Quantifying the molecular origins of opposite solvent effects on protein-protein interactions.

Although the nature of solvent-protein interactions is generally weak and non-specific, addition of cosolvents such as denaturants and osmolytes strengthens protein-protein interactions for some proteins, whereas it weakens protein-protein interactions for others. This is exemplified by the puzzling observation that addition of glycerol oppositely affects the association constants of two antibodies, D1.3 and D44.1, with lysozyme. To resolve this conundrum, we develop a methodology based on the thermodynamic principles of preferential interaction theory and the quantitative characterization of local protein solvation from molecular dynamics simulations. We find that changes of preferential solvent interactions at the protein-protein interface quantitatively account for the opposite effects of glycerol on the antibody-antigen association constants. Detailed characterization of local protein solvation in the free and associated protein states reveals how opposite solvent effects on protein-protein interactions depend on the extent of dewetting of the protein-protein contact region and on structural changes that alter cooperative solvent-protein interactions at the periphery of the protein-protein interface. These results demonstrate the direct relationship between macroscopic solvent effects on protein-protein interactions and atom-scale solvent-protein interactions, and establish a general methodology for predicting and understanding solvent effects on protein-protein interactions in diverse biological environments.

Reactive oxygen species in photochemistry of the red fluorescent protein "Killer Red".

The fluorescent protein aptly named "Killer Red" (KRed) is capable of killing transfected cells and inactivating fused proteins upon exposure to visible light in the presence of oxygen. We have investigated the source of the bioactive species through a variety of photophysical and photochemical techniques. Our results indicate a Type I (electron transfer mediated) photosensitizing mechanism.

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.