Human Rad51 mediated DNA unwinding is facilitated by conditions that favour Rad51-dsDNA aggregation

<p>Abstract</p> <p>Background</p> <p>Human Rad51 (RAD51), analogous to its bacterial homolog, RecA, binds and unwinds double stranded DNA (dsDNA) in the presence of certain nucleotide cofactors. ATP hydrolysis is not required for this process, because even ATP non hydrolysable analogs like AMP-PNP and ATPγS, support DNA unwinding. Even ADP, the product of ATP hydrolysis, feebly supports DNA unwinding.</p> <p>Results</p> <p>We find that human Rad52 (RAD52) stimulates RAD51 mediated DNA unwinding in the presence of all Adenine nucleotide cofactors, (except in AMP and no nucleotide conditions that intrinsically fail to support unwinding reaction) while enhancing aggregation of RAD51-dsDNA complexes in parallel. Interestingly, salt at low concentration can substitute the role of RAD52, in facilitating aggregation of RAD51-dsDNA complexes, that concomitantly also leads to better unwinding.</p> <p>Conclusion</p> <p>RAD52 itself being a highly aggregated protein perhaps acts as scaffold to bring together RAD51 and DNA molecules into large co-aggregates of RAD52-RAD51-DNA complexes to promote RAD51 mediated DNA unwinding reaction, when appropriate nucleotide cofactors are available, presumably through macromolecular crowding effects. Our work highlights the functional link between aggregation of protein-DNA complexes and DNA unwinding in RAD51 system.</p

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery