Accurately predicting changes in protein stability upon amino acid substitution is a much sought after goal. Destabilizing mutations are often implicated in disease, whereas stabilizing mutations are of great value for industrial and therapeutic biotechnology. Increasing protein stability is an especially challenging task, with random substitution yielding stabilizing mutations in only ∼2% of cases. To overcome this bottleneck, computational tools that aim to predict the effect of mutations have been developed; however, achieving accuracy and consistency remains challenging. Here, we combined 11 freely available tools into a meta-predictor (meieringlab.uwaterloo.ca/stabilitypredict/). Validation against ∼600 experimental mutations indicated that our meta-predictor has improved performance over any of the individual tools. The meta-predictor was then used to recommend 10 mutations in a previously designed protein of moderate thermodynamic stability, ThreeFoil. Experimental characterization showed that four mutations increased protein stability and could be amplified through ThreeFoil's structural symmetry to yield several multiple mutants with >2-kcal/mol stabilization. By avoiding residues within functional ties, we could maintain ThreeFoil's glycan-binding capacity. Despite successfully achieving substantial stabilization, however, almost all mutations decreased protein solubility, the most common cause of protein design failure. Examination of the 600-mutation data set revealed that stabilizing mutations on the protein surface tend to increase hydrophobicity and that the individual tools favor this approach to gain stability. Thus, whereas currently available tools can increase protein stability and combining them into a meta-predictor yields enhanced reliability, improvements to the potentials/force fields underlying these tools are needed to avoid gaining protein stability at the cost of solubility.

Lead is highly toxic and its detection has attracted a lot of research interests. In recent years, DNA has been used for Pb 2+ recognition and many fluorescent sensors with low to sub-nM detection limits have been reported. These figures of merit were typically measured using a spectrophotometer that can detect nM DNA with a high signal-to-noise ratio.

Highlights d Experimentally, mutations predicted to stabilize are near neutral on average d Stability predictors favor mutations that increase stability but decrease solubility d Predictor performance is quantified well by the Matthews correlation coefficient d Multi-mutants reach stability targets with higher probability than single mutants

Abstract.Metal induced nucleic acid folding has been extensively studied with ribozymes, DNAzymes, tRNA and riboswitches. These RNA/DNA molecules usually have a high content of double-stranded regions to support a rigid scaffold. On the other hand, such rigid structural features are not available for many in vitro selected or rationally designed DNA aptamers; they adopt flexible random coil structures in the absence of target molecules. Upon target binding, these aptamers adaptively fold into a compact structure with a reduced end-to-end distance, making fluorescence resonance energy transfer (FRET) a popular signaling mechanism. However, non-specific folding induced by mono-or divalent metal ions can also reduce the end-to-end distance and thus lead to false positive results. In this study we used a FRET pair labeled Hg II binding DNA and monitored metal induced folding in the presence of various cations. While non-specific electrostatically mediated folding can be very significant, at each tested salt condition, Hg II induced folding was still observed with a similar sensitivity. We also studied the biophysical meaning of the acceptor/donor fluorescence ratio that allowed us to explain the experimental observations. Potential solutions for this ionic strength problem have been discussed. For example, probes designed to signaling the formation of double-stranded DNA showed a lower dependency on ionic strength.3