protein can serve as a good source of bioactive peptides, and in silico approach can provide theoretical assistance for investigation and production of functional peptides. [ABSTRACT FROM AUTHOR]

Physicochemical n‐Grams Tool (PnGT) is an open‐source standalone software for calculating physicochemical descriptors of protein. PnGT was developed using the Python scripting language and developed the user interface using Tkinter. The software currently calculates 33 physicochemical descriptors along with the sequence length for the given protein primary sequence. The descriptor generated by this tool can be directly utilized as the feature vector for the development of proteomics statistical or machine learning predictive model. [ABSTRACT FROM AUTHOR]

ABSTRACT The structure and activity of proteins is the gold standard for functional polymeric materials. This highlight seeks to calibrate the reader with respect to recent attempts to mimic the various structural and functional traits of proteins using the techniques of modern polymer chemistry. From advances in sequence-controlled polymers (primary structure), to peptidomimetics, foldamers, single-chain nanoparticles (secondary and tertiary structure), accessing the various structural aspects of protein chemistry is a vibrant research area. Likewise, the properties and utility of proteins in applications such as catalysis and molecular recognition are being emulated in the laboratory to great effect. Rather than provide an exhaustive review on any one of these topics, this article seeks to highlight the common thread among them, encouraging discussion and collaboration that will result in the next generation of smart materials with advanced structure and function. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2017, 55, 191-206 [ABSTRACT FROM AUTHOR]

The article focuses on the renewable Energy from Wildflowers in Perennial Wild Plant Mixtures as a Social-Ecologically Sustainable Biomass Supply System.