6 TABLE IV: TopK accuracy check with different strategies, from TopK SFP check (left part) to TopK AFP’s neighbor check (right part); different coding systems, from AA (A%), CL (C%) to hp-CL (H%); different homologous level, from family (Fam), superfamily (Sup) to fold; and different SFP length L , from 6, 12 to 18. TABLE V: Alignment accuracy metric on HOMSTRAD FIG. 5: Histogram of the percentage of SFP-list’s size to the search space (moln1*moln2) between all homologous levels (from family to fold) under the self-adaptive strategy, where the moln1 (moln2) is the first (second) structure’s size. The peak value is about 1.045% and the mean value is about 1.5505%. The total pair count is 50,069. TopK SFP’s Accuracy TopK AFP’s Neighbor Accuracy Level TopK L = 6 L = 12 L = 18 Self(9-18) ∗ TopK L = 6 L = 12 L = 18 A% C% H% A% C% H% A% C% H% C% H% A% C% H% A% C% H% A% C% H% Fam 1 53.1 63.1 68.0 62.4 74.4 77.6 62.4 74.3 77.1 73.1 75.5 1 25.8 41.4 46.3 46.1 69.7 74.2 62.4 86.0 88.5 5 74.4 86.7 88.9 79.4 90.7 92.3 80.1 91.2 92.4 90.1 91.6 2 35.1 53.3 59.0 55.8 79.3 83.4 71.0 91.7 93.7 10 82.0 93.1 94.4 85.7 95.4 96.2 86.6 96.0 96.6 95.2 96.0 3 41.1 59.9 65.9 61.4 83.4 87.3 75.4 93.9 95.6 20 89.1 97.1 97.8 91.8 98.2 98.6 92.8 98.4 98.8 98.0 98.5 4 45.7 64.4 70.5 65.2 85.9 89.6 78.3 95.0 96.6 Sup 1 39.1 48.7 52.4 44.8 58.3 61.8 43.8 58.2 61.4 56.2 59.4 1 22.5 36.3 40.6 39.7 63.9 68.0 56.6 83.3 85.8 5 58.2 73.7 76.7 62.4 79.2 81.8 63.0 79.7 81.5 78.4 80.3 2 31.0 47.9 52.9 48.9 73.9 77.6 65.1 89.8 91.7 10 67.0 83.1 85.3 71.1 87.3 88.9 72.7 87.9 89.4 86.9 88.5 3 36.7 54.7 59.8 54.2 78.5 82.2 69.5 92.3 93.9 20 77.2 90.5 91.8 81.0 93.5 94.5 82.8 94.2 95.0 93.5 94.4 4 41.1 59.4 64.7 57.9 81.4 84.9 72.5 93.7 95.2 Fold 1 13.9 26.8 29.7 17.0 33.8 36.7 17.0 33.5 35.6 31.9 34.2 1 10.7 24.8 27.0 20.0 46.6 49.8 32.2 68.4 70.7 5 28.8 50.3 54.2 33.2 57.0 60.2 34.2 58.6 60.7 57.4 59.6 2 17.3 35.1 38.6 28.3 57.8 61.4 41.5 77.4 79.4 10 39.5 63.6 66.7 45.7 70.5 73.0 48.4 74.1 75.4 72.5 74.2 3 22.3 41.9 46.0 33.7 63.8 67.6 46.9 81.5 83.5 20 55.8 77.2 79.4 62.3 84.5 86.3 66.3 87.0 87.8 85.9 87.0 4 26.4 46.9 51.4 37.8 67.8 71.6 51.1 84.3 86.2 $^{∗}$: Self(9-18) means the application of self-adaptive strategy [26] of the SFP’s length L from 9 to 18. IV. DISCUSSION AND FUTURE WORK C. Implement of hydropathy conformational letters to structural alignment  0  100  200  300  400  500  600  0  1  2  3  4  5 # of structure pairs percentage of the size of SFP-list Metric CLeFAPS(CL) CLeFAPS(hp-CL) MATT C/LOA 1 0.929 0.939 0.948 C/LOR 2 0.898 0.907 0.831 $^{1}$: Correct/(Length of the algorithm). $^{2}$: Correct/(Length of the reference). ments for homologous families [42]. Its alignments were generated using structural alignment programs, then fol- lowed by a manual scrutiny of individual cases. There are totally 1033 families (633 at pairwise level). We’ll show the improvement based on hp-CL as the coding systems instead of CL under the same algorithm, say CLeFAPS, in Table V. We embed hp-CL to the pairwise protein structural alignment problem under the framework of CLeFAPS [26]. Particularly, we first transform each structures to its hp-CL strings; then search for both highly specific SFPs (SFP H) that have a high HP-CLESUM score to build an initial alignment from the best TM-score [39] SFP within TopK, and highly sensitive SFPs (SFP L) that have a low HP-CLESUM score (must above 0) to re- fine the alignment through fuzzy-add strategy. These two SFP-lists can be generated simultaneously [24]; finally we apply an elongation based on Vect-score to collect local flexible fragments. HOMSTRAD is a database of protein structural align- To explore the joint space of both AAs and SAs, en- tropic clustering is a simple but effective approach. In this work, only the reduction of AAs is considered, while we may also reduce CLs and AAs simultaneously while balancing the accuracy and the parameter numbers. For example, if reducing the CL to 9 letters, (actually, from Fig. 1, there are 4 codes for helix which can be grouped to one cluster, the same as sheet.) we may then consider up to four AA cluster instead of two, while the total al- phabet number is about the same as hp-CL. It is interesting that, hp-CL can be applied during the situation we know only a little information about the AA sequence of the structure, i.e., the hydropathy features, or even none. That is true because, from the knowledge of protein design [40, 41], the hydropathy patterns from a 3D structure may probably be deduced. Then the usage of hp-CLs and HP-CLESUM that consider the hydropa-