Biology subjects

Diss, G.

Publications and source records attributed to Diss, G..

6 recordsLinked to original sources

bioRxiv2025

Mapping the latent CRBN-molecular glue degrader interactome

Molecular glue degraders (MGDs) are a transformative modality in drug discovery. MGDs that work in concert with the E3 ligase CRL4CRBN can degrade a range of substrates through tailored MGDs. To explore CRL4CRBN reprogrammability, we tested whether reported CRBN-MGD substrates are part of a network of latent CRBN interactors, detectable with generic CRBN-MGDs. Leveraging highly parallel interaction measurement (GluePCA) between CRL4CRBN and human zinc-fingers (ZFs), we identified [~]210 ZFs bound to CRBN-pomalidomide, where top binders are already reported as degraded by dedicated MGDs. To map latent CRBN-MGDs interactions proteome-wide, and thus define the immediately accessible CRBN target space, we combined AI-derived protein surface queries (MaSIF-mimicry) with GluePCA. This pipeline identified 6 known and 43 novel CRBN-pomalidomide binders, thereby providing privileged starting points for MGD development. We expect this binding-focused, highly parallel workflow to be readily applicable to other MGD/E3 ligase systems, extending the target landscape of this emerging drug class.

molecular biology↗

bioRxiv2025

The genetic architecture of the human bZIP family

Generative biology holds the promise to transform our ability to design and understand living systems by creating novel proteins, pathways, and organisms with tailored functions that address challenges in medicine, sustainability, and technology. However, training generative models requires large quantities of data that captures the genetic architecture of protein function in all its complexity, but these are currently scarce. Here, we systematically mutagenized all 54 human basic-leucine zipper (bZIP) domains and quantified their interactions with each other using bindingPCA, a quantitative deep mutational scanning assay. This resulted in [~]2 million interaction measurements, capturing the effect of all single amino acid substitutions at each of the 35 interfacial positions. We found that mutation effects are largely additive in the vicinity of each wild-type bZIPs, but diverge across the family, indicating strong context dependency. A global additive thermodynamic model provided moderate prediction of mutation effects, while individual models per bZIP achieved higher performance, supporting a model of local simplicity and global complexity. Our results therefore suggest that the genetic architecture of protein function is more complex than previously anticipated, which could hinder predictability. However, a convolutional neural network trained on this dataset could accurately predict binding scores from sequence alone. Furthermore, the model enabled the design of synthetic bZIPs with high target specificity, demonstrating practical applicability for bioengineering purposes. Our study shows that capturing family-wide diversity is essential to reveal context dependencies and train accurate quantitative models of protein-protein interactions.

biophysics↗

bioRxiv2023

Optimization of a deep mutational scanning workflow to improve quantification of mutation effects on protein-protein interactions

Deep Mutational Scanning (DMS) assays are powerful tools to study sequence-function relationships by measuring the effects of thousands of sequence variants on protein function. During a DMS experiment, several technical artefacts might distort non-linearly the functional score obtained, potentially biasing the interpretation of the results. We therefore tested several technical parameters in the deepPCA workflow, a DMS assay for protein-protein interactions, in order to identify technical sources of non-linearities. We found that parameters common to many DMS assays such as amount of transformed DNA, timepoint of harvest and library composition can cause non-linearities in the data. Designing experiments in a way to minimize these non-linear effects will improve the quantification and interpretation of mutation effects.

systems biology↗

bioRxiv2023

The genetic architecture of protein interaction affinity and specificity

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 JUNs 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. JUNs 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.

systems biology↗

bioRxiv2022

mutscan - a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data

Multiplexed assays of variant effect (MAVE) experimentally measure the fitness of large numbers of sequence variants by selective enrichment of sequences with desirable properties followed by quantification by sequencing. mutscan is an R package for flexible analysis of such experiments, covering the entire workflow from raw reads up to statistical analysis and visualization. Core components are implemented in C++ for efficiency. Various experimental designs are supported, including single or paired reads with optional unique molecular identifiers. To find variants with changed relative abundance, mutscan employs established statistical models provided in the edgeR and limma packages. mutscan is available from https://github.com/fmicompbio/mutscan.

bioinformatics↗

bioRxiv2021

Global mapping of the energetic and allosteric landscapes of protein binding domains

Allosteric communication between distant sites in proteins is central to nearly all biological regulation but still poorly characterised for most proteins, limiting conceptual understanding, biological engineering and allosteric drug development. Typically only a few allosteric sites are known in model proteins, but theoretical, evolutionary and some experimental studies suggest they may be much more widely distributed. An important reason why allostery remains poorly characterised is the lack of methods to systematically quantify long-range communication in diverse proteins. Here we address this shortcoming by developing a method that uses deep mutational scanning to comprehensively map the allosteric landscapes of protein interaction domains. The key concept of the approach is the use of multidimensional mutagenesis: mutational effects are quantified for multiple molecular phenotypes--here binding and protein abundance--and in multiple genetic backgrounds. This is an efficient experimental design that allows the underlying causal biophysical effects of mutations to be accurately inferred en masse by fitting thermodynamic models using neural networks. We apply the approach to two of the most common human protein interaction domains, an SH3 domain and a PDZ domain, to produce the first global atlases of allosteric mutations for any proteins. Allosteric mutations are widely dispersed with extensive long-range tuning of binding affinity and a large mutational target space of network-altering edgetic variants. Mutations are more likely to be allosteric closer to binding interfaces, at Glycines in secondary structure elements and at particular sites including a chain of residues connecting to an opposite surface in the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should allow the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped.

biophysics↗