Biology subjects

Martell, C. M.

Publications and source records attributed to Martell, C. M..

3 recordsLinked to original sources

bioRxiv2025

Global Analysis of Aggregation Determinants in Small Protein Domains

Protein aggregation is an obstacle for engineering effective recombinant proteins for biotechnology and therapeutic applications. Predicting protein aggregation propensity remains challenging due to the complex interplay of sequence, structure, environmental factors, and external stress conditions, particularly for globular proteins. To understand the determinants of aggregation and improve its prediction, we quantified insoluble aggregation following high temperature and acidic stress in custom libraries of small protein domains (40-72 amino acids) using a high-throughput, in vitro, mass spectrometry-based method. In total, we quantified aggregation for 18,987 small protein domains, revealing diverse stress-dependent aggregation phenotypes that were consistent in different library contexts. We also found that aggregation measurements on individually purified proteins strongly correlated with high-throughput mixed-pool data (Pearsons r = 0.65-0.79), supporting the use of multiplexed approaches to study aggregation. Using machine learning, we identified sequence and structural features that correlate with aggregation and fine-tuned the protein language model SaProt, which explained 43-55% of the observed variation in a held-out test set of unrelated protein domains. Our model shows promising utility for engineering aggregation-resistant proteins, and our dataset serves as an important resource for developing improved models of protein aggregation.

biophysics↗

bioRxiv2025

Large-scale discovery, analysis, and design of protein energy landscapes

All folded proteins continuously fluctuate between their low-energy native structures and higher energy conformations that can be partially or fully unfolded. These rare states influence protein function, interactions, aggregation, and immunogenicity, yet they remain far less understood than protein native states. Although native protein structures are now often predictable with impressive accuracy, conformational fluctuations and their energies remain largely invisible and unpredictable, and experimental challenges have prevented large-scale measurements that could improve machine learning and physics-based modeling. Here, we introduce a multiplexed experimental approach to analyze the energies of conformational fluctuations for hundreds of protein domains in parallel using intact protein hydrogen-deuterium exchange mass spectrometry. We analyzed 5,778 domains 28-64 amino acids in length, revealing hidden variation in conformational fluctuations even between sequences sharing the same fold and global folding stability. Site-resolved hydrogen exchange NMR analysis of 13 domains showed that these fluctuations often involve entire secondary structural elements with lower stability than the overall fold. Computational modeling of our domains identified structural features that correlated with the experimentally observed fluctuations, enabling us to design mutations that stabilized low-stability structural segments. Our dataset enables new machine learning-based analysis of protein energy landscapes, and our experimental approach promises to reveal these landscapes at unprecedented scale.

biophysics↗

bioRxiv2021

Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation

Designing entirely new protein structures remains challenging because we do not fully understand the biophysical determinants of folding stability. Yet some protein folds are easier to design than others. Previous work identified the 43-residue {square}{beta}{beta}{square} fold as especially challenging: the best designs had only a 2% success rate, compared to 39-87% success for other simple folds (1). This suggested the {square}{beta}{beta}{square} fold would be a useful model system for gaining a deeper understanding of folding stability determinants and for testing new protein design methods. Here, we designed over ten thousand new {square}{beta}{beta}{square} proteins and found over three thousand of them to fold into stable structures using a high-throughput protease-based assay. Nuclear magnetic resonance, hydrogen-deuterium exchange, circular dichroism, deep mutational scanning, and scrambled sequence control experiments indicated that our stable designs fold into their designed {square}{beta}{beta}{square} structures with exceptional stability for their small size. Our large dataset enabled us to quantify the influence of universal stability determinants including nonpolar burial, helix capping, and buried unsatisfied polar atoms, as well as stability determinants unique to the {square}{beta}{beta}{square} topology. Our work demonstrates how large-scale design and test cycles can solve challenging design problems while illuminating the biophysical determinants of folding. SignificanceMost computationally designed proteins fail to fold into their designed structures. This low success rate is a major obstacle to expanding the applications of protein design. In previous work, we discovered a small protein fold that was paradoxically challenging to design (only a 2% success rate) even though the fold itself is very simple. Here, we used a recently developed high-throughput approach to comprehensively examine the design rules for this simple fold. By designing over ten thousand proteins and experimentally measuring their folding stability, we discovered the key biophysical properties that determine the stability of these designs. Our results illustrate general lessons for protein design and also demonstrate how high-throughput stability studies can quantify the importance of different biophysical forces.

synthetic biology↗