Recent advances in protein structure prediction have considerably increased the number of available structures, underscoring the need for scalable and accurate methods to compare and classify this huge amount of data. Current approaches based on sequence alignment or structural superposition often lack sensitivity and/or are computationally expensive. Here, we introduce the DeltaFold Classifier (DFC), a fast, alignment-free, protein structure classification pipeline based on topological data analysis. Protein three-dimensional structures are encoded using fixed-length vectors derived from persistent homology applied to point clouds of their spatial representation. These vectors, known as Biotopological Markers (BTMs), capture the intrinsic topological features of protein structure geometry-such as cycles and cavities-across multiple spatial scales. BTMs are invariant under translations and rotations, thereby avoiding the need for structural superposition. The DFC pipeline uses the features of these BTMs to train machine learning models for protein structure classification at various hierarchical levels defined in the SCOP and CATH classifications. It achieves performance comparable to that of structure-based comparison methods while substantially improving computational efficiency. It also outperforms sequence-based methods in tasks involving distant homology detection. In-depth analyses confirm that most BTM features contribute significantly to classification performance. These findings highlight the potential of topological vectorisations in structural bioinformatics and support the DFC pipeline as an effective and scalable tool for automated protein structure classification and annotation.
Protein Tyrosine Phosphatases (PTPs) regulate cellular signaling by balancing phosphotyrosine levels through a conserved WPD-loop that switches between open (inactive) and closed (active) conformations. While this conformational change is crucial for catalysis, the structural factors governing this motion remain poorly understood. We analyzed 551 PTP crystal structures using a modified Local Spatial Patterns (LSP) algorithm and XGBoost machine learning to identify core residues whose sidechain conformations influence WPD-loop dynamics. Our analysis revealed two distinct residue clusters: one centered on F225 (PTP1B numbering) in the protein core and another on the α2 helix approximately 20 Å from the WPD-loop. Remarkably, we discovered subfamily-specific regulatory mechanisms involving the conserved phenylalanine at position 225. Classical PTPs like PTP1B maintain this residue predominantly in a "down" conformation regardless of WPD-loop state, while SHP2 exhibits unique conformational flexibility with its equivalent residue F469 displaying dual occupancy between "up" and "down" conformations. This reveals that different PTP subfamilies evolved distinct allosteric control mechanisms while maintaining the overall catalytic framework. These subfamily-specific conformational switches have significant therapeutic implications. PTP1B's role as a negative insulin signaling regulator makes it an attractive diabetes target, while SHP2's function in growth factor signaling and oncogenesis suggests cancer therapeutic opportunities. Our results provide a structural framework for understanding subfamily-specific WPD-loop regulation and identify sites for selective therapeutic intervention, offering strategies for developing highly selective inhibitors that circumvent traditional active site-directed approach limitations.
The E2 domain of amyloid precursor protein (APP_E2) plays a central role in the protein's function. We reinvestigate how the missense mutation, ΔH382L, affects the domain's thermal stability and ligand binding propensity through differential scanning fluorometry. We report that the ΔH382L significantly impacts the thermal stability of APP_E2 with a reduction in Tm of 10.1°C ± 0.8°C when compared to wild-type APP_E2. In the presence of Cu2+ ions, WT APP_E2 showed a significant increase in Tm of 2.3°C ± 0.4°C, indicative of binding, whereas the mutant exhibited a significant decrease in Tm of 9.5°C ± 0.4°C, suggesting that Cu2+-mediated stabilization of APP_E2 is highly dependent on specific interactions. We further observed that the mutation affected heparin binding, with the mutant protein showing a 9.9°C ± 0.7°C decrease in Tm and requiring lower NaCl concentrations for elution from heparin Sepharose compared with the wild-type protein. Lastly, through molecular dynamics simulations, native contacts, dynamic cross-correlation and network analyses, we demonstrated that this point mutation may induce rigidity to one of the helical subdomains of APP_E2, change native contacts and correlated motion across the domain and reroute possible established communication pathways from the mutation site to distal residues. The global perturbations predicted in the presence of mutation may provide possible mechanistic explanation of the observed reduced thermal tolerance. Overall, our data reported that a missense mutation to an uncharged amino acid at position 382 of APP_E2 significantly decreases the thermal stability and binding propensity of the domain.
An advanced theory based on statistical physics was applied to microscopically investigate androstenone adsorption and its relationship with olfactory perception in chimpanzees and gorillas. For this purpose, dose-response curves of androstenone on olfactory receptors of chimpanzee OR7D4 and gorilla OR7D4 were analyzed by statistical physics. The two-energy adsorption model fits the chimpanzee data well, and the gorilla data are best fit by the one-energy adsorption model. The stereographic parameter analysis indicated that the studied pheromones were docked in a nonparallel manner in both species via a multimolecular mechanism. The analysis showed that the molecules interacted with different amino acid residues, resulting in distinct olfactory responses. The positive values of the molar adsorption energies indicate that an exothermic and physisorption process occurred in both olfactory systems. The docking analysis revealed that androstenone binds to the olfactory receptors of chimpanzees and gorillas through specific interactions with amino acid residues in the binding pockets. The docking results indicated distinct binding modes in each species, involving van der Waals, alkyl, and C-H interactions, which corroborated the energy trends predicted by the statistical physics model. These findings provided insights into the molecular mechanisms underlying the differential olfactory responses in chimpanzees and gorillas. Statistical physics was an excellent tool for evaluating and interpreting the interactions between androstenone and olfactory receptors.
Multidrug-resistant TB (MDR TB) disease is caused by TB bacteria that are resistant to at least isoniazid and rifampicin, the most effective first-line TB treatment drugs. MDR-TB poses a significant global health challenge due to its resistance to multiple antibiotics. World Health Organization (WHO) and other global health agencies have initiated targeted efforts to control its spread. The recent initiative taken by WHO is ENDTB Universal access to TB prevention and care. However, the eradication of the TB epidemic remains a challenging problem due to the emergence of specialized and modified drug resistance mechanisms. Among these mechanisms, the major facilitator superfamily (MFS) transporters play a crucial role in mediating drug resistance. The Rv0191 efflux transporter protein, a putative member of the MFS, has recently emerged as a potential target for investigating the molecular mechanisms behind the multidrug resistance (MDR) mechanism in Mtb, as it is involved in the efflux of PZA. To investigate the detailed molecular mechanism of Rv0191-mediated drug efflux, two conformations were studied: an outward-open (protonated) conformation for protonation analysis and an inward-open (unprotonated) conformation to examine substrate efflux. Pyrazinamide (PZA), a first-line anti-tuberculosis drug, was selected as the model substrate. Both conformations revealed a central tunnel with a single, distinct binding pocket. The efflux process was explored through a protonation-driven mechanism, highlighting the functional distinctions between the two conformational states. The systems were embedded in a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine lipid bilayer, and 500 ns of molecular dynamics simulations in triplicate were conducted for the PZA-docked structure. Trajectory-based structural analyses revealed specific interactions between PZA and the binding pocket, accompanied by conformational changes in Rv0191 that MD simulations indicate substrate movement. The observed structural dynamics suggest a plausible efflux pathway, allowing translocation of PZA from the cytoplasmic to the periplasmic side through the central tunnel, thereby suggesting a plausible mechanism of MFS-mediated drug resistance.
Recent diffusion-based approaches to protein-protein docking typically decouple structure generation from decoy ranking. We introduce DFMDock (Denoising Force Matching for Docking), a unified diffusion model that integrates generative sampling and energy-based ranking through physically motivated supervision. DFMDock predicts both denoising forces and a scalar energy, trained using force matching and energy contrastive objectives. The predicted forces guide the reverse diffusion process, while the energy enables decoy ranking without relying on a separately trained confidence model. On the Docking Benchmark 5, DFMDock achieves a 4.9% Top-1 and 31.6% Oracle success rate, outperforming DiffDock-PP (4.3% and 16.2%, respectively). Unlike co-folding models, DFMDock does not require multiple sequence alignments (MSAs) and generalizes to unseen targets. In decoy ranking, its learned energy function outperforms Rosetta energy and model-derived confidence scores, producing funnel-shaped energy landscapes enriched for near-native structures. These results suggest DFMDock as an efficient and physically grounded approach to diffusion-based protein docking.
The bromodomain and extra-terminal (BET) family of proteins, which regulate chromatin function, is an established potential drug target for treating major diseases such as cancer and inflammatory conditions. There is significant research focused on developing new BET inhibitors with innovative molecular structures to target and modulate the epigenetic mechanisms underlying major diseases, including cancer. Herein, we present the crystal structures of the second bromodomain (BD2) of hBRD2 in complex with the FDA-approved drugs, mefenamic acid (ID8) and nimesulide (NIM), and that of the first bromodomain (BD1) of hBRD4 in complex with nimesulide (NIM). Quantitative binding assays by surface plasmon resonance (SPR) confirmed the substantial binding of these drug molecules to the hBRD2 and hBRD4 bromodomains. Using these crystal structures, a series of ID8 and NIM derivatives with improved affinity relative to the parent compounds was designed and evaluated using SeeSAR (BioSolveIT GmbH). Moreover, molecular dynamics simulations were performed on the selected derivatives of ID8 and NIM against these bromodomains and confirmed the stability of these derivatives' binding throughout the simulations. We propose that the derivatives of the aforementioned parent molecules may be potential inhibitors of the function of hBRD2 and hBRD4.
Obverse Cover: The cover image is based on the article Structure of the NAD+ bound erythrose‐4‐phosphate dehydrogenase (E4PDH) reveals the stabilizing effect of polyethylene glycol on the quaternary structure by Viswanathan Vijayan et al., https://doi.org/10.1002/prot.70083 Reverse Cover: © Science Photo Library RF/Getty Images image
Computational protein design (CPD) aims to conceive new proteins or modify existing ones to achieve a functional or structural goal, using numerical methods. Among the various branches of CPD, one consists in predicting sequences given a protein backbone. This is known as the inverse folding problem. It has been particularly fruitful over the last 40 years, has given rise to numerous methodological approaches, and has obtained experimental successes, such as the design of new folds and new enzymatic functions. One criterion for distinguishing between the methods proposed to tackle this problem is the scoring or energy function, which enables different possible sequences and conformations to be compared quantitatively. A traditional classification of scoring functions distinguishes between statistical, empirical, and physics-based approaches. Recent developments in CPD have brought to the fore new approaches based on deep learning. Improvements in prediction performance are undeniable. However, physics-based methods retain advantages due to their greater explanatory power and independence from a training dataset. We review here CPD works that have been using physics-based energy functions and discuss their interests and perspectives.
The aggregation of human islet amyloid polypeptide (hIAPP) into cytotoxic oligomers and amyloid fibrils is a hallmark of type 2 diabetes mellitus (T2DM), leading to pancreatic β-cell dysfunction. In contrast, rat IAPP (rIAPP) is largely non-amyloidogenic. Osmolytes such as glucose, glycerol, and sorbitol are known to stabilize globular protein structures; however, in the case of intrinsically disordered proteins (IDPs), they modulate amyloidogenic aggregation in a concentration-dependent manner. Understanding the molecular mechanism of action of these osmolytes on IDPs remains limited. Well-tempered bias exchange metadynamics (WT-BEMD) simulations were used to study the conformational energy landscape of hIAPP and rIAPP in solution across varying osmolyte concentrations (125, 250, and 500 mM). The addition of osmolytes resulted in subtle changes in secondary structure propensity and content in both hIAPP and rIAPP. In the case of hIAPP, a general reduction in the likelihood of α-helical conformations was observed, particularly in the amyloidogenic core, suggesting a molecular mechanism for reduced aggregation in the presence of osmolytes. There was a notable lack of significant direct H-bonding and hydrophobic protein-osmolyte interactions, confirming the presence of a strong osmophobic effect. These findings suggest that these stabilizing osmolytes influence the conformational ensemble of hIAPP and rIAPP through exclusion from the protein surface, rather than by directly stabilizing specific conformations. The potential osmolyte-mediated reduction in aggregation-prone conformations in IDPs such as hIAPP may disrupt early aggregation and offer a potential strategy to mitigate hIAPP cytotoxicity.
Bone morphogenetic protein-2 (BMP-2) is a key osteoinductive growth factor employed clinically in spinal fusion and fracture repair where bone regeneration is insufficient. However, its therapeutic efficacy is limited by low solubility and aggregation at physiological pH. This study investigates BMP-2 aggregation and identifies additives that stabilize its native, biologically active dimer form under physiological conditions. We demonstrate that 3-[(3-cholamidopropyl) dimethylammonio]-1-propanesulfonate (CHAPS) efficiently refolded monomeric BMP-2 from inclusion bodies into dimeric form but failed to prevent aggregation of the folded dimer. In contrast, arginine did not promote refolding but significantly enhanced solubility and stability of the native dimer against aggregation as evidenced by turbidity, Rayleigh scattering, nanoparticle tracking analysis (NTA), dynamic light scattering (DLS) and microscopic analyzes. Functional assays, including alkaline phosphatase (ALP) activity, calcium deposition, and native PAGE, verified that BMP-2 retained its biological activity in presence of arginine. Tryptophan fluorescence and in silico analysis revealed distinct interaction mechanisms to BMP-2: CHAPS interacts with aromatic residues, enhancing flexibility and stabilizes open conformation, whereas arginine binds preferentially to acidic residues, promoting a compact, closed conformation. Collectively, arginine confers robust stabilization of BMP-2 at physiological pH, offering a rational framework for developing stable and therapeutically effective BMP-2 formulations.
One of several intriguing aspects of kinetoplastid biochemistry is the complete dependence on host purines and purine recycling due to the lack of a de novo purine biosynthesis pathway. Adenylosuccinate lyase (ASL, EC 4.3.2.2) is a key enzyme in the purine synthesis pathway responsible for the conversion of adenylosuccinate into adenosine monophosphate (AMP), representing a potential target for an effective drug design against leishmaniasis. Here, we report the in vitro kinetics studies and the crystal structure of the Leishmania major Friedlin adenylosuccinate lyase (LmASL). Furthermore, we characterize allosteric communication networks within the protein. We propose a phenylpiperazine derivative, itraconazole, as a promising candidate for selective interaction with the LmASL substrate-binding site by molecular docking and molecular dynamics simulations. Finally, we expand the current understanding on trypanosomatid ASL by demonstrating its requirement for the normal growth of Trypanosoma brucei procyclic form. Our data will substantiate future studies aimed at developing an effective and specific treatment against leishmaniasis.
The GTPase KRAS executes a conformational switch between a GTP-bound active state and a GDP-bound inactive state, a process central to oncogenic signaling. However, the structural basis of this switching at the level of residue-contact organization remains incompletely characterized by traditional binary structural models. Here, we present a statistical-mechanical generalization of the Gaussian Network Model (GNM) by constructing spanning-tree partition functions for residue-contact graphs using the weighted Kirchhoff Laplacian in conjunction with the Matrix-Tree Theorem. Within this framework, the standard GNM is recovered in the high-temperature limit, whereas the present formulation enables a continuous Boltzmann-weighted ensemble analysis. We compute the network free energy F , mean contact energy E ¯ , heat capacity C v , and thermodynamic entropy S across an effective temperature sweep that maps the combinatorial diversity of the contact network, thereby probing the topological landscape rather than structural melting. Differential analysis reveals that KRAS activation reflects a systematic entropy-enthalpy compensation mechanism: the active state incurs a systematic energetic penalty Δ E ¯ > 0 that is offset by a marked gain in conformational entropy Δ S > 0 , with a free-energy crossover occurring at kT ≈ 2.41 Å . Edge marginal inclusion probabilities, obtained via effective-resistance theory, identify Switch I (residues 25-40) as the primary allosteric locus of nucleotide-driven network reorganization. This approach provides a thermodynamically grounded perspective on KRAS allostery, quantitatively demonstrating how network architecture enables functional versatility through entropy-driven conformational flexibility.
The stable lactoferrin C-lobe offers strong potential for therapeutic applications as an antibacterial agent. Lactoferrin is a 78 kDa (Ala1Arg689) iron-binding glycoprotein which is composed of two homologous N- and C-lobes, connected by an 11-residue α-helical linker (Thr334Arg344). The limited proteolysis of lactoferrin, carried out using chymotrypsin, generated a 40 kDa, fully functional C-lobe. The structure determination revealed that the protein chain consisted of residues from Thr343 to Leu680 together with a disulfide-linked tripeptide, Ala683Cys684Ala685. It showed that the cleavage occurred specifically at the Tyr342Thr343 peptide bond within the inter-lobe 11-residue-long peptide. Remarkably, previous studies using proteinase K, trypsin, and pepsin also produced an identical C-lobe. Thus, the inter-lobe region seems to be stereochemically designed by nature for the single-site cleavage by multiple digestive enzymes. The proteolytically generated C-lobe, with three observed glycosylation sites, remains stable for 3 days in the presence of digestive enzymes. The stable C-lobe continues to sequester iron, thus showing a prolonged antibacterial property. This is a unique example of evolutionary convergence whereby multiple digestive enzymes cleave a native protein into a stable half molecule with full antibacterial action.
Proteins are built from modular domains that serve as fundamental units of structure and evolution. While individual domains have been extensively cataloged, their collective distribution across the lineages of life has remained poorly resolved. Here, we use the Evolutionary Classification of Protein Domains (ECOD) to chart the occurrence of domain homology groups (H-groups) across 44 model proteomes representing Eukaryota, Bacteria, and Archaea, in which 1.16 million domains are assigned to 3320 H-groups. H-groups are categorized as universal (occupying all three superkingdoms), shared between superkingdoms, or lineage-specific. The fold architecture distributions were examined: α/β sandwiches and other mixed architectures were abundant in universal H-groups, whereas α-rich architectures are expanded in eukaryotic H-groups and β-rich folds in bacterial H-groups. 126 (3.8%) H-groups occur in all organisms, forming a universal structural core that supports central processes of energy conversion, metabolism, and information flow. These widely distributed folds coincide with canonical superfolds-robust, adaptable architectures repeatedly repurposed for key biochemical roles. Two superkingdom groups trace evolutionary connections between lineages: bacterial metabolic and chaperone systems inherited by eukaryotes, archaeal informational machinery conserved in eukaryotic nuclei, and ancient redox scaffolds linking bacteria and archaea. Lineage-exclusive domains, in turn, highlight distinct adaptive strategies-regulatory and cytoskeletal innovation in eukaryotes, envelope and motility specialization in bacteria, and redox or replication refinements in archaea. Together, these data provide a quantitative, structure-based view of protein domain evolution across the tree of life, showing that the essential architecture of life relies on a conserved set of ancient folds, while lineage-specific diversity has largely arisen through the recombination and functional diversification of pre-existing domains.
Phospholipase D (PLD) catalyzes the hydrolysis of phospholipids to generate phosphatidic acid and free head groups such as choline. Among bacterial PLD enzymes, Streptomyces chromofuscus PLD (SchPLD), a member of the alkaline phosphatase D (PhoD) superfamily, exhibits unique Ca2+-dependent phospholipase activity. Here, we determined the crystal structure of a PhoD-type PLD from S. avermitilis (SaPLD) at a 2.2-Å resolution, which shares 86% sequence identity with SchPLD. The structure revealed the conserved Fe-Ca-Ca catalytic center characteristic of PhoD enzymes. In addition, we identified novel Ca2+ binding sites surrounding the active site pocket. SaPLD exhibited negligible activity in the absence of Ca2+ but showed strong activation in the presence of Ca2+, consistent with previous observations for SchPLD. The overall structure of SaPLD lacks the C-terminal α-helix that covers the active site in Bacillus subtilis PhoD, resulting in an expanded hydrophobic cleft suited for bulky phospholipid substrates binding. Molecular dynamics modeling with phosphatidylcholine (PC) indicated that its two oleoyl chains fit well within this cleft, and that the choline head group is accommodated by a distinct cavity formed by Asn217, Leu346, and Asn357. This cavity geometry likely disfavors phosphatidylethanolamine or phosphatidylserine, explaining the preference for PC substrates. These findings provide the first structural insights into the Ca2+-stabilized expanded active site of a PhoD-type PLD and clarify the molecular basis for its phospholipid specificity.
The role of the cell envelope-associated Rv0132c/FGD2 from Mycobacterium tuberculosis has long been a subject of debate. Importantly, FGD2 is found only in pathogenic mycobacteria, making it a potential drug target. While some suggest it functions as a glucose-6-phosphate dehydrogenase, others propose it acts instead as an F420-dependent hydroxy-mycolic acid dehydrogenase-an activity linked to cell-wall remodeling and inhibition by the anti-tubercular drug pretomanid. Yet, direct evidence for either activity has been lacking. Here, we heterologously express and purify active Mtb-FGD2, and demonstrate that the enzyme binds the F420 cofactor with nanomolar affinity. Crystal structures for both the apo-form and the F420 complex reveal that the Mtb-FGD2 active site architecture is consistent with sugar substrates but notably lacks a phosphate-binding pocket. Biochemical assays confirm that Mtb-FGD2 functions efficiently as an F420-dependent glucose dehydrogenase in vitro. Computational docking combined with molecular dynamics simulations further supports the formation of a catalytically plausible β-D-glucose:F420 ternary complex. When coupled to other F420-dependent enzymes, Mtb-FGD2 readily supports glucose-driven F420.H2-dependent oxidoreductase activity. Our data thus suggest that the Mtb-FGD2 provides reduced F420.H2 in a glucose-dependent manner to support mycobacterial F420.H2-dependent oxidoreductases in the cell envelope.
Post-translational modifications (PTMs) play a critical role in regulating the transcriptional activity of PPARγ, a nuclear receptor central to glucose and lipid homeostasis. Among these, lysine acetylation at K268 and K293 and phosphorylation at S273 are particularly relevant to insulin sensitivity. These residues form a regulatory binding interface for protein partners such as SIRT1 and CDK5, which exert opposing effects on PPARγ activity-SIRT1 promoting deacetylation and insulin sensitization, and CDK5 driving phosphorylation linked to insulin resistance. Here we show that modifications at this interface influence PPARγ's interaction with its regulators and its transcriptional activity. Acetyl-mimetic mutations at K268 and K293 reduce CDK5 binding and phosphorylation, while enhancing transcriptional activity. Phosphorylation at S273 weakens SIRT1 binding and limits its repressive function, even under overexpression. These effects likely reflect both direct interference with protein docking and changes in the global acetylation or phosphorylation landscape. Our findings reveal that this PTM-rich interface functions as a regulatory hub, integrating signals from multiple protein partners to fine-tune PPARγ activity. Unlike full receptor activation by agonists, which often triggers adverse effects, modulating this interface represents a refined therapeutic avenue for enhancing insulin sensitivity in metabolic diseases like obesity and type 2 diabetes, with improved specificity and reduced side effects.
G protein-coupled receptors (GPCRs) can form oligomers, which activate distinct signaling pathways compared to monomeric GPCRs. Oligomerization influences GPCR trafficking, ligand affinity, and signal transduction, and has been implicated in diseases such as schizophrenia and hypertension. Understanding GPCR oligomerization is essential for uncovering disease mechanisms and developing new therapeutic strategies. Previously, we developed a GPCR-GPCR interaction pair predictor (GGIP) utilizing an SVM algorithm. Features for the predictor were generated by quantifying amino acid properties, assigning scores, and averaging these values across the target sequences. In this study, we aimed to enhance the predictive accuracy of GGIP. We evaluated four methods by combining two feature generation techniques with two prediction algorithms. We tested the sequence segmentation-based method from our previous work and automatic feature generation using amino acid sequences with an autoencoder while evaluating both the SVM and gradient-boosting decision tree (GBDT) as prediction algorithms. Combining segmentation-based feature generation with GBDT yielded the highest performance, achieving an AUROC of over 0.98. Some features could be identified as a basis for predicting that a pair of GPCRs would interact, based on amino acid properties and their arrangement in the three-dimensional structure. Integrating our improved method with disease-related gene expression variation data revealed a significant association between GPCR interaction pairs and the presence of disease-related differentially expressed genes (DEGs). Specifically, around 90% of experimentally determined interaction pairs contained at least one protomer gene classified as a disease-related DEG, suggesting that GPCRs forming interaction pairs are more likely to be associated with disease-related gene expression changes. Among these pairs, we identified the interaction between mGluR2 and 5-HT2AR, which has been postulated to be linked to schizophrenia. Although this association was not registered in the database, we were able to confirm it through published literature. Given the significant association with disease-related DEGs, this approach is critical for identifying disease-associated GPCR interaction pairs and guiding future therapeutic developments.
Understanding how cellular macromolecules adapt in thermophilic and mesophilic organisms across different thermal environments provides important insights into evolutionary mechanisms. These mechanisms enable early life forms to maintain essential biological processes in diverse ecological niches. FtsZ, an important protein for bacterial and archaeal cell division, has evolved to function across different thermal environments. The present study investigates the genomic, proteomic, and structural adaptations of the pivotal bacterial cell division protein FtsZ during the transition from thermophilic to mesophilic bacteria across the prokaryotic kingdom. Through comprehensive analyses, we reveal intricate evolutionary dynamics, shedding light on the molecular strategies that underlie bacterial adaptation to diverse thermal environments. Our genomic exploration unveils key genetic variations correlating with temperature preferences, while proteomic investigations elucidate distinct expression patterns of FtsZ in response to thermal shifts. Structural insights show temperature-dependent alterations in the conformation of these proteins, providing a nuanced understanding of their functional adaptations. These findings collectively contribute to our comprehension of the molecular mechanisms governing bacterial evolution and highlight the importance of FtsZ in temperature-driven adaptations across prokaryotes. We found that three amino acids, namely lysine (K), leucine (L), and isoleucine (I), are particularly enriched in thermophilic FtsZ protein sequences compared with those of mesophiles. In addition, the mutational changes occurred in the thermophilic FtsZ protein structure to understand the thermal stability of the protein. Simultaneously, the B-factor and Tm value, these two essential parameters, established that the mutant FtsZ structures were thermodynamically unstable for losing those distinct amino acids.
