Protein Science

MAP4K4 is a serine/threonine kinase member of the mitogen activated protein kinase family. It acts through the JNK, p38 MAPK, and ERK1/2 pathways and is implicated in cancer proliferation and invasion, TNF--driven insulin resistance, macrophage-mediated inflammation, and cardiomyocyte apoptosis in heart failure. No selective small-molecule inhibitor of MAP4K4 has reached clinical use, and knowledge of the specific epitopes on MAP4K4 is lacking for some existing antibodies, leaving a role for small protein binders directed at specific surface sites. We used a computational pipeline that combines RFdiffusion, ProteinMPNN, and AlphaFold2, as well as BindCraft, an integrated "one-shot" tool, for de novo design of small protein binders (~50-130 residues) to specific MAP4K4 surface hotspots. We also created an automated hotspot determination algorithm that weighs geometry, chemistry, rigidity, and AlphaFold pLDDT. From thousands of candidate sequences, we evaluated 20 of the most promising binders with AlphaFold3 (AF3). The interface predicted template modeling (ipTM) scores ranged from 0.16 to 0.90, with nine candidates having ipTM [≥] 0.80, and five scoring [≥] 0.87. Two binders engage non-overlapping hotspots on opposite faces of MAP4K4, making them a candidate pair for a sandwich assay. BLASTp searches of all designed protein sequences returned only low-significance matches to half of them, indicating that they represent truly novel binding solutions and previously unexplored regions of protein sequence space rather than rediscovered natural motifs. We subjected five complexes spanning the observed AF3 confidence range to 100-ns explicit-solvent molecular dynamics simulation. Interchain contacts were retained throughout, with stability varying substantially between systems. Confidence in the binding specificity of the nine highest-confidence candidates was bolstered by juxtaposition with AF3 evaluations of their interaction with CDK2, a negative-control kinase. This comparison yielded a significant, consistent reduction in ipTM (p = 0.0039) for the control binding partner. ToxinPred2 and AlgPred 2.0 screening suggested that one candidate was a potential allergen and two were potential toxins. These results support that de novo design of small, site-specific protein probes for an underserved disease target is achievable using free, publicly available computational tools and minimal resources, pointing to a greater role for the public and amateur scientists to contribute to biotechnological advancement.

A challenge in synthetic biology is to transfer the knowledge acquired in vitro to in vivo applications. For instance, binding affinity between peptide binding domains (PBDs) and their binding peptides is usually measured in simple buffers and in the absence of the cell machinery responsible for synthesis and proteostasis. Consequently, there is often a discrepancy between interaction quantified in vitro and in vivo. To identify strongly binding PBD-peptide pairs for in vivo applications, we aim to better understand the determinants of synthetic interaction in living yeast cells. We designed libraries of peptides expected to bind to PBDs based on data collected by phage-display. Using an in vivo protein interaction assay, we assessed the interaction strength of >6,500 PBD-peptide variants in yeast. We found that most substitutions disrupt binding, but that 3.4% increased binding strength to levels higher than expected from the phage-display data. We asked if, by using our mutational data, it was possible to optimize peptide sequences to gain even more binding strength and show additivity effect up to four substitutions. We found that PBD specificity is consistent between in vitro and in vivo assays. We hypothesized that in vivo peptide abundance could contribute to the discrepancy between the PCA and the phage-display results. We performed assays to detect in vivo peptide availability, reporting on peptide abundance, and we found that peptides with a stronger binding also show a higher availability in cells. We show that peptide availability, which seems mainly defined by peptide hydrophobicity, is key for the design of strong synthetic interactions.

Protein design has rapidly advanced with the advent of sequence- and structure-based machine learning models. However, reasoned design, which applies physicochemical principles and rules derived from sequence-structure-function relationships, has not seen the same benefits from generative machine learning models. Here, we test the ability of common large language models (LLMs; e.g. Claude, ChatGPT, and Gemini) to consider design principles to generate de novo proteins that bind metals and lipophilic small molecules without copying existing sequences. Common LLMs alone are able to 1) generate protein sequences to adopt a desired fold and bind the target ligand and 2) explain the principles that motivate the design choices. Following structure prediction and filtering, we selected a small set of designs (6 to 12 designs per query) for experimental validation, affording metal binders in one round of LLM-based design (25% hit rate) and perfluorooctanoic acid binders in two rounds (25% hit rate in the second round of design). Importantly, the LLMs produce detailed justification to accompany the de novo designed sequences, providing a conceptual framework on which designs can be evaluated. While the successful designs have some deviations from the prompted parameters and LLM-articulated design rationale, these campaigns provide a case study that highlights the utility of LLMs in making protein design more comprehensible and accessible to users without sophisticated design expertise.

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.

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.

Structure-aware proteolysis prediction often ranks candidate cleavage sites using solvent accessibility from static protein structures, yet this assumption is rarely tested against native-state cleavage data. We examined this problem using AprX, a serralysin-family protease that readily hydrolyses caseins but shows lower activity toward native whey proteins. Across six bovine milk proteins, sequence compatibility and local scissile-bond accessibility did not separate caseins from whey proteins. In contrast, accessibility fell progressively in whey proteins as the required contiguous substrate window increased, reaching 0% across the working P4-P4' footprint, while all evaluable casein windows remained accessible in isolated-chain ensembles. Local remodelling showed that whey segments could still adopt AprX-compatible backbone geometry. An external thermolysin benchmark rejected a rigid whole-protein clash metric, but did not alter the contiguous-accessibility result. These findings identify footprint-scale accessibility as a distinct structural variable and show why single-residue solvent exposure can overestimate protease accessibility in folded proteins.

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.

Motivation: Molecular visualization is dominated by two families of software. Desktop programs such as PyMOL, VMD and UCSF ChimeraX are powerful but heavy to install and operate. Web viewers such as Jmol, 3Dmol.js, NGL, Mol* and iCn3D are light and installation-free, but typically stop at rendering, lacking deeper functionality needed for even basic protein sequence, structural analyses and design tasks. Tools building upon these often lack comprehensive sequence/structure manipulation, surface, interface and antibody-specific analytics functionality or require an external backend. There is a need for a tool that is as frictionless as a web viewer yet carries the analytical depth normally reserved for the desktop or the command line software. Results: We present Protein Design Viz (PDV), a self-contained molecular visualization studio delivered as a single offline HTML file that runs entirely in the browser. Built as an extensive modification of 3Dmol.js, PDV combines a full visualization workflow: multi-object scenes, representations, coloring palette, linked sequence track, publication-quality outline rendering and portable sessions. Additionally we re-implemented four commonly used macromolecular analyses measures from scratch in client-side JavaScript: a Shrake-Rupley solvent-accessible surface area (SASA) engine, an antibody numbering and germline-assignment engine, a non-covalent interaction detector, and a developability-liability scanner. Each engine is validated against its established reference. PDV's SASA reproduces FreeSASA at Pearson r {approx} 0.997-0.998 across 2,582 structures spanning proteins, nucleic acids and ligands; its numbering reproduces RIOT for over 99.88% of 1.3 million residue positions across 16,996 sequences and four schemes; its interaction detector reproduces PLIP at macro-F1 0.82, matching Arpeggio as closely as PLIP itself does. PDV brings validated, quantitative structural analysis into a no-install, simple to use tool. Availability and implementation: PDV is a single HTML file, free for noncommercial use under the PolyForm Noncommercial License 1.0.0, available from pdv.naturalantibody.com. It requires only a WebGL-capable browser and runs fully offline.

Human Ca2+/calmodulin (CaM)-dependent protein kinase II alpha (CaMKII) is a highly abundant and conserved protein in the brain. It is a multifunctional serine/threonine kinase that phosphorylates cellular substrates across subcellular compartments, activating signaling pathways essential for long-term memory storage. However, a detailed understanding of protein structure and enzyme-substrate interaction mechanisms require a method to produce active, stable, and pure proteins at scale. Since phosphorylation can alter catalytic activity and substrate affinity, examining the non-phosphorylated state enables an unbiased characterization of basal enzymatic function. Therefore, producing the non-phosphorylated form of the kinase is important for biochemical and structural studies aimed at understanding kinase activation mechanisms. We describe a detailed protocol for the expression and high-yield purification of full-length, non-phosphorylated human CaMKII in bacterial cells. CaMKII was co-expressed with {lambda}-phosphatase to maintain it in a non-phosphorylated state. Protein expression was induced by autoinduction at low temperature to enhance the stability and solubility of CaMKII. Recombinant CaMKII was purified by Ni-NTA affinity chromatography, followed by gel filtration chromatography. Although CaMKII has been considered difficult to purify in its non-phosphorylated form, we obtained it in high yield, and the protein was predominantly non-phosphorylated. Our results show that combining co-expression with autoinduction improves both the solubility and yield of the homogeneous non-phosphorylated form of CaMKII, which could be suitable for subsequent biochemical and structural studies.

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

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

Although surfactant protein-B (SP-B) is essential for pulmonary surfactant function, the experimental structure of mature SP-B remains undetermined, and the molecular basis of its function is unclear. Here, we characterize the structural and functional behavior of a recombinant SP-B construct (rSP-B) in DPC/SDS detergent micelles and DPPC/POPG lipid monolayers. Light scattering showed that dilute DPC/SDS (9:1, 0.2% wt) is intrinsically heterogeneous, containing both micellar (~4 nm) and supramicellar structures (~68-400 nm), consistent with non-ideal surfactant mixing near the critical micelle concentration. Dialysis consolidated these populations into a single, reproducible mesoscale structure (~600 nm) that retained predominantly -helical structure, whereas disrupting these assemblies compromised protein folding, indicating a role for supramolecular organization in stabilizing rSP-B, although the resulting complex sizes exceed the regime typically accessible to solution NMR. Higher detergent concentrations instead favored a homogeneous micellar population (~4 nm), demonstrating that detergent concentration strongly influences protein-detergent complex (PDC) size. In parallel, rSP-B incorporated into DPPC/POPG monolayers markedly improved cyclic reversibility relative to protein-free films, converging to a stable, low-hysteresis profile with compressibility signatures consistent with reversible, elastic collapse and respreading through mechanical reorganization and out-of-plane deformation. These results inform strategies for optimizing detergent conditions for future structural studies and support a model in which rSP-B stabilizes mechanically coupled reservoirs essential for surfactant recycling.

De novo design of protein binders requires a target structure. However, for flexible targets, such as intrinsically disordered proteins, this structure does not exist until the binder has stabilized the interaction. Such targets are therefore difficult for methods that separate structure generation from sequence design. We introduce GuideFlip, which co-designs structure and sequence through guided discrete flow matching: binder residues are assigned progressively while the complex is re-predicted at each step, allowing the evolving interface to affect the design process. GuideFlip reduces the hydrophobic bias of direct AlphaFold optimization and improves in silico success rates over existing approaches. We release a database of binder candidates for 177 human disordered proteins. Experimentally, we obtain de novo binders to the C-terminus of -synuclein and the disordered amino terminus of RBX1 with hit rates of 13.5% and 41.7%, respectively, and we confirm the epitopes of selected binders by NMR and mutagenesis. Applying GuideFlip to flexibility on the binder side, we design a nanobody that binds the agonist-bound {beta}1-adrenergic receptor in the active state, but not the receptor in its inactive state, with a 75% hit rate and cryo-EM structure confirming the design. GuideFlip enables protein design where bound structures emerge only upon binding.

{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.

Transition-metal ion Forster resonance energy transfer (tmFRET) measures short-range distances and conformational changes in proteins, but its distance sensitivity depends on the donor-acceptor pair. Here, we establish the metal-chelating noncanonical amino acid bipyridylalanine (bpyA) as a flexible, genetically encoded acceptor platform whose Forster distance (R0) can be tuned without changing protein labeling. Cu2+, Cu2+ complexed with nitrilotriacetic acid (NTA), and Fe2+ form distinct acceptors at the same bpyA site, allowing distance sensitivity to be adjusted by changing the solution conditions. We incorporated bpyA at two sites in maltose binding protein (MBP) and labeled nearby engineered cysteines with the donor fluorophore BODIPY. The resulting [Cu(bpyA)]2+, [CuNTA(bpyA)]2+, and [Fe(bpyA)]2+ acceptors provided R0 values ranging from 13.9 to 21.2 [A]. Analysis of time-resolved tmFRET data recovered donor-acceptor distance distributions and maltose-dependent changes consistent with structural predictions. At the 237/295 site pair, both copper and iron acceptors resolved the maltose-dependent decrease in distance. At the 309/322 site pair, [Cu(bpyA)]2+ did not appreciably resolve the distance increase, whereas adding NTA shifted R0 toward shorter distances and enabled detection of an increase from 12.1 to 14.4 [A]. These results demonstrate that a single bpyA-labeled protein preparation supports multiple tmFRET acceptors, providing a cysteine-independent acceptor-labeling strategy with adjustable distance sensitivity.

Interleukin-1{beta} (IL-1{beta}) and the interleukin-1 receptor antagonist (IL-1Ra) share the {beta}-trefoil fold, engage the same receptor (IL-1R), but produce opposite biological outcomes. IL-1{beta} recruits the accessory protein IL-1RAcP to initiate inflammatory signaling while IL-1Ra occupies the receptor without supporting co-receptor recruitment. Seeking the origin of this divergence in properties not apparent from static structures, we used solution NMR relaxation to characterize fast internal motion in both proteins. Rigorous treatment of macromolecular tumbling shows both to be nearly maximally triaxial, which admits two near-degenerate axially symmetric solutions. This ambiguity was propagated throughout and found to have negligible impact. Complete replicate data sets from independently prepared IL-1{beta} samples establish the experimental precision directly. Backbone amide and methyl-bearing side chain order parameters are indistinguishable between the two proteins, the latter averaging 0.484 {+/-} 0.030 and 0.486 {+/-} 0.033, indicating essentially equivalent residual conformational entropy. Their spatial distributions are uncorrelated when projected onto the shared fold. Importantly, the difference between the proteins is concentrated at the receptor interface, where methyl probes lying on the IL-1{beta} footprint are considerably more mobile than those of IL-1Ra. It is therefore the agonist, not the antagonist, that presents an anomalously mobile binding surface. IL-1Ra also buries some 16% less surface while ostensibly binding IL-1R more tightly, the signature of an interface predisposed toward productive contact. Conformational entropy can thus distinguish protein function where structure and average flexibility are conserved, and the distinction resides not in how much entropy a protein has but in where it is kept.

Sequence similarity networks (SSNs) are graphical representations of sequence relationship frequently used for exploring protein sequence space. Conventional SSN workflows typically use BLAST to calculate sequence similarities and rely on external visualization tools to gener-ate the final networks. Consequently, raw sequence data is often detached from the calculat-ed similarities during visualization, complicating SSN analyses that require residue-level in-formation. To bridge this gap, we present EMAP-SSN, an open-source, cross-platform soft-ware suite that integrates SSN computation, visualization, and analyses in streamlined work-flows. The program provides BLAST- and embedding-based alignment pipelines for se-quence-similarity calculation and directly links network nodes to their original sequences and multiple alignments for analyses. Modular architectures for embedding generation, com-mand integration, and browser-based utilities allow additions of research-specific functionalities and facilitate future development. Using a set of fold-type IV pyridoxal 5'-phosphate-dependent enzymes, we demonstrate how EMAP-SSN connects network topology with residue-level variation to identify sequence clusters, map functional motifs, and detect subgroup-specific conservation patterns. These capabilities provide a practical route from large protein sequence sets to experimentally verifiable hypotheses on enzyme function and targets for protein engineering. The EMAP-SSN program can be accessed from https://github.com/Xuebin-Feng/EMAP-SSN.

The folding thermodynamics of proteins are dominated by two opposing forces, the loss in backbone entropy and the packing of hydrophobic groups. The same forces are major contributors to the extension thermodynamics of elastic proteins with the distinction that both processes act in concert, favoring the higher chain and solvent entropy of a relaxed conformation. The relative entropic contributions specify the recoil mechanism; human elastin recoil is primarily driven by hydrophobic forces, whereas fly resilin has a rubber-like mechanism driven by backbone entropy. Despite the importance of elastic proteins to tissue biomechanics, few have been identified, let alone characterized to the same extent as elastin and resilin. We develop a thermodynamic framework that maps proteins by sequence-derived estimates of extension-induced backbone and solvent entropy changes. Putative elastic proteins are proposed and classified by recoil mechanism based on estimated thermodynamic features. Proteins that map to elastic regions are overrepresented by the skin proteome. The set of predicted elastic domains is further extended by incorporating sequence context embedded in protein language models. Protein domains with distinct thermodynamic recoil mechanisms cluster on the latent space manifold. Some of these domains are anticipated to have roles within molecular machines, expanding the scope of elastic protein function beyond mechanical materials like elastin and resilin.