Screening for novel long‐chain bacterial esterase activity

Designing functional peptides with specific structural and biochemical properties is critical for applications in protein engineering and therapeutic discovery. However, most peptide design approaches rely on evolutionary or local sequence optimization methods, which are limited when adapting to peptides’ shorter length, high conformational flexibility, and unique physicochemical constraints. While recent structure-based inverse folding models have shown success for proteins, these models often underperform on peptides because sequence recovery alone is not a reliable indicator of stability or foldability in short, flexible backbones. To address this challenge, we introduce InversePep, a generative diffusion model for structure-based peptide inverse folding. InversePep learns the conditional distribution of sequences that can adopt a given backbone conformation, enabling direct generation of peptides tailored to target structural geometries. The framework integrates a geometric graph neural network to encode 3D backbone features with a Transformer-based sequence refinement module that iteratively denoises candidate sequences during diffusion. Trained on a diverse set of peptide backbones sourced from Propedia and SATPdb, InversePep effectively captures structural and biochemical diversity across peptide families. In systematic evaluations on held-out peptide structures and the PepBDB benchmark, InversePep achieves a mean TM score of 0.38 and a median of 0.28, outperforming ProteinMPNN and ESM-IF1 in generating geometry-consistent peptide sequences. In-silico folding analyses confirm that sampled peptides reliably adopt the target conformations. These results highlight InversePep’s capability for designing structurally stable and sequence-diverse peptides, demonstrating its potential in antimicrobial peptide discovery, peptide therapeutics, and molecular probe development.

The Spy that links: Creation of nonlinear protein architectures and materials using SpyTag/SpyCatcher chemistry.

The Immunoglobulin fold (Ig-fold) is found in proteins from all domains of life and represents the most populous fold in the human genome, with current estimates ranging from 2 to 3% of protein coding regions. That proportion is much higher in the surfaceome where Ig and Ig-like domains orchestrate cell-cell recognition, adhesion and signaling. The ability of Ig-domains to reliably fold and self-assemble through highly specific interfaces represents a remarkable property of these domains, making them key elements of molecular interaction systems: the immune system, the nervous system, the vascular system and the muscular system. We define a universal residue numbering scheme, common to all domains sharing the Ig-fold in order to study the wide spectrum of Ig-domain variants constituting the Ig-proteome and Ig-Ig interactomes at the heart of these systems. The "IgStrand numbering scheme" enables the identification of Ig structural proteomes and interactomes in and between any species, and comparative structural, functional, and evolutionary analyses. We review how Ig-domains are classified today as topological and structural variants and highlight the "Ig-fold irreducible structural signature" shared by all of them. The IgStrand numbering scheme lays the foundation for the systematic annotation of structural proteomes by detecting and accurately labeling Ig-, Ig-like and Ig-extended domains in proteins, which are poorly annotated in current databases and opens the door to accurate machine learning. Importantly, it sheds light on the robust Ig protein folding algorithm used by nature to form beta sandwich supersecondary structures. The numbering scheme powers an algorithm implemented in the interactive structural analysis software iCn3D to systematically recognize Ig-domains, annotate them and perform detailed analyses comparing any domain sharing the Ig-fold in sequence, topology and structure, regardless of their diverse topologies or origin. The scheme provides a robust fold detection and labeling mechanism that reveals unsuspected structural homologies among protein structures beyond currently identified Ig- and Ig-like domain variants. Indeed, multiple folds classified independently contain a common structural signature, in particular jelly-rolls. Examples of folds that harbor an "Ig-extended" architecture are given. Applications in protein engineering around the Ig-architecture are straightforward based on the universal numbering.

This study examines the thermodynamic and conformational dynamics of GTRNase and lysozyme proteins under varying temperature conditions using molecular dynamics (MD) simulations. The objective is to evaluate their structural stability, folding behavior, and thermodynamic properties to understand their responses to thermal fluctuations. Protein structures were retrieved from the Protein Data Bank (PDB), refined to remove extraneous molecules, and simulated using GROMACS 2022.2 under NVT and NPT ensembles, spanning temperatures from 270 K to 380 K. The results revealed distinct behaviors for the two proteins. GTRNase exhibited a slight escalation in radius of gyration (Rg) from 1.434 nm at 270 K to 1.445 nm at 380 K, suggesting a marginal conformational expansion. In contrast, lysozyme maintained a consistent Rg of 1.38 nm over the same temperature range, indicating structural compactness. Root mean square deviation (RMSD) data demonstrated increased flexibility in both proteins, with GTRNase escalating from 0.115 nm at 270 K to 0.179 nm at 380 K and lysozyme rising from 0.102 nm to 0.142 nm across the temperature range. Solvent-accessible surface area (SASA) for GTRNase fluctuated between 69 nm² and 73 nm², with the lowest value observed at 300 K and the highest at 370 K. These findings highlight that GTRNase is more susceptible to thermal perturbations than lysozyme, showing greater conformational flexibility and expansion. This research underscores the utility of MD simulations in exploring protein behavior and provides valuable insights for applications in protein engineering and drug design.

Review: 151 refs.

Site-selective reactions of water-soluble biomolecules are being developed to produce efficient conversions in water and water­/solvent mixtures. This review focuses on the use of designs based on bis-bidentate chelation of large metal ions by diols to be acylated by a co-chelated water-stable reagent. Topics discussed include: 1. The preparation and properties of water-stable acyl phosphate monoesters and their reactions with diol-chelated metal ions. 2. Site-selective monoaminoacylation of 3′-terminal diols of RNA and their applications in protein engineering. 3. Site-selective monoacylation of sugars with acyl phosphate monoesters associated with metal ions, including lanthanum and lead. The combination of metal ion, 1,2-diol, and acyl phosphate monoester produces site-selective reactions in aqueous media­ that can produce a general approach to site-selective mono-(amino)acylation in RNA and carbohydrates.1 Introduction2 Synthetic Aminoacylation of tRNA3 Activated Amino Acids in Water4 Metal Ions and Their Effects on the Reactivity of Acyl Phosphate Monoesters5 The Challenge of Site-Selective Acylation of Carbohydrates in Water6 Conclusions and Prospects

Prions are proteinaceous infectious particles implicated in fatal neurodegenerative disorders known as prion diseases. Herein, we provide an overview of prion biology, emphasizing the structural, functional, and evolutionary aspects of prions, along with their potential applications in protein engineering. Understanding the structure-function relationships of both healthy and disease-associated prion proteins enables a deeper understanding of the mechanisms of prion-induced neurotoxicity. Furthermore, we describe how insights into prion evolution have begun to shed light on their ancient origins and evolutionary resilience, offering deeper insights into the potential roles of prions in primordial chemical processes.

Understanding the molecular mechanism underlying protein thermostability is central to the process of efficiently engineering thermostable cellulases, which can provide potential advantages in accelerating the conversion of biomass into clean biofuels. Here, we explored the general factors that diversify enzyme thermostability in the glycoside hydrolase family 12 (GH12) using comparative molecular dynamics (MD) simulations coupled to a bioinformatics approach. The results indicated that protein stability is not equally distributed over the whole structure: the N-terminus is the most thermal-sensitive region of the enzymes with a β-sandwich architecture and it tends to lose its secondary structure during the course of protein unfolding. Furthermore, we found that the total interaction energy within the N-terminus is appreciably correlated with enzyme thermostability. Interestingly, the internal interactions within the N-terminus are organized in a special amphipathic pattern in which a hydrophobic packing cluster and a hydrogen bonding cluster lie at the two ends of the N-terminus. Finally, bioinformatics analysis demonstrated that the amphipathic pattern is highly conserved in GH12 and besides that, the evolution of the amino acids in the N-terminal region is an inherent mechanism underlying the diversity of enzyme thermostability. Taken together, our results demonstrate that the N-terminus is generally the structure that determines enzyme thermostability in GH12, and thereby it is also an ideal engineering target. The dynameomics study of a protein family can give a general view of protein functions, which will offer a wide range of applications in future protein engineering.

The SpyTag–SpyCatcher system, developed by the Howarth lab, is based on splitting the CnaB2 domain from Streptococcus pyogenes into two parts: a 13-amino-acid SpyTag and a 116-amino-acid SpyCatcher. Upon incubation, they spontaneously form a covalent isopeptide bond between Asp7 (SpyTag) and Lys31 (SpyCatcher). This study explores whether the interaction specificity can be modulated by altering hydrophobic residues within the SpyCatcher binding pocket and corresponding SpyTag positions, potentially to create orthogonal SpyTag–SpyCatcher pairs. Libraries of SpyCatcher and SpyTag were created by partial saturation mutagenesis using overlap PCR and MAX randomisation, respectively. To assess the specificity of the SpyCatcher–SpyTag interaction within the resulting protein mixtures, a novel screening strategy based on mass photometry was developed to detect isopeptide bond formation. We demonstrate tolerance to mutation in the hydrophobic binding pocket of SpyCatcher in terms of binding native SpyTag and demonstrate what to our knowledge constitutes the first example of using mass photometry to examine the interactions of small libraries of proteins with a given ligand. Mass photometry detects stable interactions whether covalent or not and so this study suggests the prospect of employing mass photometry for more general application in protein engineering.

The development of noncanonical amino acids (ncAAs) provides a powerful strategy to expand the chemical space of proteins beyond the natural repertoire, thereby overcoming intrinsic pharmacodynamic limitations of peptides and proteins. Among these, boron‐containing ncAAs are of particular interest due to the versatile reactivity of boron and its proven therapeutic relevance in clinically approved drugs. However, poor aqueous solubility, instability under physiological conditions, and oxidative degradation have hindered their broader biological application. Here, we report the design and synthesis of a new class of boron‐containing ncAAs with enhanced solubility and stability. Structural modifications around the boron center and optimized substituents were employed to improve compatibility with biological systems while retaining functional reactivity. Moreover, fluorescence analysis revealed distinct photophysical properties, indicating potential applications in protein engineering and biosensing. These results highlight the utility of cyclic boron architectures as a versatile platform for the development of boron‐based amino acid analogs with broad implications in chemical biology, drug discovery, and biomolecular design.

A peptide NTH-18 was synthesized in which a N-terminal helix is stabilised by two crossed disulfide bonds to a C-terminal extension. The design was inspired by the structure of the neurotoxic peptide apamin, which has previously been used to stabilise helices in miniature enzymes. CD- and NMR-spectroscopy indicated that NTH-18 adopted a fold similar to that found in apamin. However, the arrangement of the elements of secondary structures was inverted relative to apamin; a N-terminal alpha-helix was connected by a reverse turn to a C-terminal extension of non-canonical secondary structure. NTH-18 displayed significant stability to heat and changes of pH. The high definition of the N-terminal end of the alpha-helix of NTH-18 should make this peptide a useful vehicle to stabilise alpha-helices in proteins with applications in protein engineering and molecular recognition.

Asparaginyl ligases have been extensively utilized as valuable tools for site-specific bioconjugation or surface-modification. However, the application is hindered by the laborious and poorly reproducible preparation processes, unstable activity and ambiguous substrate requirements. To address these limitations, this study employed a structure-based rational approach to obtain a high-yield and high-activity protein ligase called OaAEP1-C247A-aa55-351. It was observed that OaAEP1-C247A-aa55-351 exhibits appreciable catalytic activities across a wide pH range, and the addition of the Fe3+ metal ion effectively enhances the catalytic power. Importantly, this study provides insight into the recognition and nucleophile peptide profiles of OaAEP1-C247A-aa55-351. The ligase demonstrates a higher recognition ability for the “Asn-Ala-Leu” motif and an N-terminus “Arg-Leu” as nucleophiles, which significantly increases the reaction yield. Consequently, the catalytic activity of OaAEP1-C247A-aa55-351 with highly efficient recognition and nucleophile motif, “Asn-Ala-Leu” and “Arg-Leu” under the buffer containing Fe3+ is 70-fold and 2-fold higher than previously reported OaAEP1-C247A and the most efficient butelase-1, respectively. Thus, the designed OaAEP1-C247A-aa55-351, with its highly efficient recognition and alternative nucleophile options, holds promising potential for applications in protein engineering, chemo-enzymatic modification, and the development of drugs.

Understanding conformational dynamics is essential for elucidating protein function, yet most deep learning models in structural biology predict only static structures. Here, we introduce ESMDynamic, a deep learning model that predicts dynamic residue-residue contact probability maps directly from protein sequence. Built on the ESMFold architecture, ESMDynamic is trained on contact fluctuations from experimental structure ensembles and molecular dynamics (MD) simulations, enabling it to capture diverse modes of structural variability without requiring multiple sequence alignments. We benchmark ESMDynamic on two large-scale MD datasets (mdCATH and ATLAS), showing that it matches or outperforms state-of-the-art ensemble prediction models (AlphaFlow, ESMFlow, BioEmu) for transient contact prediction while offering orders-of-magnitude faster inference. We demonstrate the model on the ASCT2 and SWEET2b transporters, a de novo troponin C design, and the HIV-1 protease homodimer, illustrating generalization to unseen systems and recovery of experimentally validated dynamic contacts. Furthermore, we present an automated pipeline using ESMDynamic predictions to select collective variables for Markov State Model construction, producing high-quality kinetic models from unbiased MD simulations of SWEET2b. Overall, ESMDynamic provides a compact and interpretable sequence-based description of conformational dynamics, with broad applications in protein engineering, functional analysis, and simulation-guided discovery.

Computational redesign of a dimorphic protein nano-cage at the C3-symmetrical interfaces forces it to assemble into the monomorphic cage. These monodisperse assemblies are at least 20 °C more stable than the parent. This approach adds to the toolkit of bottom-up molecular design with applications in protein engineering and hybrid nano-materials.

The modification of proteins with synthetic probes is a powerful means of elucidating and engineering the functions of proteins both in vitro and in live cells or in vivo. Herein we review recent progress in chemistry-based protein modification methods and their application in protein engineering, with particular emphasis on the following four strategies: 1) the bioconjugation reactions of amino acids on the surfaces of natural proteins, mainly applied in test-tube settings; 2) the bioorthogonal reactions of proteins with non-natural functional groups; 3) the coupling of recognition and reactive sites using an enzyme or short peptide tag-probe pair for labeling natural amino acids; and 4) ligand-directed labeling chemistries for the selective labeling of endogenous proteins in living systems. Overall, these techniques represent a useful set of tools for application in chemical biology, with the methods 2-4 in particular being applicable to crude (living) habitats. Although still in its infancy, the use of organic chemistry for the manipulation of endogenous proteins, with subsequent applications in living systems, represents a worthy challenge for many chemists.