## 2 Results  
### Data Collection and Establishment of PTS-Pre for the Prediction of trans-Prenyltransferases  
In our previous work, we used the \"three floors\" model to interpret the product chain length determination mechanism of _trans_-SC-PTs and revealed that the condensation reaction pockets in these enzymes only have enough space for GPP (C10) or FPP (C15), and the longer linear polyprenyl pyrophosphates must scrunch into an additional product chain elongation tunnel formed by three \(\alpha\) helices (helix D, E, and F) [10]. The residues from each of the \(\alpha\) helices, whose side-chains face toward the center of the tunnel, form the \"floors\", and the distance between each \"floor\" is 5.4 A (a pitch of \(\alpha\) helix), which is similar to the length of a single prenyl group (C5, 5.2 A). The sizes of the \"floor\" residues determine whether the prenyl group could pass through the \"floor\" or would be terminated. Thus, each \"floor\" from the top to bottom eventually determines the product specificity as FPP, GGPP, GFPP, and PPP (\(\geq\)C30), respectively (Figure 1). The \"three floors\" model was further verified by site-directed mutagenesis and applied in the identification of new GFPPSs in the Brassicaceae clade [10]. With the above achievements, however, in the previous study, all the \"floor\" residue positions from uncharacterized enzymes were located by manually generating sequence alignment through searching template enzymes from _Arabidopsis_. Moreover, the \"blocking efficiency\" of the \"floor\" residues was simply classified into three classes as large/medium/small side-chains, while the elongation impedimental influence between the floors was not taken into consideration. Thus, the prediction accuracy may decrease when the target protein has low sequence similarity with the searched templates from _Arabidopsis_ and the method can hardly be applied in the functional prediction for such a large number of sequences in the _trans_-PTs family automatically. To increase the similarity of sequence alignment and improve the accuracy of the positioning of \"floor\" residues, a sequence similarity network (SSN) was applied to generate the gene clustering map with its advantage of an excellent globe view for thousands of sequences and a good consistency with the phylogenetic tree [20]. A total number of 7870 _trans_-PT sequences from the Structure-Function Linkage Database (SFLD) [21] were analyzed using SSN with an e-value cutoff of 1e\({}^{-50}\), and the visualization of the clustering map was performed in Cytoscape [22]. According to the clustering map, the _trans_-PTs protein family could be divided into six predominant clusters with several tiny satellite groups (Figure 2A). All the _trans_-PT sequences whose structures were determined were assigned into the SSN map, and 11 representative protein sequences were finally selected as searching templates which covered 85% of total sequences (6684/7870) in the SFLD dataset (Figure 2A). Of these representative proteins, six were from Bacteria (PDB IDs: 2 FOR, 3 OYR, 3 QOV, 3 PKO, 1 WMW, and 3Q2Q), four belonged to Eukarya (PDB IDs: 5E8H, 2E8W, 1 FPS, and 3 AQ0), and one was from Archaea (PDB ID: 1 WY0) The product specificity of these searching templates ranged from FPP (C15) to PPP (\(\geq\)C30), and their structural information was used in the coordinate location of \"floor\" residues. These selected searching templates improved the average sequence identity from 33.9% to 46.1% over 141 testing sequences. The final average sequence identity for all 7870 sequences was 42.4% (Figure S2). Next, we tried to design an automatic algorithm for high-throughput functional prediction of the _trans_-PTs protein family.