Proteins[JOURNAL]
Sun
200 papers
Engineered HSP90-MP65 Bivalent Fusion Antigen: A Novel Vaccine Candidate Against Invasive Candidiasis.
Wawiórka L
Proteins · 2026 Jun · PMID 42316901 · Publisher ↗
Invasive candidiasis represents a critical global health challenge, causing approximately 6.5 million bloodstream infections annually with mortality rates exceeding 60%. The emergence of multidrug-resistant Candida strai...
Invasive candidiasis represents a critical global health challenge, causing approximately 6.5 million bloodstream infections annually with mortality rates exceeding 60%. The emergence of multidrug-resistant Candida strains necessitates novel prophylactic strategies, with subunit vaccines offering a rational alternative to conventional antifungals. This study presents a structure-guided approach to engineer a novel, optimized HSP90-MP65 bivalent fusion protein as a vaccine candidate against invasive candidiasis. The central hypothesis posits that direct genetic fusion of the immunostimulatory HSP90 domain to the MP65 mannoprotein antigen creates a bifunctional molecule with enhanced antigen processing and presentation properties compared to co-administration of separate components. An integrative computational framework combining deep learning-based structure prediction, consensus B-cell and T-cell epitope mapping, rational sequence optimization (ProteinMPNN), and comprehensive biophysical characterization was employed to design multiple fusion constructs and systematically evaluate their structural integrity, conformational stability, aggregation propensity, and immunogenic potential. As a result, the most promising, rationally designed lead candidate has been designed and comprehensively optimized. The lead candidate combines superior thermodynamic preserved epitope accessibility while preserving all immunogenic features. Molecular docking and binding affinity predictions confirm maintained interactions with pattern recognition receptors and MHC molecules. This structure-based engineering approach yielded a manufacturable fusion antigen with preserved immunostimulatory domains, providing a molecular blueprint for bivalent vaccine development against fungal pathogens.
Physics-Based Energy Functions for Computational Protein Design.
Gaillard T
Proteins · 2026 Jun · PMID 42297629 · Publisher ↗
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...
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.
Impact of Stabilizing Osmolytes on the Conformational Dynamics of Human and Rat Islet Amyloid Polypeptides.
Kidman KA, Pedrick C, Kreck CA … +1 more , Mancera RL
Proteins · 2026 Jun · PMID 42290153 · Publisher ↗
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 (r...
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.
Stabilization of Bone Morphogenetic Protein-2 at Physiological pH: Contrasting Roles of CHAPS and Arginine in Aggregation Inhibition.
Amir H, Roy B, Gaur A … +1 more , Deep S
Proteins · 2026 Jun · PMID 42265823 · Publisher ↗
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...
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.
Structural Insights Into the Function of Leishmania major Adenylosuccinate Lyase.
E Silva IR, Mantovani M, Lino MESD … +8 more , Albuquerque AO, da Silva JHM, Nagem RAP, Rojas AL, Sartori GR, Horjales E, Campbell D, Thiemann OH
Proteins · 2026 Jun · PMID 42260760 · Publisher ↗
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....
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.
Generalizing the Gaussian Network Model: Spanning-Tree Thermodynamics Shows Entropy-Driven KRAS Activation.
Ciftci FS, Erman B
Proteins · 2026 Jun · PMID 42253221 · Publisher ↗
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...
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 , mean contact energy , heat capacity , and thermodynamic entropy 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 that is offset by a marked gain in conformational entropy , with a free-energy crossover occurring at . 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.
Structural Basis for Single-Site Cleavage of Lactoferrin by Diverse Proteases for Prolonged Antibacterial Action: Structure of the Chymotrypsin-Cleaved Lactoferrin C-Lobe.
Pandit S, Ahmad N, Sharma P … +2 more , Sharma S, Singh TP
Proteins · 2026 Jun · PMID 42230792 · Publisher ↗
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-l...
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.
Universal and Lineage-Specific Patterns in the Distribution of ECOD Domain Homology Groups Across Superkingdoms.
Guo R, Pei J, Zhang J … +3 more , Cong Q, Grishin NV, Schaeffer RD
Proteins · 2026 May · PMID 42076836 · Publisher ↗
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...
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.
Ankh-Score Produces Better Sequence Alignments Than AlphaFold3.
Malec J, Rusen K, Golding GB … +1 more , Ilie L
Proteins · 2026 May · PMID 42076825 · Publisher ↗
Protein sequence alignment is one of the most fundamental procedures in bioinformatics. Due to its many downstream applications, improvements to this procedure are of great importance. We consider two revolutionary conce...
Protein sequence alignment is one of the most fundamental procedures in bioinformatics. Due to its many downstream applications, improvements to this procedure are of great importance. We consider two revolutionary concepts that emerged recently as candidates for improving the state-of-the-art alignment methods: AlphaFold and protein language models such as Ankh, ProtT5, or ESM-C. Alignment improvements can come from the structural alignment of AlphaFold-predicted structures or the scoring based on the similarity of protein embeddings produced by the protein language models. Thorough comparison on many domains from BAliBASE and CDD demonstrates that the Ankh-score method produces much better sequence alignments than the structural alignments using US-align of AlphaFold3-predicted structures. Both are better than the traditional method using BLOSUM matrices. This suggests that Ankh embeddings may possess certain information that is not available in the AlphaFold3-predicted structures. The alignment software is freely available as a web server at e-score.csd.uwo.ca and as source code at github.com/lucian-ilie/E-score.
x-Ray Structure of Streptomyces avermitilis Phospholipase D Reveals a Ca-Stabilized Expanded Active-Site Cleft Adapted for Phospholipid Binding.
Yasutake Y, Hirata T, Nomura S … +4 more , Konishi K, Yoneda K, Sakasegawa SI, Sakuraba H
Proteins · 2026 Apr · PMID 42062768 · Publisher ↗
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 alk...
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 Ca-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 Ca binding sites surrounding the active site pocket. SaPLD exhibited negligible activity in the absence of Ca but showed strong activation in the presence of Ca, 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 Ca-stabilized expanded active site of a PhoD-type PLD and clarify the molecular basis for its phospholipid specificity.
The Mycobacterium tuberculosis Rv0132c Gene Product Mtb-FGD2 Can Act as an F-Dependent Glucose Dehydrogenase.
Aderemi AV, Snee M, Tunnicliffe RB … +10 more , Johanissen LO, Cliff MJ, Levy CW, Heyes DJ, Golovanova M, Jowitt TA, Hay S, Munro AW, Waltho JP, Leys D
Proteins · 2026 Apr · PMID 42012189 · Publisher ↗
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. Whil...
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 F-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 F cofactor with nanomolar affinity. Crystal structures for both the apo-form and the F 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 F-dependent glucose dehydrogenase in vitro. Computational docking combined with molecular dynamics simulations further supports the formation of a catalytically plausible β-D-glucose:F ternary complex. When coupled to other F-dependent enzymes, Mtb-FGD2 readily supports glucose-driven F.H-dependent oxidoreductase activity. Our data thus suggest that the Mtb-FGD2 provides reduced F.H in a glucose-dependent manner to support mycobacterial F.H-dependent oxidoreductases in the cell envelope.
Interplay of Ser273 Phosphorylation and K268 and K293 Acetylation in PPARγ: Implications for PPARγ Activation.
Malospirito CC, Dias MMG, Jara GE … +4 more , Avelino TM, Elias GB, de Oliveira PSL, Figueira ACM
Proteins · 2026 Apr · PMID 41968443 · Publisher ↗
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 K...
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.
Improved Method for Predicting GPCR-GPCR Interaction Pairs.
Fukushima A, Ouma A, Teruse H … +2 more , Toh H, Nemoto W
Proteins · 2026 Apr · PMID 41958283 · Publisher ↗
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 h...
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.
Genomic, Proteomic, and Structural Insights Into the Transition of FtsZ From Thermophiles to Mesophiles Across the Prokaryotic Kingdom.
Ghosh D, Basak P, Mandal S … +1 more , Chakrabarti G
Proteins · 2026 Apr · PMID 41944496 · Publisher ↗
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...
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 T value, these two essential parameters, established that the mutant FtsZ structures were thermodynamically unstable for losing those distinct amino acids.
In Silico Structure-Guided Design of Peptide Candidates Targeting γ-Secretase Subunit Assembly.
Yuka SA, Telli K, Yılmaz A
Proteins · 2026 Aug · PMID 41927485 · Full text
The γ-secretase complex is a membrane-embedded protease essential for intramembrane cleavage of substrates such as Notch receptors and the amyloid precursor protein (APP), processes central to cancer progression and Alzh...
The γ-secretase complex is a membrane-embedded protease essential for intramembrane cleavage of substrates such as Notch receptors and the amyloid precursor protein (APP), processes central to cancer progression and Alzheimer's disease (AD) pathology. However, catalytic inhibition of γ-secretase disrupts multiple signaling pathways, resulting in dose-limiting toxicities. In this study, we report a structure-guided approach to generate peptides with binding and stability profiles that disrupt the assembly of γ-secretase by targeting the interactions of Presenilin-1 and Nicastrin with APH1. First, molecular docking was performed for 36 248 peptides of varying lengths to assess their affinity scores to the PS1 and NCT interaction regions of APH1. Peptides filtered based on their affinity scores and physicochemical properties were then subjected to global molecular docking. 50-nanosecond molecular dynamics simulations and MM/PBSA analyses were performed on the top 10 potential candidates, identifying those with high dynamic interaction potential. Thus, seven γ-secretase inhibitor candidates with favorable affinity scores capable of providing stable interactions and thereby having the potential to disrupt the APH1:PS1 assembly were identified. This approach, which overcomes the challenges of targeting the transmembrane catalytic domain, is based on the inhibition of subunit assembly and presents promising candidates for future experimental studies.
Current Insight into Human Ornithine Aminotransferase: A Review.
Floriani F, Borri Voltattorni C, Montioli R
Proteins · 2026 Aug · PMID 41887919 · Full text
Human ornithine aminotransferase (hOAT) is a mitochondrial matrix pyridoxal-5'-phosphate enzyme (PLP) that catalyzes the reversible transfer of the δ-amino group of L-ornithine (L-Orn) to α-ketoglutarate (α-KG) yielding...
Human ornithine aminotransferase (hOAT) is a mitochondrial matrix pyridoxal-5'-phosphate enzyme (PLP) that catalyzes the reversible transfer of the δ-amino group of L-ornithine (L-Orn) to α-ketoglutarate (α-KG) yielding glutamate-5-semialdehyde (GSA) and glutamate. GSA is prone to cyclize to Δ1-pyrroline-5-carboxylate. Human OAT holds significant clinical and scientific interest because (i) its dysfunction causes gyrate atrophy (GA) of the choroid and retina, a rare autosomal recessive disease, and (ii) it is recognized as a potential target for chemotherapeutic drug development, being overexpressed in some types of cancer. Here, we review the kinetic and structural features of the enzyme, as well as the mechanistic aspects of hOAT inhibition. Moreover, we focus our attention on the characterization of the structural and functional properties of the artificial variants and of those associated with GA. Considering that great progress toward the characterization of the pathogenic variants has been reached in the last few years, we summarize here, by revisiting the data available on the hOAT and its variants as purified recombinant form, the current understanding of (i) the molecular defect(s) of studied disease-causing mutations and (ii) the residues (particularly, active site residues critical for dictating the reaction specificity) and/or regions of the enzyme crucial for its folding and/or catalytic properties.
Computational Discovery of MERS-CoV Main Protease Inhibitors Through Screening and Molecular Dynamics Simulations.
Islam SM, Rahman MS, Singla S … +2 more , Demissie R, Lee H
Proteins · 2026 Aug · PMID 41887806 · Full text
Targeting the main protease (Mpro) of coronaviruses has emerged as a promising therapeutic strategy for combating viral infections. Despite the global health threat posed by Middle East Respiratory Syndrome Coronavirus (...
Targeting the main protease (Mpro) of coronaviruses has emerged as a promising therapeutic strategy for combating viral infections. Despite the global health threat posed by Middle East Respiratory Syndrome Coronavirus (MERS-CoV), no vaccines or antiviral drugs have been approved to date for its treatment. With a mortality rate approaching 35%, MERS-CoV remains a critical concern, particularly due to its potential for increased transmissibility through mutation. The viral main protease plays a pivotal role in the proteolytic processing of viral polyproteins, making it an attractive target for antiviral drug development. In this study, an in silico high-throughput screening was performed to identify potential inhibitors of MERS-CoV Mpro. A compound library comprising small molecules was curated from diverse sources, including DrugBank, CHEMBL, and known protease inhibitors from the Protein Data Bank. Top candidates were selected using molecular docking combined with a similarity-based search strategy, which prioritized compounds known to interact with Mpro and predicted to exhibit high binding affinity at its active site. The top-ranking candidates were further evaluated through molecular dynamics (MD) simulations to assess the conformational stability of the ligand-protein complexes. Binding free energies were subsequently calculated using multiple computational approaches, including the deep learning-based K model, molecular mechanics/generalized born surface area (MM/GBSA), and free energy perturbation (FEP). Among the screened compounds, two molecules X2A and DB11779 (Danoprevir) consistently demonstrated superior binding affinities and stable interactions with MERS-CoV Mpro. These results agree well with experimental equilibrium dissociation constant (K) and half-maximal inhibitory concentrations (IC50). These findings highlight the capability of modern computational methods to generate accurate and robust binding data.
The X-Ray Crystal Structure of BorF, the Flavin Reductase Subunit of a Two-Component Flavin-Dependent Tryptophan Halogenase.
Ma Z, Rady EW, de Silva AJ … +1 more , Bellizzi JJ
Proteins · 2026 Aug · PMID 41851600 · Full text
BorF is a short-chain flavin reductase from a desert soil bacterium that uses NADH to reduce FAD to FADH, which is used by the tryptophan-6-halogenase BorH to chlorinate tryptophan in the biosynthetic pathway of borregom...
BorF is a short-chain flavin reductase from a desert soil bacterium that uses NADH to reduce FAD to FADH, which is used by the tryptophan-6-halogenase BorH to chlorinate tryptophan in the biosynthetic pathway of borregomycin A. The X-ray crystal structure of BorF bound to FAD was solved to 2.37 Å by molecular replacement. It consists of a homodimer of single-domain protomers, each with a Greek key split β-barrel topology containing a domain-swapped N-terminal α-helix, as previously seen in homologous proteins. Insertions and deletions in the region between α3 and β5 result in different conformations of the adenosine portion of FAD bound to BorF and structurally related reductases. Comparison of the FAD-bound structures of BorF and BorH suggests that FAD must completely dissociate from BorH in order to be reduced by BorF.
HAGGNN: Hydration-Aware Geometric Graph Neural Network for Protein Thermal Stability Prediction.
Jiang Y, Liu L, Ding Y
Proteins · 2026 Aug · PMID 41845179 · Publisher ↗
Water plays a fundamental thermodynamic role in determining protein structure, function, and stability. Rather than merely acting as a passive solvent, water actively participates in entropy-driven free energy changes, w...
Water plays a fundamental thermodynamic role in determining protein structure, function, and stability. Rather than merely acting as a passive solvent, water actively participates in entropy-driven free energy changes, which are crucial for stabilizing protein conformations. However, existing deep learning models for predicting protein thermal stability primarily focus on internal geometric and topological features, while neglecting the hierarchical hydration environment and its coupling with protein structure. To address this limitation, we propose a Hydration-Aware Geometric Graph Neural Network (HAGGNN), which explicitly integrates hydration environment into geometric deep learning. HAGGNN introduces a unified Hydration-Geometry Co-Modeling framework that combines invariant and equivariant GNNs. HAGGNN enables the joint capture of geometric dependencies and hydration effects, providing a more comprehensive understanding of protein-water interactions. Experiments on a large-scale protein dataset demonstrate that HAGGNN achieves superior predictive performance compared with models that do not incorporate hydration thermodynamics. Ablation studies further confirm the essential contributions of each module. HAGGNN provides a new computational paradigm that integrates geometric learning with hydration environment, offering mechanistic insights into protein thermal stability prediction.
A Machine Learning Approach to Predict Functional Performance From Measurable Protein Structural Characteristics: A Screening Tool for Protein Ingredient Quality.
Mandal R, Malvar S, Chandra R … +1 more , Ismail BP
Proteins · 2026 Aug · PMID 41813604 · Full text
The food industry is witnessing the emergence of specialized protein-based functional ingredients for the use as gelling, thickening, and/or emulsifying agents in various food applications. Different sources of protein i...
The food industry is witnessing the emergence of specialized protein-based functional ingredients for the use as gelling, thickening, and/or emulsifying agents in various food applications. Different sources of protein including species and cultivars, as well as variable processing conditions affect the protein's structural characteristics, which in turn govern their functional properties. The complex relationship between the structure and function of the protein can be modeled using machine learning (ML) algorithms. In this study, different ML algorithms were used to predict solubility, emulsifying activity index, emulsifying capacity, and gel strength of different plant proteins using structural predictors (surface hydrophobicity, zeta potential, undenatured protein content, water holding capacity, soluble protein polymer content, β-sheet content). Model performances were assessed by specific metrics ( , mean absolute error [ ], and root mean squared error [ ]) and non-violation of physical constraints. The solubility and emulsifying activity index were predicted using surface hydrophobicity, zeta potential, and undenatured protein content. Emulsifying capacity was predicted using surface hydrophobicity, solubility, undenatured protein content, while gel strength was predicted using solubility, undenatured protein content, water holding capacity, soluble protein polymer content, and β-sheet content. The based Support Vector Regression model accurately predicted solubility ( = 0.8906), emulsifying activity index ( = 0.7383), emulsifying capacity ( = 0.7978), and gel strength ( = 0.8822). Results highlighted the potential of ML algorithms for predicting of plant protein functionality using a few macromolecular structural characteristics. Such predictive models could serve as indispensable tools in the selection of protein ingredients for various food applications.
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