Identifying the interacting positions of a protein using Boolean learning and support vector machines

Identifying interacting residues using Boolean Learning and Support Vector Machines: case study on mRFP and DsRed proteins

In a protein, interactions exist between amino acid residues that influence the protein's structural integrity or stability and thus affect its catalytic function. The loss of this interaction due to mutations in these amino acids usually leads to a non-functional protein. Probing the sequence space of a protein through mutations or recombinations, as performed in directed evolution to search for an improved variant, frequently results in such inactive sequences. In this work, we demonstrate the use of machine learning to identify such interacting residues and the use of template engineering strategies to increase the fraction of active variants in a library. We show that using the sequences from recombination of monomeric red fluorescent protein (mRFP) and Discosoma red fluorescent protein (DsRed), we were able to identify a pair of interacting residues using an algorithm based on Boolean Learning and Support Vector Machines. The interaction between the identified residues was verified through point mutations on the mRFP and DsRed genes. We also show that it is possible to use such results to alter the parental genes such that the probability of disrupting the important interactions is minimized. This will result in a larger fraction of active variants in the recombinant library and allow us to access more functional space. We demonstrate this effect by comparing the recombinant library of wild-type (WT) DsRed, mRFP and an altered sequence of DsRed with mRFP WT genes.

Better library design: data-driven protein engineering

Data-driven protein engineering is increasingly used as an alternative to rational design and combinatorial engineering because it uses available knowledge to limit library size, while still allowing for the identification of unpredictable substitutions that lead to large effects. Recent advances in computational modeling and bioinformatics, as well as an increasing databank of experiments on functional variants, have led to new strategies to choose particular amino acid residues to vary in order to increase the chances of obtaining a variant protein with the desired property. Strategies for limiting diversity at each position, design of small sub-libraries, and the performance of scouting experiments, have also been developed or even automated, further reducing the library size.