Proteins[JOURNAL]
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200 papers
Structural Investigation of the Anti-CRISPR Protein AcrIE7.
Kang J, Park C, Lee G … +5 more , Koo J, Oh H, Kim EH, Bae E, Suh JY
Proteins · 2025 Sep · PMID 40318042 · Publisher ↗
The CRISPR-Cas system is an adaptive immune system in prokaryotes that provides protection against bacteriophages. As a countermeasure, bacteriophages have evolved various anti-CRISPR proteins that neutralize CRISPR-Cas...
The CRISPR-Cas system is an adaptive immune system in prokaryotes that provides protection against bacteriophages. As a countermeasure, bacteriophages have evolved various anti-CRISPR proteins that neutralize CRISPR-Cas immunity. Here, we report the structural and functional investigation of AcrIE7, which inhibits the type I-E CRISPR-Cas system in Pseudomonas aeruginosa. We determined both crystal and solution structures of AcrIE7, which revealed a novel helical fold. In binding assays using various biochemical methods, AcrIE7 did not tightly interact with a single Cas component in the type I-E Cascade complex or the CRISPR adaptation machinery. In contrast, AlphaFold modeling with our experimentally determined AcrIE7 structure predicted that AcrIE7 interacts with Cas3 in the type I-E CRISPR-Cas system in P. aeruginosa. Our findings are consistent with a model where AcrIE7 inhibits Cas3 and also highlight the effectiveness and limitations of AlphaFold modeling.
Engaging the Community: CASP Special Interest Groups.
Elofsson A, Kretsch RC, Magnus M … +1 more , Montelione GT
Proteins · 2026 Jan · PMID 40304050 · Full text
The Critical Assessment of Structure Prediction (CASP) brings together a diverse group of scientists, from deep learning experts to NMR specialists, all aimed at developing accurate prediction algorithms that can effecti...
The Critical Assessment of Structure Prediction (CASP) brings together a diverse group of scientists, from deep learning experts to NMR specialists, all aimed at developing accurate prediction algorithms that can effectively characterize the structural aspects of biomolecules relevant to their functions. Engagement within the CASP community has traditionally been limited to the prediction season and the conference, with limited discourse in the 1.5 years between CASP seasons. CASP special interest groups (SIGs) were established in 2023 to encourage continuous dialogue within the community. The online seminar series has drawn global participation from across disciplines and career stages. This has facilitated cross-disciplinary discussions fostering collaborations. The archives of these seminars have become a vital learning tool for newcomers to the field, lowering the barrier to entry.
Structure and Dynamics of Cannabinoid Binding to the GABA Receptor.
Alvarez LD, Alves NRC
Proteins · 2025 Sep · PMID 40271542 · Publisher ↗
Research on medical cannabis is progressing, with several cannabinoids emerging as promising compounds for clinical use. The available evidence suggests that cannabinoids may modulate the glycine receptor (GlyR) and GABA...
Research on medical cannabis is progressing, with several cannabinoids emerging as promising compounds for clinical use. The available evidence suggests that cannabinoids may modulate the glycine receptor (GlyR) and GABA receptor, which are part of the pentameric ligand-gated ion channels (pLGICs) superfamily and facilitate chemical communication in the nervous system. In a previous study, we employed molecular dynamics (MD) simulations to elucidate the dynamics of the GlyR/Δ-tetrahydrocannabinol (THC) complex and successfully identified a representative binding mode. Given the structural similarity between GlyR and GABAR, we employed a similar strategy to investigate GABAR-cannabinoid interactions. We initially assessed the binding mode of THC to GABAR-α1β2γ2 at the equivalent binding site of the GlyR-that is, on its two α-subunits-as well as the impact of this binding on the channel's dimensions. Our results indicate, first, that the binding modes of THC to GABAR and GlyR exhibit comparable characteristics and, second, that THC may function as a potentiator of GABA activity due to a significant opening of the channel pore. Additionally, we aimed to reduce the overall computational cost associated with exploring binding modes. To this end, we developed and validated a simplified model comprising a single-monomer system for cannabinoid binding studies. This model proved to be accurate and cost-effective, accelerating the in silico screening process and allowing for the study of GABAR-cannabinoid binding through docking and MD simulations. Moreover, the analysis of different cannabinoids in this system suggests that cannabigerol (CBG) and cannabichromene (CBC) could act as ligands for GABAR, opening unexplored avenues for research.
β-ATPase of the Insect Panstrongylus megistus: Cloning, Bioinformatics Analysis, and Study of Its Interaction With Lipophorin.
Fruttero LL, Leyria J, Ligabue-Braun R … +6 more , Clop P, Paglione PA, Perillo MA, Carlini CR, Arrese E, Canavoso LE
Proteins · 2025 Sep · PMID 40265662 · Full text
Lipophorin is the main lipoprotein of the insect's hemolymph. Although its role in lipid metabolism has been extensively analyzed, the mechanisms of lipid delivery to target tissues mediated by lipophorin are not complet...
Lipophorin is the main lipoprotein of the insect's hemolymph. Although its role in lipid metabolism has been extensively analyzed, the mechanisms of lipid delivery to target tissues mediated by lipophorin are not completely understood. It has been reported that the β-chain of the ATP synthase complex (β-ATPase) acts as a nonendocytic receptor for lipophorin in the hematophagous insect Panstrongylus megistus, and this function is relevant for the transfer of lipids. The aim of this study was to gather new information regarding the β-ATPase, including its sequence and interaction with lipophorin. A β-ATPase cDNA encoding a 521-amino acid protein was cloned from P. megistus. β-ATPase is highly conserved, and molecular phylogenetic analyses grouped the deduced amino acid sequences according to their respective taxa. Structural modeling of β-ATPase revealed a conserved folding pattern and three-dimensional architecture that allows docking with a modeled lipophorin, suggesting potential interaction between the two proteins. Recombinant β-ATPase (rβ-ATPase) was expressed in Escherichia coli, and the rβ-ATPase was purified by affinity chromatography. rβ-ATPase was combined with lipophorin at various ratios, and the sedimentation properties of these mixtures were analyzed by analytical ultracentrifugation. The changes in sedimentation behavior of the protein mixture compared to that of the individual proteins are consistent with binding between rβ-ATPase and lipophorin. This finding, which confirms the interaction of β-ATPase and lipophorin, provides additional support for the role of β-ATPase in the uptake of lipids by tissues.
In Silico Discovery of Potential Inhibitors Targeting the MEIG1-PACRG Complex for Male Contraceptive Development.
Hasse T, Zhang Z, Huang YM
Proteins · 2025 Sep · PMID 40265567 · Full text
The interaction between meiosis-expressed gene 1 (MEIG1) and Parkin co-regulated gene (PACRG) is a critical determinant of spermiogenesis, the process by which round spermatids mature into functional spermatozoa. Disrupt...
The interaction between meiosis-expressed gene 1 (MEIG1) and Parkin co-regulated gene (PACRG) is a critical determinant of spermiogenesis, the process by which round spermatids mature into functional spermatozoa. Disruption of the MEIG1-PACRG complex can impair sperm development, highlighting its potential as a therapeutic target for addressing male infertility or for the development of non-hormonal contraceptive methods. This study used virtual screening, molecular docking, and molecular dynamics (MD) simulations to identify small molecule inhibitors targeting the MEIG1-PACRG interface. MD simulations provided representative protein conformations, which were used to virtually screen a library of 821 438 compounds, resulting in 48 high-ranking candidates for each protein. PACRG emerged as a favorable target due to its flexible binding pockets and better docking scores compared to MEIG1. Key binding residues with compounds included W50, Y68, N70, and E74 on MEIG1, and K93, W96, E101, and H137 on PACRG. MD simulations revealed that compound stability in MEIG1 complexes is primarily maintained by hydrogen bonding with E74 and π-π stacking interactions with W50 and Y68. In PACRG complexes, compound stabilization is facilitated by hydrogen bonding with E101 and π-π interactions involving W96 and H137. These findings highlight distinct molecular determinants of ligand binding for each protein. Our work provides mechanistic insights and identifies promising compounds for further experimental validation, establishing a foundation for developing MEIG1-PACRG interaction inhibitors as male contraceptives.
Assessing Structural Classification Using AlphaFold2 Models Through ECOD-Based Comparative Analysis.
Kawabata T, Kinoshita K
Proteins · 2025 Sep · PMID 40251890 · Full text
Identifying homologous proteins is a fundamental task in structural bioinformatics. While AlphaFold2 has revolutionized protein structure prediction, the extent to which structure comparison of its models can reliably de...
Identifying homologous proteins is a fundamental task in structural bioinformatics. While AlphaFold2 has revolutionized protein structure prediction, the extent to which structure comparison of its models can reliably detect homologs remains unclear. In this study, we evaluate the feasibility of homology detection using AlphaFold2-predicted structures through structural comparisons. We considered the classification of the ECOD database for experimental structures as the correct standard and obtained their corresponding predicted models from AlphaFoldDB. To ensure blind assessment, we divided the structures into test and train sets according to their release date. Predicted and experimental 3D structures in the test and train sets were compared using 3D structure comparisons (MATRAS, Dali, and Foldseek) and sequence comparisons (BLAST and HHsearch). The results were evaluated based on the homology annotations in the ECOD database. For top-1 accuracy, the performance of structural comparisons was comparable to that of HHsearch. However, when considering metrics that included all structural pairs, including more remote homology, structural comparisons outperformed HHsearch. No significant differences were observed between comparisons of experimental versus experimental, predicted versus experimental, and predicted versus predicted structures with pLDDT (prediction confidence) values greater than 60. We also demonstrate that predicted protein structures, determined by NMR, had lower pLDDT values and contained fewer coils than their experimental counterparts. These findings highlight the potential of AlphaFold2 models in structural classification and suggest that 3D structural searches should be conducted not only against the PDB but also against AlphaFoldDB to identify more potential homologs.
Prediction and Evaluation of Coronavirus and Human Protein-Protein Interactions Integrating Five Different Computational Methods.
Li B, Li X, Tang X … +1 more , Wang J
Proteins · 2025 Sep · PMID 40231383 · Publisher ↗
The high lethality and infectiousness of coronaviruses, particularly SARS-Cov-2, pose a significant threat to human society. Understanding coronaviruses, especially the interactions between these viruses and humans, is c...
The high lethality and infectiousness of coronaviruses, particularly SARS-Cov-2, pose a significant threat to human society. Understanding coronaviruses, especially the interactions between these viruses and humans, is crucial for mitigating the coronavirus pandemic. In this study, we conducted a comprehensive comparison and evaluation of five prevalent computational methods: interolog mapping, domain-domain interaction methodology, domain-motif interaction methodology, structure-based approaches, and machine learning techniques. These methods were assessed using unbiased datasets that include C1, C2h, C2v, and C3 test sets. Ultimately, we integrated these five methodologies into a unified model for predicting protein-protein interactions (PPIs) between coronaviruses and human proteins. Our final model demonstrates relatively better performance, particularly with the C2v and C3 test sets, which are frequently used datasets in practical applications. Based on this model, we further established a high-confidence PPI network between coronaviruses and humans, consisting of 18,012 interactions between 3843 human proteins and 129 coronavirus proteins. The reliability of our predictions was further validated through the current knowledge framework and network analysis. This study is anticipated to enhance mechanistic understanding of the coronavirus-human relationship a while facilitating the rediscovery of antiviral drug targets. The source codes and datasets are accessible at https://github.com/covhppilab/CoVHPPI.
Protein-RNA Docking Benchmark v3.0 Integrated With Binding Affinity.
Kant S, Nithin C, Mukherjee S … +2 more , Maity A, Bahadur RP
Proteins · 2025 Sep · PMID 40202108 · Publisher ↗
We introduce an updated non-redundant protein-RNA docking benchmark version 3.0 (PRDBv3.0) containing 197 test cases curated from 288 unique protein-RNA complexes available in the Protein Data Bank until July 2024. Among...
We introduce an updated non-redundant protein-RNA docking benchmark version 3.0 (PRDBv3.0) containing 197 test cases curated from 288 unique protein-RNA complexes available in the Protein Data Bank until July 2024. Among these, 27 are unbound-unbound (UU) type where both the binding partners are available in their unbound states, 160 are unbound-bound (UB) type where only the protein is available in unbound state and remaining 10 are bound-unbound (BU) type where only the RNA is available in unbound state. The benchmark is categorized into three classes based on the conformational flexibility of the protein interface: 117 rigid-body (R) complexes with minimal structural changes, 41 semi-flexible (S) complexes showing moderate conformational changes and 29 full-flexible (F) complexes with significant conformational changes. The current benchmark represents a 62% increase in the number of test cases compared to its previous version. Binding affinity (K) values for a subset of 105 protein-RNA complexes from PRDBv3.0 are catalogued along with additional experimental details to develop a comprehensive protein-RNA affinity benchmark. Moreover, a total of 255 unique RNA-binding domains, present in RNA-binding proteins, are also catalogued in this updated benchmark. PRDBv3.0 will facilitate the evaluation of both rigid-body and flexible docking methods as well as the methods that aim to predict binding affinity. The updated benchmark is freely available at http://www.csb.iitkgp.ac.in/applications/PRDBv3/PRDBv3.php.
Protein-Ligand Structure and Affinity Prediction in CASP16 Using a Geometric Deep Learning Ensemble and Flow Matching.
Morehead A, Liu J, Neupane P … +2 more , Giri N, Cheng J
Proteins · 2026 Jan · PMID 40195868 · Full text
Predicting the structure of ligands bound to proteins is a foundational problem in modern biotechnology and drug discovery, yet little is known about how to combine the predictions of protein-ligand structure (poses) pro...
Predicting the structure of ligands bound to proteins is a foundational problem in modern biotechnology and drug discovery, yet little is known about how to combine the predictions of protein-ligand structure (poses) produced by the latest deep learning methods to identify the best poses and how to accurately estimate the binding affinity between a protein target and a list of ligand candidates. Further, a blind benchmarking and assessment of protein-ligand structure and binding affinity prediction is necessary to ensure it generalizes well to new settings. Towards this end, we introduce MULTICOM_ligand, a deep learning-based protein-ligand structure and binding affinity prediction ensemble featuring structural consensus ranking for unsupervised pose ranking and a new deep generative flow matching model for joint structure and binding affinity prediction. Notably, MULTICOM_ligand ranked among the top-5 ligand prediction methods in both protein-ligand structure prediction and binding affinity prediction in the 16th Critical Assessment of Techniques for Structure Prediction (CASP16), demonstrating its efficacy and utility for real-world drug discovery efforts. The source code for MULTICOM_ligand is freely available on GitHub.
Homocysteine Thiolactone Modification of Ribonuclease A: Thermodynamics and Kinetics.
Sabnam K, Dasgupta S
Proteins · 2025 Sep · PMID 40183160 · Publisher ↗
Homocysteine thiolactone is a metabolite associated with various diseases at elevated levels in humans. Lysine residues in proteins are modified through N-homocysteinylation and homocysteinylated proteins are prone to fo...
Homocysteine thiolactone is a metabolite associated with various diseases at elevated levels in humans. Lysine residues in proteins are modified through N-homocysteinylation and homocysteinylated proteins are prone to form dimers and oligomers through disulfide cross-linkages. This study investigates the effects of N-homocysteinylation on Ribonuclease A (RNase A). The formation of dimers and higher oligomers in RNase A have been confirmed by SDS-PAGE and MALDI-ToF. Agarose-gel assays revealed an altered ribonucleolytic activity due to Lys modification. Fluorescence spectroscopy indicates local changes in the Tyr microenvironment. CD melting studies reveal that β-sheet formation is slightly enhanced with a reduction in the α-helical content in case of modified RNase A. However, the similar melting temperature of both native and modified RNase A indicates overall structural integrity with local changes in secondary structural components. ITC and UV-visible kinetics show reduced ribonucleolytic activity in homocysteinylated RNase A compared to the unmodified enzyme. These findings provide insights into the structural and functional consequences of RNase A homocysteinylation, contributing to our understanding of hyperhomocysteinemia-related pathologies.
Enhancing Enzyme Commission Number Prediction With Contrastive Learning and Agent Attention.
Zhao W, Han Q, Yang F … +1 more , Zhao Y
Proteins · 2025 Sep · PMID 40171777 · Publisher ↗
The accurate prediction of enzyme function is crucial for elucidating disease mechanisms and identifying drug targets. Nevertheless, existing enzyme commission (EC) number prediction methods are limited by database cover...
The accurate prediction of enzyme function is crucial for elucidating disease mechanisms and identifying drug targets. Nevertheless, existing enzyme commission (EC) number prediction methods are limited by database coverage and the depth of sequence information mining, hindering the efficiency and precision of enzyme function annotation. Therefore, this study introduces ProteEC-CLA (Protein EC number prediction model with Contrastive Learning and Agent Attention). ProteEC-CLA utilizes contrastive learning to construct positive and negative sample pairs, which not only enhances sequence feature extraction but also improves the utilization of unlabeled data. This process helps the model learn the differences in sequence features, thereby enhancing its ability to predict enzyme function. Integrating the pre-trained protein language model ESM2, the model generates informative sequence embeddings for deep functional correlation analysis, significantly enhancing prediction accuracy. With the incorporation of the Agent Attention mechanism, ProteEC-CLA's ability to comprehensively capture local details and global features is enhanced, ensuring high-accuracy predictions on complex sequences. The results demonstrate that ProteEC-CLA performs exceptionally well on two independent and representative datasets. In the standard dataset, it achieves 98.92% accuracy at the EC4 level. In the more challenging clustered split dataset, ProteEC-CLA achieves 93.34% accuracy and an F1-score of 94.72%. With only enzyme sequences as input, ProteEC-CLA can accurately predict EC numbers up to the fourth level, significantly enhancing annotation efficiency and accuracy, which makes it a highly efficient and precise functional annotation tool for enzymology research and applications.
Protein Complex Structure Prediction With AlphaFold-Enhanced HDOCK in CAPRI Rounds 47-55.
Li H, Lin P, Li Y … +1 more , Huang SY
Proteins · 2025 Apr · PMID 40167205 · Publisher ↗
Protein-protein interactions play a critical role in numerous biological processes, and understanding these interactions is essential for deciphering cellular mechanisms and designing therapeutic interventions. Predictin...
Protein-protein interactions play a critical role in numerous biological processes, and understanding these interactions is essential for deciphering cellular mechanisms and designing therapeutic interventions. Predicting protein-protein complex structures by computational methods is an important approach to studying protein-protein interactions. The CAPRI (Critical Assessment of PRediction of Interactions) experiment has served as a benchmark for evaluating computational methods for predicting protein complex structures. We participated in CAPRI Rounds 47-55 and continuously refined our complex structure prediction strategies throughout this period. Initially, our approach was based on a hybrid docking strategy that combined template-based and ab initio docking methods. However, starting from Round 53, we integrated AlphaFold into our prediction pipeline. Inspired by the experiences of other participants in Round 54, we further refined our use of AlphaFold by enhancing the sampling strategy, which significantly improved our prediction accuracy in Round 55.
PCANN Program for Structure-Based Prediction of Protein-Protein Binding Affinity: Comparison With Other Neural-Network Predictors.
Lebedenko OO, Polovinkin MS, Kazovskaia AA … +1 more , Skrynnikov NR
Proteins · 2025 Sep · PMID 40116085 · Full text
In this communication, we introduce a new structure-based affinity predictor for protein-protein complexes. This predictor, dubbed PCANN (Protein Complex Affinity by Neural Network), uses the ESM-2 language model to enco...
In this communication, we introduce a new structure-based affinity predictor for protein-protein complexes. This predictor, dubbed PCANN (Protein Complex Affinity by Neural Network), uses the ESM-2 language model to encode the information about protein binding interfaces and graph attention network (GAT) to parlay this information into predictions. In the tests employing two previously unused literature-extracted datasets, PCANN performed better than the best of the publicly available predictors, BindPPI, with mean absolute error (MAE) of 1.3 versus 1.4 kcal/mol. Further progress in the development of predictors using deep learning models is faced with two problems: (i) the amount of experimental data available to train and test new predictors is limited and (ii) the available data are often not very accurate and lack internal consistency with respect to measurement conditions. These issues can be potentially addressed through an AI-leveraged literature search followed by careful human curation and by introducing additional parameters to account for variations in experimental conditions.
An Overview of Property, Design, and Functionality of Linkers for Fusion Protein Construction.
Chatrdooz H, Sargolzaei J
Proteins · 2025 Sep · PMID 40099816 · Publisher ↗
Linkers are naturally occurring short amino acid sequences that are used to separate domains within a protein. The advent of recombinant DNA technology has made it possible to combine two interacting partners by introduc...
Linkers are naturally occurring short amino acid sequences that are used to separate domains within a protein. The advent of recombinant DNA technology has made it possible to combine two interacting partners by introducing artificial linkers that often, allow for the production of stable and functional proteins. Glycine-rich linkers are useful for transient interactions, especially where the interaction is weak, by covalently linking proteins and forming a stable protein-protein complex. These linkers have also been used to generate covalently stable dimers and to connect two independent domains that create a ligand binding site or recognition sequence. Various structures of covalently linked protein complexes have been described using nuclear magnetic resonance methods, cryo-electron microscopy techniques, and X-ray crystallography; in addition, several structures where linkers have been used to generate stable protein-protein complexes, improve protein solubility, and obtain protein dimers are investigated, and also the design and engineering of the linker in fusion proteins is discussed. Therefore, one of the main factors for linker design and optimization is their flexibility, which can directly contribute to the physical distance between the domains of a fusion protein and describe the tendency of a linker to maintain a stable conformation during expression. We summarize the research on design and bioinformatics can be used to predict the spatial structure of the fusion protein. To perform simulations of spatial structures and drug molecule design, future research will concentrate on various correlation models.
Integrative Protein Assembly With LZerD and Deep Learning in CAPRI 47-55.
Christoffer C, Kagaya Y, Verburgt J … +10 more , Terashi G, Shin WH, Jain A, Sarkar D, Aderinwale T, Maddhuri Venkata Subramaniya SR, Wang X, Zhang Z, Zhang Y, Kihara D
Proteins · 2025 Mar · PMID 40095385 · Full text
We report the performance of the protein complex prediction approaches of our group and their results in CAPRI Rounds 47-55, excluding the joint CASP Rounds 50 and 54, as well as the special COVID-19 Round 51. Our approa...
We report the performance of the protein complex prediction approaches of our group and their results in CAPRI Rounds 47-55, excluding the joint CASP Rounds 50 and 54, as well as the special COVID-19 Round 51. Our approaches integrated classical pipelines developed in our group as well as more recently developed deep learning pipelines. In the cases of human group prediction, we surveyed the literature to find information to integrate into the modeling, such as assayed interface residues. In addition to any literature information, generated complex models were selected by a rank aggregation of statistical scoring functions, by generative model confidence, or by expert inspection. In these CAPRI rounds, our human group successfully modeled eight interfaces and achieved the top quality level among the submissions for all of them, including two where no other group did. We note that components of our modeling pipelines have become increasingly unified within deep learning approaches. Finally, we discuss several case studies that illustrate successful and unsuccessful modeling using our approaches.
The Actin-Binding Prolyl-Isomerase Par17 Sustains Its Substrate Selectivity by Interdomain Allostery.
Sternberg A, Borger JL, Thies M … +7 more , Matena A, Blueggel M, Kamba BE, Beuck C, Kaschani F, Kaiser M, Bayer P
Proteins · 2025 Sep · PMID 40071814 · Full text
The human peptidyl-prolyl-cis/trans isomerases (PPIases), Parvulin 14 and Parvulin 17, accelerate the cis/trans isomerization of Xaa-Pro moieties within protein sequences. By modulating the respective binding interfaces...
The human peptidyl-prolyl-cis/trans isomerases (PPIases), Parvulin 14 and Parvulin 17, accelerate the cis/trans isomerization of Xaa-Pro moieties within protein sequences. By modulating the respective binding interfaces of their target proteins, they play a crucial role in determining the fate of their substrates within the cell. Although both enzymes share the same amino acid sequence, they have different cellular functions. This difference is due to a 25 residue N-terminal extension present in Par17 but absent in Par14. Using activity assays, NMR spectroscopy, and mass spectrometry, we demonstrate that the N-terminal extension of Par17 determines substrate selectivity by an intramolecular allosteric mechanism and exhibits a target-binding motif that interacts with actin.
Insights Into the Conformational Dynamics of the Cytoplasmic Domain of Metal-Sensing Sensor Histidine Kinase ZraS.
Mahapatra N, Mahanta P, Pandey S … +1 more , Acharya R
Proteins · 2025 Sep · PMID 40062583 · Full text
ZraS is a metal sensor integral to ZraPSR, a two-component signaling system found in enterobacters. It belongs to a family of bifunctional sensor histidine kinases (SHKs) and is speculated to sense zinc-induced stress on...
ZraS is a metal sensor integral to ZraPSR, a two-component signaling system found in enterobacters. It belongs to a family of bifunctional sensor histidine kinases (SHKs) and is speculated to sense zinc-induced stress on the bacterial envelope. Information on the structure-function relationship of sensor kinases is elusive due to the lack of full-length structures, intrinsically dynamic behavior, and difficulty trapping them in active state conformations. While the kinase domains (KDs) of a few SHKs are well characterized, they exhibit significant functional diversity attributed to their modular multi-domain arrangement in the cytoplasmic region, combined with other signal transducing elements such as simple helices, HAMP, and PAS domains. We report the crystal structure of the entire cytoplasmic region of Escherichia coli ZraS (EcZraS-CD) resolved at a resolution of 2.49 Å, comprising a unique helical linker and the KD. In the asymmetric unit, four molecules of ZraS assemble as homodimers trapped as two ligand-bound occluded conformers. Our analysis using these conformers shows that modulation of the dimer bundle through segmental helical bending, sliding, and rotation leads to the reorganization of the dimerization interface during kinase activation. Further, our analysis reveals the significance of aromatic amino acid interactions and loop residues at the dimer base in regulating the directionality of rotation during autophosphorylation. We also performed an in vitro coupled assay to determine ATPase activity. Overall, our findings provide structure-based mechanistic insights into the process of autophosphorylation in trans-acting SHKs.
Understanding the Role of RING-Between-RING E3 Ligase of the Human Malaria Parasite.
Kumari V, Vidyarthi S, Tripathi A … +9 more , Chaurasia N, Rai N, Shukla R, Noorie SN, Bhati G, Anjum S, Anas M, Ahmed S, Kumar N
Proteins · 2025 Sep · PMID 40035193 · Publisher ↗
E3 ligases constitute an important component of proteostasis machinery, which plays a critical role in the survival of malaria parasites through post-translational modifications of their protein substrates. In contrast t...
E3 ligases constitute an important component of proteostasis machinery, which plays a critical role in the survival of malaria parasites through post-translational modifications of their protein substrates. In contrast to humans, parasite E3 ligases have not been extensively studied. Here, we characterize a unique Plasmodium E3 ligase that has both RING and HECT-like features with zinc-coordinating domains. Plasmodium encodes a single RING-between-RING (RBR) E3 ligase that has evolutionarily diverged from human and other intracellular parasites. This RBR-E3 ligase is expressed throughout the erythrocytic phase of the P. falciparum lifecycle. Immunoprecipitation experiments showed that Pf RBR-E3 ligase catalyzes K6, K11, K48, and K63 mediated polyubiquitination, hinting towards its probable biological roles (DNA repair, proteasomal degradation, mitochondrial quality control). We observed that Pf RBR-E3 ligase interacts with UBCH5 and UBC13 family of E2-conjugating enzymes. Through mutational analysis in Pf RBR-E3 ligase, we identified residues in RING1 and RING2 domains that are critical for ubiquitination activity and its protein stability. Pf RBR-E3 ligase exhibits differences in immunofluorescence profile upon exposure of the parasite to different genotoxic (MMS) and proteotoxic (MG132, FCCP and artemisinin derivative) stress. Our study opens up avenues for exploring the client substrates of Pf RBR-E3 ligase and using this knowledge to design substrate-specific protein degradation-based alternative intervention strategies for malaria.
Investigating Local Sequence-Structural Attributes of Amyloidogenic Light Chain Variable Domains.
Rawat P, Prabakaran R, Sharma D … +4 more , Mandala V, Greiff V, Kumar S, Gromiha MM
Proteins · 2025 Sep · PMID 40034034 · Full text
Light chain amyloidosis is a medical condition characterized by the aggregation of misfolded antibody light chains into insoluble amyloid fibrils in the target organs, causing organ dysfunction, organ failure, and death....
Light chain amyloidosis is a medical condition characterized by the aggregation of misfolded antibody light chains into insoluble amyloid fibrils in the target organs, causing organ dysfunction, organ failure, and death. Despite extensive research to understand the factors contributing to amyloidogenesis, accurately predicting whether a given protein will form amyloids under specific conditions remains a formidable challenge. In this study, we have conducted a comprehensive analysis to understand the amyloidogenic tendencies within a dataset containing 1828 (348 amyloidogenic and 1480 non-amyloidogenic) antibody light chain variable region (V) sequences obtained from the AL-Base database. Physicochemical and structural features often associated with protein aggregation, such as net charge, isoelectric point (pI), and solvent-exposed hydrophobic regions did not reveal a consistent association with the aggregation capability of the antibody light chains. However, the solvent-exposed aggregation-prone regions (APRs) occur with higher frequencies among the amyloidogenic light chains when compared with the non-amyloidogenic ones, with the difference ranging from 2% to 15% at various relative solvent-accessible surface area (rASA) cutoffs. We have, for the first time, identified structural gatekeeping residues around the APRs and assessed their impact on the amyloidogenicity of the antibody light chains. The non-amyloidogenic light chains contain these structural gatekeeper residues vicinal to their APRs more often than the amyloidogenic ones. We observed that the rASA cutoff of 35% is optimal for identifying the surface-exposed APRs, and a 4 Å distance cutoff from the APR motif(s) is optimal for identifying the structural gatekeeper residues. Moreover, lambda light chains were found to contain solvent-exposed APRs more often and surrounded by fewer gatekeepers, rendering them more susceptible to aggregation. The insights gained from this report have significant implications for understanding the molecular origins of light-chain amyloidosis in humans and the design of aggregation-resistant therapeutic antibodies.
Towards a Greener AlphaFold2 Protocol for Antibody-Antigen Modeling: Insights From CAPRI Round 55.
Savaş B, Yılmazbilek İ, Özsan A … +1 more , Karaca E
Proteins · 2025 Mar · PMID 40028659 · Publisher ↗
In the 55th round of CAPRI, we used enhanced AlphaFold2 (AF2) sampling and data-driven docking. Our AF2 protocol relies on Wallner's massive sampling approach, which combines different AF2 versions and sampling parameter...
In the 55th round of CAPRI, we used enhanced AlphaFold2 (AF2) sampling and data-driven docking. Our AF2 protocol relies on Wallner's massive sampling approach, which combines different AF2 versions and sampling parameters to produce thousands of models per target. For T231 (an antibody-peptide complex) and T232 (PP2A:TIPRL complex), we employed a 50-fold reduced MinnieFold sampling and a custom ranking approach, leading to a top-ranking medium prediction in both cases. For T233 and T234 (two antibody bound MHC I complexes), we followed data-driven docking, which did not lead to an acceptable model. Our post-CAPRI55 analysis showed that if we had used our MinnieFold approach on T233 and T234, we could have submitted a medium-quality model for T233 as well. In the scoring challenge, we utilized the scoring function of FoldX, which was effective in selecting acceptable models for T231 and medium-quality models for T232. Our success, especially in predicting and ranking a medium-quality model for T231 and potentially for T233, underscores the feasibility of green and accurate enhanced AF2 sampling in antibody complex prediction.
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