Structure-based learning to model complex protein-DNA interactions and transcription-factor co-operativity incis-regulatory elements

Abstract

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, a web server implementing a structure homology-modelling approach to predict TF motifs and automatically model higher-order TF regulatory complexes. Starting from a TF sequence or structure, ModCRE predicts a set of motifs for that TF. The predicted motifs are then used to scan the DNA for occurrences of each of them, 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. Moreover, we demonstrate that incorporating high-throughput TF binding data, such as from protein binding microarrays, addresses the protein-DNA structure scarcity problem for deriving statistical potentials. In turn, these statistical potentials are proven to be capable predictors of TF motifs. We also show the conditional advantage of using ModCRE over a nearest-neighbor approach for predicting TF binding sites as well as an improvement in prediction accuracy when using a rank-enrichment selection system. Finally, as case examples, we apply ModCRE to model the interferon beta enhanceosome and the complex of SOX2 and 11 with a nucleosome.

Recruitment of Homodimeric Proneural Factors by Conserved CAT-CAT E-Boxes Drives Major Epigenetic Reconfiguration in Cortical Neurogenesis

“…This structural insight further supported our hypothesis regarding the role of distinct dimer compositions in influencing E-box usage. To delve deeper into the binding compatibility of different combinations of proneural factors and co-exiting E-proteins during neurogenesis, we employed a recent structure homology modeling approach called ModCRE 77 . Using this method, we systematically investigated the TF-DNA binding affinities for each type of E-box in various bHLH homo and heterodimers.…”

SBILib: a handle for protein modeling and engineering

“…Its design strength lies in providing several common analysis tools under one umbrella, resulting in a streamlined approach to structural bioinformatics projects, including the prediction of loop conformations ( Fernandez-Fuentes et al 2006 ), redesign of super-secondary protein structures ( Bonet et al 2014a , b ), quality assessment of protein folds ( Aguirre-Plans et al 2021 ), or analyses of protein–protein and protein–DNA interactions. For example, SBILib is a core component of ArchDB, Frag’r’Us ( Bonet et al 2014a , b ), MODPIN ( Meseguer et al 2020 ), InteractoMIX ( Mirela-Bota et al 2021 ), and ModCRE ( Fornes et al 2022 ). We would like to place particular emphasis on the extended functionality of SBILib as it compares to other Python packages such as atomium ( Ireland and Martin 2020 ), ProDy ( Zhang et al 2021 ), Biotite ( Kunzmann et al 2023 ) with regards to protein modeling and engineering.…”