Biochemistry and Pharmacology - Research Publications

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Author: Ascher, David × Author: Portelli, Stephanie × Author: Valente Pires, Douglas Eduardo × End date: 2022 × Start date: 2020 ×

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    Exploring the structural distribution of genetic variation in SARS-CoV-2 with the COVID-3D online resource (vol 52, pg 999, 2020)
    Portelli, S ; Olshansky, M ; Rodrigues, CHM ; D'Souza, EN ; Myung, Y ; Silk, M ; Alavi, A ; Pires, DEV ; Ascher, DB (NATURE RESEARCH, 2021-02)
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    ThermoMutDB: a thermodynamic database for missense mutations
    Xavier, JS ; Thanh-Binh, N ; Karmarkar, M ; Portelli, S ; Rezende, PM ; Velloso, JPL ; Ascher, DB ; Pires, DE (OXFORD UNIV PRESS, 2021-01-08)
    Proteins are intricate, dynamic structures, and small changes in their amino acid sequences can lead to large effects on their folding, stability and dynamics. To facilitate the further development and evaluation of methods to predict these changes, we have developed ThermoMutDB, a manually curated database containing >14,669 experimental data of thermodynamic parameters for wild type and mutant proteins. This represents an increase of 83% in unique mutations over previous databases and includes thermodynamic information on 204 new proteins. During manual curation we have also corrected annotation errors in previously curated entries. Associated with each entry, we have included information on the unfolding Gibbs free energy and melting temperature change, and have associated entries with available experimental structural information. ThermoMutDB supports users to contribute to new data points and programmatic access to the database via a RESTful API. ThermoMutDB is freely available at: http://biosig.unimelb.edu.au/thermomutdb.
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    Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.
    Portelli, S ; Myung, Y ; Furnham, N ; Vedithi, SC ; Pires, DEV ; Ascher, DB (Nature Publishing Group, 2020-10-22)
    Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/ .