The rational modification of protein stability is an important goal of protein design. Protein surface electrostatic interactions are not evolutionarily optimized for stability and are an attractive target for the rational redesign of proteins. We show that surface charge mutants can exert stabilizing effects in distinct and unanticipated ways, including ones that are not predicted by existing methods, even when only solvent-exposed sites are targeted. Individual mutation of three solvent-exposed lysines in the villin headpiece subdomain significantly stabilizes the protein, but the mechanism of stabilization is very different in each case. One mutation destabilizes native-state electrostatic interactions but has a larger destabilizing effect on the denatured state, a second removes the desolvation penalty paid by the charged residue, whereas the third introduces unanticipated native-state interactions but does not alter electrostatics. Our results show that even seemingly intuitive mutations can exert their effects through unforeseen and complex interactions.

Recent studies have pointed to protein S-nitrosylation as a critical regulator of cellular redox homeostasis. For example, S-nitrosylation of peroxiredoxin-2 (Prx2), a peroxidase widely expressed in mammalian neurons, inhibits both enzymatic activity and protective function against oxidative stress. Here, using in vitro and in vivo approaches, we identify a role and reaction mechanism of the reductase sulfiredoxin (Srxn1) as an enzyme that denitrosylates (thus removing -SNO) from Prx2 in an ATP-dependent manner. Accordingly, by decreasing S-nitrosylated Prx2 (SNO-Prx2), overexpression of Srxn1 protects dopaminergic neural cells and human-induced pluripotent stem cell (hiPSC)-derived neurons from NO-induced hypersensitivity to oxidative stress. The pathophysiological relevance of this observation is suggested by our finding that SNO-Prx2 is dramatically increased in murine and human Parkinson's disease (PD) brains. Our findings therefore suggest that Srxn1 may represent a therapeutic target for neurodegenerative disorders such as PD that involve nitrosative/oxidative stress.

Shotgun proteomics generates valuable information from large-scale and target protein characterizations, including protein expression, protein quantification, protein post-translational modifications (PTMs), protein localization, and protein-protein interactions. Typically, peptides derived from proteolytic digestion, rather than intact proteins, are analyzed by mass spectrometers because peptides are more readily separated, ionized and fragmented. The amino acid sequences of peptides can be interpreted by matching the observed tandem mass spectra to theoretical spectra derived from a protein sequence database. Identified peptides serve as surrogates for their proteins and are often used to establish what proteins were present in the original mixture and to quantify protein abundance. Two major issues exist for assigning peptides to their originating protein. The first issue is maintaining a desired false discovery rate (FDR) when comparing or combining multiple large datasets generated by shotgun analysis and the second issue is properly assigning peptides to proteins when homologous proteins are present in the database. Herein we demonstrate a new computational tool, ProteinInferencer, which can be used for protein inference with both small- or large-scale data sets to produce a well-controlled protein FDR. In addition, ProteinInferencer introduces confidence scoring for individual proteins, which makes protein identifications evaluable. This article is part of a Special Issue entitled: Computational Proteomics.