Pervasive degeneracy and epistasis in a protein-protein interface

By Anna I. Podgornaia, M. Laub. 2015

191 citations recorded by the source corpus.

Exploring the limits of protein sequence space Exploring the variability of individual functional proteins is complicated by the vast number of combinations of possible amino acid sequences. Podgornaia and Laub take on this challenge by analyzing four amino acids critical for the interaction between two signaling proteins in Escherichia coli. They build all the possible 160,000 variants of one of the two proteins and find that over 1650 are functional. Even though there can be very high variability in the composition of the interface between the two proteins, there are nonetheless strong context-dependent constraints for some amino acids, which suggests why many functional variants are not seen in nature. Science, this issue p. 673 Four key residues in the Escherichia coli protein kinase PhoQ drive recognition of its substrate PhoP. Mapping protein sequence space is a difficult problem that necessitates the analysis of 20N combinations for sequences of length N. We systematically mapped the sequence space of four key residues in the Escherichia coli protein kinase PhoQ that drive recognition of its substrate PhoP. We generated a library containing all 160,000 variants of PhoQ at these positions and used a two-step selection coupled to next-generation sequencing to identify 1659 functional variants. Our results reveal extensive degeneracy in the PhoQ-PhoP interface and epistasis, with the effect of individual substitutions often highly dependent on context. Together, epistasis and the genetic code create a pattern of connectivity of functional variants in sequence space that likely constrains PhoQ evolution. Consequently, the diversity of PhoQ orthologs is substantially lower than that of functional PhoQ variants.

Subfields: Molecular Biology

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Citations in the corpus

Evolving new protein-protein interaction specificity through promiscuous intermediates (2015, unknown): “Similar approaches have also been used to probe subsets of all possible double and higher-order mutants (Melamed et al., 2013), or to systematically probe all possible mutants at a limited set of positions (Podgornaia and Laub, 2015).”
Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes (2016, background): “Even with these limitations, the model is still able to distinguish between functional multi-point mutations that are composed of non-functional single-point mutations for the most favorable predictions, in accordance with experiment (Podgornaia and Laub 2015).”
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design (2019, background): “For both PhoQ and GB1, we initiated the simulation by selecting an initial batch of sequences consisting of all the single mutants plus 100 randomly-selected sequences.”
Epistasis in protein evolution (2016, background): “For example, in a high-throughput screen of a mutant protein library for variants that maintained the wild-type function, about 95% of the functional variants recovered would have been predicted to be nonfunctional from the effects of single mutations alone.(36) These deep mutational scanning studie”
Machine learning in protein engineering (2018, background): “There are now deep mutational scanning datasets for a variety of proteins and properties, including green ﬂuorescent protein (Sarkisyan et al., 2016), amidase (Wrenbeck et al., 2017), β -lactamase (Klesmith et al., 2017), β -glucosidase (Romero et al., 2015), HIV envelope protein (Haddox et al., 201”