GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects

The systematic and accurate description of protein mutational landscapes is a question of utmost importance in biology, bioengineering, and medicine. Recent progress has been achieved by leveraging on the increasing wealth of genomic data and by modeling intersite dependencies within biological sequences. However, state-of-the-art methods remain time consuming. Here, we present Global Epistatic Model for predicting Mutational Effects (GEMME) (www.lcqb.upmc.fr/GEMME), an original and fast method that predicts mutational outcomes by explicitly modeling the evolutionary history of natural sequences. This allows accounting for all positions in a sequence when estimating the effect of a given mutation. GEMME uses only a few biologically meaningful and interpretable parameters. Assessed against 50 high- and low-throughput mutational experiments, it overall performs similarly or better than existing methods. It accurately predicts the mutational landscapes of a wide range of protein families, including viral ones and, more generally, of much conserved families. Given an input alignment, it generates the full mutational landscape of a protein in a matter of minutes. It is freely available as a package and a webserver at www.lcqb.upmc.fr/GEMME/.

GEMME: a simple and fast global epistatic model predicting mutational effects

AbstractsThe systematic and accurate description of protein mutational landscapes is a question of utmost importance in biology, bioengineering and medicine. Recent progress has been achieved by leveraging on the increasing wealth of genomic data and by modeling inter-site dependencies within biological sequences. However, state-of-the-art methods require numerous highly variable sequences and remain time consuming. Here, we present GEMME (www.lcqb.upmc.fr/GEMME), a method that overcomes these limitations by explicitly modeling the evolutionary history of natural sequences. This allows accounting for all positions in a sequence when estimating the effect of a given mutation. Assessed against 41 experimental high-throughput mutational scans, GEMME overall performs similarly or better than existing methods and runs faster by several orders of magnitude. It greatly improves predictions for viral sequences and, more generally, for very conserved families. It uses only a few biologically meaningful and interpretable parameters, while existing methods work with hundreds of thousands of parameters.

Cytoplasmic effects on grain resistance to yellowberry in durum wheat

Parental, F₁, reciprocal F₁ (RF₁), F₂, reciprocal F₂ (RF₂), BC₁P₁and BC₁P₂ generations of four crosses involving four cultivars of durum wheat (Triticum durum Desf.) were evaluated for grain resistance to yellowberry. Significant differences were reported for F₁, F₂and their reciprocals in all crosses. A generation means analysis indicated the inadequacy of additive-dominance model and additive-dominance model considering maternal effects. However, the variation in generation means in the four crosses could be explained by a digenic epistatic model with cytoplasmic effects. Cytoplasmic effects were significant and consistent in all the crosses. Dominance effects and additive × dominance epistasis were more important than additive effects and other epistatic components. The choice of a female parent possessing grain resistance to yellowberry appeared to be decisive in durum wheat breeding for resistance to this serious seed disorder.

Genetic Analysis of Cut-flower Longevity in Antirrhinum majus

Genetics of Antirrhinum majus L. (snapdragon) cut flower postharvest longevity (PHL) was investigated by generation means analysis using a white short-lived inbred (WS) and white long-lived inbred (WL) to determine mode of inheritance and heritability. Broad and narrow sense PHL heritability was estimated at 78% and 30%, respectively. Scaling tests for adequacy of an additive-dominance model in explaining PHL inheritance suggested absence of epistasis. However, joint scaling indicated digenic or higher order epistatic interactions. Fitting of a digenic epistatic model revealed significant additive effects and nonsignificant dominance and epistatic interactions. Additionally, based on sequential model fittings all six parameters [mean, additive (a), dominance (d), a×a, d×d, and a×d] proved necessary to explain observed PHL variation. Continuous variation for PHL observed in the F2 and backcross generations suggests PHL is quantitative. Assessment of associated traits revealed a positive relationship between number of flowers opening postharvest on a cut flower and PHL. In addition, floret wilting led to short PHL while floret browning was associated with long PHL.