Proteins[JOURNAL]
Sun
200 papers
Human Citrate Synthase Post-Translational Modification Mimics and Molecular Dynamic Simulations Demonstrate Attenuation of Acetyl-CoA/CoA Binding.
Shackelford N, Zavodny Z, Fancher N … +1 more , Moxley MA
Proteins · 2026 Apr · PMID 41185119 · Full text
Human citrate synthase (hCS) is a mitochondrial enzyme that catalyzes the aldol condensation of acetyl coenzyme A (AcCoA) to oxaloacetate to form citrate in the TCA cycle. CS activity is important for aerobic exercise pe...
Human citrate synthase (hCS) is a mitochondrial enzyme that catalyzes the aldol condensation of acetyl coenzyme A (AcCoA) to oxaloacetate to form citrate in the TCA cycle. CS activity is important for aerobic exercise performance and basic metabolic function as a housekeeping enzyme. It has been shown through several mass spectrometry-based physiological studies that CS is post-translationally modified (PTM) on numerous residues via acetylation, phosphorylation, and methylation reactions. Few follow-up studies have been reported on the impact of PTMs on CS activity. Thus, we kinetically characterized several hCS PTM mimics near and distant from the active site by site-directed mutagenesis coupled with steady-state kinetics. Most modifications had a negative impact on AcCoA k/K but to a much lesser extent on oxaloacetate k/K. Most notably, the K393 acetylation mimic, K393Q displays an increase in K for AcCoA relative to WT by about 30-fold, with no significant change in k. To complement our kinetic analyses, we performed molecular dynamics simulations on 26 PTM and mutant CS-substrate complexes, providing a combined kinetic and MD simulation approach. Among the MD results, CS K393AcK showed the greatest reduction in AcCoA/CoA binding.
Progress and Bottlenecks for Deep Learning in Computational Structure Biology: CASP Round XVI.
Kryshtafovych A, Schwede T, Topf M … +2 more , Fidelis K, Moult J
Proteins · 2026 Jan · PMID 41178755 · Full text
CASP16 is the most recent in a series of community experiments to rigorously assess the state of the art in areas of computational structural biology. The field has advanced enormously in recent years: in early CASPs, th...
CASP16 is the most recent in a series of community experiments to rigorously assess the state of the art in areas of computational structural biology. The field has advanced enormously in recent years: in early CASPs, the assessments centered around whether the methods were at all useful. Now they mostly focus on how near we are to not needing experiments. In most areas, deep learning methods dominate, particularly AlphaFold variants and associated technology. In this round, there is no significant change in overall agreement between calculated monomer protein structures and their experimental counterparts, not because of method deficiencies but because, for most proteins, agreement is likely as high as can be obtained given experimental uncertainty. For protein complexes, huge gains in accuracy were made in the previous CASP, but there still appears to be room for further improvement. In contrast to these encouraging results, for RNA structures, the deep learning methods are notably unsuccessful at present and are not superior to traditional approaches. Both approaches still produce very poor results in the absence of structural homology. For macromolecular ensembles, the small CASP target set limits conclusions, but generally, in the absence of structural templates, results tend to be poor and detailed structures of alternative conformations are usually of relatively low accuracy. For organic ligand-protein structures and affinities (important for aspects of drug design), deep learning methods are substantially more successful than traditional ones on the relatively easy CASP target set, though the results often fall short of experimental accuracy. In the less glamorous but essential area of methods for estimating the accuracy, previous results found reliable accuracy estimates at the amino acid level. The present CASP results show that the best methods are also largely effective in selecting models of protein complexes with high interface accuracy. Will upcoming method improvements overcome the remaining barriers to reaching experimental accuracy in all categories? We will have to wait until the next CASP to find out, but there are two promising trends. One is the combination of traditional physics-inspired methods and deep learning, and the other is the expected increase in training data, especially for ligand-protein complexes.
Assessment of Protein Complex Predictions in CASP16: Are We Making Progress?
Zhang J, Yuan R, Kryshtafovych A … +7 more , Pei J, Kretsch RC, Schaeffer RD, Zhou J, Das R, Grishin NV, Cong Q
Proteins · 2026 Jan · PMID 41170922 · Full text
The assessment of oligomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) suggests that complex structure prediction remains an unsolved challenge. Even the leading groups can only predict s...
The assessment of oligomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) suggests that complex structure prediction remains an unsolved challenge. Even the leading groups can only predict slightly more than half of the targets to high accuracy. Most CASP16 groups relied on AlphaFold-Multimer (AFM) or AlphaFold3 (AF3) as their core modeling engines. By optimizing input MSAs, refining modeling constructs (using partial rather than full sequences), and employing massive model sampling and selection, top-performing groups were able to significantly outperform the default AFM/AF3 predictions. CASP16 also introduced two additional challenges: Phase 0, which required predictions without stoichiometry information, and Phase 2, which provided participants with thousands of models generated by MassiveFold (MF) to enable large-scale sampling for resource-limited groups. Across all phases, the MULTICOM series and Kiharalab emerged as top performers based on the quality of their best models. However, these groups did not have a strong advantage in model ranking, and thus their lead over other teams, such as Yang-Multimer and kozakovvajda, was less pronounced when evaluating only the first submitted models. Compared to CASP15, CASP16 showed moderate overall improvement, likely driven by the release of AF3 and the extensive model sampling employed by top groups. Several notable trends highlight frontiers for future development. First, the kozakovvajda group significantly outperformed others on antibody-antigen targets, achieving over a 60% success rate without relying on AFM or AF3 as their primary modeling framework, suggesting that alternative approaches may offer promising solutions for these difficult targets. Second, model ranking and selection continue to be major bottlenecks. The PEZYFoldings group demonstrated a notable advantage in selecting their best models as first models, suggesting that their pipeline for model ranking may offer important insights for the field. Finally, the Phase 0 experiment indicated moderate success in stoichiometry prediction; however, stoichiometry prediction remains challenging for high-order assemblies and targets that differ from available homologous templates. Overall, CASP16 demonstrated steady progress in multimer prediction while emphasizing the need for more effective model ranking strategies, improved stoichiometry prediction, and new modeling methods that extend beyond the current AF-based paradigm.
In Silico Structural Analysis of Human β-Glucuronidase for Antibody-Drug Conjugates Optimization.
Canini G, Saporiti S, Coppa C … +3 more , Rossi M, Centola F, Arcovito A
Proteins · 2026 Mar · PMID 41169255 · Full text
Human β-glucuronidase (HGUSB), a key lysosomal glycosyl hydrolase for the degradation pathway of glycosaminoglycans (GAGs), plays a crucial role in cell proliferation and inflammation, making it a promising target for no...
Human β-glucuronidase (HGUSB), a key lysosomal glycosyl hydrolase for the degradation pathway of glycosaminoglycans (GAGs), plays a crucial role in cell proliferation and inflammation, making it a promising target for novel therapeutic strategies including antibody-drug conjugates (ADCs) with β-glucuronic linkers. In this study, molecular docking and molecular dynamics (MD) simulations were performed to investigate the conformational stability of HGUSB in complex with different ligands, including substrates, inhibitors, and β-glucuronic linkers. Our rationale approach includes the evaluation of commercial substrates and a known inhibitor with different binding stoichiometries to identify the most favorable configuration and the most stable conformation of the enzyme. Based on the binding mechanism of HGUSB to these well-known ligands, the interaction with commercial linkers was evaluated, providing a structural determination of the recognition mechanism between the enzyme and ADCs. MD simulations on HGUSB::Linker complexes revealed that the maleimide-containing hydrophilic β-glucuronide, exhibited the most stable binding making it the best fitting linker among those analyzed in this study. Overall, this study identifies the optimal binding configuration of the HGUSB enzyme for investigating small molecule interactions and, despite the endogenous homotetrameric assembly, justifies the use of a simplified monomeric model for the study of larger macromolecular complexes, like linker analysis, ensuring an efficient and accurate computational approach. These findings lay the groundwork for a rationale optimization of β-glucuronic linker-based ADCs, offering new perspectives for targeted cancer therapies.
Assessment of Nucleic Acid Structure Prediction in CASP16.
Kretsch RC, Hummer AM, He S … +6 more , Yuan R, Zhang J, Karagianes T, Cong Q, Kryshtafovych A, Das R
Proteins · 2026 Jan · PMID 41165252 · Full text
Consistently accurate 3D nucleic acid structure prediction would facilitate studies of the diverse RNA and DNA molecules underlying life. In CASP16, blind predictions for 42 targets canvassing a full array of nucleic aci...
Consistently accurate 3D nucleic acid structure prediction would facilitate studies of the diverse RNA and DNA molecules underlying life. In CASP16, blind predictions for 42 targets canvassing a full array of nucleic acid functions, from dopamine binding by DNA to formation of elaborate RNA nanocages, were submitted by 65 groups from 46 different labs worldwide. In contrast to concurrent protein structure predictions, performance on nucleic acids was generally poor, with no predictions of previously unseen natural RNA structures achieving TM-scores above 0.8. Even though automated server performance has improved, all top-performing groups were human expert predictors: Vfold, GuangzhouRNA-human, and KiharaLab. Good performance on one template-free modeling target (OLE RNA) and accurate global secondary structure prediction suggested that structural information can be extracted from multiple sequence alignments. However, 3D accuracy generally appeared to depend on the availability of closely related 3D structure templates, and predictions still did not achieve consistent recovery of pseudoknots, singlet Watson-Crick-Franklin pairs, non-canonical pairs, or tertiary motifs like A-minor interactions. For the first time, blind predictions of nucleic acid interactions with small molecules, proteins, and other nucleic acids could be assessed in CASP16. As with nucleic acid monomers, prediction accuracy for nucleic acid complexes was generally poor unless 3D templates were available. Accounting for template availability, there has not been a notable increase in nucleic acid modeling accuracy between previous blind challenges and CASP16.
Modeling Alternative Conformational States in CASP16.
Dube N, Ramelot TA, Benavides TL … +4 more , Huang YJ, Moult J, Kryshtafovych A, Montelione GT
Proteins · 2026 Jan · PMID 41147497 · Full text
The CASP16 Ensemble Prediction experiment assessed advances in methods for modeling proteins, nucleic acids, and their complexes in multiple conformational states. Targets included systems with experimental structures de...
The CASP16 Ensemble Prediction experiment assessed advances in methods for modeling proteins, nucleic acids, and their complexes in multiple conformational states. Targets included systems with experimental structures determined in two or three states, evaluated by direct comparison to experimental coordinates, as well as domain-linker-domain (D-L-D) targets assessed against statistical models generated from NMR and SAXS data. This paper focuses on the former class of multi-state targets. Ten ensembles were released as community challenges, including ligand-induced conformational changes, protein-DNA complexes, a trimeric protein, a stem-loop RNA, and multiple oligomeric states of a single RNA. For five targets, some groups produced reasonably accurate models of both reference states (best TM-score > 0.75). However, with the exception of one protein-ligand complex (T1214), where an apo structure was available as a template, predictors generally failed to capture key structural details distinguishing the states. Overall, accuracy was significantly lower than for single-state targets in other CASP experiments. The most successful approaches generated multiple AlphaFold2 models using enhanced multiple sequence alignments and sampling protocols, followed by model quality-based selection. Although the AlphaFold3 server performed well on several targets, individual groups outperformed it in specific cases. By contrast, predictions for one protein-DNA complex, three RNA targets, and multiple oligomeric RNA states consistently fell short (TM-score < 0.75). These results highlight both progress and persistent challenges in multi-state prediction. Despite recent advances, accurate modeling of conformational ensembles, particularly RNA and large multimeric assemblies, remains an important frontier for structural biology.
Membrane Curvature During Membrane Rupture and Formation of Pentagonal Pyramidal Superassemblies by a Pore-Forming Toxin, Vibrio cholerae Cytolysin, Using Single Particle Cryo-EM.
Mishra S, Chattopadhyay K, Dutta S
Proteins · 2026 Mar · PMID 41126589 · Publisher ↗
In this cryo-electron microscopy study, we provide mechanistic insights into how an archetypical β-barrel pore-forming toxin (β-PFT), Vibrio cholerae Cytolysin (VCC), ruptures the membrane lipid bilayer by inducing membr...
In this cryo-electron microscopy study, we provide mechanistic insights into how an archetypical β-barrel pore-forming toxin (β-PFT), Vibrio cholerae Cytolysin (VCC), ruptures the membrane lipid bilayer by inducing membrane curvature. We demonstrate how VCC oligomers cluster together and drastically increase local membrane curvature, thereby causing membrane blebbing. In addition, we also show how these PFTs, after rupturing the host membrane, tend to form symmetric supermolecular assemblies to stabilize their hydrophobic transmembrane rim domains. We further provide another example of membrane rupture with gamma hemolysin, a Staphylococcal bicomponent β-PFT. These insights will usher in new studies on membrane curvature due to protein crowding and broaden our mechanistic understanding of how this largest class of bacterial protein toxins induces host cellular death.
Structural and Comparative Stability of a Truncated N-Terminal Domain of DNA Gyrase A From Salmonella Typhi.
Salman M, Sachdeva E, Negi S … +3 more , Das U, Ethayathulla AS, Kaur P
Proteins · 2026 Mar · PMID 41116293 · Publisher ↗
DNA Gyrase, a Type II topoisomerase, introduces negative supercoiling in dsDNA through the cleavage and religation activity at the expense of ATP. DNA Gyrase forms a hetero-tetrameric complex with two Gyrase A and Gyrase...
DNA Gyrase, a Type II topoisomerase, introduces negative supercoiling in dsDNA through the cleavage and religation activity at the expense of ATP. DNA Gyrase forms a hetero-tetrameric complex with two Gyrase A and Gyrase B subunits. These two subunits interact dynamically to physically transfer one DNA duplex through another by coupling ATP binding and hydrolysis with DNA binding, cleavage, and strand transport. The N-terminal domain of Gyrase A (GyrA-NTD) mediates the cleavage of the DNA strand and forms the target site for quinolones class of antibiotics. While structures of GyrA-NTD from several prokaryotes have been determined, the N-terminal segment (residues 1-32) remains unresolved in apo forms. Here, we present the crystal structure of a truncated GyrA-NTD (ΔGyrA-NTD; residues 33-530) from Salmonella Typhi at 2.43 Å resolution, alongside comparative biophysical characterization with the wild type. Thermal and chemical denaturation assays revealed that the wild-type GyrA-NTD is more prone to unfolding than the truncated variant, indicating that deletion of the unresolved N-terminal segment enhances domain stability. These findings uncover a structural element influencing GyrA-NTD stability.
Modeling Protein-Protein and Protein-Ligand Interactions by the ClusPro Team in CASP16.
Ashizawa R, Kotelnikov S, Khan O … +24 more , Li SX, Glukhov E, Cao X, Lazou M, Bekar-Cesaretli A, Hailegeorgis D, Averkava V, Zhu Y, Jones G, Yu H, Kalitin D, Stepanenko D, Koirala K, Patsahan T, Beglov D, Lukin M, Joseph-McCarthy D, Simmerling C, Tropsha A, Coutsias E, Dill KA, Padhorny D, Vajda S, Kozakov D
Proteins · 2026 Jan · PMID 41115690 · Full text
In the CASP16 experiment, our team employed hybrid computational strategies to predict both protein-protein and protein-ligand complex structures. For protein-protein docking, we combined physics-based sampling-using Clu...
In the CASP16 experiment, our team employed hybrid computational strategies to predict both protein-protein and protein-ligand complex structures. For protein-protein docking, we combined physics-based sampling-using ClusPro FFT docking and molecular dynamics-with AlphaFold (AF)-based sampling, followed by AF-based refinement. Our method produced numerous high-accuracy complex models, including cases where AF alone failed, underscoring the critical role of physics-based sampling alongside deep learning-based refinement. For protein-ligand docking, we integrated the ClusPro LigTBM template-based approach with a machine learning-based confidence model for rescoring. The method preserves conserved interaction fragments derived from homologous complexes, followed by local resampling using physics-based sampling and a diffusion model. Our template-based strategy achieved a mean lDDT-PLI of 0.69 across 233 targets, which was highly competitive. These results demonstrate that combining physics-based modeling with AI-driven refinement can significantly enhance the accuracy of both protein-protein and protein-ligand structure predictions.
Understanding the Roles of Secondary Shell Hotspots in Protein-Protein Complexes.
Parvathy J, Yazhini A, Srinivasan N … +1 more , Sowdhamini R
Proteins · 2026 Mar · PMID 41108522 · Publisher ↗
Hotspots are interfacial residues in protein-protein complexes that contribute significantly to complex stability. Methods for identifying interfacial residues in protein-protein complexes are based on two approaches, na...
Hotspots are interfacial residues in protein-protein complexes that contribute significantly to complex stability. Methods for identifying interfacial residues in protein-protein complexes are based on two approaches, namely, (a) distance-based methods, which identify residues that form direct interactions with the partner protein and (b) Accessibility Surface Area (ASA)-based methods, which identify those residues that are solvent-exposed in the isolated form of the protein and become buried upon complex formation. In this study, we introduce the concept of secondary shell hotspots, which are hotspots uniquely identified by the distance-based approach, staying buried in both the bound and isolated forms of the protein and yet forming direct interactions with the partner protein. From the analysis of the dataset curated from Docking Benchmark 5.5, comprising 94 protein-protein complexes, we find that secondary shell hotspots are more evolutionarily conserved and have distinct Chou-Fasman propensities and interaction patterns compared to other hotspots. Finally, we present detailed case studies to show that the interaction network formed by the secondary shell hotspots is crucial for complex stability and activity. Further, they act as potentially allosteric propagators and bridge interfacial and non-interfacial sites in the protein. Their in silico mutations to any other amino acid types cause significant destabilization. Overall, this study sheds light on the uniqueness and importance of secondary shell hotspots in protein-protein complexes.
Unraveling the Structure-Function Relationship and Mechanism of an Important Spiro-Forming Nitrilase Using Metadynamics and Quantum Molecular Dynamics.
Kumar A, Muthuraj L, Sigamani G … +3 more , Lalitha R, Tingirikari JMR, Kumar P
Proteins · 2026 Mar · PMID 41108205 · Publisher ↗
The nitrilase from Bacillus safensis (BsNIT) is a spiro-forming enzyme with significant potential in the bioremediation of nitrile pollutants such as benzonitrile and glutaronitrile. Despite its environmental and industr...
The nitrilase from Bacillus safensis (BsNIT) is a spiro-forming enzyme with significant potential in the bioremediation of nitrile pollutants such as benzonitrile and glutaronitrile. Despite its environmental and industrial relevance, its structure-function relationships and mechanistic details remain poorly understood. This study employs metadynamics and quantum molecular dynamics (QMD) simulations to delineate BsNIT's structure-function relationships with relevant substrates. Metadynamics simulations identified distinct substrate association and dissociation pathways, with the T1 tunnel emerging as the primary diffusion route for substrates and products. Tyrosine-gated residues within the tunnel, alongside conserved active site residues, were crucial for orienting nitrile substrates and enabling efficient binding. Comparing BsNIT to Spirosoma linguale DSM 74 (SINIT) provides a clearer understanding of how variations in active site architecture and mechanisms, particularly the events revealed in our QM studies, favor certain nitrilases for amide formation while others preferentially catalyze hydrolysis. QMD simulations further revealed mechanistic insights, including Cys164's nucleophilic attack and Glu48's proton hopping via a water-mediated relay, which plays a critical role for nitrile hydrolysis. The critical transition state (TS1), corresponding to covalent substrate binding, exhibited an energy barrier of 14.8 kcal mol, defining it as the rate-limiting step. Based on these studies' key mutations in the tunnel gating residues (Y276, Y278, and Y279) and mutations of salt-bridge residues (R67-D275, K75-E271, and K68-E229) are proposed to enhance BsNIT's substrate specificity for more bulky nitrile pollutants with increased efficiency. This computational analysis highlighted BsNIT's structural adaptations for catalytic efficiency, particularly in its interactions with benzonitrile and glutaronitrile. The study provides mechanistic insights into substrate binding, product release, and active site dynamics, and a comparative study with amide-forming nitrilase SlNIT for enhancing our understanding of how BsNIT's structure facilitates its function. These insights pave the way for the development of engineered BsNIT variants with enhanced activity and specificity toward specific nitrile pollutants, potentially leading to more effective bioremediation strategies.
Predicting Pose Distribution of Protein Domains Connected by Flexible Linkers Is an Unsolved Problem.
McBride AC, Yu F, Cheng EH … +7 more , Mpouli A, Soe AC, Hammel M, Montelione GT, Oas TG, Tsutakawa SE, Donald BR
Proteins · 2026 Jan · PMID 41102979 · Full text
In CASP16, we assessed the ability of computational methods to predict the distribution of relative orientations of two domains tethered by a flexible linker. The range of interdomain distances and orientations (poses) o...
In CASP16, we assessed the ability of computational methods to predict the distribution of relative orientations of two domains tethered by a flexible linker. The range of interdomain distances and orientations (poses) of such domain-linker-domain (D-L-D) proteins can play an important role in protein function, allostery, aggregation, and the thermodynamics of binding. The CASP16 Conformational Ensembles Experiment included two challenges to predict the interdomain pose distribution of a Staphylococcal protein A (SpA) D-L-D construct, called ZLBT-C, in which two of SpA's five nearly identical domains are connected by either (1) a six-residue wild-type (WT) linker (kadnkf), or (2) an all-glycine (Gly6) linker. The wild-type linker has a highly conserved sequence and is thought to contribute to the energetic barrier for binding with host antibodies. Ground truth was provided by nuclear magnetic resonance (NMR) residual dipolar coupling (RDC) data on WT protein and small angle X-ray scattering (SAXS) data on both proteins in solution. Twenty-five predictor groups submitted 35 sets of predicted conformational distributions, in the form of population-weighted finite ensembles of discrete structures. Unlike traditional CASP assessments that compare predicted atomic models to experimental atomic models, the accuracy of these predictions was assessed by back-calculating NMR RDCs and SAXS curves from each ensemble of atomic models and comparing these results to respective experimental data. Accuracy was also assessed by using kernelization to compare ensembles to the continuous orientational distributions optimally fit to experimental data. In our assessment, predictions spanned a wide range of accuracy, but none were close fits to the combined NMR and SAXS data. In addition, none were able to recapitulate the observed difference between WT and Gly6 proteins, as observed in the SAXS data. These results, and our analysis, highlighted strengths and weaknesses, plus complementarity of NMR RDC and SAXS analysis.
Practical Outcomes From CASP16 for Users in Need of Biomolecular Structure Prediction.
Abriata LA, Dal Peraro M
Proteins · 2026 Jan · PMID 41088961 · Full text
The 16th Critical Assessment of Structure Prediction benchmarked advancements in biomolecular modeling, particularly in the context of AlphaFold 2 and 3 systems. Protein monomer and domain prediction is largely solved, w...
The 16th Critical Assessment of Structure Prediction benchmarked advancements in biomolecular modeling, particularly in the context of AlphaFold 2 and 3 systems. Protein monomer and domain prediction is largely solved, with barely any space for further improvements at the backbone level except for very specific details, irregular secondary structures, and mutational effects that remain challenging to predict. For protein assemblies, AF-based methods, especially when expertly guided or enhanced by servers like those from the Yang, Zheng/Zhang, and Cheng lab, show progress, though complex topologies and in particular antibody-antigen interactions are still difficult. Notably, a priori knowledge of stoichiometry significantly aids assembly prediction. Protein-ligand co-folding with AF3 demonstrated strong potential for pose prediction, outperforming many participants and some dedicated docking tools in baseline tests, but several caveats hold as discussed. Ligand affinity prediction is totally unreliable. Nucleic acid structure prediction lags considerably, heavily relying on 3D templates and expert human intervention, even AF3 showing substantial limitations. Overall, on all fronts, AF3's modeling capabilities are at or close to the state of the art; additionally, it shows slight improvements over AF2 and more detailed confidence metrics than it. We guide users on tool selection, realistic accuracy expectations, and persistent challenges, emphasizing the critical role of confidence metrics in interpreting AI-generated models.
Fibrinogen αC-Domain Derived From Group 1 Allergen of Dermatophagoides microceras Modulates Cell Adhesion in Human Bronchial Epithelial Cells.
Lin CY, Hsu HR, Ko JL … +1 more , Liu YF
Proteins · 2026 Mar · PMID 41085358 · Publisher ↗
House dust mites (HDMs) allergens are major contributors to allergic asthma, with their protease activity playing a critical role in airway inflammation. Der m 1, a Group 1 HDMs allergen from Dermatophagoides microceras,...
House dust mites (HDMs) allergens are major contributors to allergic asthma, with their protease activity playing a critical role in airway inflammation. Der m 1, a Group 1 HDMs allergen from Dermatophagoides microceras, is a cysteine protease known for its ability to degrade host proteins. In this study, we identified novel fibrinogen cleavage sites targeted by Der m 1, which are distinct from those cleaved by thrombin or plasmin. By employing biochemical and bioinformatic approaches, we identified the fibrinogen αC domain as a key component of Der m 1-derived fibrinogen cleavage products (FCPs). To assess their functional effects, we treated human bronchial epithelial cells with Der m 1-derived FCPs and the fibrinogen αC domain. Both treatments significantly enhanced cell adhesion, with effects peaking at 2-4 h post-treatment before gradually declining. Transcriptomic analysis, including RNA sequencing and gene set enrichment analysis (GSEA), revealed that both Der m 1-derived FCPs and the αC domain induced similar transcriptional responses, particularly in adhesion-related pathways involving integrin signaling. Functional validation using Cilengitide, a cyclic RGD peptide that antagonizes αVβ and αVβ integrins, confirmed that the pro-adhesive effects were integrin αV-dependent. These findings reveal that Der m 1 not only cleaves fibrinogen but also produces bioactive fragments that influence epithelial adhesion and signaling, offering new insight into airway remodeling in allergic asthma.
Protein Target Highlights in CASP16: Insights From the Structure Providers.
Alexander LT, Follonier OM, Kryshtafovych A … +32 more , Abesamis K, Bibi-Triki S, Box HG, Breyton C, Bringel F, Carrique L, d'Acapito A, Dong G, DuBois R, Fass D, Fiesco JM, Fox DR, Grimes JM, Grinter R, Jenkins M, Kamyshinsky R, Keown JR, Lackner G, Lammers M, Liu S, Lovering AL, Malinauskas T, Masquida B, Palm GJ, Siebold C, Su T, Zhang P, Zhou ZH, Fidelis K, Topf M, Moult J, Schwede T
Proteins · 2026 Jan · PMID 41065010 · Full text
This article presents an in-depth analysis of selected CASP16 targets, with a focus on their biological and functional significance. The authors highlight the most relevant features of the target proteins and discuss how...
This article presents an in-depth analysis of selected CASP16 targets, with a focus on their biological and functional significance. The authors highlight the most relevant features of the target proteins and discuss how well these were reproduced in the submitted predictions. While the overall performance of structure prediction methods remains impressive, challenges persist, particularly in modeling rare structural motifs, flexible regions, small molecule interactions, posttranslational modifications, and biologically important interfaces. Addressing these limitations can strengthen the role of structure prediction in complementing experimental efforts and advancing both basic research and biomedical applications.
Protein-Ligand Structure Prediction by Template-Guided Ensemble Docking Strategy.
Zhang K, Wu Q, Huang SY
Proteins · 2026 Jan · PMID 41047853 · Publisher ↗
In the 15th Critical Assessment of Techniques for Structure Prediction (CASP15), the category of protein-ligand complexes was introduced to advance the development of protein-ligand structure prediction techniques. CASP1...
In the 15th Critical Assessment of Techniques for Structure Prediction (CASP15), the category of protein-ligand complexes was introduced to advance the development of protein-ligand structure prediction techniques. CASP16 further expanded this category by introducing four sets of pharmaceutical targets as super-targets. Each super-target consists of multiple protein-ligand complexes involving the same protein but different ligands. Given the outstanding performance of template-based methods in CASP15, we employed a template-guided ensemble docking strategy for ligand (LG) tasks in CASP16. MODELER, AlphaFold3, and AlphaFold-Multimer were used to generate structural ensembles for each target protein. Then, we searched the Protein Data Bank (PDB) for reliable template complexes based on sequence identity, ligand similarity, and maximum common substructure (MCS) coverage score. If templates were identified, we used LSalign to perform ligand 3D alignment. For targets without a template, XDock and MDock were used to predict the binding poses. Finally, a knowledge-based scoring function, ITScore, was employed for energy evaluation. It is shown that our method performed well in the CASP16's LG tasks, ranking 4th out of 38 participating teams.
Distinctive Properties of Mla Proteins Differentiate Them From Classical ABC Transporter Components.
Dutta A, Patel S, Kanaujia SP
Proteins · 2026 Mar · PMID 41047745 · Publisher ↗
In Gram-negative bacteria, the non-canonical ABC transporter, namely, maintenance of lipid asymmetry (Mla) system, ferries phospholipids (PLs) between the inner (IM) and outer (OM) membranes to preserve the PL asymmetry...
In Gram-negative bacteria, the non-canonical ABC transporter, namely, maintenance of lipid asymmetry (Mla) system, ferries phospholipids (PLs) between the inner (IM) and outer (OM) membranes to preserve the PL asymmetry of the OM. The system utilizes three sub-cellular complexes-lipoprotein MlaA-OmpC/F (OM), MlaC (periplasmic), and MlaFEDB complex (IM). The structural studies on the Mla system have primarily been dedicated to its organization in IM and transport mechanisms. The characteristics of the individual components of the Mla system are lacking in the literature. In this study, individual components, namely MlaA, MlaB, MlaE, and MlaF were analyzed using computational tools. This has resulted in the identification of unique features and their characterization, including understanding the dynamicity of the C-terminal extension (CTE) of MlaA, which protrudes into the periplasm and the orientation of the protein, as well as binding patterns. Utilization of artificial intelligence has led to the understanding of the conformational landscape of MlaA and the validation of the macromolecular arrangement of Mla systems. Based on the results obtained, we were able to propose a fascinating mechanism of ligand transport, namely, bait-capture-pull. Our results reveal the poorly understood interfaces of the MlaB-MlaF complex. Furthermore, the results also suggest that MlaE possesses an EQ loop, which helps maintain a unique orientation. Overall, the findings of this study provide a new perspective on non-vesicular PL transport mediated by the enigmatic Mla system, thereby providing a holistic understanding.
Comparative Analysis of Deep Learning-Based Algorithms for Peptide Structure Prediction.
Sauvestre C, Zagury JF, Langenfeld F
Proteins · 2026 Mar · PMID 41047732 · Full text
While of primary importance in both the biomedical and therapeutic fields, peptides suffer from a relative lack of dedicated tools to predict efficiently and accurately their 3D structures despite being a crucial step in...
While of primary importance in both the biomedical and therapeutic fields, peptides suffer from a relative lack of dedicated tools to predict efficiently and accurately their 3D structures despite being a crucial step in understanding their physio-pathological function or designing new drugs. In recent years, deep-learning methods have enabled a major breakthrough for the protein 3D structure prediction approaches, allowing to predict protein 3D structures with a near-experimental accuracy for nearly any protein sequence. This present study aims at confronting some of these new methods (AlphaFold2, RoseTTAFold2, and ESMFold) for the peptides' 3D structure prediction problem and evaluating their performance. All methods produced high-quality results, but their overall performance is lower as compared to the prediction of protein 3D structures. We also identified a few structural features that impede the ability to produce high-quality peptide structure predictions. These findings point out the discrepancy that still exists between the protein and peptide 3D structure prediction methods and underline a few cases where the generated peptide structures should be used very cautiously.
Assessment of Pharmaceutical Protein-Ligand Pose and Affinity Predictions in CASP16.
Gilson MK, Eberhardt J, Škrinjar P … +3 more , Durairaj J, Robin X, Kryshtafovych A
Proteins · 2026 Jan · PMID 41045049 · Full text
The protein-ligand component of the 16th Critical Assessment of Structure Prediction (CASP16) challenged participants to predict both binding poses and affinities of small molecules to protein targets, with a focus on dr...
The protein-ligand component of the 16th Critical Assessment of Structure Prediction (CASP16) challenged participants to predict both binding poses and affinities of small molecules to protein targets, with a focus on drug-like compounds from pharmaceutical discovery projects. Thirty research groups submitted predictions for 229 protein-ligand pose targets and 140 affinity targets across five protein systems. Among the submitted predictions, template-based pose-prediction methods did particularly well, with the best groups achieving mean LDDT-PLI values of 0.69 (scale of 0-1 with 1 best). For comparison, we also ran a set of automated baseline pose-prediction methods, including ones using deep neural networks. Of these, AlphaFold 3 did particularly well, with a mean LDDT-PLI of 0.8, thus outscoring the best CASP16 predictor. The CASP affinity predictions showed modest correlation with experimental data (maximum Kendall's τ = 0.42), well below the theoretical maximum possible given experimental uncertainty (~0.73). As seen in prior challenges, providing experimental structures did not improve affinity predictions in the second stage of the challenge, suggesting that the scoring functions used here are a key limiting factor. Overall, the accuracy achieved by CASP participants is similar to that observed in the prior Drug Design Data Resource (D3R) blinded prediction challenges. The present results highlight the progress and persistent challenges in computational protein-ligand modeling and provide valuable benchmarks for the field of computer-aided drug design.
In Silico Analysis of Human NEK10 Reveals Novel Domain Architecture and Protein-Protein Interactions.
Eichner AS, Zimmerman N, San A … +1 more , Singh S
Proteins · 2026 Mar · PMID 41044802 · Publisher ↗
Cancer is the second leading cause of death worldwide, with an estimated 27.5 million new cases projected by 2040. Disruptions in cell cycle control cause DNA replication errors to accumulate during cell growth, leading...
Cancer is the second leading cause of death worldwide, with an estimated 27.5 million new cases projected by 2040. Disruptions in cell cycle control cause DNA replication errors to accumulate during cell growth, leading to genomic instability and tumor development. Proteins that regulate cell cycle progression and checkpoint mechanisms are crucial targets for cancer therapy. NIMA-related kinases (NEKs) are a family of serine/threonine kinases involved in regulating various aspects of the cell cycle and mitotic checkpoints in humans. Among these, NEK10 is the most divergent member and has been associated with both cancer and ciliopathies, a group of disorders caused by defects in cilia structure or function. Despite its biological significance and distinctive domain architecture, the structural details of NEK10 remain largely unknown. To address this gap, we employed computational modeling techniques to predict the complete structure of the NEK10 protein. Our analysis revealed a catalytic domain flanked by two coiled-coil domains, armadillo repeats (ARM repeats), an ATP binding site, two putative ubiquitin-associated (UBA) domains, and a PEST sequence known to regulate protein degradation. Furthermore, we mapped a comprehensive interactome of NEK10, uncovering previously unreported interactions with the cancer-related proteins MAP3K1 and HSPB1. MAP3K1, a serine/threonine kinase and E3 ubiquitin ligase frequently mutated in cancers, interacts with the catalytic region of NEK10. The interaction with HSPB1, a molecular chaperone associated with poor cancer prognosis, is mediated by NEK10's ARM repeats. Our findings highlight a potential connection between NEK10, ciliogenesis, and cancer, suggesting an important role in cancer development and progression.
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