Protein engineering and synthetic biology stand to benefit immensely from recent advances 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 analyzed into a single easily interpreted output describing overall performance. The strategy comprises of (a) 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) and (b) 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 the accessibility of synthetic protein building and de novo protein design.

中文翻译：

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Function2Form Bridge-Toward合成蛋白整体性能预测。

蛋白质工程和合成生物学将从用于蛋白质的结构和功能分析的计算机工具的最新进展中受益匪浅。在设计新型蛋白质的情况下，当前的计算机软件工具会向用户通知查询蛋白质的各个参数，并为每个参数提供唯一的输出得分/指标。实际上，蛋白质具有多个“部分” /功能，并且旨在改变给定部分的蛋白质修饰通常会对其他蛋白质部分产生附带影响。不存在用于预测设计参数对最终蛋白质整体性能的综合影响的系统。Function2Form桥（F2F-Bridge）试图通过将与要分析的蛋白质有关的不同设计参数的得分组合成一个简单易懂的描述整体性能的输出来解决此问题。该策略包括（a）将来自多种计算机工具的数据组合到OP分数中的数学策略（分数表示用户定义的整体性能），以及（b）F2F图表（通知的图形方式） Wetlab生物学家从多方面考虑了设计结构的适用性，突出了改进的范围。F2F预测输出与本研究设计，构建和测试的一系列合成蛋白的wetlab数据进行了比较。统计/机器学习方法，用于预测整体性能，与F2F图一起使用，还进行了检查。在wetlab性能和F2F预测之间的比较证明了紧密而可靠的相关性。这种用户友好的策略代表了增加合成蛋白构建和从头蛋白设计的可及性的关键推动力。