Biology subjects

Rastogi, C.

Publications and source records attributed to Rastogi, C..

7 recordsLinked to original sources

bioRxiv2024

Accurate sequence-to-affinity models for SH2 domains from multi-round peptide binding assays coupled with free-energy regression

Short linear peptide motifs play important roles in cell signaling. They can act as modification sites for enzymes and as recognition sites for peptide binding domains. SH2 domains bind specifically to tyrosine-phosphorylated proteins, with the affinity of the interaction depending strongly on the flanking sequence. Quantifying this sequence specificity is critical for deciphering phosphotyrosine-dependent signaling networks. In recent years, protein display technologies and deep sequencing have allowed researchers to profile SH2 domain binding across thousands of candidate ligands. Here, we present a concerted experimental and computational strategy that improves the predictive power of SH2 specificity profiling. Through multi-round affinity selection and deep sequencing with large randomized phosphopeptide libraries, we produce suitable data to train an additive binding free energy model that covers the full theoretical ligand sequence space. Our models can be used to predict signaling network connectivity and the impact of missense variants in phosphoproteins on SH2 binding.

systems biology↗

bioRxiv2024

Optimized reporters for multiplexed detection of transcription factor activity

In any given cell type, dozens of transcription factors (TFs) act in concert to control the activity of the genome by binding to specific DNA sequences in regulatory elements. Despite their considerable importance in determining cell identity and their pivotal role in numerous disorders, we currently lack simple tools to directly measure the activity of many TFs in parallel. Massively parallel reporter assays (MPRAs) allow the detection of TF activities in a multiplexed fashion; however, we lack basic understanding to rationally design sensitive reporters for many TFs. Here, we use an MPRA to systematically optimize transcriptional reporters for 86 TFs and evaluate the specificity of all reporters across a wide array of TF perturbation conditions. We thus identified critical TF reporter design features and obtained highly sensitive and specific reporters for 60 TFs, many of which outperform available reporters. The resulting collection of "prime" TF reporters can be used to uncover TF regulatory networks and to illuminate signaling pathways. HIGHLIGHTSO_LISystematic design and optimization of transcriptional reporters for 86 TFs C_LIO_LICharacterization of TF-specific reporter design optimization rules C_LIO_LIEvaluation of reporter TF-specificity across a wide array of TF perturbations C_LIO_LIIdentification of a collection of 60 "prime" TF reporters with optimized performance C_LI

genomics↗

bioRxiv2023

Optimisation of TP53 reporters by systematic dissection of synthetic TP53 response elements

TP53 is a transcription factor that controls multiple cellular processes, including cell cycle arrest, DNA repair, and apoptosis. The relation between TP53 binding site architecture and transcriptional output is still not fully understood. Here, we systematically examined in three different cell lines the effects of binding site affinity and copy number on TP53-dependent transcriptional output, and also probed the impact of spacer length and sequence between adjacent binding sites, and of core promoter identity. Paradoxically, we found that high-affinity TP53 binding sites are less potent than medium-affinity sites. TP53 achieves supra-additive transcriptional activation through optimally spaced adjacent binding sites, suggesting a cooperative mechanism. Optimally spaced adjacent binding sites have a [~]10-bp periodicity, suggesting a role for spatial orientation along the DNA double helix. We leveraged these insights to construct a log-linear model that explains activity from sequence features, and to identify new highly active and sensitive TP53 reporters.

genomics↗

bioRxiv2022

Transcription factor paralogs orchestrate alternative gene regulatory networks by context-dependent cooperation with multiple cofactors

In eukaryotes, members of large transcription factor families often exhibit similar DNA binding properties in vitro, yet initiate paralog-specific gene regulatory networks in vivo. The serially homologous first (T1) and third (T3) thoracic legs of Drosophila, which result from alternative gene regulatory networks specified by the Hox proteins Scr and Ubx, respectively, offer a unique opportunity to address this paradox in vivo. Genome-wide analyses using epitope-tagged alleles of both Hox loci in the T1 and T3 leg imaginal discs, which are the precursors to the adult appendages and ventral body regions, show that [~]8% of Hox binding is paralog-specific. Binding specificity is mediated by interactions with distinct cofactors in different domains: the known Hox cofactor Exd acts in the proximal domain and is necessary for Scr to bind many of its paralog-specific targets, while in the distal leg domain, we identified the homeodomain protein Distal-less (Dll) as a novel Hox cofactor that enhances Scr binding to a different subset of genomic loci. Reporter genes confirm the in vivo roles of Scr+Dll and suggest that [~]1/3 of paralog-specific Hox binding in enhancers is functional. Together, these findings provide a genome-wide view of how Hox paralogs, and perhaps paralogs of other transcription factor families, orchestrate alternative downstream gene networks and suggest the importance of multiple, context-specific cofactors.

molecular biology↗

bioRxiv2021

Probing molecular specificity with deep sequencing and biophysically interpretable machine learning

Quantifying sequence-specific protein-ligand interactions is critical for understanding and exploiting numerous cellular processes, including gene regulation and signal transduction. Next-generation sequencing (NGS) based assays are increasingly being used to profile these interactions with high-throughput. However, these assays do not provide the biophysical parameters that have long been used to uncover the quantitative rules underlying sequence recognition. We developed a highly flexible machine learning framework, called ProBound, to define sequence recognition in terms of biophysical parameters based on NGS data. ProBound quantifies transcription factor (TF) behavior with models that accurately predict binding affinity over a range exceeding that of previous resources, captures the impact of DNA modifications and conformational flexibility of multi-TF complexes, and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with a new assay called Kd-seq, it determines the absolute affinity of protein-ligand interactions. It can also profile the kinetics of kinase-substrate interactions. By constructing a biophysically robust foundation for profiling sequence recognition, ProBound opens up new avenues for decoding biological networks and rationally engineering protein-ligand interactions.

genomics↗

bioRxiv2019

Context-dependent gene regulation by transcription factor complexes

Eukaryotic transcription factors (TFs) form complexes with various partner proteins to recognize their genomic target sites. Yet, how the DNA sequence determines which TF complex forms at any given site is poorly understood. Here we demonstrate that high-throughput in vitro binding assays coupled with unbiased computational analysis provides unprecedented insight into how complexes of homeodomain proteins adapt their stoichiometry and configuration to the bound DNA. Using inferred knowledge about minor groove width readout, we design targeted protein mutations that destabilize homeodomain binding in a complex-specific manner. By performing parallel SELEX-seq, ChIP-seq, RNA-seq and Hi-C assays, we not only reveal complex-specific functions, but also show that TF binding sites that lack a canonical sequence motif emerge as a consequence of direct interaction with functionally bound sites.

genomics↗

bioRxiv2017

SelexGLM differentiates androgen and glucocorticoid receptor DNA-binding preference over an extended binding site

The DNA-binding interfaces of the androgen (AR) and glucocorticoid (GR) receptors are virtually identical, yet these transcription factors share only about a third of their genomic binding sites and regulate similarly distinct sets of target genes. To address this paradox, we determined the intrinsic specificities of the AR and GR DNA binding domains using a refined version of SELEX-seq. We developed an algorithm, SelexGLM, that quantifies binding specificity over a large (31 bp) binding-site by iteratively fitting a feature-based generalized linear model to SELEX probe counts. This analysis revealed that the DNA binding preferences of AR and GR homodimers differ significantly, both within and outside the 15bp core binding site. The relative preference between the two factors can be tuned over a wide range by changing the DNA sequence, with AR more sensitive to sequence changes than GR. The specificity of AR extends to the regions flanking the core 15bp site, where isothermal calorimetry measurements reveal that affinity is augmented by enthalpy-driven readout of poly-A sequences associated with narrowed minor groove width. We conclude that the increased specificity of AR is correlated with more enthalpy-driven binding than GR. The binding models help explain differences in AR and GR genomic binding, and provide a biophysical rationale for how promiscuous binding by GR allows functional substitution for AR in some castration-resistant prostate cancers.

systems biology↗