Biology subjects

Finneran, P.

Publications and source records attributed to Finneran, P..

3 recordsLinked to original sources

bioRxiv2024

EvoSeq-ML: Advancing Data-Centric Machine Learning with Evolutionary-Informed Protein Sequence Representation and Generation

From protein structure prediction to novel protein generation, challenging protein engineering tasks have been made possible by advancements in machine learning (ML). While largely driven by ML architecture refinements, these advancements in ML-based protein engineering campaigns have left the impact of data curation underexplored. In light of the growing wealth of labeled sequence data, data-centric advances (e.g. prioritizing improvements in ML protein engineering tools through the curation of high-quality, domain-specific training data) are increasingly preferred over model-centric advancements. Implementing datasets that accurately reflect biological complexity and diversity has been shown to improve the efficiency of training protein engineering ML tools. Here, we evaluate an ancestral sequence reconstruction (ASR)-informed data augmentation strategy for training generative and representation-learning models in protein engineering. Using ethylene-forming enzyme (EFE) as a model system, we show that variational autoencoder models trained on ancestral and near-ancestral sequence datasets generate variants with improved predicted and experimentally measured thermostability relative to variants generated from modern-sequence training data. All experimentally tested ancestral and ML-generated EFEs produced detectable ethylene, although ML-generated variants showed reduced activity relative to wild-type EFE, indicating that the approach more strongly captured stability-associated features than catalytic optimization. We further evaluated ASR-enriched sequence sets for fine-tuning ESM2 representations in endolysin and lysozyme C stability-classification tasks, where ancestral representations were competitive with modern-sequence fine-tuning in selected settings. Overall, this work supports ASR-informed data augmentation as a promising strategy for stability-oriented protein sequence generation and motivates future work to couple ancestral sequence diversity with explicit functional selection.

bioinformatics↗

bioRxiv2022

Allele-specific activation, enzyme kinetics, and inhibitor sensitivities of EGFR exon 19 deletion mutations in lung cancer

Oncogenic mutations within the epidermal growth factor receptor (EGFR) are found in 15-30% of all non-small cell lung carcinomas. The term exon 19 deletion (ex19del) is collectively used to refer to more than 20 distinct genomic alterations within exon 19 that comprise the most common EGFR mutation subtype in lung cancer. Despite this heterogeneity, clinical treatment decisions are made irrespective of which EGFR ex19del variant is present within the tumor, and there is a paucity of information regarding how individual ex19del variants influence protein structure and function. Herein, we identify allele-specific functional differences among ex19del variants attributable to recurring sequence and structure motifs. We build all-atom structural models of 60 ex19del variants identified in patients and combine ~400 s of molecular dynamics simulations with biochemical and biophysical experiments to analyze three ex19del mutations (E746_A750, E746_S752>V, and L747_A750>P). We demonstrate that sequence variation in ex19del alters oncogenic cell growth, dimerization propensity, and tyrosine kinase inhibitor (TKI) sensitivity. We show that in contrast to E746_A750 and E746_S752>V, the L747_A750>P variant forms highly active ligand-independent dimers. E746_S752>V displays the least TKI sensitivity among the variants tested, which enzyme kinetic analysis and TKI inhibition experiments suggest is due to increased ATP Km relative to the common E746_A750 variant. Through these analyses, we propose an expanded framework for interpreting ex19del variants and new considerations for therapeutic intervention. SignificanceEGFR mutations are detected in approximately 30% of all lung adenocarcinomas, and the most common EGFR mutation occurring in ~50% of patients is termed "exon 19 deletion" (ex19del). Despite the existence of dozens of different genomic variants comprising what is generically referred to clinically as ex19del, clinicians currently do not distinguish between ex19del variants in considering treatment options, and the differences between ex19del variants are largely unstudied in the broader scientific community. Herein, we describe functional differences between distinct EGFR ex19del variants attributable to the structural features of each variant. These findings suggest a possible explanation for observed differences in patient outcomes stratified by ex19del subtype and reinforce the need for allele-specific considerations in clinical treatment decision making.

cancer biology↗

bioRxiv2020

Bimodal evolution of Src and Abl kinase substrate specificity revealed using mammalian cell extract as substrate pool

The specificity of phosphorylation by protein kinases is essential to the integrity of biological signal transduction. While peptide sequence specificity for individual kinases has been examined previously, here we explore the evolutionary progression that has led to the modern substrate specificity of two non-receptor tyrosine kinases, Abl and Src. To efficiently determine the substrate specificity of modern and reconstructed ancestral kinases, we developed a method using mammalian cell lysate as the substrate pool, thereby representing the naturally occurring substrate proteins. We find that the oldest tyrosine kinase ancestor was a promiscuous enzyme that evolved through a more specific last common ancestor into a specific human Abl. In contrast, the parallel pathway to human Src involved a loss of substrate specificity, leading to general promiscuity. These results add a new facet to our understanding of the evolution of signaling pathways, with both subfunctionalization and neofunctionalization along the evolutionary trajectories.

biochemistry↗