Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1

By Matteo Figliuzzi, H. Jacquier, Alexander Schug, O. Tenaillon, Martin Weigt. 2015

252 citations recorded by the source corpus.

The quantitative characterization of mutational landscapes is a task of outstanding importance in evolutionary and medical biology: It is, e.g., of central importance for our understanding of the phenotypic effect of mutations related to disease and antibiotic drug resistance. Here we develop a novel inference scheme for mutational landscapes, which is based on the statistical analysis of large alignments of homologs of the protein of interest. Our method is able to capture epistatic couplings between residues, and therefore to assess the dependence of mutational effects on the sequence context where they appear. Compared to recent large-scale mutagenesis data of the beta-lactamase TEM-1, a protein providing resistance against beta-lactam antibiotics, our method leads to an increase of about 40% in explicative power as compared to approaches neglecting epistasis. We find that the informative sequence context extends to residues at native distances of about 20 from the mutated site, reaching thus far beyond residues in direct physical contact.

Subfields: Genetics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, methodology): “…inductive bias of the models and frequency biases in the data, we compare against (1) untrained contextual language models: an untrained LSTM, and the Transformer at its initial state prior to training; and (2) a frequency baseline that maps a sequence to a vector of normalized amino acid counts.”
Generating functional protein variants with variational autoencoders (2020, methodology): “‘Mapped’ refers to the robust rank correlation described in [57], which maps model scores to the experimental scale by assigning the sequence with the n th highest score the n th highest experimental value, then computes the Pearson correlation.”
Language models enable zero-shot prediction of the effects of mutations on protein function (2021, methodology): “Unsupervised models of mutational effects can be learned from sequences [3, 4].”
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, background): “…the collective behavior of neurons (41,42), infer gene-interaction networks from experimentally observed transcription proﬁles (43), predict residue-residue contacts from multiple sequence alignments (37,44,45), study protein ﬁtness landscapes (46,47), and infer epistatic effects from ﬁtness (48).”
Inferring the shape of global epistasis (2018, methodology): “Here we apply this approach to a study of β-lactamase, an enzyme that degrades β-lactam antibiotics, which has been a model system for deep mutational scanning [39, 40, 41] and protein evolution [42, 43, 44, 45].”
Inferring interaction partners from protein sequences (2016, background): “[14] Figliuzzi M, Jacquier H, Schug A, Tenaillon O, Weigt”
Variational Inference for Sparse and Undirected Models (2016, background): “The ability to generalize well to new sequences will likely be important to the related problem of predicting mutation effects with unsupervised models of sequence variation (Hopf et al., 2017; Figliuzzi et al., 2015).”
Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis (2017, background): “Direct coupling analysis (DCA) (1, 2) and related tools (3, 4) develop a global model mimicking evolutionary fitness landscapes of protein families (5, 6) and quantify the coevolution of amino acid residue positions (7–9).”
Epistasis in protein evolution (2016, background): “tion terms have been experimentally validated as strong epistatic interactions.(61,62) Further, efforts to”
Efficient generative modeling of protein sequences using simple autoregressive models (2021, background): “Strong correlations to mutational effects determined experimentally via deep mutational scanning have been reported [23, 30].”
Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes (2016, background): “Two separate studies, which focused on antibiotic drug resistance in E. coli (Figliuzzi et al. 2016) and viral fitness of HIV-1 proteins (Ferguson et al. 2013), respectively, inferred a Potts model (i.e., additive fitness landscape), H , and calculated mutational changes as D H .”