Protein Science

Protein aggregation is central to amyloid-related disorders and remains a major developability challenge for protein therapeutics. Over the past two decades, significant advances have been made to predict aggregation-prone regions (APRs) and estimate aggregation propensity in proteins and peptides. In contrast, the prediction of aggregation kinetics has received relatively less attention due to the limited availability and heterogeneity of experimental data. Consequently, aggregation propensities from APR prediction algorithms were widely accepted as a means to predict relative changes in the aggregation kinetics of proteins and mutants. Previous studies have demonstrated, using large-scale datasets, that aggregation propensity shows a weak or inconsistent correlation with aggregation kinetics. In the present study, we have integrated complementary state-of-the-art mechanistic and kinetic prediction tools for protein aggregation into a unified, user-friendly web framework entitled "Amylo-Pipe". Amylo-Pipe also implements practical features that are especially useful for protein engineering, such as gatekeeper-residue mutational scanning to support the design of aggregation-resistant variants. By consolidating multiple prediction tasks in a single interface, Amylo-Pipe enables a more comprehensive assessment of aggregation behavior than APR-only workflows. The web server is freely accessible at: https://web.iitm.ac.in/bioinfo2/amylopipe/.

Over the past decade, protein design has evolved from a specialized discipline into a broadly accessible approach for engineering and interrogating biological systems. Despite these advances, protein design continues to be a technically challenging task, often requiring knowledge of programming to be able to use and combine the different software packages. To address this challenge, we have developed Prosculpt, an easy-to-use protein design pipeline. Prosculpt integrates RFdiffusion for backbone generation, ProteinMPNN for sequence design and multiple structure-prediction platforms (AF2, AF3, Colabfold, Boltz2). Candidate designs are evaluated using customizable Rosetta-based scoring protocols. Each project is specified through a single configuration file, enabling users with minimal computational expertise to perform sophisticated protein design tasks without writing code, while also allowing advanced users to access the full capabilities of the underlying programs. Prosculpt supports a wide range of applications, including design of symmetric homo-oligomers, design of binders, motif scaffolding, partial diffusion and fixed-backbone sequence redesign. By combining these capabilities within a single, user-friendly platform, Prosculpt provides a practical entry point to modern protein design for both novice and expert users.

Antifreeze proteins (AFPs) found in various cold-adapted organisms inhibit ice growth and are of interest for applications in food products, cryopreservation, agriculture, and materials science. Although high-resolution structures are available for several AFPs, the amino acids required for full antifreeze activity remain incompletely defined, and the development of AFP variants with properties such as enhanced solubility, high expression yield, and improved thermostability may further facilitate applications. Here, we used the deep learning model ProteinMPNN to redesign the globular fish antifreeze protein AFPIII, keeping the previously reported ice-binding residues fixed. We readily obtained sequences confidently predicted to adopt AFPIIIs structure and we selected five designed variants for expression, all of which expressed efficiently in E. coli. Circular dichroism spectroscopy showed that two of these variants retained secondary structure elements consistent with AFPIII, whereas the other three exhibited structural differences. One design was predicted and experimentally confirmed to have increased thermostability. All five variants displayed measurable thermal hysteresis activity. However, none reached the activity of wild-type AFPIII, suggesting that maintaining the currently established set of ice-binding residues is not sufficient to fully preserve this AFPs function; other, unidentified residues can also impact its activity. Our findings highlight the value of deep learning-based protein design methods both for generating AFP variants with desirable properties and for uncovering gaps in existing knowledge of well-characterized AFPs.

For structural-biology and protein-production pipelines, the hardest part of a difficult protein is not the biology -- it is obtaining a well-behaved sample for functional studies. Programs routinely stall at construct design, expression, and purification: deciding where to truncate, which tags to use, how to express, and how to purify so the protein survives concentration and handling. These decisions are still made largely by literature precedent and experimental experience, and they require trial-and-error before arriving at a functional construct for hard targets. We present a prospective, single-pair wet-lab case study testing whether an integrated computational platform can improve these decisions. For human fibroblast growth factor 21 (FGF21) -- a clinically important and stability-challenged metabolic hormone -- we compared two expression constructs produced side by side under the same experimental workflow, using two different design strategies: one designed by a scientist from the literature (reproducing the published core-domain construct, PDB 6M6E), and one designed by the Orbion platform -- an AI, prediction-guided protein-design system (orbion.life) -- which additionally generated the expression and purification protocols (executed scientist-in-the-loop). The platforms construct used an unconventional, longer C-terminal boundary not found in public sequence databases. Since the two constructs differ in more than one feature, we treat them as workflow-level designs throughout. The scientist construct gave a higher initial yield ([~]2.4 xmore protein recovered at affinity capture). The platform-designed construct, however, showed a more favourable downstream developability profile: it concentrated higher (1.4 vs 0.7 mg/mL) while remaining more monodisperse by dynamic light scattering (DLS). The scientist construct, in contrast, aggregated on concentration, so its initial-yield advantage did not survive: in the final concentrated sample the Orbion construct provided the more usable material for downstream studies. Computed for the mammalian host used, the platform had prospectively scored its own design higher (composite 68.7 vs 59.0 for the scientist-designed construct), and its predictions of yield, solubility, and disorder matched the wet-lab outcome. This is a single, deliberately scoped case study, not a population-level benchmark; the two constructs differ in more than one feature, and biological activity was not assayed. Alongside the bottlenecks of this approach discussed here, used as a decision aid, prediction-guided construct and protocol design has the potential to remove costly iteration cycles of protein production campaigns.

T cells are important components of the adaptive immune system and develop through a selection process regulated by signaling through the T-cell receptor (TCR). Thymocyte-Expressed Molecule Expressed in Selection (THEMIS) is a TCR-proximal protein that modulates the activity of Shp1 phosphatase to influence TCR signaling during development. THEMIS has been shown to both activate and inhibit Shp1, but the molecular mechanisms of these functions are poorly understood. THEMIS contains two rare Cysteine All-Beta In THEMIS (CABIT) domains, the N-terminal of which interacts with Shp1 and is likely responsible for modulation of its phosphatase activity. Herein, we report the first crystal structure of the THEMIS CABIT1 domain. While a portion of the CABIT1 domain is poorly resolved, it appears to share the same overall fold observed in our recent CABIT2 crystal structure and AlphaFold predictions. We show that phosphorylation of the CABIT1 domain by LCK is required for association with SHP1 and that phosphorylated CABIT1 can protect Shp1 from oxidation and inhibition by reactive oxygen species (ROS), which may serve as a mechanism by which THEMIS enhances Shp1 activity.

Proteins and their associated complexes often adopt multiple conformations, with the transitions between these states playing a critical role in biological function. However, the resulting structural heterogeneity can be challenging to visualize and communicate, often requiring manual inspection and time-consuming annotation of biomolecular structures. To address this, we developed ResiRuler, a local, browser-based tool that uses inter-residue distance measurements to quickly quantify atomic displacements and map changes in internal geometry across ensembles of related protein structures. By converting structural differences into residue-pair distance changes, ResiRuler enables rapid identification of regions undergoing coordinated motion, local rearrangement, or large-scale conformational change. The resulting visualizations can be exported as scripts for PyMOL and ChimeraX, allowing users to explore conformational differences and generate publication-quality molecular figures in their preferred visualization environment. Using atomic models in Macromolecular Crystallographic Information File (mmCIF) file format, ResiRuler aligns multiple structures and measures structural variation across models facilitating visualization and presentation of these differences. This allows for rapid visualization of which regions of proteins change among ensembles of structures. The program is available for download at https://github.com/tbaker67/ResiRuler on macOS and Linux operating systems.

Membrane contact sites are organized by protein assemblies that physically couple organelles and coordinate lipid metabolism, yet the structural principles that enable lipid exchange across these junctions remain poorly defined. At the nuclear-vacuolar junction (NVJ) in budding yeast, the tethering protein Mdm1 and its binding partner Nvj3 form a complex that regulates lipid metabolic pathways, but the structural features underlying their interaction have not been resolved. Here, we use AlphaFold-based complex prediction and comparative structural analysis to define the organization of Nvj3-Mdm1 complex assembly. We identify a high-confidence heterodimer in which conserved PXA and PXC domains generate an extended tunnel spanning both proteins. Tunnel analysis predicts a core hydrophobic conduit traversing the Nvj3-Mdm1 interface, consistent with a lipid-compatible architecture. Evolutionary conservation is enriched at the Nvj3-Mdm1 interface. The predicted conduit shares geometric and physicochemical properties with bridge-like lipid transfer proteins, including Atg2, Fmp27, and Hob2, suggesting that heteromeric tether assemblies may contribute directly to inter-organelle lipid transfer. Cophylogenetic analysis reveals coordinated coevolution of Nvj3 and Mdm1 across Saccharomycetes. Together, these findings define Nvj3 as a structural partner of Mdm1 and support a conduit-based model of lipid transfer at the NVJ.

Alpha-synuclein is an intensely studied intrinsically disordered protein whose aggregation into amyloid fibrils is connected to the progression of several neurodegenerative diseases, most commonly Parkinsons Disease. A remarkable feature that has emerged from this research is how easy it is to induce the protein to aggregate in vitro into a wide range of amyloid fibrils that appear to resemble the aggregates found in Lewy bodies in diseases like Parkinsons while at the same time how difficult it is to produce aggregates whose fold truly represents the disease-associated amyloids at the atomic level. In an effort to produce the disease-relevant fibrils in vitro we have analyzed over 60 independent samples by cryo-electron microscopy using helical reconstruction to obtain atomic resolution models for most of the samples. While not yet achieving our original goal, we have found that several overlooked parameters influence the structural outcomes of alpha-synuclein aggregation, in particular protein purity, preparation of the monomeric starting material and agitation method.

Assigning peaks in NMR spectra to specific residues is an essential but time-consuming step in the study of intrinsically disordered proteins (IDPs). Conventional approaches rely on building chains of sequential connectivities between peaks, which are particularly prone to failure in disordered systems due to spectral overlap, missing peaks, and proline-rich sequences. Here we present SpinForecast, a tool that performs chain-free probabilistic backbone assignment of IDPs from chemical shifts alone, without requiring peaks to be linked into sequential chains. SpinForecast predicts residue-specific chemical shift distributions from a disorder-filtered subset of the Biomolecular Magnetic Resonance Data Bank (BMRB), incorporating nearest-neighbour residue effects and corrections for temperature and pH. Experimental chemical shifts are then assigned to residues by Bayes theorem, using residue-specific chemical shift distributions as likelihoods. We validate SpinForecast on three disordered proteins, IAPP (37 residues), NUPR1 (82 residues), and JPT2 (218 residues), achieving 100% confidence assignments for 74%, 57%, and 44% of in-distribution peaks, respectively, all with at least 99% accuracy. Where single assignments cannot be determined for these systems, the correct assignment was contained within the returned set of candidate assignments in greater than 97% of cases. SpinForecast is freely available at https://tools.bindresearch.org/bindbox/SpinForecast.

Insulin can misfold and assemble into amyloid fibrils, a process linked not only to complications of insulin therapy but also to proteotoxic stress in pancreatic {beta}-cells. Despite growing interest in the pathological consequences of insulin aggregation, prevention efforts are limited by an incomplete understanding of the endogenous mechanisms that counteract it. Here, we identify Fas apoptosis inhibitory molecule (FAIM) as an endogenous suppressor of insulin amyloid formation. FAIM reduces {beta}-sheet formation and redirects insulin toward disordered, growth-incompetent assemblies. Further, FAIM attenuates the cytotoxicity of insulin aggregates in vitro. We hypothesize that this effect arises from masking aggregation-prone regions of insulin and show through structural modeling that FAIM interacts with both insulin chains. These findings extend the anti-aggregation function of FAIM to insulin and suggest a mechanism for endogenous suppression of insulin amyloid formation. More broadly, our results provide insight into the regulation of insulin assembly and highlight FAIM as a candidate modulator of proteostasis in metabolic disease. Statement for a broader audienceInsulin can clump together into harmful aggregates, contributing to complications of insulin therapy and potentially damaging the insulin-producing cells of the pancreas. This study identifies the naturally occurring protein FAIM as a protective factor that inhibits the formation of these harmful aggregates and reduces their toxicity. These findings improve our understanding of how cells protect insulin from harmful aggregation and may open new avenues for developing therapies to combat diabetes-related protein aggregation.

More than two thirds of proteins in the human proteome are predicted to contain intrinsically disordered regions (IDRs), which lack stable folded structure. IDRs are critical for biological regulation and organization, as targets for post-translational modifications, and as mediators of biomolecular condensates. To address the pressing need for better structural models enabling functional insight, we developed AlphaFlex to model fully atomistic conformer ensembles for proteins predicted to have IDRs, modeled in the context of AlphaFold folded domains and an implicit bilayer for transmembrane proteins. The AlphaFlex resource provides conformational ensembles of human proteins from the AlphaFold database with identified IDRs in the Protein Ensemble Database that is mirrored in UniProt. This transformative resource of AlphaFlex ensembles provides physically and biologically relevant full-length models for IDR proteins, including scaffold proteins, those with IDR:folded-domain interactions, regulatory and condensate proteins requiring exposed binding elements, conditionally folding IDRs, and transmembrane proteins containing IDRs.

Motivation: Continuing advances in genome and metagenome sequencing expand the number of identified conserved protein families that remain functionally uncharacterized and contain domains of unknown function (DUFs). Functional-association resources such as STRING provide biological context, but mostly do not distinguish indirect association from physical interaction. We assessed whether AlphaFold 3 complex prediction, combined with STRING evidence and domain-level analysis of interfaces and interaction partners, can help identify and characterize DUF-containing proteins. Results: We generated four structural-prediction cohorts from STRING associations involving DUF-containing proteins and evaluated the predicted complexes using interface ipSAE, average pLDDT and buried surface area. An L2-regularized logistic regression model was trained on an initial cohort of predictions from high-confidence STRING associations to prioritize DUF-containing candidates likely to produce structurally confident AlphaFold 3 complexes. The model was then applied across all 12,535 organisms represented in STRING v12.0, followed by grouping into DUF-family and partner-architecture modules, covering 2,076 unique DUF families. The final L2-model screen contained 12,298 successfully modelled protein pairs, including 1,208 (9.82%) complexes meeting a strict-confidence criterion and 2,433 (19.78%) meeting a more liberal confidence criterion. Two examples suggest roles for DUF4130 in nucleic-acid-associated radical-SAM biology and DUF5819 in a bacterial system related to vitamin-K-dependent carboxylation. Availability and implementation: Predicted structures and associated metadata are available through Zenodo at https://doi.org/10.5281/zenodo.21875362. The model implementation and code used to generate the analyses and figures are available at https://github.com/linoriep/Proteome-scale-structure-prediction-of-DUF-containing-protein-protein-interactions.

The thermal stability of collagen triple helices is strongly influenced by the amino acid sequence of the repeating Gly-X-Y tripeptides, yet how these residue-specific interactions are integrated within an extended triple helix to determine thermal behavior remains poorly understood. Here, we addressed this question using recombinant collagen mimetic peptides (rCMPs) containing extended native sequences from the 1(I) and 2(I) chains of human type I collagen. Triple-helix formation was nucleated by a C-terminal foldon domain and further stabilized by interchain disulfide crosslinking, allowing the apparent melting temperature (T) to reflect interactions within the triple-helical domain independent of nucleation. The stabilizing effects of Pro and Y-position Arg identified in host-guest peptides were largely preserved in extended triple helices, whereas the proposed Lys-Gly-Glu (KGE) interchain salt bridge produced little measurable stabilization, demonstrating the influence of sequence context. Remarkably, identical triple-helical sequences exhibited markedly different thermal behavior when unfolding was initiated under different conditions. Nevertheless, extended triple helices differing substantially in sequence and length retained an apparently two-state thermal transition. These findings support a mechanism in which unfolding is preferentially initiated within regions of lower intrinsic stability, while the continuity of the triple helix couples neighboring regions into a cooperative unfolding process throughout the helix. This mechanism provides a plausible explanation for the longstanding paradox that extended collagen triple helices exhibit persistent sequence-dependent thermodynamic heterogeneity despite a two-state thermal transition, and a framework for investigating how sequence-dependent stability contributes to the structure and function of collagen molecules. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC="FIGDIR/small/741348v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@f311aorg.highwire.dtl.DTLVardef@160dc76org.highwire.dtl.DTLVardef@29e989org.highwire.dtl.DTLVardef@1a3279c_HPS_FORMAT_FIGEXP M_FIG C_FIG

A universal mechanism in molecular evolution is functional and structural divergence of members of a protein family. The ability of AlphaFold to predict atomic-resolution protein structures promises to accelerate insights into this process. We study the interplay of changes in sequence, structure, and function in photoactive yellow protein (PYP), a family of bacterial blue light photoreceptors. Halorhodospira halophila contains two PYP homologs that diverged to 60% sequence identity, differ 100-fold in the lifetime ({tau}pB) of their pB signaling intermediate, and display altered peak wavelengths ({lambda}max) for color sensing. We resurrected ancestral PYPs and determined these properties along the resulting recapitulating evolutionary divergence. The resurrected ancestral PYP is functionally similar to PYP1, indicating divergence on the path to PYP2. AlphaFold predictions for PYP2 and these ancestral proteins revealed the absence of structural changes compared to the crystal structure of PYP1. To experimentally validate these predictions, we optimized second-derivative Fourier transform infrared (FTIR) spectroscopy. The FTIR spectra of PYP1 and 2 and their resurrected ancestral proteins demonstrated clear differences in their secondary structure. These results demonstrate an important limitation of AlphaFold and show how ancestral sequence reconstruction combined with spectroscopic approaches yields insights into divergence in a protein family.

A central challenge in molecular biology is understanding how amino acid substitutions modulate various features of protein function and stability. To illuminate the complexities of this relationship, high-throughput (HTP) assays are increasingly used to assess site-saturating mutagenesis libraries. A common downstream analysis is to average the set of twenty outcomes at each amino acid position for comparison with structural and evolutionary features. Average values clearly identify positions that tolerate most substitutions (neutral positions) and positions where most substitutions abolish activity (toggle positions). However, average values conceal the existence of rheostat positions, where different amino acid substitutions sample a wide range of outcomes. To quantitatively identify rheostat positions, we previously developed a histogram-based analysis that we here expand by: (i) incorporating new position classes observed in experimental studies of rheostat positions; (ii) formalizing a hierarchy of class assignments; (iii) refining error-based identification of neutral positions; and (iv) statistically assessing the robustness of class assignments to changes in experimental and computational parameters. RheoScale 2.0 is implemented in Excel and newly implemented in Python for facile integration with existing HTP pipelines; all parameters are customizable. Example analyses are shown for three HTP datasets of the SARS-CoV-2 papain-like protease. Results illustrate two aspects that influence interpretation of HTP data: First, position assignments (and substitution outcomes) depend highly on the measured feature. Second, many protein positions play multiple roles in the sequence-structure-function relationship. The recognition of varied position roles will advance understanding of pathogen evolution, protein engineering, and variant interpretation for personalized medicine. SummaryRheoScale 2.0 improves how high-throughput mutational data are interpreted by identifying protein positions where amino acid substitutions act like biological dimmer switches. By enabling more nuanced assignment of position behavior, beyond neutral or deleterious outcomes, this analysis framework advances studies of sequence-structure-function relationships and has broad relevance for understanding protein evolution, engineering proteins with desired properties, and interpreting variants linked to human disease. SOFTWARE AVAILABILITYhttps://github.com/liskinsk/RheoScale-calculator

Tropomyosins are coiled-coil dimers that polymerize head-to-tail along actin filaments. They stabilize distinct filament populations and regulate the access of myosins and actin-binding proteins in both muscle and non-muscle contexts. Despite their central regulatory role, how filament length and isoform identity of different tropomyosin homologues might modulate actin affinity is not completely understood, especially across species. Here, we present a stepwise computational docking pipeline combining AlphaFold2-Multimer coiled-coil models, experimentally informed residue-level restraints, and pseudo-energy analysis via PPCheck to build and evaluate actin-tropomyosin co-polymer models for three isoforms: human TPM1 (hTPM1; 284 residues), human TPM4 (hTPM4; 248 residues), and Schizosaccharomyces pombe Cdc8 (SpCdc8; 161 residues). Interface energetics reveal a consistent hierarchy in which the shortest filament, SpCdc8, achieves the most stabilizing and residue-rich actin contacts, consistent with reduced cumulative geometric penalty along the actin helix. Among human isoforms, hTPM1 forms stronger interfaces with actin than hTPM4. The hTPM1-actin model also exhibits higher contact density and additional energetic hotspots, in agreement with the experimentally established slower exchange kinetics of TPM1 isoforms on actin filaments relative to TPM4. Hotspot mapping identifies conserved acidic residues at equivalent positions across all three isoforms, emphasizing the importance of electrostatic anchor points in maintaining interface integrity across diverse evolutionary contexts. Modeling of four temperature-sensitive SpCdc8 mutations (A18T, R21H, E31K and E129K) reveals that these substitutions substantially destabilize the coiled-coil dimer without significantly affecting actin interactions, suggesting that subtle regulatory failure arises from compromised longitudinal cable continuity rather than from direct loss of actin affinity. Taken together, our results support a hierarchical model of tropomyosin dimer stability, actin-tropomyosin recognition in which filament length imposes a geometric baseline on interface stability, onto which isoform-specific sequence evolution superimposes functional tuning. The tropomyosin homologues we studied appear to retain conserved electrostatic hotspots thereby providing a common structural scaffold across tissues and organisms.

Electrostatic interactions often play a role in determining the thermodynamic and kinetic properties of protein-protein interactions. However, the role of long-range electrostatic interactions in intrinsically disordered protein (IDP) binding is less clear, as they often bind in multiple steps including initial formation of a disordered encounter complex, followed by rearrangement into the bound state. We varied the salt concentration to probe the role of long-range electrostatic interactions in the binding of the highly charged AbpSH3 domain and the oppositely charged IDP ArkA. Using isothermal titration calorimetry, we observe that salt enthalpically destabilizes the bound complex. Molecular dynamics and NMR experiments reveal that salt has little effect on the bound state structure. However, simulations show that salt destabilizes the encounter complex intermediate, which primarily affects the association rate as measured by NMR. Consistent with these results, salt has the largest stabilizing effect on the apo SH3 domain, as cations substitute for the transient and long-range electrostatic interactions that can form with ArkA in the complex. We reveal a detailed picture of how a highly charged domain uses long-range, fuzzy, electrostatic interactions to help reach the bound state, a mechanism that is likely common among other highly charged domains that bind IDPs. TOC Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/741257v1_ufig1.gif" ALT="Figure 1000"> View larger version (21K): org.highwire.dtl.DTLVardef@dfc7acorg.highwire.dtl.DTLVardef@1ae0438org.highwire.dtl.DTLVardef@19704adorg.highwire.dtl.DTLVardef@1b40b15_HPS_FORMAT_FIGEXP M_FIG C_FIG

S-acylation, the reversible addition of fatty acids to proteins, has emerged as an abundant post-translational modification that drives protein localization and function. With no known consensus sequence, current prediction programs rely on machine learning algorithms that use short peptide sequences and large proteomic datasets. However, current prediction programs often suggest incorrect sites of S-acylation, leading to wasted experimental time and effort following site-directed mutagenesis and low-throughput validation experiments. Using only experimentally confirmed sites of S-acylation, we sought to identify primary sequence, secondary structure, and tertiary structure features common amongst S-acylation sites to aid in developing more robust prediction tools. In doing so, we identified an S-acylation motif including a cysteine cluster flanked by a hydrophobic stretch, and a positively charged polybasic region found within a helical stretch. These features were combined with known or AlphaFold-predicted structures and additional features including residue depth and solvent accessibility into a random forest model to generate a new and more accurate S-acylation prediction program (SAPP), named SAPPTree. All the processed datasets and complete model training pipeline are available at https://github.com/neurdyphagy-lab/palm-prediction-model, while the webserver is available at http://martintools.sci.uwaterloo.ca/.

The structural diversity of alpha-synuclein amyloid fibrils is closely linked to the pathogenesis of Parkinsons disease and related synucleinopathies. However, reproducing disease-associated fibril conformations from recombinant full-length protein in vitro has remained challenging. Inspired by successful truncation strategies developed for the Tau protein, we investigated whether removing the disordered terminal regions (<<fuzzy coat>>) of alpha-synuclein could bias fibril assembly toward disease-relevant folds. We designed a truncated construct comprising residues 31-100, corresponding to the structured core of patient-derived Parkinsons disease fibrils, and systematically screened aggregation conditions across a broad range of pH values and ionic environments. Cryo-electron microscopy revealed four previously undescribed fibril structures, including new subtypes of the established type 1 and type 3 polymorphs and a novel fibril fold, termed type 10, which reproducibly formed under acidic conditions. Type 10 was observed as two distinct dimeric assemblies (10A and 10B) that share a common protofilament fold but differ in their inter-filament interfaces. Structural comparison with the patient-derived Parkinsons disease polymorph revealed local similarities, including conserved {beta}-strand organization and loop conformations within the fibril core, but remains structurally distinct overall. Our results demonstrate that rational construct design combined with systematic environmental screening reshapes the alpha-synuclein polymorphic landscape and promotes structural motifs characteristic of disease-associated fibrils.

{beta}-Glucosidases are essential for lignocellulosic biomass conversion, yet their industrial utility is limited by inhibition from glucose, the reaction product. Although glucose inhibition has been extensively documented, its molecular basis remains poorly understood. Here, we demonstrate that glucose inhibition in the GH1 {beta}-glucosidase BglB from Paenibacillus polymyxa is mediated by multiple surface-exposed secondary binding sites (SBSs) that function as regulatory elements rather than passive glucose-binding patches. Kinetic analyses revealed a mixed mode of inhibition, with modest activation at low glucose concentrations followed by progressive inhibition at higher concentrations. Glucose association occurred through multiple SBSs in a concentration-dependent manner and induced localized rigidification, particularly at gatekeeper regions surrounding the active-site entrance. These surface interactions propagated through long-range coupling pathways, resulting in structural, energetic, and residue interaction network reorganization that reshaped the catalytic pocket. Functional interrogation of representative SBSs revealed distinct roles for individual sites. Mutation of a gatekeeper-associated SBS reduced local glucose association, increased glucose tolerance by more than 30%, and improved substrate affinity by approximately 38%, whereas disruption of a distal SBS compromised structural integrity and soluble protein production. Together, these findings establish a direct link between surface glucose recognition and active-site regulation, providing, to our knowledge, the first integrated evidence for the existence and functional significance of secondary glucose-binding sites in {beta}-glucosidases. More broadly, this work identifies surface SBSs as promising targets for engineering glucose-tolerant and catalytically robust enzymes for biomass conversion and related biotechnological applications.