Abstract
Modern computational tools can predict the mutational effects on protein stability, sometimes at the expense of activity or solubility. Here, we investigate two homologous computationally stabilized haloalkane dehalogenases: (i) the soluble thermostable DhaA115 (T(m)(app) = 74 °C) and (ii) the poorly soluble and aggregating thermostable LinB116 (T(m)(app) = 65 °C), together with their respective wild-type variants. The intriguing difference in the solubility of these highly homologous proteins has remained unexplained for three decades. We combined experimental and in-silico techniques and examined the effects of stabilization on solubility and aggregation propensity. A detailed analysis of the unfolding mechanisms in the context of aggregation explained the negative consequences of stabilization observed in LinB116. With the aid of molecular dynamics simulations, we identified regions exposed during the unfolding of LinB116 that were later found to exhibit aggregation propensity. Our analysis identified cryptic aggregation-prone regions and increased surface hydrophobicity as key factors contributing to the reduced solubility of LinB116. This study reveals novel molecular mechanisms of unfolding for hyperstabilized dehalogenases and highlights the importance of contextual information in protein engineering to avoid the negative effects of stabilizing mutations on protein solubility.