Biology subjects

Phoumyvong, C. M.

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

4 recordsLinked to original sources

bioRxiv2026

Molecular and functional profiling distinguishes PACS1 syndrome variant from PACS1 loss-of-function in iNeurons

PACS1 syndrome is a rare neurodevelopmental disorder caused by a recurrent de novo missense variant (p.R203W) in the PACS1 protein. However, it remains unclear whether the p.R203W variant acts through a loss-of-function or alternative mechanism. Here, we used isogenic iPSC-derived neurons (iNs) to directly compare the effects of PACS1 p.R203W to complete loss of PACS1 function. Using a combination of proteomic, biochemical and electrophysiological approaches, we identified molecular and functional phenotypes associated with each genotype. While PACS1(+/R203W) and PACS1(-/-) iNs shared phenotypic abnormalities, the overall molecular and functional consequences of the p.R203W variant were distinct from those caused by PACS1 deficiency. Notably, PACS1(+/R203W) presented with unique proteomic and kinase signaling signatures and a shift in stimulus dependent excitability. These findings demonstrate that PACS1 syndrome is not caused by a simple loss of function and instead support a non-loss-of-function mechanism. Lastly, our interactome analysis suggests that the p.R203W variant retains aspects of canonical PACS1 function while acquiring novel molecular interactions that could contribute to PACS1 syndrome pathogenesis. Altogether, these findings provide a framework for future mechanistic studies and therapeutic development in PACS1 syndrome. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/747101v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@d1522corg.highwire.dtl.DTLVardef@69e4dforg.highwire.dtl.DTLVardef@30eebcorg.highwire.dtl.DTLVardef@899b9d_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Accurate protein stability prediction for small domains using mega-scale experiments

Predicting absolute protein folding stability is a long-standing challenge in biophysics, with broad applications in protein design and in understanding genetic variation and evolution. Physics-based simulations have shown limited success at predicting stability and are often computationally intractable, and machine learning methods have been constrained by the lack of sufficiently large experimental datasets. We recently introduced cDNA display proteolysis, a cell-free approach that can measure folding stability for nearly one million protein domains in parallel. Here, we applied this method to measure stability for 1.8 million diverse protein domains 60-80 amino acids in length primarily taken from the MGnify metagenomic database and spanning over 200,000 sequence families. Using this new "MGnify Stability dataset", we developed the predictive models SaProt{Delta}G and ESM3{Delta}G, which accurately predict absolute folding stability for small domains with root mean squared error of 0.8 kcal/mol over a 6 kcal/mol range (Spearman rank correlation of 0.88). These predictors show high accuracy at predicting effects of substitutions, insertions, and deletions, successfully identify global trends toward higher stability in thermophilic organisms, and improve discrimination of stable and unstable computationally designed proteins. Our results illustrate how megascale biophysical measurements can complement existing evolutionary and structural data to enable accurate absolute stability prediction for small domains.

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↗