Interface Residues Research Articles

Design and Evaluation of Peptide Inhibitors Targeting the Dimerization of SARS-CoV-2 Main Protease.

The severe acute respiratory syndrome virus 2 (SARS-CoV-2) seriously impacted public health. The evolutionarily conserved viral chymotrypsin-like main protease (Mpro) is an important target for anti-SARS-CoV-2 drug development. Previous studies have shown that the eight N-terminal amino acids (N8) of SARS-CoV Mpro are essential for its dimerization, and are used to design inhibitors against SARS-CoV Mpro dimerization. Here, we established a simple readout assay using SDS-PAGE and Coomassie blue staining to measure inhibitory activity of N8 peptide derived from SARS-CoV-2 Mpro. To optimize its inhibitory effect, we then modified the side-chain length, charge, and hydrophilicity of the N8 peptide, and introduced a mutated Mpro recognition sequence. As a result, we obtained a series of potent peptide inhibitors against SARS-CoV-2 Mpro, with N8-A24 being the most efficient with an IC50 value of 1.44 mM. We observed that N8-A24 reduced Mpro dimerization with an IC50 value of 0.86 mM. Molecular docking revealed that N8-A24 formed hydrogen bond interactions with critical dimeric interface residues, thus inhibiting its dimerization and activity. In conclusion, our study not only discovers a series of peptide inhibitors targeting the SARS-CoV-2 Mpro dimerization, but also provides a promising strategy for the rational design of new inhibitors against COVID-19.

Massively Parallel Screening of Toll/Interleukin-1 Receptor (TIR)-Derived Peptides Reveals Multiple Toll-Like Receptors (TLRs)-Targeting Immunomodulatory Peptides.

Toll-like receptors (TLRs) are critical regulators of the immune system, and altered TLR responses lead to a variety of inflammatory diseases. Interference of intracellular TLR signaling, which is mediated by multiple Toll/interleukin-1 receptor (TIR) domains on all TLRs and TLR adapters, is an effective therapeutic strategy against immune dysregulation. Peptides that inhibit TIR-TIR interactions by fragmenting interface residues have potential as therapeutic decoys. However, a systematic method for discovering TIR-targeting moieties has been elusive, limiting exploration of the vast, unsequenced space of the TIR domain family. A comprehensive parallel screening method is developed to uncover novel TIR-binding peptides derived from previously unexplored surfaces on a wide range of TIR domains. A large peptide library is constructed, named TIR surfacesome, by tiling surface sequences of the large TIR domain family and screening against MALTIR and MyD88TIR, TIRs of two major TLR adaptor proteins, resulting in the discovery of hundreds of TIR-binding peptides. The selected peptides inhibited TLR signaling and demonstrated anti-inflammatory effects in macrophages, and therapeutic potential in mouse inflammatory models. This approach may facilitate the development of TLR-targeted therapeutics.

Pair-EGRET: Enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models.

Proteins are responsible for most biological functions, many of which require the interaction of more than one protein molecule. However, accurately predicting protein-protein interaction (PPI) sites (the interfacial residues of a protein that interact with other protein molecules) remains a challenge. The growing demand and cost associated with the reliable identification of PPI sites using conventional experimental methods call for computational tools for automated prediction and understanding of PPIs. We present Pair-EGRET, an edge-aggregated graph attention network that leverages the features extracted from pre-trained transformer-like models to accurately predict PPI sites. Pair-EGRET works on a k-nearest neighbor graph, representing the three-dimensional structure of a protein, and utilizes the cross-attention mechanism for accurate identification of interfacial residues of a pair of proteins. Through an extensive evaluation study using a diverse array of experimental data, evaluation metrics, and case studies on representative protein sequences, we demonstrate that Pair-EGRET can achieve remarkable performance in predicting PPI sites. Moreover, Pair-EGRET can provide interpretable insights from the learned cross-attention matrix. Pair-EGRET is freely available in open source form at the GitHub Repository https://github.com/1705004/Pair-EGRET. Supplementary data are available at Bioinformatics online.

Customizable OpenGUS immunoassay: A homogeneous detection system using β-glucuronidase switch and label-free antibody

We developed a customizable OpenGUS immunoassay that enables rapid and sensitive detection of analytes without requiring antibody modification. This immunoassay employs label-free whole antibodies, an antibody-binding Z domain (ZD) derived from Staphylococcal protein A, and a β-glucuronidase (GUS) switch mutant, allowing for easy replacement of antibodies to tailor the immunoassays for various targeted antigens. The working principle is that the OpenGUS probe, the fusion protein of ZD and a GUS switch, converts the antibody-antigen interaction into GUS activation in a one-pot reaction. To enhance the signal-to-background ratio of the immunoassay, a GUS switch mutant that exhibits reduced background activation was developed by screening several additional mutations at the diagonal interface residue H514. Moreover, we optimized the composition of the reaction buffer, including organic solvents, salt, and surfactant. Under optimal conditions, we customized OpenGUS immunoassays for Cry j 1, human C-reactive protein, and human lactoferrin, achieving around 10–20-fold maximum fluorescence (15 min) or colorimetric (2 h) responses with picomolar to low nanomolar level detection limit, simply by using commercially available IgGs. Additionally, the three analytes were successfully detected in complex matrices similar to those used in practical applications. We believe that this customizable OpenGUS immunoassay will pave the way for the prompt development of rapid and sensitive homogeneous immunoassays for point-of-care diagnostics, high-throughput testing, and onsite environmental assessments.

PPI-hotspotID for detecting protein–protein interaction hot spots from the free protein structure

Experimental detection of residues critical for protein–protein interactions (PPI) is a time-consuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot prediction methods have been developed, but they have been validated using relatively small datasets, which may compromise their predictive reliability. Here, we introduce PPI-hotspotID, a novel method for identifying PPI-hot spots using the free protein structure, and validated it on the largest collection of experimentally confirmed PPI-hot spots to date. We explored the possibility of detecting PPI-hot spots using (i) FTMap in the PPI mode, which identifies hot spots on protein–protein interfaces from the free protein structure, and (ii) the interface residues predicted by AlphaFold-Multimer. PPI-hotspotID yielded better performance than FTMap and SPOTONE, a webserver for predicting PPI-hot spots given the protein sequence. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-hotspotID yielded better performance than either method alone. Furthermore, we experimentally verified several PPI-hotspotID-predicted PPI-hot spots of eukaryotic elongation factor 2. Notably, PPI-hotspotID can reveal PPI-hot spots not obvious from complex structures, including those in indirect contact with binding partners. PPI-hotspotID serves as a valuable tool for understanding PPI mechanisms and aiding drug design. It is available as a web server (https://ppihotspotid.limlab.dnsalias.org/) and open-source code (https://github.com/wrigjz/ppihotspotid/).

PPI-hotspotID for detecting protein-protein interaction hot spots from the free protein structure.

Experimental detection of residues critical for protein-protein interactions (PPI) is a time-consuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot prediction methods have been developed, but they have been validated using relatively small datasets, which may compromise their predictive reliability. Here, we introduce PPI-hotspotID, a novel method for identifying PPI-hot spots using the free protein structure, and validated it on the largest collection of experimentally confirmed PPI-hot spots to date. We explored the possibility of detecting PPI-hot spots using (i) FTMap in the PPI mode, which identifies hot spots on protein-protein interfaces from the free protein structure, and (ii) the interface residues predicted by AlphaFold-Multimer. PPI-hotspotID yielded better performance than FTMap and SPOTONE, a webserver for predicting PPI-hot spots given the protein sequence. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-hotspotID yielded better performance than either method alone. Furthermore, we experimentally verified several PPI-hotspotID-predicted PPI-hot spots of eukaryotic elongation factor 2. Notably, PPI-hotspotID can reveal PPI-hot spots not obvious from complex structures, including those in indirect contact with binding partners. PPI-hotspotID serves as a valuable tool for understanding PPI mechanisms and aiding drug design. It is available as a web server (https://ppihotspotid.limlab.dnsalias.org/) and open-source code (https://github.com/wrigjz/ppihotspotid/).

Rewiring protein binding specificity in paralogous DRG/DFRP complexes

Eukaryotes have two paralogous developmentally regulated GTP-binding (DRG) proteins: DRG1 and DRG2, both of which have a conserved binding partner called DRG family regulatory protein 1 and 2 (DFRP1 and DFRP2), respectively. DFRPs are important for the function of DRGs and interact with their respective DRG via a conserved region called the DFRP domain. Despite being highly similar, DRG1 and DRG2 have strict binding specificity for their respective DFRP. Using AlphaFold generated structure models of the human DRG/DFRP complexes, we have biochemically characterized their interactions and identified interface residues involved in determining specificity. This analysis revealed that as few as five mutations in DRG1 can switch binding from DFRP1 to DFRP2. Moreover, while DFRP1 binding confers increased stability and GTPase activity to DRG1, DFRP2 binding only supports increased stability. Overall, this work provides new insight into the structural determinants responsible for the binding specificities of the DRG/DFRP complexes.

Biophysical and Solution Structure Analysis of Critical Residues Involved in the Interaction between the PupB N-Terminal Signaling Domain and PupR C-Terminal Cell Surface Signaling Domain from Pseudomonas capeferrum.

Cell surface signaling (CSS) is a means of rapidly adjusting transcription in response to extracellular stimuli in Gram-negative bacteria. The pseudobactin BN7/8 uptake (Pup) system not only imports iron but also upregulates its own transcription through CSS in Pseudomonas capeferrum. In the absence of ferric pseudobactin BN7/8, the signaling components are maintained in a resting state via the formation of a periplasmic complex between the N-terminal signaling domain (NTSD) of the outer membrane iron-transporter, PupB, and the C-terminal CSS domain (CCSSD) of the sigma regulator, PupR. The previously determined 1.6 Å crystal structure of this periplasmic complex has allowed us to probe the structural and thermodynamic consequences of mutating key interfacial residues. In this report, we describe the solution structure of the PupB NTSD and use Nuclear Magnetic Resonance spectroscopy, Isothermal Titration Calorimetry, and Circular Dichroism spectroscopy together with thermal denaturation to investigate whether three PupB point mutations, Q69K, H72D, and L74A, influence the interaction merely due to the chemical nature of the amino acid substitution or also cause changes in overall protein structure. Our results demonstrate that binding to the PupR CCSSD does not alter the structure of PupB NTSD and that the individual mutations have only minor effects on structure. The mutations generally lower thermodynamic stability of the NTSD and weaken binding to the CCSSD. These findings validate the X-ray crystal structure interface, emphasizing the importance of amino acid chemical nature at the interface.

Proton-triggered rearrangement of the AMPA receptor N-terminal domains impacts receptor kinetics and synaptic localization.

AMPA glutamate receptors (AMPARs) are ion channel tetramers that mediate the majority of fast excitatory synaptic transmission. They are composed of four subunits (GluA1-GluA4); the GluA2 subunit dominates AMPAR function throughout the forebrain. Its extracellular N-terminal domain (NTD) determines receptor localization at the synapse, ensuring reliable synaptic transmission and plasticity. This synaptic anchoring function requires a compact NTD tier, stabilized by a GluA2-specific NTD interface. Here we show that low pH conditions, which accompany synaptic activity, rupture this interface. All-atom molecular dynamics simulations reveal that protonation of an interfacial histidine residue (H208) centrally contributes to NTD rearrangement. Moreover, in stark contrast to their canonical compact arrangement at neutral pH, GluA2 cryo-electron microscopy structures exhibit a wide spectrum of NTD conformations under acidic conditions. We show that the consequences of this pH-dependent conformational control are twofold: rupture of the NTD tier slows recovery from desensitized states and increases receptor mobility at mouse hippocampal synapses. Therefore, a proton-triggered NTD switch will shape both AMPAR location and kinetics, thereby impacting synaptic signal transmission.

Geometric deep learning of protein–DNA binding specificity

Predicting protein–DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein–DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding specificity, to a wide range of DNA sequences. This information is not directly accessible in a single structure. Here, to access this information, we present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep-learning model designed to predict binding specificity from protein–DNA structure. DeepPBS can be applied to experimental or predicted structures. Interpretable protein heavy atom importance scores for interface residues can be extracted. When aggregated at the protein residue level, these scores are validated through mutagenesis experiments. Applied to designed proteins targeting specific DNA sequences, DeepPBS was demonstrated to predict experimentally measured binding specificity. DeepPBS offers a foundation for machine-aided studies that advance our understanding of molecular interactions and guide experimental designs and synthetic biology.

Autoinhibition of ubiquitin-specific protease 8: Insights into domain interactions and mechanisms of regulation

Ubiquitin-specific proteases (USPs) are a family of multi-domain deubiquitinases (DUBs) with variable architectures, some containing regulatory auxiliary domains. Among the USP family, all occurrences of intramolecular regulation presently known are autoactivating. USP8 remains the sole exception as its putative WW-like domain, conserved only in vertebrate orthologs, is autoinhibitory. Here, we present a comprehensive structure-function analysis describing the autoinhibition of USP8 and provide evidence of the physical interaction between the WW-like and catalytic domains. The solution structure of full-length USP8 reveals an extended, monomeric conformation. Coupled with DUB assays, the WW-like domain is confirmed to be the minimal autoinhibitory unit. Strikingly, autoinhibition is only observed with the WW-like domain in cis and depends on the length of the linker tethering it to the catalytic domain. Modelling of the WW:CD complex structure and mutagenesis of interface residues suggests a novel binding site in the S1 pocket. To investigate the interplay between phosphorylation and USP8 autoinhibition, we identify AMP-activated protein kinase as a highly selective modifier of S718 in the 14-3-3 binding motif. We show that 14-3-3γ binding to phosphorylated USP8 potentiates autoinhibition in a WW-like domain-dependent manner by stabilizing an autoinhibited conformation. These findings provide mechanistic details on the autoregulation of USP8 and shed light on its evolutionary significance.

Interplay of PROTAC Complex Dynamics for Undruggable Targets: Insights into Ternary Complex Behavior and Linker Design.

Protein degraders, such as bifunctional proteolysis-targeting chimeras (PROTACs), selectively eliminate target proteins by leveraging the natural protein degradation machinery. PROTACs bridge the target protein with an E3 ligase, which induces ubiquitination and degradation. Investigating ternary complex structures elucidates the molecular mechanisms of their formation and degradation. This study examines the binding dynamics of E3 ligases (VHL, CRBN, and cIAP) with proteins of interest, focusing on dynamics, cooperativity, selectivity, linker length, and PROTAC conformations. The influence of interface residues and linker lengths on specific conformations for target proteins and E3 ligases is highlighted. Utilizing molecular dynamics and steered molecular dynamics simulations, the study provides comprehensive parameters on the behavior and stability of diverse ternary complexes. These insights are crucial for designing PROTACs targeting disease-causing proteins and advancing the development of degradable ternary complexes for therapeutic applications.

Identification of Immunodominant Epitopes of Dengue Virus 2 Envelope and NS1 Proteins: Evaluating the Diagnostic Potential of a Synthetic Peptide.

Dengue is a major infectious disease with potential for outbreaks and epidemics. A specific and sensitive diagnosis is a prerequisite for clinical management of the disease. We designed our study to identify epitopes on theDengue virus (DENV) envelope (E) and non-structural protein 1 (NS1) with potential for diagnosis. Serology and immunoinformatic approaches were employed. We collected DENV-positive, DENV-negative and Japanese encephalitis virus-positive samples from collaborating hospitals in 2019 and 2022-2023. Seropositive peptides in 15-18 mer peptide arrays of E and NS1 proteins of DENV2 were determined by an indirect enzyme-linked immunosorbent assay. B-cell linear and conformational epitopes were predicted using BepiPred2.0 and ElliPro, respectively. A consensus recombinant peptide was designed, synthesised and evaluated for its diagnostic potential using patient sera. Eight peptides of E protein and six peptides of NS1 protein were identified to be the most frequently recognised by Dengue-positive patients. These peptide sequences were compared with B-cell epitope regions and found to be overlapped with predicted B-cell linear and conformational epitopes. EP11 and NSP15 showed a 100% amino acid sequence overlap with B-cell epitopes. EP1 and NSP15 had 14 whereas EP28, EP31, EP60 16, NSP12 and NSP32 had more than 15 interacting interface residues with a neutralising antibody, suggesting a strength of interaction. Interestingly, potential epitopes identified were localised on the surface of proteins as visualised by PyMOL. Validation with a recombined synthetic peptide yielded 92.3% sensitivity and 91.42% specificity. Immunodominant regions identified by serology and computationally predicted epitopes overlapped, thereby showing the robustness of the methodology and the peptide designedfor diagnosis.

After the Smoke Clears: Wildland-Urban Interface Fires and Residues in Nearby Homes.

Scientists are scrambling to fill major research gaps about the types of pollutants released when wildfires burn not just vegetation, but also structures and vehicles-and how nearby residents can protect themselves.

Structural basis of TMPRSS2 zymogen activation and recognition by the HKU1 seasonal coronavirus

The human seasonal coronavirus HKU1-CoV, which causes common colds worldwide, relies on the sequential binding to surface glycans and transmembrane serine protease 2 (TMPRSS2) for entry into target cells. TMPRSS2 is synthesized as a zymogen that undergoes autolytic activation to process its substrates. Several respiratory viruses, in particular coronaviruses, use TMPRSS2 for proteolytic priming of their surface spike protein to drive membrane fusion upon receptor binding. We describe the crystal structure of the HKU1-CoV receptor binding domain in complex with TMPRSS2, showing that it recognizes residues lining the catalytic groove. Combined mutagenesis of interface residues and comparison across species highlight positions 417 and 469 as determinants of HKU1-CoV host tropism. The structure of a receptor-blocking nanobody in complex with zymogen or activated TMPRSS2 further provides the structural basis of TMPRSS2 activating conformational change, which alters loops recognized by HKU1-CoV and dramatically increases binding affinity.

Elucidating the Molecular Basis of 14-3-3 Interaction with α-Synuclein: Insights from Molecular Dynamics Simulations and the Design of a Novel Protein-Protein Interaction Inhibitor.

Parkinson's disease is a widespread age-related neurodegenerative disorder characterized by the loss of dopaminergic neurons in the midbrain along with the appearance of protein aggregates, termed as "Lewy bodies" in the surviving neuronal cells. The components of Lewy bodies include proteins such as α-synuclein, 14-3-3, Parkin, and LRRK2, along with other cellular organelles, which, in their native state, perform a plethora of vital biological functions within the human biome. Formation of these aggregates renders these components inactive, thereby interfering with homeostasis. In this regard, the current study attempts to investigate the complexation behavior of all human-based 14-3-3 isoforms with α-synuclein via a combination of classical and enhanced sampling techniques and thereby determine the causality of these protein-protein interactions. The study indicated that upon complexation, the aggregation propensity of both 14-3-3 and α-synuclein increases, and this increment is propelled by the interfacial residues on either protein. Furthermore, mutagenesis studies revealed that Lys214 of 14-3-3 (henceforth termed K214A) is crucial for the formation of this binary complex. Principal component analysis combined with clustering studies unveiled the stability of these complexes in terms of their conformational distribution across the entire MD trajectory. For K214A, these clustered states were sparsely located, thereby making the transitions between them slightly difficult. Dynamic cross-correlation maps (DCCM) revealed the role of residues in the range 80-130 of 14-3-3 having a potential allosteric role in driving this complexation process. Finally, a novel peptide-based supramolecular inhibitor was designed, which exhibited higher proficiency in limiting the 14-3-3/α-synuclein interaction compared to the previous inhibitor model. It was also revealed that the presence of this inhibitor induces structural rigidity in α-synuclein, making changes in its conformations extremely difficult, as observed through Umbrella Sampling studies. Based on available information, the current study provides an insight into the molecular-level understanding of protein-protein interactions underlying Parkinson's disease and adds on to the methods of devising novel therapeutic approaches to treat the same.

Structure-guided Evolutionary Analysis of Interactome Network Rewiring at Single Residue Resolution in Yeasts

Protein-protein interactions (PPIs) are known to rewire extensively during evolution leading to lineage-specific and species-specific changes in molecular processes. However, the detailed molecular evolutionary mechanisms underlying interactome network rewiring are not well-understood. Here, we combine high-confidence PPI data, high-resolution three-dimensional structures of protein complexes, and homology-based structural annotation transfer to construct structurally-resolved interactome networks for the two yeasts S. cerevisiae and S. pombe. We then classify PPIs according to whether they are preserved or different between the two yeast species and compare site-specific evolutionary rates of interfacial versus non-interfacial residues for these different categories of PPIs. We find that residues in PPI interfaces evolve significantly more slowly than non-interfacial residues when using lineage-specific measures of evolutionary rate, but not when using non-lineage-specific measures. Furthermore, both lineage-specific and non-lineage-specific evolutionary rate measures can distinguish interfacial residues from non-interfacial residues for preserved PPIs between the two yeasts, but only the lineage-specific measure is appropriate for rewired PPIs. Finally, both lineage-specific and non-lineage-specific evolutionary rate measures are appropriate for elucidating structural determinants of protein evolution for residues outside of PPI interfaces. Overall, our results demonstrate that unlike tertiary structures of single proteins, PPIs and PPI interfaces can be highly volatile in their evolution, thus requiring the use of lineage-specific measures when studying their evolution. These results yield insight into the evolutionary design principles of PPIs and the mechanisms by which interactions are preserved or rewired between species, improving our understanding of the molecular evolution of PPIs and PPI interfaces at the residue level.

Human cytokine and coronavirus nucleocapsid protein interactivity using large-scale virtual screens

Understanding the interactions between SARS-CoV-2 and the human immune system is paramount to the characterization of novel variants as the virus co-evolves with the human host. In this study, we employed state-of-the-art molecular docking tools to conduct large-scale virtual screens, predicting the binding affinities between 64 human cytokines against 17 nucleocapsid proteins from six betacoronaviruses. Our comprehensive in silico analyses reveal specific changes in cytokine-nucleocapsid protein interactions, shedding light on potential modulators of the host immune response during infection. These findings offer valuable insights into the molecular mechanisms underlying viral pathogenesis and may guide the future development of targeted interventions. This manuscript serves as insight into the comparison of deep learning based AlphaFold2-Multimer and the semi-physicochemical based HADDOCK for protein-protein docking. We show the two methods are complementary in their predictive capabilities. We also introduce a novel algorithm for rapidly assessing the binding interface of protein-protein docks using graph edit distance: graph-based interface residue assessment function (GIRAF). The high-performance computational framework presented here will not only aid in accelerating the discovery of effective interventions against emerging viral threats, but extend to other applications of high throughput protein-protein screens.

Hydrophobic core evolution of major histocompatibility complex class I chain-related protein A for dramatic enhancing binding affinity

Interface residues at sites of protein–protein interaction (PPI) are the focus for affinity optimisation. However, protein hydrophobic cores (HCs) play critical roles and shape the protein surface. We hypothesise that manipulating protein HCs can enhance PPI interaction affinities. A cell stress molecule, major histocompatibility complex class I chain-related protein A (MICA), binds to the natural killer group 2D (NKG2D) homodimer to form three molecule interactions. MICA was used as a study subject to support our hypothesis. We redesigned MICA HCs by directed mutagenesis and isolated high-affinity variants through a newly designed partial-denature panning (PDP) method. A few mutations in MICA HCs increased the NKG2D–MICA interaction affinity by 325–5613-fold. Crystal structures of the NKG2D–MICA variant complexes indicated that mutagenesis of MICA HCs stabilised helical elements for decreasing intermolecular interactive free energy (ΔG) of the NKG2D–MICA heterotrimer. The repacking of MICA HC mutants maintained overall surface residues and the authentic binding specificity of MICA. In conclusion, this study provides a new method for MICA redesign and affinity optimisation through HC manipulation without mutating PPI interface residues. Our study introduces a novel approach to protein manipulation, potentially expanding the toolkit for protein affinity optimisation.

Biochemical, Bioinformatic, and Structural Comparisons of Transketolases and Position of Human Transketolase in the Enzyme Evolution.

Transketolases (TKs) are key enzymes of the pentose phosphate pathway, regulating several other critical pathways in cells. Considering their metabolic importance, TKs are expected to be conserved throughout evolution. However, Tittmann et al. (J Biol Chem, 2010, 285(41): 31559-31570) demonstrated that Homo sapiens TK (hsTK) possesses several structural and kinetic differences compared to bacterial TKs. Here, we study 14 TKs from pathogenic bacteria, fungi, and parasites and compare them with hsTK using biochemical, bioinformatic, and structural approaches. For this purpose, six new TK structures are solved by X-ray crystallography, including the TK of Plasmodium falciparum. All of these TKs have the same general fold as bacterial TKs. This comparative study shows that hsTK greatly differs from TKs from pathogens in terms of enzymatic activity, spatial positions of the active site, and monomer-monomer interface residues. An ubiquitous structural pattern is identified in all TKs as a six-residue histidyl crown around the TK cofactor (thiamine pyrophosphate), except for hsTK containing only five residues in the crown. Residue mapping of the monomer-monomer interface and the active site reveals that hsTK contains more unique residues than other TKs. From an evolutionary standpoint, TKs from animals (including H. sapiens) and Schistosoma sp. belong to a distinct structural group from TKs of bacteria, plants, fungi, and parasites, mostly based on a different linker between domains, raising hypotheses regarding evolution and regulation.