Protein engineering and synthetic biology stand to benefit immensely from recent advances in in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple ‘parts’/functions, and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F-Bridge), attempts to address this by combining the scores of different design parameters pertaining to the protein being analysed into a single easily interpreted output describing overall performance. The strategy comprises 1. A mathematical strategy combining data from a myriad of in silico tools into an OP-score (a singular score informing on a user-defined overall performance); 2. The F2F-Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user-friendly strategy represents a pivotal enabler in increasing accessibility of synthetic protein building and de novo protein design. This article is protected by copyright. All rights reserved.