Ben Lehner

This study maps allosteric communication in KRAS, revealing three distinct modes of allostery and highlighting the complexity of regulatory networks in single-domain proteins.

The paper identifies CRBN-pomalidomide binders using a combination of GluePCA and AI-derived protein surface queries, revealing known and novel interactors for MGD development.

Energy-model maps of five human kinases show conserved distance-dependent decay of inhibitory allostery, but distal allosteric pockets and activating sites diverge between kinases.

This paper systematically mutagenizes the genome and proteome of PhiX174, revealing that most mutations impair fitness, with protein interaction disruptions being a major cause.

This study quantifies sequence determinants of human programmed stop codon readthrough using deep mutational scanning and identifies gene-specific and context-dependent effects.

The paper shows allostery is a common cause of loss-of-function variants in human genetic diseases and identifies a distance-dependent decay of mutational effects.

OpenSplice quantifies the impact of over 590,000 variants on the splicing of 600 human exons, providing insights into splicing regulatory mechanisms and enabling machine learning model evaluation.

The paper maps how mutations alter the input-output function of an allosteric hormone receptor using a high-throughput approach, revealing genetic mechanisms that tune signaling parameters.

The paper develops a model to predict nonsense-mediated mRNA decay (NMD) across human genes using genomic data and mutational scanning, showing improved accuracy and revealing quantitative rules of NMD implementation.

This paper presents a method to identify allosteric sites in enzymes and applies it to Src kinase, producing the first comprehensive map of its allosteric control.

This study identifies splice-switching antisense oligonucleotides that correct splicing defects in SCN8A, reducing seizures and improving survival in a mouse model of genetic epilepsy.

The paper introduces TF-MAPS, a platform to map functional and allosteric sites of DNA-binding proteins, showing distance-dependent allostery and druggable pockets in three human TFs.

The paper uses deep learning to predict splicing regulatory effects and identify effective antisense oligonucleotides by analyzing systematic deletion scans of human exons.

This paper uses deep learning models and MPRAs to predict gene expression from sequence and identify causal regulatory motifs and grammars.

The paper evaluates the stability and function of randomized hydrophobic protein cores across evolutionary distances, finding that allostery disrupts function despite stable cores.

This study quantifies how domain extensions in a PDZ protein reshape its energy and allosteric landscapes through extensive mutational analysis.

The paper constructs allosteric maps for five human proteins to study how allostery evolves, finding conserved core interactions and protein-specific extensions.

The paper uses genetic perturbation and machine learning to analyze the energetics of amyloid beta nucleation, revealing its genetic architecture and transition state structure.

The paper shows that C. elegans transmit environmental information through lipid changes across generations, affecting offspring thermotolerance via a neuron-intestine-oocyte pathway.

The paper introduces GPCR-MAPS, a method to generate high-resolution functional and allosteric maps of GPCRs, revealing modular architecture and novel allosteric pockets.

The paper analyzes deep indel mutagenesis data to understand the effects of amino acid insertions and deletions on protein stability and function, introducing a new model to predict these effects.

The paper tests a pharmacological chaperone's ability to rescue expression of destabilizing variants in the V2R gene, finding it effective for most variants.

This paper introduces a microchip-based DNA synthesis system that significantly improves DNA product concentration and simplifies downstream processes for large-scale gene synthesis.

The paper quantifies how mutations in JUN affect its binding to 54 human basic leucine zipper transcription factors, revealing that specificity is encoded by both positive and negative determinants.

The paper quantifies and predicts pathogenic stop codon readthrough by small molecules across ~5,800 human PTCs using 8 drugs, revealing drug-specific readthrough patterns and building predictive models.

The paper experimentally quantifies amyloid nucleation for >100,000 sequences, trains an interpretable deep learning model to predict it, and reveals the model's decision-making process.

This paper extends the Walsh-Hadamard transform to model epistasis in genetic landscapes with multiple alleles per position, enabling analysis of pairwise and higher-order interactions in simulated and empirical data.

The paper performs site saturation mutagenesis on 500 human protein domains to assess the impact of missense variants on protein stability and disease.

The paper maps how specificity in protein-ligand interactions is encoded and shows how mutations can reprogram binding specificity across multiple sites.