Journal of Molecular Biology最新文献

Proteins of halophilic microorganisms thrive in high-salt environments. Compared to mesophilic proteins, they are enriched in acidic residues and small polar/apolar amino acids while being depleted in large hydrophobic residues, features that strongly influence their structure and stability. Here we critically examine experimental and computational studies investigating the mechanistic connection between the halophilic proteome and thermodynamic stability of halophilic proteins as a function of salt concentration. A defining feature of halophilic proteins is their highly negative surface charge, arising from abundant acidic residues. Some studies suggest this property is essential to ensure proteins remain folded at high salt concentrations, while others argue that a net negative protein charge might always be destabilizing. Alternative views propose that reducing solvent-exposed hydrophobic surface area is more critical than charge for stability at high salt concentrations. Advancing our understanding on this topic will require addressing multiple knowledge gaps. The unfolded states of both protein classes remain poorly characterized, leaving differences in local and non-local entropy contributions between the folded and the unfolded states to salt-dependent protein stability largely unexplored. Packing, cation-carbonyl and hydrophobic SASA differences between both protein classes are also insufficiently quantified. Atomistic molecular dynamics simulations with explicit solvent can advantageously be used to investigate these issues. Simultaneously, theoretical frameworks to understand how small perturbations in protein composition impact its stability as a function of salt concentration need to be expanded to include these contributions, which to date have been neglected, to fully understand how a halophilic proteome impacts salt-dependent stability.

WASP family proteins activate Arp2/3 complex to nucleate branched actin filaments in diverse cellular processes. To activate, WASP proteins recruit actin monomers to Arp2/3 complex through their conserved WASP homology 2 domains, but how these recruited monomers contribute to activation of Arp2/3 complex has been unclear. Previous studies suggested potential species-specific differences: work on human Arp2/3 complex indicated that direct actin monomer delivery by WASP is required to assemble the filament nucleus, while studies on budding yeast Arp2/3 complex showed that direct monomer recruitment is required specifically for WASP-mediated activation of the complex and is not a fundamental requirement for activation. Here we show that mutations that shift the actin-related subunits (Arp2 and Arp3) in human Arp2/3 complex toward a filament-like conformation enable nucleation without WASP. This demonstrates that direct monomer delivery by WASP is not a fundamental requirement for formation of the nucleus, but instead a specific requirement for WASP-mediated activation-consistent with findings in yeast Arp2/3 complex. Using kinetic models of actin polymerization, we provide evidence that while the Arp2-Arp3 filament-like dimer is insufficient for nucleation by human Arp2/3 complex, actin monomers diffuse and bind to the complex to create the filament nucleus in the absence of recruitment. We show that this diffusion-based mechanism can stimulate nucleation by mammalian Arp2/3 complex at rates near tethered actin monomer delivery. These results provide important insights into Arp2/3 complex activation with or without monomer delivery and demonstrate that fundamental aspects of activation are conserved across fungal and mammalian species.

Primary liver cancer represents a significant global health burden, with limited therapeutic options for advanced disease. Long non-coding RNAs (lncRNAs) are increasingly found to play crucial roles in hepatic biology and disease progression. Here, we identify FAM99A as a highly liver-enriched lncRNA that is systematically downregulated across liver malignancies, with reduced expression correlating with poor clinical outcomes. FAM99A exhibits remarkable tissue specificity with minimal expression outside the liver, and its levels rapidly decline during primary hepatocyte dedifferentiation in culture. Through isoform analysis, we establish FAM99A-203 as the predominant transcript in normal liver tissue and observe altered isoform distribution in liver cancers. Functionally, FAM99A overexpression inhibits anchorage-independent growth in liver cancer cell lines. Transcriptomic analysis reveals that FAM99A negatively regulates translation-related pathways in both liver cancer cells and primary hepatocytes. This is corroborated by protein synthesis assays showing that FAM99A overexpression substantially reduces global translation rates. Targeted RNase H-mediated extraction coupled with mass spectrometry identifies multiple components of the translation machinery as direct FAM99A binding partners, including eukaryotic translation initiation factors and RNA helicases involved in ribosome biogenesis. Clinical data analysis demonstrates significant inverse correlations between FAM99A expression and ribosomal protein genes in liver cancer patients. Additionally, hepatitis B virus appears to downregulate FAM99A expression, potentially contributing to its oncogenic properties. Our findings establish FAM99A as a liver-enriched translational regulator that exerts tumor-suppressive effects by restraining protein synthesis rates, offering insights into hepatocarcinogenesis and the potential of FAM99A as both a biomarker and agent in new therapeutic avenues.

The human reference proteome is routinely modeled with predictive tools such as AlphaFold2 and ESMFold. The two methods, based on different procedures, can behave differently depending on the experimental information available for a protein. We previously released a public database that stores pairs of predicted models, allowing us to obtain insights into the two methods and providing a resource where users can select the better model for downstream analysis. Here, we update the database after the latest release of UniProt (2025_04), we functionally characterize the models by mapping Pfam entries on the 3D structures, and we introduce external quality assessment metrics to evaluate and compare the models. We observe that, regardless of the quality and similarity of the predicted models, both AlphaFold2 and ESMFold converge with high pLDDT values in regions covered by Pfam entries. Alpha&ESMhFolds, including all its features, is freely available at https://alpha-esmhfolds.biocomp.unibo.it/.

Heavy metal contamination poses a significant threat to environmental health, agriculture, and microbial ecosystems, necessitating the identification of molecular components that confer resistance. Heavy metal resistance (HMR) proteins enable organisms to survive toxic metal exposure through mechanisms such as efflux transport, enzymatic detoxification, and metal sequestration. However, the diversity and functional overlap of these proteins across taxa present challenges for reliable identification using conventional homology-based methods. Furthermore, current machine learning approaches for resistance gene prediction primarily focus on antibiotics, with no comprehensive resource available for systematically classifying HMR proteins across multiple metals and biological domains. To address this, we developed HMRPred, a machine learning-based predictive framework for the identification of HMR proteins across ten metals of concern: arsenic, cadmium, chromium, copper, iron, lead, mercury, nickel, silver, and zinc. Curated datasets comprising experimentally validated resistance and non-resistance proteins were used to extract a comprehensive set of sequence-derived features, including amino acid composition and physicochemical descriptors. For each metal, optimized classifiers were trained using various machine learning algorithms, achieving high performance with an AUC-ROC of more than 98% in both cross-validation and independent testing. HMRPred is deployed as a web-accessible resource (available at https://hmrpred.streamlit.app/), allowing researchers to submit protein sequences and obtain predictions with confidence scores. By facilitating genome-wide annotation of metal resistance determinants, HMRPred supports applications in bioremediation, environmental microbiology, phytoremediation, and synthetic biology.

Understanding how mutations affect protein function remains critical yet challenging, particularly for variants in clinical databases lacking experimental characterisation and for intrinsically disordered regions. Current computational approaches often operate as black boxes, providing predictions without sufficient transparency or quality assessment of the underlying data. Here we present ProteoCast, a user-friendly web server that predicts variant effects through evolutionary constraint analysis and structural context integration. ProteoCast provides a three-tier variant classification (impactful, mild, neutral) to help prioritise mutations for clinical interpretation and experimental validation. It incorporates multiple sequence alignment quality controls to ensure prediction reliability and flag positions with insufficient evolutionary information. Beyond single-variant classification, ProteoCast employs a novel segmentation approach based on mutational sensitivity to identify functional linear peptides in disordered regions. Interactive visualisations guide users through results interpretation, from variant-level predictions to protein-wide functional landscapes. Evaluation on 63,000 ClinVar variants demonstrates 77 % sensitivity and 87 % specificity for pathogenicity prediction, with performance maintained across species (85 % accuracy on Drosophila lethal mutations). ProteoCast successfully identifies twice as many functional motifs in intrinsically disordered regions compared to conservation-based phylogenetic methods. Predictions can be tuned to specific conformations, such as bound forms in protein complexes, for improved accuracy and interpretability. With its transparent, unsupervised methodology and computational efficiency (minutes per protein), ProteoCast democratises access to variant effect prediction and functional site discovery for the broader research community. The web server is freely available at: https://proteocast.ijm.fr/.

The recent development of highly accurate protein structure prediction tools has led to a rapid expansion in the scope of computational structural biology, enabling a much wider range of modelling studies than ever before. These new in silico opportunities help life science researchers understand how proteins interact with their environment and support design of new molecules with desired properties. Ultimately, they have broad applications, e.g. in medicine, drug discovery or engineering. To ensure reproducibility and to facilitate data exchange and reuse, predicted structures or computed structure models can be stored using ModelCIF, a rich data representation designed to include the atomic coordinates/metadata. The previously published version of ModelCIF (1.4.4; 2022-12-21) mainly covered protein structure predictions generated by homology and ab initio modelling. In this work, we present an extension of the ModelCIF (https://github.com/ihmwg/ModelCIF) data standard and its associated tools. This extension supports important new use cases, including modelling protein-ligand and protein-protein interactions, sampling multiple conformational states and designing proteins de novo. We define guidelines for storage and validation of modelling results for those use cases by applying new and existing ModelCIF categories to capture protocols, inputs and outputs. Additionally, we outline updates to the software tools and resources that implement these new standards and provide functionality for model generation, validation, archiving, and visualisation. By enabling consistent metadata capture across different modelling workflows, this framework aims to support the FAIR dissemination of computational models, thereby promoting reproducibility and reusability in downstream applications.

Several if not all manifestations of aging can be postponed by a healthy lifestyle involving a balanced diet coupled with regular exercise and sufficient sleep. Similarly, various genetic and pharmacological longevity interventions can exert beneficial effects across species in a conserved manner, extending both lifespan and healthspan. While all these interventions-ranging from genetic perturbations to pharmacological supplementation to lifestyle changes-affect diverse biological processes, a common candidate mechanism underpinning at least some of their benefits is autophagy, a cellular recycling process essential for maintaining cellular homeostasis. In this review, we summarize how autophagy is affected by various pharmacological and lifestyle factors, with a focus on studies in which autophagy have been shown to play a causal role in promoting healthy aging. Specifically, we review the molecular mechanisms through which pharmacological agents, dietary restriction, exercise, sleep adjustments, as well as temperature modulation affect autophagy to extend lifespan and often also healthspan in model organisms and humans. Still, major gaps remain in human research due to limited assays to monitor autophagy and the scarcity of longitudinal studies linking autophagy dynamics to health outcomes. Closing this gap is a key challenge in converting discoveries from model organisms into interventions that consistently enhance healthy aging in humans. By summarizing current findings and highlighting remaining uncertainties, this review aims to provide a roadmap for translating insights on autophagy from model organisms into strategies to promote healthy aging in humans.

Accurate determination of initial reaction rates (v₀) is essential for characterizing enzyme function, designing inhibitors, and modeling biological systems. Traditional methods rely on linear approximations valid for reaction phases difficult to capture, while substrate excess over the enzyme does not ensure constant rates. To overcome these limitations, we developed ACCU-RATES, a user-friendly web tool that analyzes heuristically product accumulation or substrate depletion curves containing at least two time points. Using a differential form of the Michaelis-Menten equation, ACCU-RATES numerically fits progress curves to interpolate v₀, enabling precise determination of the Michaelis constant (K_m) and limiting rate (V). Simulations across diverse scenarios, including data noise and low sampling rates, show that ACCU-RATES delivers reliable, user-independent parameter estimates without relying on linear phases. Compared to existing methods, it offers superior accuracy and robustness against assay interferences, with applications in inhibitor discovery, synthetic biology, and biomarker assays. ACCU-RATES is freely available at https://accu-rates.i3s.up.pt.