## 2 Results
### Validation of PTS-Pre Prediction Using Experimentally Determined trans-PTs Sequences  
To date, there is only one method that has been published for predicting the function of _trans_-PTs, which used structural modeling and covalent ligand docking (referred to as MD-Pre, \"Modeling Docking Prediction method\", in the latter part of this paper) to predict the chain length specificity [14]. Since our proposed PTS-Pre tool utilizes a different mechanism, it was interesting to compare these two methods using the same testing dataset which was used for the MD-Pre as the initial testing. A total of 74 experimentally determined _trans_-PTs protein sequences were selected in MD-Pre which resulted in correct chain-length prediction for 58% (43/74) [14]. The same testing sequences were used for functional prediction by PTS-Pre which finally resulted in correct prediction for 77% (57/74) (Figure 3A, detailed prediction results are shown in Table S3). A more detailed comparison shows that 37 sequences were correctly predicted by both methods, 20 sequences were correctly predicted only by the PTS-Pre method, and six sequences were correctly predicted only by PTS-Pre (Table S4). Interestingly, among the 20 sequences which were correctly predicted only by PTS-Pre, 11 of them had activities for synthesizing final products longer than C30, while the predicted results by MD-Pre were much shorter (Table S3). This may be because the longer final products (compared with shorter chain length products) have more flexibility and complexity for MD-Pre prediction based on ligand docking. Nevertheless, the different prediction results by MD-Pre and PTS-Pre suggest that the mechanisms behind these two methods are dissimilar, and the prediction accuracy may be improved by combining these two methods in the future. Considering that experimental assays may also have a bias in identifying the product outcomes of _trans_-PTs (for example, the product chain length profile may change when using different protein purification tags or ratios of IPP/allylic co-substrates), and the detection methods may also have various sensitivities, we extended the testing dataset with more experimentally verified sequences which were analyzed via different detection methods including radio-gas chromatography (radio-GC), radio-high-performance liquid chromatography (radio-HPLC), thin-layer chromatography (TLC), and HPLC-MS/MS (Table S5). A testing dataset consisting of 147 sequences was generated and applied to the functional prediction using PTS-Pre. The prediction accuracy for the new dataset was 86% (121/141), and detailed results are shown in Tables S3 and S5. To explore the scope of this method, we further analyzed the results by classifying these sequences into three super kingdoms and calculated the prediction accuracy for each of them. There were nine sequences in Archaea, 84 sequences in Bacteria, 46 sequences in Eukarya, and two unclassified sequences. Among the three categories, the prediction accuracies for eukaryotic, archaeal, and bacterial sequences were 95% (44/46), 88% (8/9), and 80% (67/84), respectively (Figure 3B). These results are not surprising since the \"three floors\" model was proposed on the basis of crystal structures from _Arabidopsis_; thus, the eukaryotic sequences had the highest prediction accuracy. However, the 88% and 80% accuracies for Archaea and Bacteria suggest that the \"three floors\" model could also be applied to these two kingdoms. We further analyzed the wrongly predicted cases. As the incorrectly predicted number was too small in Archaea (1/9) and Eukarya (2/46), only bacterial cases (17/84) were taken into consideration. A more detailed classification by phylum revealed that the distribution of wrongly predicted sequences was mainly concentrated in specific phyla (Figure S4A). The 84 bacterial sequences from the testing dataset could be divided into 11 phyla, with the incorrectly predicted cases found only in six of them.