Biology subjects

Hudson, B. P.

Publications and source records attributed to Hudson, B. P..

3 recordsLinked to original sources

bioRxiv2025

Q-score as a reliability measure for protein, nucleic acid, and small molecule atomic coordinate models derived from 3DEM density maps

Atomic coordinate models are important in the interpretation of 3D maps produced with cryoEM and sub-tomogram averaging in cryoET, or more generically, 3D electron microscopy (3DEM). In addition to visual inspection of such maps and models, quantitative metrics convey the reliability of the atomic coordinates, in particular how well the model is supported by the experimentally determined 3DEM map. A recently introduced metric, Q-score, was shown to correlate well with the reported resolution of the map for well-fitted models. Here we present new statistical analyses of Q-scores based on its application to [~]10,000 maps and models archived in EMDB and PDB. Further we introduce two new metrics based on Q-score: Q-relative-all and Q-relative-resolution to compare a map and model to all entries in the EMDB and those with similar resolution respectively. We also explore through illustrative examples of proteins, nucleic acids, and small molecules how Q-scores can indicate whether the atomic coordinates are well-fitted to 3DEM maps and whether some parts of a map may be poorly resolved due to factors such as molecular flexibility, radiation damage, and/or conformational heterogeneity. Lastly, we show examples of how Q-scores can effectively be converted to atomic B-factors. These analyses provide a basis for how Q-scores can be interpreted effectively to evaluate 3DEM maps and atomic coordinate models prior to publication and archiving. SynopsisQ-scores are calculated each atom in models fitted to 3DEM (3D electron microscopy) maps. They measure how well the model fits the map, and also reflect the quality of the map as they correlate to resolution. Here we develop a statistical model for Q-scores applied to many maps and models in the EMDB (Electron Microscopy Database) and PDB (Protein Data Bank) respectively, and show how it can be used to assess the reliability of entire models as well as their subcomponents.

biochemistry↗

bioRxiv2023

A comprehensive survey of coronaviral main protease active site diversity in 3D: Identifying and analyzing drug discovery targets in search of broad specificity inhibitors for the next coronavirus pandemic

Although the rapid development of therapeutic responses to combat SARS-CoV-2 represents a great human achievement, it also demonstrates untapped potential for advanced pandemic preparedness. Cross-species efficacy against multiple human coronaviruses by the main protease (MPro) inhibitor nirmatrelvir raises the question of its breadth of inhibition and our preparedness against future coronaviral threats. Herein, we describe sequence and structural analyses of 346 unique MPro enzymes from all coronaviruses represented in the NCBI Virus database. Cognate substrates of these representative proteases were inferred from their polyprotein sequences. We clustered MPro sequences based on sequence identity and AlphaFold2-predicted structures, showing approximate correspondence with known viral subspecies. Predicted structures of five representative MPros bound to their inferred cognate substrates showed high conservation in protease:substrate interaction modes, with some notable differences. Yeast-based proteolysis assays of the five representatives were able to confirm activity of three on inferred cognate substrates, and demonstrated that of the three, only one was effectively inhibited by nirmatrelvir. Our findings suggest that comprehensive preparedness against future potential coronaviral threats will require continued inhibitor development. Our methods may be applied to candidate coronaviral MPro inhibitors to evaluate in advance the breadth of their inhibition and identify target coronaviruses potentially meriting advanced development of alternative countermeasures.

molecular biology↗

bioRxiv2020

Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic

Three-dimensional structures of SARS-CoV-2 and other coronaviral proteins archived in the Protein Data Bank were used to analyze viral proteome evolution during the first six months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48,000 viral proteome sequences showed how each one of the 29 viral study proteins have undergone amino acid changes. Structural models computed for every unique sequence variant revealed that most substitutions map to protein surfaces and boundary layers with a minority affecting hydrophobic cores. Conservative changes were observed more frequently in cores versus boundary layers/surfaces. Active sites and protein-protein interfaces showed modest numbers of substitutions. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for six drug discovery targets and four structural proteins comprising the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and functional interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.

molecular biology↗