Douglas L. Theobald

Analysis of Drosophila TRPA1 reveals an ancient origin for human chemical nociception

Chemical nociception, the detection of tissue-damaging chemicals, is important for animal survival and causes human pain and inflammation, but its evolutionary origins are largely unknown. Reactive electrophiles are a class of noxious compounds humans find pungent and irritating, such as allyl isothiocyanate (in wasabi) and acrolein (in cigarette smoke). Diverse animals, from insects to humans, find reactive electrophiles aversive, but whether this reflects conservation of an ancient sensory modality has been unclear. Here we identify the molecular basis of reactive electrophile detection in flies. We demonstrate that Drosophila TRPA1 (Transient receptor potential A1), the Drosophila melanogaster orthologue of the human irritant sensor, acts in gustatory chemosensors to inhibit reactive electrophile ingestion. We show that fly and mosquito TRPA1 orthologues are molecular sensors of electrophiles, using a mechanism conserved with vertebrate TRPA1s. Phylogenetic analyses indicate that invertebrate and vertebrate TRPA1s share a common ancestor that possessed critical characteristics required for electrophile detection. These findings support emergence of TRPA1-based electrophile detection in a common bilaterian ancestor, with widespread conservation throughout vertebrate and invertebrate evolution. Such conservation contrasts with the evolutionary divergence of canonical olfactory and gustatory receptors and may relate to electrophile toxicity. We propose that human pain perception relies on an ancient chemical sensor conserved across ∼500 million years of animal evolution.

THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures

UNLABELLED THESEUS is a command line program for performing maximum likelihood (ML) superpositions and analysis of macromolecular structures. While conventional superpositioning methods use ordinary least-squares (LS) as the optimization criterion, ML superpositions provide substantially improved accuracy by down-weighting variable structural regions and by correcting for correlations among atoms. ML superpositioning is robust and insensitive to the specific atoms included in the analysis, and thus it does not require subjective pruning of selected variable atomic coordinates. Output includes both likelihood-based and frequentist statistics for accurate evaluation of the adequacy of a superposition and for reliable analysis of structural similarities and differences. THESEUS performs principal components analysis for analyzing the complex correlations found among atoms within a structural ensemble. AVAILABILITY ANSI C source code and selected binaries for various computing platforms are available under the GNU open source license from http://monkshood.colorado.edu/theseus/ or http://www.theseus3d.org.

Likelihood-based classification of cryo-EM images using FREALIGN

We describe an implementation of maximum likelihood classification for single particle electron cryo-microscopy that is based on the FREALIGN software. Particle alignment parameters are determined by maximizing a joint likelihood that can include hierarchical priors, while classification is performed by expectation maximization of a marginal likelihood. We test the FREALIGN implementation using a simulated dataset containing computer-generated projection images of three different 70S ribosome structures, as well as a publicly available dataset of 70S ribosomes. The results show that the mixed strategy of the new FREALIGN algorithm yields performance on par with other maximum likelihood implementations, while remaining computationally efficient.

A formal test of the theory of universal common ancestry

Universal common ancestry (UCA) is a central pillar of modern evolutionary theory. As first suggested by Darwin, the theory of UCA posits that all extant terrestrial organisms share a common genetic heritage, each being the genealogical descendant of a single species from the distant past. The classic evidence for UCA, although massive, is largely restricted to ‘local’ common ancestry—for example, of specific phyla rather than the entirety of life—and has yet to fully integrate the recent advances from modern phylogenetics and probability theory. Although UCA is widely assumed, it has rarely been subjected to formal quantitative testing, and this has led to critical commentary emphasizing the intrinsic technical difficulties in empirically evaluating a theory of such broad scope. Furthermore, several researchers have proposed that early life was characterized by rampant horizontal gene transfer, leading some to question the monophyly of life. Here I provide the first, to my knowledge, formal, fundamental test of UCA, without assuming that sequence similarity implies genetic kinship. I test UCA by applying model selection theory to molecular phylogenies, focusing on a set of ubiquitously conserved proteins that are proposed to be orthologous. Among a wide range of biological models involving the independent ancestry of major taxonomic groups, the model selection tests are found to overwhelmingly support UCA irrespective of the presence of horizontal gene transfer and symbiotic fusion events. These results provide powerful statistical evidence corroborating the monophyly of all known life.

Rapid calculation of RMSDs using a quaternion- based characteristic polynomial

A common measure of conformational similarity in structural bioinformatics is the minimum root mean square deviation (RMSD) between the coordinates of two macromolecules. In many applications, the rotations relating the structures are not needed. Several common algorithms for calculating RMSDs require the computationally costly procedures of determining either the eigen decomposition or matrix inversion of a 3x3 or 4x4 matrix. Using a quaternion-based method, here a simple algorithm is developed that rapidly and stably determines RMSDs by circumventing the decomposition and inversion problems.

A gustatory receptor paralogue controls rapid warmth avoidance in Drosophila

Behavioural responses to temperature are critical for survival, and animals from insects to humans show strong preferences for specific temperatures. Preferred temperature selection promotes avoidance of adverse thermal environments in the short term and maintenance of optimal body temperatures over the long term, but its molecular and cellular basis is largely unknown. Recent studies have generated conflicting views of thermal preference in Drosophila, attributing importance to either internal or peripheral warmth sensors. Here we reconcile these views by showing that thermal preference is not a singular response, but involves multiple systems relevant in different contexts. We found previously that the transient receptor potential channel TRPA1 acts internally to control the slowly developing preference response of flies exposed to a shallow thermal gradient. We now find that the rapid response of flies exposed to a steep warmth gradient does not require TRPA1; rather, the gustatory receptor GR28B(D) drives this behaviour through peripheral thermosensors. Gustatory receptors are a large gene family, widely studied in insect gustation and olfaction, and are implicated in host-seeking by insect disease vectors, but have not previously been implicated in thermosensation. At the molecular level, GR28B(D) misexpression confers thermosensitivity upon diverse cell types, suggesting that it is a warmth sensor. These data reveal a new type of thermosensory molecule and uncover a functional distinction between peripheral and internal warmth sensors in this tiny ectotherm reminiscent of thermoregulatory systems in larger, endothermic animals. The use of multiple, distinct molecules to respond to a given temperature, as observed here, may facilitate independent tuning of an animal’s distinct thermosensory responses.

Fast determination of the optimal rotational matrix for macromolecular superpositions

Finding the rotational matrix that minimizes the sum of squared deviations between two vectors is an important problem in bioinformatics and crystallography. Traditional algorithms involve the inversion or decomposition of a 3 × 3 or 4 × 4 matrix, which can be computationally expensive and numerically unstable in certain cases. Here, we present a simple and robust algorithm to rapidly determine the optimal rotation using a Newton‐Raphson quaternion‐based method and an adjoint matrix. Our method is at least an order of magnitude more efficient than conventional inversion/decomposition methods, and it should be particularly useful for high‐throughput analyses of molecular conformations.

Accurate structural correlations from maximum likelihood superpositions.

The cores of globular proteins are densely packed, resulting in complicated networks of structural interactions. These interactions in turn give rise to dynamic structural correlations over a wide range of time scales. Accurate analysis of these complex correlations is crucial for understanding biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related molecules, including families of nuclear magnetic resonance (NMR) models, different crystal forms of a protein, and structural alignments of homologous proteins, as well as molecular dynamics trajectories. Dominant modes of structural correlation are determined using principal components analysis (PCA) of the maximum likelihood estimate of the correlation matrix. The correlations we identify are inherently independent of the statistical uncertainty and dynamic heterogeneity associated with the structural coordinates. We additionally present an easily interpretable method (“PCA plots”) for displaying these positional correlations by color-coding them onto a macromolecular structure. Maximum likelihood PCA of structural superpositions, and the structural PCA plots that illustrate the results, will facilitate the accurate determination of dynamic structural correlations analyzed in diverse fields of structural biology.

Using ancient protein kinases to unravel a modern cancer drug’s mechanism

Evolution of dynamics affects function The drug Gleevac inhibits Abl kinases and is used to treat multiple cancers. The closely related Src kinases also play a role in cancer but are not inhibited effectively by Gleevac. Nevertheless, Gleevac-bound structures of Src and Abl are nearly identical. Based on this structural information and protein sequence data, Wilson et al. reconstructed the common ancestor of Src and Abl. Mutations that affected conformational dynamics caused Gleevac affinity to be gained on the evolutionary trajectory toward Abl and lost on the trajectory toward Src. Science, this issue p. 882 Characterization of ancestors of the kinases Src and Abl reveals why they respond differently to the cancer drug Gleevec. Macromolecular function is rooted in energy landscapes, where sequence determines not a single structure but an ensemble of conformations. Hence, evolution modifies a protein’s function by altering its energy landscape. Here, we recreate the evolutionary pathway between two modern human oncogenes, Src and Abl, by reconstructing their common ancestors. Our evolutionary reconstruction combined with x-ray structures of the common ancestor and pre–steady-state kinetics reveals a detailed atomistic mechanism for selectivity of the successful cancer drug Gleevec. Gleevec affinity is gained during the evolutionary trajectory toward Abl and lost toward Src, primarily by shifting an induced-fit equilibrium that is also disrupted in the clinical T315I resistance mutation. This work reveals the mechanism of Gleevec specificity while offering insights into how energy landscapes evolve.

Empirical Bayes hierarchical models for regularizing maximum likelihood estimation in the matrix Gaussian Procrustes problem

Procrustes analysis involves finding the optimal superposition of two or more “forms” via rotations, translations, and scalings. Procrustes problems arise in a wide range of scientific disciplines, especially when the geometrical shapes of objects are compared, contrasted, and analyzed. Classically, the optimal transformations are found by minimizing the sum of the squared distances between corresponding points in the forms. Despite its widespread use, the ordinary unweighted least-squares (LS) criterion can give erroneous solutions when the errors have heterogeneous variances (heteroscedasticity) or the errors are correlated, both common occurrences with real data. In contrast, maximum likelihood (ML) estimation can provide accurate and consistent statistical estimates in the presence of both heteroscedasticity and correlation. Here we provide a complete solution to the nonisotropic ML Procrustes problem assuming a matrix Gaussian distribution with factored covariances. Our analysis generalizes, simplifies, and extends results from previous discussions of the ML Procrustes problem. An iterative algorithm is presented for the simultaneous, numerical determination of the ML solutions.