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Next-generation single-molecule protein sequencing technologies have the potential to significantly accelerate biomedical research. These technologies offer sensitivity and scalability for proteomic analysis. One auspicious method is fluorosequencing, which involves: cutting naturalized proteins into peptides, attaching fluorophores to specific amino acids, and observing variations in light intensity as one amino acid is removed at a time. The original peptide is classified from the sequence of light-intensity reads, and proteins can subsequently be recognized with this information. The amino acid step removal is achieved by attaching the peptides to a wall on the C-terminal and using a process called Edman Degradation to remove an amino acid from the N-Terminal. Even though a framework (Whatprot) has been proposed for the peptide classification task, processing times remain restrictive due to the massively parallel data acquisicion system. In this paper, we propose a new beam search decoder with a novel state formulation that obtains considerably lower processing times at the expense of only a slight accuracy drop compared to Whatprot. Furthermore, we explore how our novel state formulation may lead to even faster decoders in the future. Author summary: Proteomic analyses frequently rely on mass spectrometry, a method characterized by its limited dynamic range, potentially overlooking low-abundant proteins. To address this limitation, single-molecule protein sequencing methods offer a solution. Fluorosequencing is a cutting-edge single-molecule protein sequencing method, which can distinguish peptides or protein molecules massively parallelly. This method has attracted interest from investors, as evidenced by the recent funding of Erisyon, a company developing this technology. This technique contains a challenging classification task: determining the original peptide sequence from light-intensity observations obtained after several Edman cycles. A classifier based on a combination of k Nearest Neighbors (kNN) with Hidden Markov Models (HMM) had been shown to have close-to-optimal accuracy with tractable complexity. We propose in this paper a new algorithm that reduces computation time significantly at the expense of a slight reduction in accuracy compared to state-of-the-art method. [ABSTRACT FROM AUTHOR]

The alteration of amino acid register in peptides could affect their folding properties and thus functions. The effect of sequence in metal ion induced folding of peptide/protein is important in the design of new functional protein structures, such as biomimetics. In this paper, a set of hairpin peptides with identical composition but different histidine locations have been designed to exploit the metal ion‐mediated peptide intramolecular folding transformation through the adjustment of histidine and lysine residue locations. The results demonstrated that the histidine positions in peptide sequence could dramatically affect the coordination modes between peptides and Cu(II) ion. The Cu(II) coordination could affect the secondary structure transformation and self‐assembly along with electrostatic interaction and hydrogen bonding synergistically. Three molecules with VHVH sequence at each side of the turn segment (VDPPT) were apt to coordinate with Cu(II) ion with three or four histidines. The closer the VHVH motifs were to the turn segment, the more easier the molecules were to form β sheet structure with the promotion of Cu(II) ions. These results demonstrated that the purposeful location of key amino acids in peptides could achieve the controllable conformation transformation of hairpin peptides, which provides useful strategies for biomimetic design, such as metalloenzymes. [ABSTRACT FROM AUTHOR]

[Display omitted] • UMP789 database was collected in this paper. • The Umami-BERT model comprehensively outperformed other deep learning models in terms of umami peptides prediction. • The contribution of amino acids to umami peptide was ranked. • The motif pattern concerning A, D, E, and G were analyzed. Umami peptides have received extensive attention due to their ability to enhance flavors and provide nutritional benefits. The increasing demand for novel umami peptides and the vast number of peptides present in food call for more efficient methods to screen umami peptides, and further exploration is necessary. Therefore, the purpose of this study is to develop deep learning (DL) model to realize rapid screening of umami peptides. The Umami-BERT model was devised utilizing a novel two-stage training strategy with Bidirectional Encoder Representations from Transformers (BERT) and the inception network. In the pre-training stage, attention mechanisms were implemented on a large amount of bioactive peptides sequences to acquire high-dimensional generalized features. In the re-training stage, umami peptide prediction was carried out on UMP789 dataset, which is developed through the latest research. The model achieved the performance with an accuracy (ACC) of 93.23% and MCC of 0.78 on the balanced dataset, as well as an ACC of 95.00% and MCC of 0.85 on the unbalanced dataset. The results demonstrated that Umami-BERT could predict umami peptides directly from their amino acid sequences and exceeded the performance of other models. Furthermore, Umami-BERT enabled the analysis of attention pattern learned by Umami-BERT model. The amino acids Alanine (A), Cysteine (C), Aspartate (D), and Glutamicacid (E) were found to be the most significant contributors to umami peptides. Additionally, the patterns of summarized umami peptides involving A, C, D, and E were analyzed based on the learned attention weights. Consequently, Umami-BERT exhibited great potential in the large-scale screening of candidate peptides and offers novel insight for the further exploration of umami peptides. [ABSTRACT FROM AUTHOR]

Cancer is a Toxic health concern worldwide, it happens when cellular modifications cause the irregular growth and division of human cells. Several traditional approaches such as therapies and wet laboratory-based methods have been applied to treat cancer cells. However, these methods are considered less effective due to their high cost and diverse side effects. According to recent advancements, peptide-based therapies have attracted the attention of scientists because of their high selectivity. Peptide therapy can efficiently treat the targeted cells, without affecting the normal cells. Due to the rapid increase of peptide sequences, an accurate prediction model has become a challenging task. Keeping the significance of anticancer peptides (ACPs) in cancer treatment, an intelligent and reliable prediction model is highly indispensable. In this paper, a FastText-based word embedding strategy has been employed to represent each peptide sample via a skip-gram model. After extracting the peptide embedding descriptors, the deep neural network (DNN) model was applied to accurately discriminate the ACPs. The optimized parameters of DNN achieved an accuracy of 96.94 %, 93.41 %, and 94.02 % using training, alternate, and independent samples, respectively. It was observed that our proposed cACP-DeepGram model outperformed and reported ~10 % highest prediction accuracy than existing predictors. It is suggested that the cACP-DeepGram model will be a reliable tool for scientists and might play a valuable role in academic research and drug discovery. The source code and the datasets are publicly available at https://github.com/shahidakbarcs/cACP-DeepGram. [ABSTRACT FROM AUTHOR]

Short peptides are of extreme interest in clinical and food research fields, nevertheless they still represent a crucial analytical issue. The main aim of this paper was the development of an analytical platform for a considerable advancement in short peptides identification. For the first time, short sequences presenting both natural and post-translationally modified amino acids were comprehensively studied thanks to the generation of specific databases. Short peptide databases had a dual purpose. First, they were employed as inclusion lists for a suspect screening mass-spectrometric analysis, overcoming the limits of data dependent acquisition mode and allowing the fragmentation of such low-abundance substances. Moreover, the databases were implemented in Compound Discoverer 3.0, a software dedicated to the analysis of short molecules, for the creation of a data processing workflow specifically dedicated to short peptide tentative identification. For this purpose, a detailed study of short peptide fragmentation pathways was carried out for the first time. The proposed method was applied to the study of short peptide sequences in enriched urine samples and led to the tentative identification more than 200 short natural and modified short peptides, the highest number ever reported. Image 1 • 216 natural and modified short peptides (SPs) tentatively identified in urine sample. • Post translational modification on SPs were investigated for the first time. • Suspect screening is more suitable than untargeted analysis for short peptides. • Detailed study of SPs fragmentation pathways was provided for the first time. • The proposed method avoids highly time-consuming data analysis. [ABSTRACT FROM AUTHOR]

Identifying single-amino-acid variants (SAVs) from mass spectrometry-based experiments is critical for validating single-nucleotide variants (SNVs) at the protein level to facilitate biomedical research. Currently, two approaches are usually applied to convert SNV annotations into SAV-harboring protein sequences. One approach generates one sequence containing exactly one SAV, and the other all SAVs. However, they may neglect the possibility of SAV combinations, e.g., haplotypes, existing in bio-samples. Therefore, it is necessary to consider all SAV combinations of a protein when generating SAV-harboring protein sequences. In this paper, we propose MinProtMaxVP, a novel approach which selects a minimized number of SAV-harboring protein sequences generated from the exhaustive approach, while still accommodating all possible variant peptides, by solving a classic set covering problem. Our study on known haplotype variations of TAS2R38 justifies the necessity for MinProtMaxVP to consider all combinations of SAVs. The performance of MinProtMaxVP is demonstrated by an in silico study on OR2T27 with five SAVs and real experimental data of the HEK293 cell line. Furthermore, assuming simulated somatic and germline variants of OR2T27 in tumor and normal tissues demonstrates that when adopting the appropriate somatic and germline SAV integration strategy, MinProtMaxVP is adaptable to labeling and label-free mass spectrometry-based experiments. We present MinProtMaxVP, a novel approach to generate SAV-harboring protein sequences for constructing a customized protein sequence database, which is used in database searching for variant peptide identification. This approach outperforms the existing approaches in generating all possible variant peptides to be included in protein sequences and possibly leading to identification of more variant peptides in proteogenomic analysis. Unlabelled Image • MinProtMaxVP is a novel approach for converting single amino acid variations into protein sequences. • MinProtMaxVP considers the possibility of haplotype variations and heterogeneity of a bio-sample during the conversion. • MinProtMaxVP generates protein sequences containing all possible variant peptides by solving a set covering problem. • The efficacy of MinProtMaxVP were validated by in silico studies and an MS data set of HEK293 cell line. [ABSTRACT FROM AUTHOR]