On the characterization of protein-DNA interactions using statistical potentials and protein-protein interactions

Protein-DNA interactions are indispensable players in the daily activities of cells. DNA-binding proteins regulate gene expression and are responsible of DNA replication, packaging, repair and recombination. Among them, transcription factors activate/repress gene transcription by binding to specific genomic sites. Hence, the characterization of transcription factor binding sites turns out to be crucial in order to understand gene regulation. In this context, the development of computational tools is foremost. Here, I show the prediction of redundant transcription factors in yeast using a combination of homology-based tools and protein-protein interactions. The approach was automated and incorporated into ModLink+, an online and user-friendly tool to infer the fold of remote homologs. Moreover, I describe split-statistical potentials for protein-DNA interactions. Finally, I present SHAITAN, a statistical/homology-based approach that can be used to both predict transcription factor binding sites and infer the more likely transcription factors to bind a DNA sequence of interest.Les interaccions proteïna-ADN són indispensables en l’activitat diària de les cèl•lules. Les proteïnes que participen en aquestes interaccions s’encarreguen de la regulació de l'expressió gènica i són responsables de la replicació, l'empaquetament, la reparació i la recombinació de l’ADN. Entre aquestes proteïnes, els factors de transcripció activen/reprimeixen la transcripció de gens mitjançant la unió a llocs específics dins el genoma. Per tant, la caracterització dels llocs d'unió dels diferents factors de transcripció és crucial per tal d’entendre com funciona la regulació gènica. En aquest context, desenvolupar eines computacionals és importantíssim. En aquesta tesi predict redundància entre factors de transcripció de llevat eines fent servir eines basades en homologia i interaccions proteïna-proteïna. Aquesta aproximació va ser automatitzada i incorporada a ModLink+, una eina accessible des d’internet i fàcil d'usar per a inferir el plegament de proteïnes a partir d’homòlegs remots. D'altra banda, descric potencials estadístics fraccionats per a interaccions proteïna-ADN. Finalment presento SHAITAN, una aproximació basada en homologia i potencials estadistics que pot ser utilitzada per a predir els llocs d'unió de factors de transcripció així com per saber quins factors de transcripció són més probables que s’uneixin a una determinada seqüència d'ADN

Backup in gene regulatory networks explains differences between binding and/nknockout results

The complementarity of gene expression and protein-DNA interaction data led to several successful models of biological systems. However, recent studies in multiple species raise doubts about the relationship between these two datasets. These studies show that the overwhelming majority of genes bound by a particular transcription factor (TF) are not affected when that factor is knocked out. Here, we show that this surprising result can be partially explained by considering the broader cellular context in which TFs operate. Factors whose functions are not backed up by redundant paralogs show a fourfold increase in the agreement between their bound targets and the expression levels of those targets. In addition, we show that incorporating protein interaction networks provides physical explanations for knockout effects. New double knockout experiments support our conclusions. Our results highlight the robustness provided by redundant TFs and indicate that in the context of diverse cellular systems, binding is still largely functional.This work was supported in part by NIH grant 1RO1 GM085022 and NSF CAREER award 0448453 to ZBJ. In addition, this material is based on work supported under a National Science Foundation Graduate Research Fellowship to A

SBILib: a handle for protein modeling and engineering

Summary: The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library. Availability and implementation: SBILib is made available on Github at https://github.com/structuralbioinformatics/SBILib.This work was supported by grants [PID2020-113203RB-I00] and “Unidad de Excelencia María de Maeztu” [ref: CEX2018-000792-M], funded by the MCIN and the AEI [DOI: 10.13039/501100011033]. J.P.-I. acknowledges support from the Czech Ministry of Education [EXCELES LX22NPO5102]

On the prediction of DNA-binding preferences of C2H2-ZF domains using structural models: application on human CTCF

Cis2-His2 zinc finger (C2H2-ZF) proteins are the largest family of transcription factors in human and higher metazoans. To date, the DNA-binding preferences of many members of this family remain unknown. We have developed a computational method to predict their DNA-binding preferences. We have computed theoretical position weight matrices (PWMs) of proteins composed by C2H2-ZF domains, with the only requirement of an input structure. We have predicted more than two-third of a single zinc-finger domain binding site for about 70% variants of Zif268, a classical member of this family. We have successfully matched between 60 and 90% of the binding-site motif of examples of proteins composed by three C2H2-ZF domains in JASPAR, a standard database of PWMs. The tests are used as a proof of the capacity to scan a DNA fragment and find the potential binding sites of transcription-factors formed by C2H2-ZF domains. As an example, we have tested the approach to predict the DNA-binding preferences of the human chromatin binding factor CTCF. We offer a server to model the structure of a zinc-finger protein and predict its PWM.Spanish Ministry of Economy (MICINN) [BIO2017-85329-R, RYC2015-17519, MDM2014-0370] and European Regional Development Fund (FEDER) [BIO2017-85329-R, RYC-2015-17519, MDM-2014-0370]; Erasmus+ Fellowship 2019 by EU (to F.Å.); Research Formation of ‘Generalitat de Catalunya’ (FI) Fellowship (to A.M)