Protein Science最新文献

{"title":"Damping amyloid-associated conformational fluctuations in a protein by an engineered diselenide bridge.","authors":"Yanwu Yang, Balamurugan Dhayalan, Andreas Ehnbom, Orit Weil-Ktorza, Norman Metanis, Michael A Weiss","doi":"10.1002/pro.70697","DOIUrl":"10.1002/pro.70697","url":null,"abstract":"<p><p>Polypeptide cross-β assembly, characteristic of diverse proteotoxic diseases, defines a general thermodynamic ground state and limits the shelf lives of peptide- and protein therapeutics. A model is provided by insulin. Although the hormone contains a predominance of α-helix, its fibrils exhibit cross-β reorganization. In the real world, aggregation-coupled fibrillation of insulin underlies its degradation above room temperature, impairing activity and imposing a complex global \"cold chain\" of transport and storage. Here, we describe biophysical protection of an insulin analog at an elevated temperature by an engineered diselenide bridge. Our studies focused on insulin glargine, the active ingredient of long-acting formulations in broad clinical use. Insoluble in a subcutaneous depot due to its shifted isoelectric point, the analog dissolves at pH 4.0 and so, unlike neutral formulations of the wild-type hormone, is unprotected by zinc-mediated hexamer assembly. Whereas at 37°C the fibrillation lag time of insulin glargine is accelerated by fourfold relative to WT insulin, such instability is circumvented by pairwise substitution of Cys^A6 and Cys^A11 by selenocysteine. Protection from fibrillation correlates with augmented resistance to pepsin cleavage, guanidine denaturation, and thermal unfolding. Although NMR structures of insulin glargine and its diselenide analog are similar, damping of conformational fluctuations is evidenced by patterns of ¹H-NMR chemical shifts, helix-associated NOEs, amide-resonance line widths, and ¹H-²H amide-proton exchange. Such damping is discussed in relation to molecular dynamics simulations. Demonstrating a likely mechanistic relationship between fibrillation and native-state conformational fluctuations, our findings highlight the translational promise of \"dynamic engineering\" via nonstandard mutagenesis.</p>","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"35 8","pages":"e70697"},"PeriodicalIF":5.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13386395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148536684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational design of blue melanin by motif-scaffolding a pentapeptide-repeat protein.","authors":"Di Sheng Lee, Bomi Park, Sergio Salgado, James Dolgin, David L Kaplan","doi":"10.1002/pro.70718","DOIUrl":"10.1002/pro.70718","url":null,"abstract":"<p><p>Blue coloration in natural and synthetic melanins arises from light reflection rather than absorption, that is, structural rather than pigmentary color. Producing melanin with blue pigmentary color is challenging as it requires the formation of long, homogeneous melanin polymer from tyrosine (Tyr) amino acid. Here, we describe a bottom-up design strategy for producing blue melanin by polymerizing Tyr-containing pentapeptides into a ~60-unit polymer that absorbs at 617 nm. To narrow the sequence space from 160,000 possible amino acid combinations, we used RFdiffusion, a protein deep learning model, to motif-scaffold a pentapeptide-repeat protein (PRP) to identify sequences ideal for blue melanin formation. Top peptide designs were experimentally validated, generating de novo melanin colors (blue, purple, green) alongside known colors (red, yellow, brown). Under acidic conditions, blue melanin remained thermally stable at autoclave temperature of 121°C and photostable for weeks under 600 lux illumination. We also demonstrated the application of blue melanin as an electrophoretic ink. De novo melanin design from simple peptides with sequence-to-color tunability could potentially transform how colorants are sourced and produced. Our approach with computational design should also inspire the development of new deep-learning tools to directly predict colors from amino acid sequences.</p>","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"35 8","pages":"e70718"},"PeriodicalIF":5.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13343731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148405534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering the Function and Structure of PA1216 as an S-Adenosyl-l-Methionine Binding Protein Using Differential Scanning Fluorimetry and Circular Dichroism.","authors":"Amaan M Fruitwala, Jade X Tiszler, Daniel Watson, Mansi Sinha, Tony D Davis","doi":"10.1002/pro.70719","DOIUrl":"10.1002/pro.70719","url":null,"abstract":"<p><p>Microbes produce bioactive secondary metabolites as toxins, pigments, or virulence factors. These specialized compounds are produced by nonribosomal peptide synthetases (NRPS), polyketide synthases (PKS), or hybrid NRPS/PKS pathways. The genes encoding NRPS and PKS reside in biosynthetic gene clusters (BGCs), some of which have no identified metabolite associated with them. Characterization of these orphan BGCs could provide insights into potential bioactive compounds that have yet to be discovered. Here, we characterize PA1216, a putative methyltransferase embedded within an NRPS BGC in Pseudomonas aeruginosa strain PAO1. We cloned, expressed, and purified PA1216, and developed an optimized differential scanning fluorimetry assay to measure its thermal stability, demonstrating concentration-dependent stabilization in the presence of established methyltransferase cofactors and inhibitors. We then adapted this assay for high-throughput screening of potential PA1216 substrates, identifying destabilizing compounds, including glycyl-glycine dipeptides, amino esters with aromatic or basic side chains, and N-Boc-protected amino acids. In contrast, sodium salts of organic acids stabilized PA1216. Lastly, we employed AlphaFold to construct a predictive model, revealing that PA1216 contains a Rossmann-like fold and a glycine-rich loop, typical of class I methyltransferases, and we corroborated these secondary structural elements using circular dichroism spectroscopy. Overall, these studies illuminate PA1216 function and establish a platform for characterizing cryptic gene clusters within secondary metabolic pathways.</p>","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"35 8","pages":"e70719"},"PeriodicalIF":5.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13351317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148423439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privateer for UCSF ChimeraX: Carbohydrate structure validation and visualization.","authors":"Lou Holland, Jordan S Dialpuri, Lucy C Schofield, Phuong Thao Pham, Shuai Wang, William Cook, Tristan I Croll, Jon Agirre","doi":"10.1002/pro.70716","DOIUrl":"10.1002/pro.70716","url":null,"abstract":"<p><p>Modeling 3D carbohydrate structures, either as ligands or covalently bonded to proteins, still presents a number of challenges. As such, carbohydrate structure validation is a key step in the modeling process. The Privateer software package has long provided methods to aid in this validation, considering unlikely ring conformations of the sugars, stereochemistry, comparison to known glycans, and unlikely linkage torsions. However, uptake of these tools depends on the pipeline used for structure solution. It is therefore important to make glycan validation available in as many model building programs as possible. To this end, we have created a Privateer bundle for ChimeraX. This bundle provides intuitive access to key validation metrics for carbohydrates present in a structure model within the molecular visualization program while working on your structure solution, as well as providing the ability to visualize monosaccharides in the structure using 3D glycan symbols to aid in understanding of interactions. The Privateer bundle for ChimeraX can be installed from ChimeraX's toolshed: https://cxtoolshed.rbvi.ucsf.edu/apps/chimeraxprivateer.</p>","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"35 8","pages":"e70716"},"PeriodicalIF":5.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13351607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148423464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep mutational scanning of SARS-CoV-2 nucleocapsid reveals regional stability patterns.","authors":"Alasdair D Keith, Meredith M Keen, Filipp Frank, Eric A Ortlund","doi":"10.1002/pro.70706","DOIUrl":"10.1002/pro.70706","url":null,"abstract":"<p><p>Point mutations within the SARS-CoV-2 nucleocapsid protein (NP) have the potential to impact not only detection but the viral life cycle, and therefore pathogenicity. Leveraging deep mutational scanning (DMS) we determine the effect of 7876 (98.9%) of all possible point mutations on NP surface expression as a proxy for stability, which we term surface-expression-based folding fitness (SEBFF). We show NP susceptibility to SEBFF changes varies with region, with mutations to the structured RNA-binding domain (RBD) and dimerization domain (DD) being more likely to cause decreases in SEBFF. We show that the R203K mutation in the linker region increases the folding fitness of NP, suggesting thermostability may have been one factor driving the adoption of the R203K/G204R set of mutations arising in the alpha, gamma and omicron lineages. Our DMS results appear consistent with the limited recombinant protein and biophysical data for NP variants available from the literature. We tested three computational methods to determine whether any could accurately predict the DMS data; per-variant predictions were poor, but per-site predictions were good, with machine learning methods outperforming the physics-based strategy, and ThermoMPNN-D performing most effectively with the RBD and ProteinMPNN with the DD.</p>","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"35 8","pages":"e70706"},"PeriodicalIF":5.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13347322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148412644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-based prediction of protein folding and unfolding rates: Examining the roles of fold topology and core packing.","authors":"Mohammad Abdulqader, Victor Muñoz","doi":"10.1002/pro.70705","DOIUrl":"10.1002/pro.70705","url":null,"abstract":"<p><p>The (un)folding rates of natural proteins determine their native stability and functional homeostasis, making them important targets for protein engineering and design. From a prediction standpoint, the rates have been a long-standing puzzle. We have known for decades that folding rates empirically correlate with properties of the native three dimensional (3D) structures and that both, folding and unfolding rates, scale with protein size. Whereas such rate correlations are too rough for being of practical use, no significant progress in prediction accuracy has occurred since then, despite many efforts even including machine learning approaches. Here, we retake on this challenge by expanding the simple one-dimensional free energy surface (1D-FES) model that originally led to demonstrate the size scaling of both rates, and a curated database with rates for 75 single-domain proteins. We define the weighted sequence order (WSO) as a novel parameter that allows incorporating structural information into the 1D-FES model explicitly. Via the WSO, we examine the role of global structural properties such as fold topology and core packing in defining the (un)folding rates within the context of a physics-based model of protein folding. After introducing fold topology and packing at a coarse-grained level, the model uses three floating parameters to predict the folding and unfolding rates within 6.5- and 10-fold, respectively, resulting in ±6.5 kJ/mol accuracy in native stability, equivalent to the typical perturbation induced by one single-point mutation. The net improvement over the 2-parameter size-only prediction is of 2.5-fold. These new rate predictions are significantly closer to the threshold of usefulness for engineering and design. More importantly, this WSO-modified 1D-FES model can now directly accommodate atomistic, high-resolution, force-fields to further optimize the rate predictions, and/or to use rate information as a testbed for force-field refinement. Finally, the WSO-1D-FES model could also serve as foundation for developing more complex models capable of dealing with multi-domain proteins as well as with the evolutionary information cryptically encoded in natural protein sequences.</p>","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"35 8","pages":"e70705"},"PeriodicalIF":5.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13413589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148606945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolutionary history and recombination in the mitochondrial carrier SLC25 superfamily analyzed by similarities in the exon and transmembrane α-helix sequences.","authors":"Magnus Monné, Daniela Valeria Miniero, Rosa Calvello, Antonia Cianciulli, Luigi Palmieri, Ferdinando Palmieri","doi":"10.1002/pro.70727","DOIUrl":"10.1002/pro.70727","url":null,"abstract":"<p><p>Mitochondrial carriers (MCs), which constitute a superfamily also called the solute carrier family 25 (SLC25), are characterized by conserved signature motif sequences and a six-transmembrane α-helical transporter domain. They transport a wide variety of substrates ranging from protons, inorganic ions, citric acid cycle intermediates, and amino acids to nucleotides and cofactors. The superfamily members can be divided into subfamilies, each with a distinct substrate specificity. In an attempt to understand how different subfamilies have evolved, we analyzed the protein sequences of the exons (with conserved boundaries) and the six transmembrane α-helices of MCs from highly diverged organisms. The results show that some MC subfamilies have all exons and transmembrane α-helices most similar to a closely related subfamily, which is consistent with a scenario of gene duplication and mutational divergence from a last common ancestor. However, several MC subfamilies appear to be mosaics of exons and transmembrane α-helices most similar to different and distant subfamilies, which in some cases could be explained by recombination between the superfamily genes during evolution. It seems that this latter mechanism could have played a role in the formation of new subfamilies with different substrate specificities by the combination of MC transporter domain segments that had been optimized previously for binding specific portions of the substrates. This study presents novel evolutionary relationships between MC subfamilies and may provide clues for how protein superfamilies have expanded and how to investigate their evolution.</p>","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"35 8","pages":"e70727"},"PeriodicalIF":5.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13385207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148536794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing machine learning-based binder design with high-throughput screening: A comparison of mRNA and yeast display technologies.","authors":"Zhiyuan Yao, McGuire Metts, Avery K Huber, Jingjing Li, Tomoaki Kinjo, Henry Dieckhaus, Amrita Nallathambi, Albert A Bowers, Brian Kuhlman","doi":"10.1002/pro.70696","DOIUrl":"10.1002/pro.70696","url":null,"abstract":"<p><p>Recent advances in machine learning (ML)-based protein design methods have enabled the rapid in silico generation of large libraries of miniprotein binders with minimal manual input. While computational design capacity has scaled rapidly, experimental validation methods have lagged, creating a bottleneck in binder discovery pipelines. Here, we apply mRNA display to screen an ML-designed miniprotein binder library and directly compare its performance with the more widely used yeast surface display platform using a single shared DNA library. We screened 2009 designs targeting the platelet receptor TLT-1 and 3159 designs targeting the immune receptor B7-H3 across both platforms. While both selection protocols reliably identified functional binders, we found that mRNA display preferentially enriched binders with slower dissociation rates. In addition, mRNA display achieved higher library coverage than yeast display, likely rescuing functional designs that are penalized in a cell-based expression system. Biophysical characterization of selected binders from both platforms revealed strong binding affinities and high thermal stabilities. These results showcase the power of integrating ML-based computational design tools with rapid in vitro selection technologies, providing a scalable framework for therapeutic miniprotein discovery.</p>","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"35 8","pages":"e70696"},"PeriodicalIF":5.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13352141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148423420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Divergent CRD-dependent mechanisms govern RAS isoform-selective recruitment of CRAF and ARAF.","authors":"Shrhea Banerjee, Sravani Malasani, Shriti Banerjee, Maria Celeste López Vásquez, Shane McSorley, Zhihong Wang","doi":"10.1002/pro.70687","DOIUrl":"10.1002/pro.70687","url":null,"abstract":"<p><p>RAF kinases interpret signals from the three major RAS isoforms to initiate MAPK pathway activation, yet the molecular logic that governs isoform-specific RAS recruitment and the early events that relieve RAF autoinhibition are not yet fully understood. In particular, how the modular N-terminal regulatory architecture of CRAF and ARAF, anchored by the multifunctional cysteine-rich domain (CRD), discriminates among HRAS, KRAS, and NRAS has remained a central unresolved question. Here, we combine quantitative biophysical measurements with structural and dynamic analyses to define how RAS isoform identity and CRD engagement shape the earliest steps of RAF activation. These studies reveal unexpectedly divergent modes of RAS recognition between CRAF and ARAF and expose previously unappreciated functions of the CRD in modulating RAS affinity and intramolecular regulatory contacts. We further identify a direct link between RAS binding and destabilization of RAF autoinhibition, suggesting a mechanism by which RAS engagement facilitates the transition from an inactive monomer to an activation-competent assembly. Finally, we show that emerging KRAS inhibitors variably perturb KRAS-CRAF interactions, offering insight into how these therapeutics influence early RAS-RAF signaling events. Together, this work uncovers distinct biophysical principles that govern RAS-RAF selectivity and reveals a regulatory role for the CRD that reframes our understanding of RAF activation and its dysregulation in RAS-driven cancers.</p>","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"35 8","pages":"e70687"},"PeriodicalIF":5.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13347321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148412653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engineering selective amyloid precursor protein inhibitors by machine learning and deep mutational scanning.","authors":"Reut Meiri, Oz Reuveni, Michal Levi, Evette S Radisky, Niv Papo, Yaron Orenstein","doi":"10.1002/pro.70712","DOIUrl":"10.1002/pro.70712","url":null,"abstract":"<p><p>Deep mutational scanning (DMS) has proven effective for mapping protein-protein interactions (PPIs), but it cannot provide complete coverage of the mutation landscape, particularly for multi-mutant variants. To address this limitation, we trained machine-learning (ML) models on previously generated DMS data for a stabilized amyloid precursor protein inhibitor (APPI) binding to either of two serine proteases, mesotrypsin and kallikrein-6 (KLK6), which are implicated in various human disorders. We combined the models to accurately predict the binding selectivity of APPI variants, including double-mutant variants, for the two serine proteases. We achieved a Pearson correlation of 0.937 between predicted log₂ selectivity enrichment ratios and DMS-derived values. We further validated the predictions of our combined model by yeast-surface-display measurements and inhibition assays of purified APPI variants and revealed epistatic interactions that shape protease selectivity. Guided by binding selectivity predictions, we identified highly selective APPI variants, including the most selective mesotrypsin inhibitor reported to date. Together, these findings support the use of DMS and ML as a framework for predicting PPI selectivity and prioritizing selective therapeutic protein variants.</p>","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"35 8","pages":"e70712"},"PeriodicalIF":5.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13385218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148536773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}