diSBPred: A machine learning based approach for disulfide bond prediction

Unveiling the relationship between heat-resistant structure characteristics and inhibitory activity in colored highland barley proteinaceous α-amylase inhibitors

Natural α-amylase inhibitors (α-AIs) serve as food processing additives, capable of mitigating postprandial blood glucose levels, but heat resistance limits their application in high-temperature processing. This study delved into the correlation between protein structural characteristics and heat-resistance of colored highland barley (CHB) α-AIs and evaluated the inhibitory activity during chemical modification and in vitro digestion. Results demonstrated that CHB α-AIs were glycoproteins, the inhibitory activity retention rate of black highland barley α-AI salted-out with 0-60 % (NH4)2SO4 (BK1 α-AI) was 56.23 % ± 0.64 %. The protein structure analysis revealed that the preservation of three-dimensional structure was attributed to hydrogen bonds and hydrophobic interactions, and disulfide bonds played a crucial role in maintaining protein folding and activity. Succinylation increased the content of disulfide bonds after heating, and the inhibitory activity retention rate of α-AI noodles increased from 37.72 % ± 2.49 % to 42.79 % ± 0.39 %. These findings provide a theoretical foundation for the application of α-AI in thermally processed foods.

Amino acid sequence-based IDR classification using ensemble machine learning and quantum neural networks

Biologically traditional methods, such as the Uversky plot, which rely on hydrophobicity and net charge, have inherent limitations in accurately distinguishing intrinsically disordered regions (IDRs) from ordered protein regions. To overcome these constraints, we propose a novel ensemble framework integrating Machine Learning (ML), Deep Neural Networks (DNN), and Quantum Neural Networks (QNN) to enhance IDR classification accuracy. Notably, this study is the first to employ QNNs for IDR classification, leveraging quantum entanglement to model intricate feature interactions. Amino acid sequences were analyzed to extract biophysical features, including charge distribution, hydrophobicity, and structural properties, which served as inputs for the predictive models. ML was utilized for independent feature learning, DNN for hierarchical interaction modeling, and QNN for capturing high-order dependencies. Our meta-model demonstrated an accuracy of 0.85, surpassing individual classifiers and highlighting the importance of buried amino acids and feature interactions between scaled hydrophobicity and large, buried, and charged residues. This study advances computational protein science by demonstrating the applicability of QNNs in bioinformatics and establishing a robust framework for IDR classification.

BrainPepPass: A Framework Based on Supervised Dimensionality Reduction for Predicting Blood-Brain Barrier-Penetrating Peptides

Peptides that pass through the blood-brain barrier (BBB) not only are implicated in brain-related pathologies but also are promising therapeutic tools for treating brain diseases, e.g., as shuttles carrying active medicines across the BBB. Computational prediction of BBB-penetrating peptides (B3PPs) has emerged as an interesting approach because of its ability to screen large peptide libraries in a cost-effective manner. In this study, we present BrainPepPass, a machine learning (ML) framework that utilizes supervised manifold dimensionality reduction and extreme gradient boosting (XGB) algorithms to predict natural and chemically modified B3PPs. The results indicate that the proposed tool outperforms other classifiers, with average accuracies exceeding 94% and 98% in 10-fold cross-validation and leave-one-out cross-validation (LOOCV), respectively. In addition, accuracy values ranging from 45% to 97.05% were achieved in the independent tests. The BrainPepPass tool is available in a public repository for academic use (https://github.com/ewerton-cristhian/BrainPepPass).

DRBpred: A sequence-based machine learning method to effectively predict DNA- and RNA-binding residues

DNA-binding and RNA-binding proteins are essential to an organism's normal life cycle. These proteins have diverse functions in various biological processes. DNA-binding proteins are crucial for DNA replication, transcription, repair, packaging, and gene expression. Likewise, RNA-binding proteins are essential for the post-transcriptional control of RNAs and RNA metabolism. Identifying DNA- and RNA-binding residue is essential for biological research and understanding the pathogenesis of many diseases. However, most DNA-binding and RNA-binding proteins still need to be discovered. This research explored various properties of the protein sequences, such as amino acid composition type, Position-Specific Scoring Matrix (PSSM) values of amino acids, Hidden Markov model (HMM) profiles, physiochemical properties, structural properties, torsion angles, and disorder regions. We utilized a sliding window technique to extract more information from a target residue's neighbors. We proposed an optimized Light Gradient Boosting Machine (LightGBM) method, named DRBpred, to predict DNA-binding and RNA-binding residues from the protein sequence. DRBpred shows an improvement of 112.00 %, 33.33 %, and 6.49 % for the DNA-binding test set compared to the state-of-the-art method. It shows an improvement of 112.50 %, 16.67 %, and 7.46 % for the RNA-binding test set regarding Sensitivity, Mathews Correlation Coefficient (MCC), and AUC metric.

Analysis of key genes for the survival of Pantoea agglomerans under nutritional stress

The absolute amount of nutrients on plant leaves is usually low, and the growth of epiphytic bacteria is typically limited by nutrient content. Thus, is of great significance to study the survival mechanism of epiphytes under nutritional stress for plant disease control. In this paper, Pantoea agglomerans CHTF15 isolated from walnut leaves was used to detect the key genes for the survival of the bacterium under simulated nutrient stress in artificial medium. Genome sequencing was combined with transposon insertion sequencing (Tn-seq) for the detection technique. A total of 105 essential genes were screened from the whole genome. The genes were mainly related to the nucleotide metabolism, protein metabolism, biological oxidation and the gene repair of bacteria analyzed by gene ontology (GO) enrichment analysis. Volcano map analysis demonstrated that the functions of the 15 genes with the most significant differences were generally related to the synthesis of amino acids or proteins, the nucleotide metabolism and homologous recombination and repair. Competitive index analysis revealed that the deletion of the genes dksA and epmA regulating protein synthesis, the gene ribB involved in the nucleotide metabolism and the gene xerD involved in recombination repair induced a significant reduction in the survival ability of the corresponding mutants in the 0.10 % YEP medium and the walnut leaf surface. The results act as a foundation for further in-depth research on the infection process and the mechanisms of pathogenic bacteria.

Identification and structural prediction of the unrevealed amidohydrolase enzyme: Pterin deaminase from Agrobacterium tumefaciens LBA4404

Microbes make a remarkable contribution to the health and well-being of living beings all over the world. Interestingly, pterin deaminase is an amidohydrolase enzyme that exhibits antitumor, anticancer activities and antioxidant properties. With the existing evidence of the presence of pterin deaminase from microbial sources, an attempt was made to reveal the existence of this enzyme in the unexplored bacterium Agrobacterium tumefaciens LBA4404. After, the cells were harvested and characterized as intracellular enzymes and then partially purified through acetone precipitation. Subsequently, further purification step was carried out with an ion-exchange chromatogram (HiTrap Q FF) using the Fast-Protein Liquid Chromatography technique (FPLC). Henceforward, the approximate molecular weight of the purified pterin deaminase was determined through SDS-PAGE. Furthermore, the purified protein was identified accurately by MALDI-TOF, and the sequence was explored through a Mascot search engine. Additionally, the three-dimensional structure was predicted and then validated, as well as ligand-binding sites, and the stability of this enzyme was confirmed for the first time. Thus, the present study revealed the selected parameters showing a considerable impact on the identification and purification of pterin deaminase from A. tumefaciens LBA4404 for the first time. The enzyme specificity makes it a favorable choice as a potent anticancer agent.

Redox proteomics and structural analyses provide insightful implications for additional non-catalytic thiol-disulfide motifs in PDIs

Protein disulfide isomerases (PDIs) catalyze redox reactions that reduce, oxidize, or isomerize disulfide bonds and act as chaperones of proteins as they fold. The characteristic features of PDIs are the presence of one or more catalytic thioredoxin (TRX)-like domains harboring typical CXXC catalytic motifs responsible for redox reactions, as well as non-catalytic TRX-like domain. As increasing attention is paid to oxidative post-translational modifications of cysteines (Cys ox-PTMs) with the recognition that they control cellular signaling, strategies to identify sites of Cys ox-PTM by redox proteomics have been optimized. Exploration of an available Cys redoxome dataset supported by modeled structure provided arguments for the existence of an additional non-catalytic thiol-disulfide motif, distinct from those contained in the TRX type patterns, typical of PDIAs. Further structural analysis of PDIA3 and 6 allows us to consider the possibility that this hypothesis could be extended to other members of PDI. These elements invite future studies to decipher the exact role of these non-catalytic thiol-disulfide motifs in the functions of PDIs. Strategies that would allow to validate this hypothesis are discussed.

Evolution as a Guide to Designing xeno Amino Acid Alphabets

Here, we summarize a line of remarkably simple, theoretical research to better understand the chemical logic by which life’s standard alphabet of 20 genetically encoded amino acids evolved. The connection to the theme of this Special Issue, “Protein Structure Analysis and Prediction with Statistical Scoring Functions”, emerges from the ways in which current bioinformatics currently lacks empirical science when it comes to xenoproteins composed largely or entirely of amino acids from beyond the standard genetic code. Our intent is to present new perspectives on existing data from two different frontiers in order to suggest fresh ways in which their findings complement one another. These frontiers are origins/astrobiology research into the emergence of the standard amino acid alphabet, and empirical xenoprotein synthesis.