arXiv subjects

D. Allan Drummond

Publications and source records attributed to D. Allan Drummond.

6 recordsLinked to original sources

arXiv2024

Single-molecule fluorescence multiplexing by multi-parameter spectroscopic detection of nanostructured FRET labels

Multiplexed, real-time fluorescence detection at the single-molecule level is highly desirable to reveal the stoichiometry, dynamics, and interactions of individual molecular species within complex systems. However, traditionally fluorescence sensing is limited to 3-4 concurrently detected labels, due to low signal-to-noise, high spectral overlap between labels, and the need to avoid dissimilar dye chemistries. We have engineered a palette of several dozen fluorescent labels, called FRETfluors, for spectroscopic multiplexing at the single-molecule level. Each FRETfluor is a compact nanostructure formed from the same three chemical building blocks (DNA, Cy3, and Cy5). The composition and dye-dye geometries create a characteristic Förster Resonance Energy Transfer (FRET) efficiency for each construct. In addition, we varied the local DNA sequence and attachment chemistry to alter the Cy3 and Cy5 emission properties and thereby shift the emission signatures of an entire series of FRET constructs to new sectors of the multi-parameter detection space. Unique spectroscopic emission of each FRETfluor is therefore conferred by a combination of FRET and this site-specific tuning of individual fluorophore photophysics. We show single-molecule identification of a set of 27 FRETfluors in a sample mixture using a subset of constructs statistically selected to minimize classification errors, measured using an Anti-Brownian ELectrokinetic (ABEL) trap which provides precise multi-parameter spectroscopic measurements. The ABEL trap also enables discrimination between FRETfluors attached to a target (here: mRNA) and unbound FRETfluors, eliminating the need for washes or removal of excess label by purification. We show single-molecule identification of a set of 27 FRETfluors in a sample mixture using a subset of constructs selected to minimize classification errors.

physics.bio-ph↗

arXiv2006

Population genetics of translational robustness

Recent work has shown that expression level is the main predictor of a gene’s evolutionary rate, and that more highly expressed genes evolve slower. A possible explanation for this observation is selection for proteins which fold properly despite mistranslation, in short selection for translational robustness. Translational robustness leads to the somewhat paradoxical prediction that highly expressed genes are extremely tolerant to missense substitutions but nevertheless evolve very slowly. Here, we study a simple theoretical model of translational robustness that allows us to gain analytic insight into how this paradoxical behavior arises.

q-bio.PE↗

arXiv2005

Why highly expressed proteins evolve slowly

Much recent work has explored molecular and population-genetic constraints on the rate of protein sequence evolution. The best predictor of evolutionary rate is expression level, for reasons which have remained unexplained. Here, we hypothesize that selection to reduce the burden of protein misfolding will favor protein sequences with increased robustness to translational missense errors. Pressure for translational robustness increases with expression level and constrains sequence evolution. Using several sequenced yeast genomes, global expression and protein abundance data, and sets of paralogs traceable to an ancient whole-genome duplication in yeast, we rule out several confounding effects and show that expression level explains roughly half the variation in Saccharomyces cerevisiae protein evolutionary rates. We examine causes for expression's dominant role and find that genome-wide tests favor the translational robustness explanation over existing hypotheses that invoke constraints on function or translational efficiency. Our results suggest that proteins evolve at rates largely unrelated to their functions, and can explain why highly expressed proteins evolve slowly across the tree of life.

q-bio.PE↗

arXiv2005

A single determinant for the rate of yeast protein evolution

A gene's rate of sequence evolution is among the most fundamental evolutionary quantities in common use, but what determines evolutionary rates has remained unclear. Here, we show that the two most commonly used methods to disentangle the determinants of evolutionary rate, partial correlation analysis and ordinary multivariate regression, produce misleading or spurious results when applied to noisy biological data. To overcome these difficulties, we employ an alternative method, principal component regression, which is a multivariate regression of evolutionary rate against the principal components of the predictor variables. We carry out the first combined analysis of seven predictors (gene expression level, dispensability, protein abundance, codon adaptation index, gene length, number of protein-protein interactions, and the gene's centrality in the interaction network). Strikingly, our analysis reveals a single dominant component which explains 40-fold more variation in evolutionary rate than any other, suggesting that protein evolutionary rate has a single determinant among the seven predictors. The dominant component explains nearly half the variation in the rate of synonymous and protein evolution. Our results support the hypothesis that selection against the cost of translation-error-induced protein misfolding governs the rate of synonymous and protein sequence evolution in yeast.

q-bio.PE↗

arXiv2005

Why high-error-rate random mutagenesis libraries are enriched in functional and improved proteins

Recently, several groups have used error-prone polymerase chain reactions to construct mutant libraries containing up to 27 nucleotide mutations per gene on average, and reported a striking observation: although retention of protein function initially declines exponentially with mutations as has previously been observed, orders of magnitude more proteins remain viable at the highest mutation rates than this trend would predict. Mutant proteins having improved or novel activity were isolated disproportionately from these heavily mutated libraries, leading to the suggestion that distant regions of sequence space are enriched in useful cooperative mutations and that optimal mutagenesis should target these regions. If true, these claims have profound implications for laboratory evolution and for evolutionary theory. Here, we demonstrate that properties of the polymerase chain reaction can explain these results and, consequently, that average protein viability indeed decreases exponentially with mutational distance at all error rates. We show that high-error-rate mutagenesis may be useful in certain cases, though for very different reasons than originally proposed, and that optimal mutation rates are inherently protocol-dependent. Our results allow optimal mutation rates to be found given mutagenesis conditions and a protein of known mutational robustness.

q-bio.QM↗

arXiv2004

Thermodynamic Prediction of Protein Neutrality

We present a simple theory that uses thermodynamic parameters to predict the probability that a protein retains the wildtype structure after one or more random amino acid substitutions. Our theory predicts that for large numbers of substitutions the probability that a protein retains its structure will decline exponentially with the number of substitutions, with the severity of this decline determined by properties of the structure. Our theory also predicts that a protein can gain extra robustness to the first few substitutions by increasing its thermodynamic stability. We validate our theory with simulations on lattice protein models and by showing that it quantitatively predicts previously published experimental measurements on subtilisin and our own measurements on variants of TEM1 beta-lactamase. Our work unifies observations about the clustering of functional proteins in sequence space, and provides a basis for interpreting the response of proteins to substitutions in protein engineering applications.

q-bio.BM↗