arXiv subjects

Claus O. Wilke

Publications and source records attributed to Claus O. Wilke.

At least 19 recordsLinked to original sources

arXiv2026

StabilityArc: Decoding Protein Sequence Embeddings into Generalizable Stability Landscapes

Every protein has a unique stability landscape, but the physical consequences of mutation are governed by recurring biochemical constraints. We test whether a shared decoder, trained on measurements from diverse proteins, can interpret these constraints in an unseen target, enabling cross-protein transfer for initial experimental round prescreening. We present StabilityArc , which maps frozen ESMC-600M residue representations through a shared RoPE transformer to an Lx20 matrix of substitution effects; a symmetric, contact-aware residual aids in predicting epistasis in simultaneous substitutions. In 66 strict leave-one-protein-out evaluations covering 134,794 ProteinGym variants, StabilityArc achieves 0.7134 Spearman correlation, exceeding the strongest zero-shot baseline, ProSST-2048 (0.6526), by 0.0608. We further explore the utility of this method by providing the score as a prior for Kermut, achieving Spearman correlation of 0.8280 across three supervised split schemes, improving on Kermut's reported 0.8167.

q-bio.BM↗

arXiv2025

p2smi: A Python Toolkit for Peptide FASTA-to-SMILES Conversion and Molecular Property Analysis

Converting peptide sequences into useful representations for downstream analysis is a common step in computational modeling and cheminformatics. Furthermore, peptide drugs (e.g., Semaglutide, Degarelix) often take advantage of the diverse chemistries found in noncanonical amino acids (NCAAs), altered stereochemistry, and backbone modifications. Despite there being several chemoinformatics toolkits, none are tailored to the task of converting a modified peptide from an amino acid representation to the chemical string nomenclature Simplified Molecular-Input Line-Entry System (SMILES), often used in chemical modeling. Here we present p2smi, a Python toolkit with CLI, designed to facilitate the conversion of peptide sequences into chemical SMILES strings. By supporting both cyclic and linear peptides, including those with NCAAs, p2smi enables researchers to generate accurate SMILES strings for drug-like peptides, reducing the overhead for computational modeling and cheminformatics analyses. The toolkit also offers functionalities for chemical modification, synthesis feasibility evaluation, and calculation of molecular properties such as hydrophobicity, topological polar surface area, molecular weight, and adherence to Lipinski's rules for drug-likeness.

q-bio.BM↗

arXiv2023

Mining Patents with Large Language Models Elucidates the Chemical Function Landscape

The fundamental goal of small molecule discovery is to generate chemicals with target functionality. While this often proceeds through structure-based methods, we set out to investigate the practicality of orthogonal methods that leverage the extensive corpus of chemical literature. We hypothesize that a sufficiently large text-derived chemical function dataset would mirror the actual landscape of chemical functionality. Such a landscape would implicitly capture complex physical and biological interactions given that chemical function arises from both a molecule's structure and its interacting partners. To evaluate this hypothesis, we built a Chemical Function (CheF) dataset of patent-derived functional labels. This dataset, comprising 631K molecule-function pairs, was created using an LLM- and embedding-based method to obtain functional labels for approximately 100K molecules from their corresponding 188K unique patents. We carry out a series of analyses demonstrating that the CheF dataset contains a semantically coherent textual representation of the functional landscape congruent with chemical structural relationships, thus approximating the actual chemical function landscape. We then demonstrate that this text-based functional landscape can be leveraged to identify drugs with target functionality using a model able to predict functional profiles from structure alone. We believe that functional label-guided molecular discovery may serve as an orthogonal approach to traditional structure-based methods in the pursuit of designing novel functional molecules.

q-bio.QM↗

arXiv2023

Prompt Engineering for Transformer-based Chemical Similarity Search Identifies Structurally Distinct Functional Analogues

Chemical similarity searches are widely used in-silico methods for identifying new drug-like molecules. These methods have historically relied on structure-based comparisons to compute molecular similarity. Here, we use a chemical language model to create a vector-based chemical search. We extend implementations by creating a prompt engineering strategy that utilizes two different chemical string representation algorithms: one for the query and the other for the database. We explore this method by reviewing the search results from five drug-like query molecules (penicillin G, nirmatrelvir, zidovudine, lysergic acid diethylamide, and fentanyl) and three dye-like query molecules (acid blue 25, avobenzone, and 2-diphenylaminocarbazole). We find that this novel method identifies molecules that are functionally similar to the query, indicated by the associated patent literature, and that many of these molecules are structurally distinct from the query, making them unlikely to be found with traditional chemical similarity search methods. This method may aid in the discovery of novel structural classes of molecules that achieve target functionality.

physics.chem-ph↗

arXiv2019

colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes

The R package colorspace provides a flexible toolbox for selecting individual colors or color palettes, manipulating these colors, and employing them in statistical graphics and data visualizations. In particular, the package provides a broad range of color palettes based on the HCL (Hue-Chroma-Luminance) color space. The three HCL dimensions have been shown to match those of the human visual system very well, thus facilitating intuitive selection of color palettes through trajectories in this space. Using the HCL color model general strategies for three types of palettes are implemented: (1) Qualitative for coding categorical information, i.e., where no particular ordering of categories is available. (2) Sequential for coding ordered/numeric information, i.e., going from high to low (or vice versa). (3) Diverging for coding ordered/numeric information around a central neutral value, i.e., where colors diverge from neutral to two extremes. To aid selection and application of these palettes the package also contains scales for use with ggplot2, shiny (and tcltk) apps for interactive exploration, visualizations of palette properties, accompanying manipulation utilities (like desaturation and lighten/darken), and emulation of color vision deficiencies.

stat.CO↗

arXiv2014

Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on 9 non-homologous viral protein structures and from variation in homologous variants of those proteins, where available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1 to 0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than was structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than was buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than are more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design.

physics.bio-ph↗

arXiv2014

Analyzing Machupo virus-receptor binding by molecular dynamics simulations

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non-alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-virus protein-protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-versus-distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein-protein interactions.

q-bio.BM↗

arXiv2013

Maximum allowed solvent accessibilites of residues in proteins

The relative solvent accessibility (RSA) of a residue in a protein measures the extent of burial or exposure of that residue in the 3D structure. RSA is frequently used to describe a protein's biophysical or evolutionary properties. To calculate RSA, a residue's solvent accessibility (ASA) needs to be normalized by a suitable reference value for the given amino acid; several normalization scales have previously been proposed. However, these scales do not provide tight upper bounds on ASA values frequently observed in empirical crystal structures. Instead, they underestimate the largest allowed ASA values, by up to 20%. As a result, many empirical crystal structures contain residues that seem to have RSA values in excess of one. Here, we derive a new normalization scale that does provide a tight upper bound on observed ASA values. We pursue two complementary strategies, one based on extensive analysis of empirical structures and one based on systematic enumeration of biophysically allowed tripeptides. Both approaches yield congruent results that consistently exceed published values. We conclude that previously published ASA normalization values were too small, primarily because the conformations that maximize ASA had not been correctly identified. As an application of our results, we show that empirically derived hydrophobicity scales are sensitive to accurate RSA calculation, and we derive new hydrophobicity scales that show increased correlation with experimentally measured scales.

q-bio.BM↗

arXiv2012

Membrane environment imposes unique selection pressures on transmembrane domains of G protein-coupled receptors

We have investigated the influence of the plasma membrane environment on the molecular evolution of G protein-coupled receptors (GPCRs), the largest receptor family in Metazoa. In particular, we have analyzed the site-specific rate variation across the two primary structural partitions, transmembrane (TM) and extramembrane (EM), of these membrane proteins. We find that transmembrane domains evolve more slowly than do extramembrane domains, though TM domains display increased rate heterogeneity relative to their EM counterparts. Although the majority of residues across GPCRs experience strong to weak purifying selection, many GPCRs experience positive selection at both TM and EM residues, albeit with a slight bias towards the EM. Further, a subset of GPCRs, chemosensory receptors (including olfactory and taste receptors), exhibit increased rates of evolution relative to other GPCRs, an effect which is more pronounced in their TM spans. Although it has been previously suggested that the TM's low evolutionary rate is caused by their high percentage of buried residues, we show that their attenuated rate seems to stem from the strong biophysical constraints of the membrane itself, or by functional requirements. In spite of the strong evolutionary constraints acting on the transmembrane spans of GPCRs, positive selection and high levels of evolutionary rate variability are common. Thus, biophysical constraints should not be presumed to preclude a protein's ability to evolve.

q-bio.GN↗

arXiv2007

The stochastic edge in adaptive evolution

In a recent article, Desai and Fisher (2007) proposed that the speed of adaptation in an asexual population is determined by the dynamics of the stochastic edge of the population, that is, by the emergence and subsequent establishment of rare mutants that exceed the fitness of all sequences currently present in the population. Desai and Fisher perform an elaborate stochastic calculation of the mean time $τ$ until a new class of mutants has been established, and interpret $1/τ$ as the speed of adaptation. As they note, however, their calculations are valid only for moderate speeds. This limitation arises from their method to determine $τ$: Desai and Fisher back-extrapolate the value of $τ$ from the best-fit class' exponential growth at infinite time. This approach is not valid when the population adapts rapidly, because in this case the best-fit class grows non-exponentially during the relevant time interval. Here, we substantially extend Desai and Fisher's analysis of the stochastic edge. We show that we can apply Desai and Fisher's method to high speeds by either exponentially back-extrapolating from finite time or using a non-exponential back-extrapolation. Our results are compatible with predictions made using a different analytical approach (Rouzine et al. 2003, 2007), and agree well with numerical simulations.

q-bio.PE↗

arXiv2007

The look-ahead effect of phenotypic mutations

The evolution of complex molecular traits such as disulphide bridges often requires multiple mutations. The intermediate steps in such evolutionary trajectories are likely to be selectively neutral or deleterious. Therefore, large populations and long times may be required to evolve such traits. We propose that errors in transcription and translation may allow selection for the intermediate mutations if the final trait provides a large enough selective advantage. We test this hypothesis using a population based model of protein evolution. If an individual acquires one of two mutations needed for a novel trait, the second mutation can be introduced into the phenotype due to transcription and translation errors. If the novel trait is advantageous enough, the allele with only one mutation will spread through the population, even though the gene sequence does not yet code for the complete trait. The first mutation then has a higher frequency than expected without phenotypic mutations giving the second mutation a higher probability of fixation. Thus, errors allow protein sequences to ''look-ahead'' for a more direct path to a complex trait.

q-bio.PE↗

arXiv2007

The traveling wave approach to asexual evolution: Muller's ratchet and speed of adaptation

We use traveling-wave theory to derive expressions for the rate of accumulation of deleterious mutations under Muller's ratchet and the speed of adaptation under positive selection in asexual populations. Traveling-wave theory is a semi-deterministic description of an evolving population, where the bulk of the population is modeled using deterministic equations, but the class of the highest-fitness genotypes, whose evolution over time determines loss or gain of fitness in the population, is given proper stochastic treatment. We derive improved methods to model the highest-fitness class (the stochastic edge) for both Muller's ratchet and adaptive evolution, and calculate analytic correction terms that compensate for inaccuracies which arise when treating discrete fitness classes as a continuum. We show that traveling wave theory makes excellent predictions for the rate of mutation accumulation in the case of Muller's ratchet, and makes good predictions for the speed of adaptation in a very broad parameter range. We predict the adaptation rate to grow logarithmically in the population size until the population size is extremely large.

q-bio.PE↗

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

Tradeoff between short-term and long-term adaptation in a changing environment

We investigate the competition dynamics of two microbial or viral strains that live in an environment that switches periodically between two states. One of the strains is adapted to the long-term environment, but pays a short-term cost, while the other is adapted to the short-term environment and pays a cost in the long term. We explore the tradeoff between these alternative strategies in extensive numerical simulations, and present a simple analytic model that can predict the outcome of these competitions as a function of the mutation rate and the time scale of the environmental changes. Our model is relevant for arboviruses, which alternate between different host species on a regular basis.

q-bio.PE↗

arXiv2005

Quasispecies can exist under neutral drift at finite population sizes

We investigate the evolutionary dynamics of a finite population of RNA sequences adapting to a neutral fitness landscape. Despite the lack of differential fitness between viable sequences, we observe typical properties of adaptive evolution, such as increase of mean fitness over time and punctuated equilibrium transitions. We discuss the implications of these results for understanding evolution at high mutation rates, and extend the relevance of the quasispecies concept to finite populations and time scales. Our results imply that the quasispecies concept and neutral drift are not complementary concepts, and that the relative importance of each is determined by a combination of population size and mutation rate.

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↗

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↗