Dynamical comparison between myoglobin and hemoglobin

Biofilm Microenvironment Activated Antibiotic Adjuvant for Implant-Associated Infections by Systematic Iron Metabolism Interference

Hematoma, a risk factor of implant-associated infections (IAIs), creates a Fe-rich environment following implantation, which proliferates the growth of pathogenic bacteria. Fe metabolism is a major vulnerability for pathogens and is crucial for several fundamental physiological processes. Herein, a deferiprone (DFP)-loaded layered double hydroxide (LDH)-based nanomedicine (DFP@Ga-LDH) that targets the Fe-rich environments of IAIs is reported. In response to acidic changes at the infection site, DFP@Ga-LDH systematically interferes with bacterial Fe metabolism via the substitution of Ga3+ and Fe scavenging by DFP. DFP@Ga-LDH effectively reverses the Fe/Ga ratio in Pseudomonas aeruginosa, causing comprehensive interference in various Fe-associated targets, including transcription and substance metabolism. In addition to its favorable antibacterial properties, DFP@Ga-LDH functions as a nano-adjuvant capable of delaying the emergence of antibiotic resistance. Accordingly, DFP@Ga-LDH is loaded with a siderophore antibiotic (cefiderocol, Cefi) to achieve the antibacterial nanodrug DFP@Ga-LDH-Cefi. Antimicrobial and biosafety efficacies of DFP@Ga-LDH-Cefi are validated using ex vivo human skin and mouse IAI models. The pivotal role of the hematoma-created Fe-rich environment of IAIs is highlighted, and a nanoplatform that efficiently interferes with bacterial Fe metabolism is developed. The findings of the study provide promising guidance for future research on the exploration of nano-adjuvants as antibacterial agents.

Analysis of Fluctuation in the Heme-Binding Pocket and Heme Distortion in Hemoglobin and Myoglobin

Heme is located in the active site of proteins and has diverse and important biological functions, such as electron transfer and oxygen transport and/or storage. The distortion of heme porphyrin is considered an important factor for the diverse functions of heme because it correlates with the physical properties of heme, such as oxygen affinity and redox potential. Therefore, clarification of the relationship between heme distortion and the protein environment is crucial in protein science. Here, we analyzed the fluctuation in heme distortion in the protein environment for hemoglobin and myoglobin using molecular dynamics (MD) simulations and quantum mechanical (QM) calculations as well as statistical analysis of the protein structures of hemoglobin and myoglobin stored in Protein Data Bank. Our computation and statistical analysis showed that the protein environment for hemoglobin and myoglobin prominently affects the doming distortion of heme porphyrin, which correlates with its oxygen affinity, and that the magnitude of distortion is different between hemoglobin and myoglobin. These results suggest that heme distortion is affected by its protein environment and fluctuates around its fitted conformation, leading to physical properties that are appropriate for protein functions.

How good are comparative models in the understanding of protein dynamics?

The 3D structure of a protein is essential to understand protein dynamics. If experimentally determined structure is unavailable, comparative models could be used to infer dynamics. However, the effectiveness of comparative models, compared to experimental structures, in inferring dynamics is not clear. To address this, we compared dynamics features of ~800 comparative models with their crystal structures using normal mode analysis. Average similarity in magnitude, direction, and correlation of residue motions is >0.8 (where value 1 is identical) indicating that the dynamics of models and crystal structures are highly similar. Accuracy of 3D structure and dynamics is significantly higher for models built on multiple and/or high sequence identity templates (>40%). Three-dimensional (3D) structure and residue fluctuations of models are closer to that of crystal structures than to templates (TM score 0.9 vs 0.7 and square inner product 0.92 vs 0.88). Furthermore, long-range molecular dynamics simulations on comparative models of RNase 1 and Angiogenin showed significant differences in the conformational sampling of conserved active-site residues that characterize differences in their activity levels. Similar analyses on two EGFR kinase variant models highlight the effect of mutations on the functional state-specific αC helix motions and these results corroborate with the previous experimental observations. Thus, our study adds confidence to the use of comparative models in understanding protein dynamics.

Dynamical comparison between Drosha and Dicer reveals functional motion similarities and dissimilarities

Drosha and Dicer are RNase III family members of classes II and III, respectively, which play a major role in the maturation of micro-RNAs. The two proteins share similar domain arrangement and overall fold despite no apparent sequence homology. The overall structural and catalytic reaction similarity of both proteins, on the one hand, and differences in the substrate and its binding mechanisms, on the other, suggest that both proteins also share dynamic similarities and dissimilarities. Since dynamics is essential for protein function, a comparison at their dynamics level is fundamental for a complete understanding of the overall relations between these proteins. In this study, we present a dynamical comparison between human Drosha and Giardia Dicer. Gaussian Network Model and Anisotropic Network Model modes of motion of the proteins are calculated. Dynamical comparison is performed using global and local dynamic programming algorithms for aligning modes of motion. These algorithms were recently developed based on the commonly used Needleman-Wunsch and Smith-Waterman algorithms for global and local sequence alignment. The slowest mode of Drosha is different from that of Dicer due to its more bended posture and allow the motion of the double-stranded RNA-binding domain toward and away from its substrate. Among the five slowest modes dynamics similarity exists only for the second slow mode of motion of Drosha and Dicer. In addition, high local dynamics similarity is observed at the catalytic domains, in the vicinity of the catalytic residues. The results suggest that the proteins exert a similar catalytic mechanism using similar motions, especially at the catalytic sites.

Dynamics based clustering of globin family members

A methodology to cluster proteins based on their dynamics’ similarity is presented. For each pair of proteins from a dataset, the structures are superimposed, and the Anisotropic Network Model modes of motions are calculated. The twelve slowest modes from each protein are matched using a local mode alignment algorithm based on the local sequence alignment algorithm of Smith–Waterman. The dynamical similarity distance matrix is calculated based on the top scoring matches of each pair and the proteins are clustered using a hierarchical clustering algorithm. The utility of this method is exemplified on a dataset of protein chains from the globin family and a dataset of tetrameric hemoglobins. The results demonstrate the effect of the quaternary structure of globin members on their intrinsic dynamics and show good ability to distinguish between different states of hemoglobin, revealing the dynamical relations between them.