Thursday, 11th May, 2023, 12:00

Automated structure-based learning to model complex protein-DNA interactions and co-operativity in cis-regulatory modules

PRBB Computational Genomics Seminar. Chair: Baldo Oliva

Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ~25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. Here, we introduce ModCRE, an automated structure homology-modelling approach to predict TF motifs and model higher-order TF regulatory complexes. We demonstrate the conditional advantage of using ModCRE over the state-of-the-art nearest-neighbor prediction as well as an improvement in prediction accuracy when using a rank-enrichment selection system. Starting from a TF sequence or structure, ModCRE predicts a set of binding motifs. The predicted motifs are then used to scan the DNA for occurrences of each, and the best matches are either profiled with a binding score or collected for their subsequent modeling into a higher-order regulatory complex with DNA, as well as other TFs and co-factors. Cooperativity is modelled by: i) the co-localization of TFs; and ii) the structural modeling of protein-protein interactions between TFs and with co-factors. Finally, as case examples, we apply ModCRE to model the interferon beta enhanceosome and the complex of OCT4, SOX2 and SOX11 with a nucleosome and compare these to experimentally determined structures.

Zoom webinar:https://us02web.zoom.us/j/84212222322?pwd=VmVIdHo3czlFcVZUSDhscnQ0RzN5Zz09

Speaker: Patrick Gohl, Structural Bioinformatics group (Baldo Oliva group), GRIB

Room online