Biology subjects

Schmiedel, J. M.

Publications and source records attributed to Schmiedel, J. M..

5 recordsLinked to original sources

bioRxiv2023

The genetic architecture of protein stability

There are more ways to synthesize a 100 amino acid protein (20100) 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 >1010, 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.

biophysics↗

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↗

bioRxiv2020

DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies

Deep mutational scanning (DMS) enables multiplexed measurement of the effects of thousands of variants of proteins, RNAs and regulatory elements. Here, we present a customizable pipeline - DiMSum - that represents an end-to-end solution for obtaining variant fitness and error estimates from raw sequencing data. A key innovation of DiMSum is the use of an interpretable error model that captures the main sources of variability arising in DMS workflows, outperforming previous methods. DiMSum is available as an R/Bioconda package and provides summary reports to help researchers diagnose common DMS pathologies and take remedial steps in their analyses.

bioinformatics↗

bioRxiv2019

The mutational landscape of a prion-like domain

Specific insoluble protein aggregates are the hallmarks of many neurodegenerative diseases1-5. For example, cytoplasmic aggregates of the RNA-binding protein TDP-43 are observed in 97% of cases of Amyotrophic Lateral Sclerosis (ALS)6,7. However, it is still unclear for ALS and other diseases whether it is the insoluble aggregates or other forms of the mutated proteins that cause these diseases that are actually toxic to cells8-13. Here we address this question for TDP-43 by systematically mutating14 the protein and quantifying the effects on cellular toxicity. We generated >50,000 mutations in the intrinsically disordered prion-like domain (PRD) and observed that changes in hydrophobicity and aggregation potential are highly predictive of changes in toxicity. Surprisingly, however, increased hydrophobicity and cytoplasmic aggregation actually reduce cellular toxicity. Mutations have their strongest effects in a central region of the PRD, with variants that increase toxicity promoting the formation of more dynamic liquid-like condensates. The genetic interactions in double mutants reveal that specific structures exist in this unstructured region in vivo. Our results demonstrate that deep mutagenesis is a powerful approach for probing the sequence-function relationships of intrinsically disordered proteins as well as their in vivo structural conformations. Moreover, we show that aggregation of TDP-43 is not harmful but actually protects cells, most likely by titrating the protein away from a toxic liquid-like phase.

genetics↗

bioRxiv2018

Empirical noise-mean fitness landscapes and the evolution of gene expression

The effects of cell-to-cell variation (noise) in gene expression have proven difficult to quantify, in part due to the mechanistic coupling of noise to mean expression. To independently evaluate the effects of changes in expression mean and noise we determined the fitness landscapes in mean-noise expression space for 33 genes in yeast. The landscapes can be decomposed into two principal topologies: the fitness effects of protein shortage and surplus. For most genes, the fitness impact of sustained (mean) and short-lived (noise) deviations away from the expression optimum are linked and of similar magnitude. Sensitivity to both protein shortage and surplus creates a fitness landscape in which an epistatic ratchet uncouples the evolution of noise from mean expression, promoting noise minimization. These results demonstrate that noise is detrimental for many genes and reveal non-trivial consequences of mean-noise-fitness topologies for the evolution of gene expression systems.\n\nHighlights- Expression fitness landscapes in mean-noise space allow quantitative independent assessment of the effects of noise and mean expression on fitness for 33 genes\n- Landscapes are described by a combination of just two principal topologies: fitness defects due protein shortage, or due to protein surplus\n- Direct evidence that high expression noise is detrimental to fitness for 50% of genes\n- Mean expression and noise have equivalent impact on organismal fitness\n- Landscapes created by sensitivities to protein shortage and surplus facilitate independent evolution of gene expression noise via an epistatic ratchet

systems biology↗