Journal of Molecular Biology

Short, linear sequence motifs within the human prion protein (PrP) may encode local aggregation tendencies that are not apparent from full-length sequence analysis. To map intrinsic amyloidogenic potential across PrP, we generated a complete one-residue-step library of overlapping 15-mer peptides from the 253-residue human PrP sequence and evaluated each peptide using WALTZ in both high-specificity and best-overall-performance modes, with full-length SNPeffect4/WALTZ output used for comparison. Peptide-level WALTZ analysis identified several candidate amyloidogenic regions, including an N-terminal signal-peptide segment spanning approximately residues 8-21/22 that was not detected in the publicly available full-length WALTZ/SNPeffect4 output. Predictions made using AlphaFold 3.0 also identified a short C-terminal area whose representative peptides formed either -helix or pair of {beta}-strands, indicating a region of potential higher susceptibility to conformational dynamics. This computational study supports the use of peptide tiling as a complementary screening strategy for identifying candidate short aggregation-prone motifs in PrP and other misfolding-associated proteins.

The androgen receptor is a hormone-dependent transcription factor that regulates a wide range of physiological processes and plays a pivotal role in the development of prostate cancer. Its 555-residue, intrinsically disordered, N-terminal domain is involved in the modulation of transcriptional activity by recruiting co-regulators and mediating the formation of biomolecular condensates. This study reports on the characterisation of a conserved domain located in the C-terminal region of the androgen receptor N-terminal domain, at atomic level, using nuclear magnetic resonance spectroscopy. This proline rich region exhibits extensive conformational heterogeneity driven by highly populated cis proline conformers that are stabilised through interactions with adjacent aromatic residues. We demonstrate that the cis-proline population is modulated by phosphorylation as well as cancer-associated mutations. This suggests that proline driven conformational heterogeneity at the C-terminal region of androgen receptor N-terminal domain is involved in the regulatory function of this transcription factor.

Collagen I provides mechanical stability to organs and plays a critical role in several physiological processes such as wound healing and bone remodeling. Its ability to form fibrils depends on the proteolytic conversion of soluble procollagen I into mature protomers. However, the mechanistic aspects of this critical step have been difficult to analyze due to the lack of recombinant procollagen I suitable for structural and biochemical analyses. Here, we describe the successful production and purification of a heterotrimeric mini-procollagen I [1(I)22(I)] together with its homotrimeric counterpart [1(I)3] in HEK293-F cells. These tools were used to probe the specific role of the 2(I) chain both in procollagen I assembly and proteolytic maturation. While the presence of 2(I) in the heterotrimer does not strengthen trimer interfaces on its own, as revealed by cryo-electron microscopy, it provides a clear functional advantage to the procollagen substrate that is cleaved more efficiently by bone morphogenetic protein 1 (BMP-1) than the homotrimer. Furthermore, we demonstrate that the first cleavage event in the heterotrimer is at the level of the 2(I) chain, through a mechanism involving its C-telopeptide and a specific PRS motif close to the cleavage site. The preferential cleavage of 2(I) by BMP-1 can be partially corrected in the presence of PCPE-1 (procollagen C-proteinase enhancer 1) but the efficacy of the correction depends on its position on the procollagen trimer. Finally, we solved the high-resolution structure of the complex between the heterotrimer and PCPE-1 and proposed a new mechanism to explain the enhancing activity of PCPE-1.

Understanding protein mechanisms in health and disease requires characterizing the functional roles of individual amino acid residues. To explore the role of residues and their mutations, we have developed Atlantis, a database that integrates structural and functional information at the human proteome residue level. A graph database enables complex queries and the retrieval of integrated information for multiple functional analysis of protein systems. A Model Context Protocol (MCP) connector allows the interrogation of the resource through Large Language Models (LLMs) or agentic frameworks for biomedical research. Atlantis annotates over 11M residues across 20k human proteins, identifying hundreds thousands intra- and inter-protein contacts in PDB as well as AlphaFoldDB structures. We also provide the possibility to analyze and integrate predicted 3D complexes inputted by the user, and we showcased these features on hundreds of AlphaFold-multimer complexes of GPCRs and LRRK2 interaction networks. The tool is freely accessible at https://atlantis.bioinfolab.sns.it/.

Summary: Protein mutation datasets are widely used in variant effect prediction and protein engineering, but datasets from different sources often lack consistent conventions for mutation representation, sequence representation, data organization, and experimental labels, making these resources difficult to integrate directly and limiting their use in downstream analyses and modeling. MutCleaner is an extensible Python framework that cleans, validates, and standardizes protein- and codon-level mutation datasets through composable cleaning pipelines, unified sequence and mutation data structures, and dataset-specific cleaners. It provides standardized resources covering 16 protein and codon mutation datasets with more than 12.14 million mutation records. Availability and implementation: MutCleaner is an open-source Python package released under the Apache License 2.0. The source code is available on https://github.com/xulab-research/MutCleaner, the package is distributed through https://pypi.org/project/mutcleaner/, and the documentation is available https://xulab-research.github.io/MutCleaner/.

Protein-protein interaction (PPI) information is distributed across resources that differ in organism coverage, identifier systems, evidence models, confidence scores and access mechanisms, so assembling and comparing evidence for a protein requires source-specific queries, identifier conversion and extensive post-processing. We present KlinkPPI, a web server that retrieves, compares and exports PPI evidence from STRING, BioGRID, IntAct, CORUM, HuRI and Predictomes from a single query. KlinkPPI accepts UniProtKB accessions, NCBI Gene and Ensembl identifiers and gene names, and performs taxonomy-aware mapping to a common identifier space. Users can query individual proteins across all resources available for an organism, or retrieve organism-wide interaction sets. Results are presented per source so that database-specific evidence, annotations and confidence values are retained, while an integrated view exposes coverage and agreement between resources. KlinkPPI deliberately does not merge heterogeneous confidence scores, nor collapse functional associations, complex co-membership, binary interactions and structural predictions into a single consensus network. Results can be exported in PSI-MI TAB 2.8-compatible or Apache Parquet format with user-selected evidence fields. KlinkPPI is freely available at https://rappsilberlab.org/KlinkPPI/ and the source code at https://github.com/Rappsilber-Laboratory/KlinkPPI. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/742057v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@12dcaceorg.highwire.dtl.DTLVardef@15d9d0aorg.highwire.dtl.DTLVardef@f0fc39org.highwire.dtl.DTLVardef@13f172e_HPS_FORMAT_FIGEXP M_FIG C_FIG

Binding of ligands to partially or fully unfolded proteins can play a key role in the mechanism of cellular and pharmacological chaperones, facilitating proper folding. However, it is challenging to quantify the binding affinity of ligands for unfolded states in a protein that is normally folded, as the methods standardly used to destabilize the native fold also affect ligand binding. We used single-molecule force spectroscopy to unfold single protein molecules without altering solution condi-tions and observe interactions of a ligand with unfolded states. Focusing on pentosan polysulfate (PPS), an anti-prion pharmacological chaperone previously shown to interact with both the native and partially or fully unfolded states of the prion protein, we measured the concentration-dependent effects of PPS binding on the conformational dynamics of bank vole prion protein (BvPrP) molecules held in optical tweezers. We found that PPS stabilized certain partially unfolded intermediate states of BvPrP as well as the fully unfolded state. Strikingly, the tendency to bind unfolded states instead of the folded state increased as the PPS concentration was reduced, implying a higher affinity to unfolded states. From the relative amount of binding to unfolded versus folded states, we estimated that PPS bound roughly 100-fold more tightly to unfolded states than to the native state of PrP. These results reinforce the likely importance of unfolded states in prion misfolding and propagation. More generally, they show how binding affinity to transient, unstable states can be estimated.

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

Post-translational modifications (PTMs) and genetic variants regulate protein function, signalling, and disease, but their interpretation requires integration of sequence annotations with structural, interaction, and biophysical context. Although resources such as Scop3P, UniProt, the Protein Data Bank, and AlphaFold provide extensive annotations and structural information, integrating these data into reproducible structure-aware analyses still requires custom scripting and manual coordination between multiple independent tools. To address this challenge, we developed Scop3P-Toolkit, an open-source executable analytical environment for interactive analysis of PTMs, mutations, and proteomics-derived peptides in their structural context. The toolkit integrates protein annotation retrieval with structural mapping, residue interaction network analysis, comparative structural analysis, and residue-level biophysical profiling within a unified framework. Experimentally supported phosphosites, phosphopeptides, and phosphoproteomics evidence are provided for human proteins through Scop3P, with optional integration of curated UniProt PTM annotations. UniProt-derived PTMs, sequence features, and genetic variants are available for proteins from any species, extending the framework beyond the human phosphoproteome. Scop3P-Toolkit supports structure-centric analyses including interpretation of PTMs and disease-associated variants, analysis of residue interaction networks and their rewiring across alternative conformations, structural localisation of peptides, and exploration of protein-protein, protein-ligand, and host-pathogen interfaces. Interactive visualisation links sequence annotations, three-dimensional structures, residue interaction networks, and biophysical profiles, enabling coordinated exploration across multiple molecular representations. The toolkit is distributed as Jupyter notebooks, browser-based Voila applications, and a Galaxy interactive tool, providing transparent, accessible, and reproducible workflows for both computational and experimental researchers. By integrating biological annotation resources into executable, structure-aware workflows, Scop3P-Toolkit enables reproducible interpretation of PTMs, mutations, and proteomics data.

AlphaFold 3 (AF3) has broadened the computational structural biology landscape by predicting all-atom complexes across proteins, nucleic acids, small-molecule ligands, and ions, including chemical modifications. For non-experts, Google provides a fully web-based service to utilize AlphaFold 3. Unfortunately, despite a flexible and rich interface for inputs, the server's outputs are incomplete and complicated for many users, as witnessed hands-on. Not all confidence metrics are displayed, and those that are shown do not present much interactivity; besides, only model number 1 is shown among the 5 produced by the software, and upon download the user finds the models are provided only in CIF format, which is not yet well-known especially among highly practical users. Here, we present the AlphaFold 3 Multi-Model Inspector (AF3 Inspector, https://pdbms.altervista.org/af3viewer/afviewer8.html), an open, client-side, zero-install web application designed to dissect, align, and interactively explore the complete ensemble of structural models produced by AlphaFold 3. Part of the PDB Manipulation Suite (https://pdbms.altervista.org/), the AF3 Inspector operates entirely within web browsers, meaning it is available out of the box in all devices and operating systems. AF3 Inspector delivers 3D visualization in various styles and colors for all models, overlay with backbone alignment if requested, interactive plots for pLDDT profiles, pAE matrices and contact maps, chain-pair interaction heatmaps, automated detection of ligands, ions and post-translational modifications, and rapid mmCIF-to-PDB conversion allowing users to download the more familiar files. The platform addresses critical practical considerations highlighted in recent community benchmarks, such as CASP16, and extends the lightweight, client-side biophysical toolkit paradigm established by the PDB Manipulation Suite (PDBMS).

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.

RNA function is dictated by 3D architecture. Although 2D structural models based on canonical Watson-Crick base pairs are widely used, they often fail to capture the non-canonical interactions, tertiary contacts, and coaxial stacking important for biological activity. We have developed RNAbridge, a comprehensive database and web application that systematically identifies, quantifies, and visualizes extended non-canonical helices and multi-way junctions. Using a geometry- and stacking-based pipeline, we analyzed the Protein Data Bank and compiled a catalog of 135,541 structural motifs. RNAbridge includes a user-friendly interface with interactive filters, synchronized 2D and 3D visualizations, and detailed structural data, including helical bend angles and stacking paths. By connecting simplified 2D topologies with complex 3D structures, RNAbridge serves as a valuable resource for structural biologists and lays the foundation for future machine learning applications in RNA structure prediction. The database, available at https://rnabridge.cs.put.poznan.pl/, is automatically updated once a week.

In the post-AlphaFold era, advances in protein complex structure prediction have enabled the generation of thousands of candidate models per target, making the accurate and efficient selection of high-quality models from large candidate pools a critical challenge. Existing model-selection approaches commonly employ estimated model accuracy (EMA) methods, which include single-model and consensus-based methods. The former lack cross-model comparative information, whereas the latter often incur high computational costs from pairwise structural alignments and are influenced by candidate-pool quality. Here, we present DeepUMQA-Selection, a two-stage framework combining single-model accuracy estimators for preliminary selection with Ultrafast Shape Recognition (USR)-based structural alignment for consensus selection. Preliminary selection enriches the candidate pool with high-quality models and reduces subsequent structural comparisons, while USR-based structural alignment enables efficient consensus selection without explicit structural superposition. On the retrospective CASP16 QMODE3 benchmark dataset, DeepUMQA-Selection outperformed all participating methods and predictor-derived confidence metrics, as measured by mean Top-5 weighted penalty. The advantage was particularly pronounced for heteromeric targets, with a 13.9% improvement in model-selection performance relative to the best-performing CASP16 participating method. Furthermore, a confidence-assisted strategy enabled the framework to accommodate larger protein complexes while maintaining superior model-selection performance over competing methods, supporting its scalability. Runtime benchmarking showed that the proposed USR-based structural alignment achieved a substantial 317-fold speedup over US-align for candidate pools containing 1,000 models, demonstrating its computational efficiency for large-scale model selection. Our results demonstrate that DeepUMQA-Selection enables accurate and computationally efficient selection of high-quality protein complex models from large-scale candidate pools.

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.

Accurate assessment of ligand coordinate-density consistency across different resolutions remains challenging in macromolecular crystallography. We introduce the atomic Box Correlation Coefficient (aBCC), an atom-level metric for evaluating the consistency between ligand atomic coordinates and electron density in a resolution-standardized framework. To predict aBCC values from electron-density maps, we developed QAEmap, a machine-learning model based on three-dimensional convolutional neural networks (3D-CNNs). The model was trained using Fourier-truncated electron-density maps and corresponding ligand coordinates generated from high-resolution structures in the Protein Data Bank. It was evaluated using both Fourier-truncated electron-density maps and experimentally determined PDB structures. was evaluated using both Fourier-truncated electron-density maps and experimentally determined PDB structures.The prediction accuracy gradually decreased with decreasing resolution, but remained reliable up to [~]3.5 [A]. These results demonstrate that aBCC enables resolution-standardized atom-wise evaluation of coordinate-density consistency across different resolutions and provide a foundation for further development and refinement of machine learning-based coordinate validation. SynopsisWe introduce the atomic box correlation coefficient (aBCC), a machine learning-based metric for the resolution-standardized atom-level evaluation of ligand coordinate-density consistency in crystallographic structures. aBCC provides a common framework for assessing and communicating the local coordinate reliability between structural biologists and researchers in structure-based drug discovery.

UvrD-family Superfamily 1A helicases are processive ATP-dependent motor proteins that function during DNA replication, recombination, repair, and transcription. UvrD-family monomers translocate along single stranded (ss) DNA with 3-to-5 directionality but must be activated by dimerization to become helicases in the absence of force or accessory factors. Mycobacterium tuberculosis (Mtb) UvrD1 helicase forms dimers via a disulfide bond between native cysteines in the 2B sub-domains of each monomer. E. coli UvrD forms non-covalent dimers using the same 2B domain interface as in Mtb UvrD1. Using both ensemble and single DNA molecule approaches we examined an E. coli UvrD variant (R421C), which forms covalent dimers with constitutive helicase activity. For the first time this has enabled us to compare the ssDNA translocation and helicase activities of covalent dimers and monomers. Crosslinked UvrD dimers exhibit much higher ssDNA translocation processivities than monomers, although with similar translocation rates. Crosslinked UvrD dimers also show highly processive DNA unwinding of thousands of base pairs, much higher than non-crosslinked UvrD dimers, while monomers show no DNA unwinding activity. DNA unwinding rates of crosslinked UvrD dimers are only [~]20% slower than ssDNA translocation rates, indicating they are "active" helicases that directly facilitate duplex destabilization.

As biological AI models become more powerful, practical biosecurity approaches are needed to support beneficial applications while reducing misuse risks. Sequence-similarity-based screening approaches are no longer adequate to safeguard biological AI models because these models can design molecules with novel sequences and structures. Therefore, a screening approach that takes function into account is needed. To address this need, we propose a new screening method for AI-enabled protein binder design tools. Our framework screens protein binding targets, with a focus on the human proteome, as opposed to the binder molecule itself. We constructed a database of 14,541 potentially harmful proteoform targets from the human proteome (7.1% of all human protein proteoforms) classified by biosecurity risk level. To discern structural and functional features, we evaluated constructs with an embedding-based screening method using the ESM-C protein language model. ESM-C achieved high accuracy for detecting variants of known targets (F1 scores >97%), with performance similar to BLASTP. However, ESM-C proved to be more effective at capturing functional relationships, distinguishing benign mutations from damaging ones where BLASTP did not. To characterize how screening would affect bioscience research, we measured flagging rates across diverse protein datasets. Flagging rates were significant for mammalian proteins weighted by publication frequency (23% for human, 20% for mouse), and rates for organisms distantly related to humans were minimal (<1.1% for bacteria, fungi, plants, and viruses). Among commercially relevant targets, 63% of antibody patent targets were classified as dual-use, reflecting that therapeutically important proteins often perform critical biological functions. To identify and flag risky user requests from protein binder design tools without placing an undue burden on scientific research and innovation, it will be essential to deploy this screening approach in a way that addresses the overlap our analysis showed between targets of concern and therapeutic targets-possibly in concert with tiered trusted access frameworks. This new method provides a foundation for proportionate safeguards for biological AI models that reduce misuse risks while preserving their benefits for legitimate research and demonstrates a concrete proof of principle that can be generalized to other protein design tools and biological AI models.

Enzyme activity increases with temperature up to a maximum, beyond which it declines, a behaviour traditionally attributed to thermal denaturation. However, some enzymes show activity decline well below the melting temperature. Macromolecular rate theory (MMRT) explains this phenomenon by introducing a negative activation heat capacity [Formula], reflecting a transition-state ensemble more conformationally restricted than the ground state. Recently, [Formula] has been shown to be temperature-dependent and proposed as a general catalytic feature, though its variation within and across homologous families from distinct thermal niches remains unexplored. We characterized the glucokinase activity of three homologous bifunctional ADP-dependent PFK/GK enzymes: MbPFK/GK from the psychrotolerant Methanococcoides burtonii, MmPFK/GK from the mesophilic Methanococcus maripaludis, and ancM, the inferred ancestor of the Methanococcales order, which displays enhanced thermostability. MmPFK/GK and ancM display two [Formula] regimes, with abrupt changes in kcat vs temperature: zero to moderately negative values at low temperatures, shifting sharply at elevated temperatures to highly negative values (-44 kJ mol-1 K-1 and -36 kJ mol-1 K-1, respectively), exceeding previous reports. Circular dichroism spectroscopy confirms that these extreme values reflect pre-melting conformational changes rather than denaturation. Despite being psychrotolerant, MbPFK/GK displayed the highest thermal stability [Formula] and a single [Formula] regime throughout all temperatures (-2.6 kJ mol-1 K-1). Domain-closure dynamics explain thermal adaptation and moderate-temperature [Formula] values; whereas the basis of the extreme high-temperature [Formula] values remain unknown. To account for these two regimes, we present a two-pathway model incorporating a conformational equilibrium in which free enzyme and enzyme-substrate complex populate two catalytically competent conformations.

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.

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.