The genetic architecture of protein interaction affinity and specificity
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1027833 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26382000.fastq.gz | Fastqsanger.gz | |||
| SRR26382001.fastq.gz | Fastqsanger.gz | |||
| SRR26382002.fastq.gz | Fastqsanger.gz | |||
| SRR26382003.fastq.gz | Fastqsanger.gz | |||
| SRR26382004.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 20 |
Similar Datasets
Project description:Proteins function in crowded cellular environments in which they must bind to specific target proteins but also avoid binding to many other off-target proteins. In large protein families this task is particularly challenging because many off-target proteins have very similar structures. How this specificity of physical protein-protein interactions in cellular networks is encoded and evolves is not very well understood. Here we address the question of specificity-encoding by comprehensively quantifying the effects of all mutations in one protein, JUN, on its binding to all other members of a protein family, the 54 human basic leucine zipper transcription factors. Fitting a global thermodynamic model to the data reveals that most affinity changing mutations equally affect JUN’s propensity to bind to all its interaction partners. Mutations that alter the specificity of binding are much rarer. These specificity-altering mutations are, however, distributed throughout the JUN interaction interface. JUN’s interaction specificity is encoded by both positive determinants that promote on-target interactions and negative determinants that prevent off-target interactions. Indeed, about half of the specificity-defining residues in JUN have dual functions and both promote on-target binding and prevent off-target binding. Whereas nearly all mutations that alter specificity are pleiotropic and also alter the affinity of binding to all interaction partners, the converse is not true with mutations outside of the interface able to tune affinity without affecting specificity. Our results provide the first global view of how mutations in a protein affect binding to all its potential interaction partners and reveal the distributed encoding of specificity and affinity in an interaction interface. They also show how the modular architecture of coiled-coils provides an elegant solution to the challenge of optimising specificity and affinity in a large protein family.
2024-05-02 | GSE245326 | GEO
Project description:Thousands of protein domains encoded in the human genome function by binding up to a million short linear motifs embedded in intrinsically disordered regions of other proteins. How affinity and specificity are encoded in these binding domains and the motifs themselves is not well understood. The evolvability of binding specificity - how rapidly and extensively it can change upon mutation - is also largely unexplored, as is the contribution of ‘fuzzy’ dynamic residues to affinity and specificity in protein-protein interactions. Here we produce the first global map of affinity and specificity encoding in a globular protein domain. Quantifying >200,000 energetic interactions between the domain and ligand allows us to identify 20 major energetically coupled pairs of sites. These are organised into six modules, with the vast majority of mutations in each module only reprogramming specificity for a single position in the ligand. Nine of the major energetic couplings encoding specificity are direct structural contacts and 11 have an allosteric mechanism of action. The dynamic tail of the ligand is more robust to mutation than the structured portion but contributes additively to binding affinity and communicates with structured residues to enable changes in specificity. Our results present how affinity and specificity are encoded in a globular protein domain interacting with a disordered peptide and a direct comparison of the encoding of affinity and specificity in structured and dynamic molecular recognition.
2024-04-25 | GSE265816 | GEO
Project description:A complete map of affinity and specificity encoding for a partially fuzzy protein interaction
| PRJNA1104259 | ENA
Project description:Protein-protein interaction specificity is often accomplished by molecular recognition and mediated by a small set of interfacial residues. However, the extent to which these residues serve positive specificity roles (encourage interaction with the cognate protein) or negative specificity roles (destabilize interaction with the non-cognate protein) is poorly understood. To systematically dissect these roles, we built a library of interface variants using a bacterial toxin-antitoxin system, Mesorhizobium opportunistum ParD3-ParE3, as a model. The ParD3 antitoxin is specific for its cognate toxin ParE3 and does not neutralize the non-cognate toxin ParE2. We built a saturation mutagenesis library at three key interface positions in ParD3 and measured the ability of each variant to neutralize ParE3 and ParE2 via a 10-hour bulk selection assay. A fitness score was assigned to each variant in the presence of each toxin based on the relative expansion of the variant during the experiment (between t = 0 and t = 600 min).
2020-08-17 | GSE153897 | GEO
Project description:Uncovering the basis of protein-protein interaction specificity with a combinatorially complete library
| PRJNA644451 | ENA
Project description:The genetic architecture of protein stability
| PRJNA1032554 | ENA
Project description:There are more ways to synthesize a 100 amino acid protein (20^100) than atoms in the universe. Only a miniscule fraction of such a vast sequence space can ever be experimentally or computationally surveyed. Deep neural networks are increasingly being used to navigate high-dimensional sequence spaces. However, these models are extremely complicated and provide little insight into the fundamental genetic architecture of proteins. Here, by experimentally exploring sequence spaces >10^10, we show that the genetic architecture of at least some proteins is remarkably simple, allowing accurate genetic prediction in high-dimensional sequence spaces with fully interpretable biophysical models. These models capture the non-linear relationships between free energies and phenotypes but otherwise consist of additive free energy changes with a small contribution from pairwise energetic couplings. These energetic couplings are sparse and caused by structural contacts and backbone propagations. Our results suggest that artificial intelligence models may be vastly more complicated than the proteins that they are modeling and that protein genetics is actually both simple and intelligible.
2023-10-27 | GSE246322 | GEO
Project description:We applied the high-throughput interaction assay SORTCERY to measure thousands of protein-peptide binding affinities and used the data to parameterize models of the peptide-binding landscape for three members of the Bcl-2 family of proteins. We applied the models to design peptides that bound with high affinity and specificity to just one of Bcl-xL, Mcl-1, or Bfl-1. We designed additional peptides that bound selectively to two out of three of these proteins. The raw data provided are the multiplexed fastq files that serve as inputs to our analysis pipeline. Additional detail is available in our corresponding publication and at the following github repository. https://github.com/KeatingLab/sortcery_design
2018-10-01 | GSE118147 | GEO
Project description:The regulatory specificity of enhancers and their interaction with gene promoters is thought to be controlled by their sequence and the binding of transcription factors. By studying Pitx1, a regulator of hindlimb development, we show that dynamic changes in chromatin conformation can restrict the activity of enhancers. Inconsistent with its hindlimb-restricted expression, Pitx1 is controlled by an enhancer (Pen) that shows activity in forelimbs and hindlimbs. By Capture Hi-C and three-dimensional modeling of the locus, we demonstrate that forelimbs and hindlimbs have fundamentally different chromatin configurations, whereby Pen and Pitx1 interact in hindlimbs and are physically separated in forelimbs. Structural variants can convert the inactive into the active conformation, thereby inducing Pitx1 misexpression in forelimbs, causing partial arm-to-leg transformation in mice and humans. Thus, tissue-specific three-dimensional chromatin conformation can contribute to enhancer activity and specificity in vivo and its disturbance can result in gene misexpression and disease.
2018-10-01 | GSE103676 | GEO
Project description:Genome-wide association studies (GWAS) have identified hundreds of susceptibility loci for chronic and inflammatory disease phenotypes in humans. There is increasing evidence that chronic inflammation is a crucial driver in the pathogenesis of cardiovascular diseases (CVD), which may be genetically determined. To understand the genetic architecture underlying chronic inflammation and CVD we performed a systematic analysis of (1) common risk alleles coming from published GWAS, (2) of protein-protein interaction (PPI) networks informed by (3) gene expression data with a defined molecular target involved in the inflammatory processes promoting CVD, MRP8. (4) through analysis of integrated haplotype scores (iHS) and FST values in HapMap phase 2 data, we investigated whether recent selection pressure acting upon inflammatory genes affected CVD susceptibility loci. Our findings provide significant evidence for a PPI network, which connects inflammatory and cardiovascular susceptibility genes, and establish a genetic framework of inflammatory CVD. 41.59% of PPI genes are associated with immune functions. 28.3% of integrated genes can be linked to both, an inflammatory and cardiovascular disease phenotype. Interestingly, CDKN2B, and CELSR2/PSRC1/MYBPHL/SORT1, unequivocally replicated CVD loci, are integrated within this network as are several SNPs located in transcription factor recognition sequences, i.e. NFKB1, STAT3, which are key factors in inflammation. Finally, we observed a significant enrichment of inflammatory variants within CVD cluster loci that are targets of selection. Overall, 32 genes exhibit traces of selection, 16 of which are part of the PPI, further suggesting that recent selective sweeps may have affected the genomic architecture underlying CVD. 6 samples, no replicates.
2015-09-01 | E-GEOD-54989 | biostudies-arrayexpress