Thermostability Enhancement of Tagatose 4-Epimerase through Protein Engineering and Whole-Cell Immobilization

Journal of Agricultural and Food Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

d-Tagatose, a rare sugar endowed with low-calorie property, superior taste quality, and probiotic functionality, has garnered significant research attention. However, the prevailing biological production methods relying on β-galactosidase l-arabinose isomerase face challenges including high cost suboptimal conversion efficiency. Consequently, it is of great significance to find efficient alternative routes for d-tagatose synthesis. Previously, Thermotoga petrophila tagaturonate 3-epimerase was modified function as tagatose 4-epimerase (T4E) enabling direct d-fructose d-tagatose. In this study, T4E further engineered through directed evolution, specifically targeting enhancement its thermostability application. This endeavor yielded promising variants superiority over those original enzyme. I430P exhibits half-life (t1/2) at 70 °C that 1.83-fold T4E, an increased melting temperature (Tm) 5.1 compared T4E. Additionally, G90S/T272A/I430P demonstrated 21.4% increase in specific activity At °C, t1/2 1.69-fold Tm 2.9 higher than Furthermore, whole-cell immobilization integrating these into robust biocatalytic system employed. innovative approach not only underscores practical feasibility modifying enzymes evolution but also establishes foundation cost-effective, large-scale

Language: Английский

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Conformationally adaptive therapeutic peptides for diseases caused by intrinsically disordered proteins (IDPs). New paradigm for drug discovery: Target the target, not the arrow

Pharmacology & Therapeutics, Journal Year: 2025, Volume and Issue: unknown, P. 108797 - 108797

Published: Jan. 1, 2025

The traditional model of protein structure determined by the amino acid sequence is today seriously challenged fact that approximately half human proteome made up proteins do not have a stable 3D structure, either partially or in totality. These proteins, called intrinsically disordered (IDPs), are involved numerous physiological functions and associated with severe pathologies, e.g. Alzheimer, Parkinson, Creutzfeldt-Jakob, amyotrophic lateral sclerosis (ALS), type 2 diabetes. Targeting these challenging for two reasons: i) we need to preserve their functions, ii) drug design molecular docking possible due lack reliable starting conditions. Faced this challenge, solutions proposed artificial intelligence (AI) such as AlphaFold clearly unsuitable. Instead, suggest an innovative approach consisting mimicking, short synthetic peptides, conformational flexibility IDPs. which call adaptive derived from domains IDPs become structured after interacting ligand. Adaptive peptides designed aim selectively antagonizing harmful effects IDPs, without targeting them directly but through selected ligands, affecting properties. This"target target, arrow" strategy promised open new route discovery currently undruggable proteins.

Language: Английский

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Lessons from Deep Learning Structural Prediction of Multistate Multidomain Proteins—The Case Study of Coiled-Coil NOD-like Receptors

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(2), P. 500 - 500

Published: Jan. 9, 2025

We test here the prediction capabilities of new generation deep learning predictors in more challenging situation multistate multidomain proteins by using as a case study coiled-coil family Nucleotide-binding Oligomerization Domain-like (NOD-like) receptors from A. thaliana and few extra examples for reference. Results reveal truly remarkable ability these platforms to correctly predict 3D structure modules that fold well-established topologies. A lower performance is noticed modeling morphing regions proteins, such coiled coils. Predictors also display good sensitivity local sequence drifts upon solution overall modular configuration. In multivalued 1D mappings, marked tendency model most compact configuration must be retrained information filtering drive toward sparser ones. Bias order compactness seen at secondary level well. All all, AI when global templates are hand fruitful, but above challenges have taken into account. absence templates, piecewise approach with experimentally constrained reconstruction architecture might give realistic results.

Language: Английский

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AlphaFold and what is next: bridging functional, systems and structural biology

Expert Review of Proteomics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

The DeepMind's AlphaFold (AF) has revolutionized biomedical research by providing both experts and non-experts with an invaluable tool for predicting protein structures. However, while AF is highly effective structures of rigid globular proteins, it not able to fully capture the dynamics, conformational variability, interactions proteins ligands other biomacromolecules. In this review, we present a comprehensive overview latest advancements in 3D model predictions biomacromolecules using AF. We also provide detailed analysis its strengths limitations, explore more recent iterations, modifications, practical applications strategy. Moreover, map path forward expanding landscape toward every peptide proteome most physiologically relevant form. This discussion based on extensive literature search performed PubMed Google Scholar. While significant progress been made enhance AF's modeling capabilities, argue that combined approach integrating various silico vitro methods will be beneficial future structural biology, bridging gaps between static dynamic features their functions.

Language: Английский

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A quantitative intracellular peptide binding assay reveals recognition determinants and context dependence of short linear motifs

Journal of Biological Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 108225 - 108225

Published: Jan. 1, 2025

Language: Английский

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Machine learning approaches for predicting protein-ligand binding sites from sequence data

Frontiers in Bioinformatics, Journal Year: 2025, Volume and Issue: 5

Published: Feb. 3, 2025

Proteins, composed of amino acids, are crucial for a wide range biological functions. Proteins have various interaction sites, one which is the protein-ligand binding site, essential molecular interactions and biochemical reactions. These sites enable proteins to bind with other molecules, facilitating key Accurate prediction these pivotal in computational drug discovery, helping identify therapeutic targets facilitate treatment development. Machine learning has made significant contributions this field by improving interactions. This paper reviews studies that use machine predict from sequence data, focusing on recent advancements. The review examines embedding methods architectures, addressing current challenges ongoing debates field. Additionally, research gaps existing literature highlighted, potential future directions advancing discussed. study provides thorough overview sequence-based approaches predicting offering insights into state possibilities.

Language: Английский

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Integrating chemical artificial intelligence and cognitive computing for predictive analysis of biological pathways: a case for intrinsically disordered proteins

Biophysical Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 15, 2025

Language: Английский

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Leveraging AlphaFold2 and residual dipolar couplings for side-chain methyl group assignment: A case study with S. cerevisiae Xrs2

Journal of Magnetic Resonance, Journal Year: 2025, Volume and Issue: 374, P. 107865 - 107865

Published: March 6, 2025

Side-chain methyl group NMR spectroscopy provides invaluable insights into macromolecular structure, dynamics, and function, particularly for large biomolecular complexes. Accurate assignment of resonances in two-dimensional spectra is essential structural dynamics studies. Traditional strategies rely on either transferring assignments from backbone resonance data or NOESY high-resolution experimental structures; however, these methods are often limited by molecular size availability information, respectively. Here, we describe the use AlphaFold2 models as a basis manual, distance-based side-chain folded domains S. cerevisiae Xrs2. While facilitated initial resonances, inaccuracies coordinates highlighted need improved models. By generating >500 ColabFold-derived filtering with residual dipolar couplings (RDCs), identified superior agreement to data. These refined enabled additional while suggesting an iterative approach simultaneously improve structure prediction assignment. Our findings outline workflow that integrates machine learning-based predictions data, offering pathway advancing systems lacking structures.

Language: Английский

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Predicting reverse-bound peptide conformations in MHC Class II with PANDORA

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: March 24, 2025

Recent discoveries have transformed our understanding of peptide binding in Major Histocompatibility Complex (MHC) molecules, showing that peptides, for some MHC class II alleles, can bind a reverse orientation (C-terminus to N-terminus) and still effectively activate CD4+ T cells. These finding challenges established concepts immune recognition suggests new pathways therapeutic intervention, such as vaccine design. We present an updated version PANDORA, which, the best knowledge, is first tool capable modeling reversed-bound peptides. Modeling these peptides presents unique challenge due limited structural data available orientations existing databases. PANDORA has overcome this through integrative using algorithmically reversed templates. validated feature two targeted experiments, achieving average backbone binding-core L-RMSD value 0.63 Å. Notably, it maintained low RMSD values even when templates from different alleles sequences. Our results suggest will be invaluable resource immunology community, aiding development immunotherapies

Language: Английский

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Navigating the unstructured by evaluating alphafold’s efficacy in predicting missing residues and structural disorder in proteins

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0313812 - e0313812

Published: March 25, 2025

The study investigated regions with undefined structures, known as “missing” segments in X-ray crystallography and cryo-electron microscopy (Cryo-EM) data, by assessing their predicted structural confidence disorder scores. Utilizing a comprehensive dataset from the Protein Data Bank (PDB), residues were categorized “modeled”, “hard missing” “soft based on visibility datasets. Key features determined, including score local distance difference test (pLDDT) AlphaFold2, an advanced prediction tool, IUPred, traditional method. To enhance performance for unstructured residues, we employed Long Short-Term Memory (LSTM) model, integrating both scores amino acid sequences. Notable patterns such composition, region lengths observed identified through experiments over our studied period. Our findings also indicate that often align low scores, whereas exhibit dynamic behavior can complicate predictions. incorporation of pLDDT, IUPred sequence data into LSTM model has improved differentiation between structured particularly shorter regions. This research elucidates relationship established computational predictions experimental enhancing ability to target structurally significant areas guiding designs toward functionally relevant

Language: Английский

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