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Bandak, A. F.

Publications and source records attributed to Bandak, A. F..

2 recordsLinked to original sources

Using energy to go downhill - a genoprotective role for ATPase activity in DNA topoisomerase II

SO_SCPLOWUMMARYC_SCPLOWType II topoisomerases effect topological changes in DNA by cutting a single duplex, passing a second duplex through the break, and resealing the broken strand in an ATP-coupled reaction. Curiously, most type II topoisomerases (topos II, IV, and VI) catalyze DNA transformations that are energetically favorable, such as the removal of superhelical strain; why ATP is required for such reactions is unknown. Here, using human topoisomerase II {beta} (hTOP2{beta}) as a model, we show that the ATPase domains of the enzyme are not required for DNA strand passage, but that their loss leads to increased DNA nicking and double strand break formation by the enzyme. The unstructured C-terminal domains (CTDs) of hTOP2{beta} strongly potentiate strand passage activity in the absence of the ATPase regions, as do cleavage-prone mutations that confer hypersensitivity to the chemotherapeutic agent etoposide. The presence of either the CTD or the mutations lead ATPase-less enzymes to promote even greater levels of DNA cleavaingevitro, as well as in vivo. By contrast, the aberrant cleavage phenotypes of these topo II variants is significantly repressed when the ATPase domains are restored. Our findings are consistent with the proposal that type II topoisomerases acquired an ATPase function to maintain high levels of catalytic activity while minimizing inappropriate DNA damage.

biochemistry↗

Finding Druggable Sites in Proteins using TACTICS

Structure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (Trajectory-based Analysis of Conformations To Identify Cryptic Sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at https://github.com/Albert-Lau-Lab/tactics_protein_analysis.

biophysics↗