Proteins-Structure Function and Bioinformatics最新文献

Friedreich's ataxia (FRDA) is a neurodegenerative disorder caused by frataxin (FXN) deficiency, where protein replacement therapy is hampered by the inherent instability and aggregation propensity of wild-type (WT) FXN. The structural flexibility of Loop-1 (residues 115-123), a critical region within the acidic ridge, represents a key determinant of protein stability. This study introduces a computational pipeline integrating evolutionary conservation analysis (ConSurf) with diffusion-based de novo design (RFdiffusion) to redesign both the backbone and sequence of Loop-1. Through systematic filtration of 1000 ProteinMPNN-generated variants using aggregation propensity screening (AGGRESCAN) and 450 ns of molecular dynamics (MD) simulations, four lead candidates were identified. Design_188 (EERVGGREI) demonstrated optimal performance with 2.3-fold improvement in aggregation resistance (Na4vSS: -53.8 vs. -23.5 for WT), superior structural stability (RMSD: 0.486 nm), reduced conformational diversity (62.3% dominant cluster occupancy), and 93% retention of ISCU binding capacity (ΔΔG: +6.4 kcal/mol). Experimental validation through ¹⁵N NMR relaxation analysis confirmed computational predictions, with Design_188 exhibiting uniform backbone rigidification (S² = 0.81-0.95) and strong MD-NMR correlation (Pearson r = 0.675, p = 0.003). SEC-MALS analysis demonstrated near-complete monomeric behavior (> 98% monomer content) compared to WT's heterogeneous oligomerization (68% monomer, 32% oligomers), directly confirming the predicted anti-aggregation properties. K-means clustering analysis revealed an inverse relationship between conformational heterogeneity and stability, while correlation analysis identified a fundamental trade-off between aggregation resistance and structural stability (r = -0.82, p < 0.01). This work establishes a generalizable framework for therapeutic protein engineering where backbone redesign enables conformational ensemble modulation beyond the limitations of sequence optimization alone.

Yersinia pestis was responsible for the Black Plague, one of the worst epidemiological disasters in recorded history. Today, Y. pestis, Y. enterocolitica, and Y. pseudotuberculosis remain clinically relevant human pathogens. Each of these pathogenic Yersinia species relies on a Type Three Secretion System (T3SS) for virulence, with the ATPase YscN playing a critical role in T3SS function. T3SS ATPases are responsible for powering apparatus formation and effector protein secretion through ATP hydrolysis. This study provides an extensive enzymatic characterization of recombinant YscN under several conditions, including variable pH and temperature, substrate and protein concentrations, and in the presence of putative inhibitors. Thermal stability data, assessed by circular dichroism, demonstrate that YscN exhibits increased stability in alkaline conditions, coinciding with greatest ATPase activity. Further, we report the first high-resolution crystal structure of YscN and leverage homology data to model an oligomeric active site. Mutational analysis of a predicted active site residue confirms oligomerization as necessary for YscN ATPase activity and corroborates our oligomeric model and enzyme concentration-dependent specific activity. Interestingly, however, AUC analysis reveals that the purified YscN predominantly exists as a monomer, despite oligomerization-dependent active site formation. Thus, we propose that transient oligomeric interactions support the observed ATP hydrolysis. Together, these data uncover structural and environmental impacts on YscN activity that may support the highly specialized Yersinia pathogenic lifecycle and leverage its role in virulence in search of pan-effective small molecule T3SS ATPase inhibitors.

We present the first x-ray crystallographic structural evidence of an archaeal DNA ligase showing the AMP covalent adduct together with further cofactor hydrolysis, capturing a transient intermediary in the first step of the ligation reaction, triggered by the pyrophosphate hydrolysis. Our crystallographic models of Thermococcus gammatolerans DNA ligase (LigTgam), coupled with bioinformatic analysis of at least 28 crystallographic structures from ATP- and NAD⁺-dependent DNA ligases, highlight the central role of domain mobility. Notably, elevated B-values are consistently observed in key catalytic and binding regions, suggesting a link between structural flexibility and enzymatic efficiency. Remarkably, this pattern of high B-values is conserved in replicative ligases, including bacterial Lig A, indicating a broader evolutionary relevance. These fluctuations emphasize the importance of conformational adaptability in accommodating substrate DNA and facilitating catalytic steps, including adenylation and phosphodiester bond formation. In this work, we delve deeper into this dynamic behavior, providing evidence of its critical role in ligase function.

The rise of flaviviral diseases, including West Nile virus (WNV), presents a growing threat to global public health and underscores the urgent need for new therapeutic strategies. The non-structural protein 3 helicase (NS3h) of the Orthoflavivirus genus, including WNV, is essential for viral replication and a promising antiviral target. Previously [Roy et al., Nucleic Acids Research, 52 (13), 2024, 7447-7464], we showed that the motif VI loop (VIL) in WNV NS3h functions as a nucleotide valve, regulating ADP affinity during hydrolysis. In this study, we uncover an ATP-dependent coupling between nucleotide affinity at motif VIL and RNA affinity at motifs IVa and V, suggesting a coordinated mechanism of ssRNA translocation. Using microsecond-scale all-atom molecular dynamics simulations of hydrolysis-cycle intermediates, we find that key VIL residues (R461, R464) correlate strongly with RNA phosphate affinity of motif V. Structural analyses reveal an ATP-sensitive interaction between E413 (motif V) and R461 (motif VIL) that modulates the conformation of the motif V 3₁₀-helix, thereby influencing RNA binding. This dynamic interaction is lost in catalytically deficient VIL mutants, which have been experimentally shown to impair hydrolysis and attenuate viral replication. These findings provide mechanistic insights into NS3h function and identify new opportunities for structure-based antiviral design.

GnRH1 binds to its receptor GnRH1R to stimulate release of FSH and LH. Earlier NMR analysis had reported several possible conformers of GnRH1; however, the biologically active conformation of GnRH1 is not identified so far. Here, molecular docking of different NMR conformers of GnRH1 to GnRH1R is performed. Based on: (a) residues of GnRH1R interacting with antagonist elagolix (as ligand-binding pocket), (b) intermolecular hydrogen bonds (for specificity of interaction), and (c) total intermolecular non-covalent interactions (for stability of interaction), one NMR conformation, having an asymmetric U-turn reverse coil structure with a beta strand comprised of residues Gly6 and Leu7, is identified as the bioactive conformation of GnRH1. Further, the identified bioactive NMR conformation of GnRH1 is used to explain in vivo GnRH1-neutralizing ability of monoclonal antibody (mAb) F1D3C5 and lack of neutralization by another mAb E2D2H12. In mice, F1D3C5 completely blocks estrus cycle, while E2D2H12, despite having a relatively higher affinity for GnRH1 in ELISA, does not alter the estrus cycle. Molecular docking of the identified bioactive NMR conformation of GnRH1 to homology models of scFv attributes in vivo neutralizing ability of F1D3C5 to structure-specific recognition of GnRH1. The bioactive conformation of GnRH1 identified here could guide co-crystallization studies, design of analogs and GnRH1 vaccination efforts.

Iron homeostasis in various pathogenic bacteria is regulated by bacterioferritins (Bfr) which function to store Fe³⁺ and release Fe²⁺ as needed for metabolic processes. The Bfr structure consists of 18 kDa subunits in which dimer pairs bind a heme molecule and are assembled into a highly symmetrical 24-meric spherical structure with an internal core diameter of approximately 80 Å. Release of iron is facilitated by the binding of a 7 kDa [2Fe-2S] ferredoxin (Bfd) to specific sites on the surface of Bfr which transfers electrons to the core thereby reducing the stored Fe³⁺ to Fe²⁺ for mobilization. The crystal structures of Bfr from Brucella abortus (Ba) in the apo and iron bound forms are presented and compared with those from Acinetobacter baumannii (Ab) and Pseudomonas aeruginosa (Pa). Additionally, models of the Bfr:Bfd complexes for Ba and Ab are provided and compared with the Pa complex. Finally, compounds known to target the Bfr:Bfd interaction in Pa were docked to the Ba and Ab structures which provided insight regarding the potential binding mode and inhibitory mechanism.

This paper presents the experimental protein-ligand datasets used as benchmarks in the CASP 16 blind prediction experiment-the first CASP round to incorporate targets from pharmaceutical discovery projects. We have assembled and characterized protein-ligand complexes for four proteins that are known or candidate drug targets: human chymase, human cathepsin G, human autotaxin, and the SARS-CoV-2 main protease. The collection encompasses over 200 co-crystal structures at resolutions better than 2.7 Å, paired with binding affinity measurements for approximately 160 compounds covering a broad affinity range (nanomolar to high micromolar). These data enabled the CASP16 pose-prediction and affinity-prediction challenges. Many systems feature potentially challenging characteristics, including chymase's electropositive surface and acidic ligands, which require proper handling of titratable ligand groups; autotaxin complexes with and without zinc coordination; and a SARS-CoV-2 protease crystal form exhibiting an unusually open active site conformation. We describe the experimental approaches-from protein production and crystallization to binding assay development-that yielded these reference data. Contributed by scientists at F. Hoffmann-La Roche and Idorsia Pharmaceuticals, these datasets represent actual drug discovery projects and therefore provide a realistic testbed for assessing how computational methods perform on pharmaceutically relevant targets. An accompanying paper in the present special journal issue provides a comprehensive assessment of the pose and affinity predictions for these pharmaceutical protein-ligand systems.

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.

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.

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.