Solubility-Weighted Index: fast and accurate prediction of protein solubility

Bikash K. Bhandari; Paul P. Gardner; Chun Shen Lim

We performed Spearman's correlation analysis for both the PSI:Biology and eSOL datasets. SWI shows the strongest correlation with solubility compared to the standard and 9,920 protein sequence properties (Fig 3 and Supplementary Fig S2, respectively) . SWI also strongly correlates with flexibility, suggesting that SWI is also a good proxy for global structural flexibility. We asked whether protein solubility can be predicted by surface amino acid residues. To address this question, we examined a previously published dataset for the protein surface 'stickiness' of 397 E. coli proteins (Levy, De, and Teichmann 2012) . This dataset has the annotation for surface residues based on previously solved protein crystal structures. We observed little correlation between the protein surface 'stickiness' and the solubility data from eSOL (Spearman's rho = 0.05, P = 0.34, N = 348; Supplementary Fig S6A) . Next, we evaluated if amino acid composition scoring using surface residues is sufficient, optimising only the weights of surface residues should achieve similar or better results than SWI. As above, we iteratively refined the weights of surface residues using the Nelder-Mead optimisation algorithm. The method was initialised with Smith et al. 's normalised B-factors and a maximised correlation coefficient was the target. However, a low correlation was obtained upon convergence (Spearman's rho = 0.18, P = 7.20 ✕ 10 -4 ; Supplementary Fig S6B) . In contrast, the SWI of the full-length sequences has a much stronger correlation with solubility (Spearman's rho = 0.46, P = 2.97 ✕ 10 -19 ; Supplementary Fig S6C) . These results suggest that the full-length of sequences contributes to protein solubility, not just surface residues, in which solubility is modulated by cotranslational folding (Natan et al. 2018 ) .