Thus, stop-point orders could be scored from 0 to 28; a sequence ordered perfectly would receive a score of 0 and a sequence ordered as poorly as possible would receive a score of 28.
Step 3. Protein structures are compared using a sequence order-independent profile-profile alignment (SOIPPA) algorithm.
An empirical distance cutoff of 0.85 that maps to an RMSD of 0.7 Å and pocket sequence similarity of 60% for a sequence order-independent alignment of 12 15 atoms is used in this study.
These values are sorted obtaining a sequencing order.
In this article, we propose a sequence-order-independent method that distinguishes App-DDoS attacks from normal traffic.
For that reason, we constructed a sequence-order-based defense method, which is modeled by the HsMM [8].
This article introduces a sequence-order-independent method for the profiling of network traffic and the detection of a new type of App-DDoS attacks.
The binding site is then compared against target 3D protein structures using a sequence-order independent profile profile alignment (SOIPPA) algorithm [39] that is able to detect similar binding sites between structurally unrelated proteins.
We compared our method with a sequence-order-based defense method on the standard metrics such as the detection rate (DR), false positive rate (FPR), and receiver operating characteristic (ROC) curves.
This is a good demonstration of the value behind a sequence-order independent alignment algorithm that ignores fold and residue connectivity.
Tm-Align is a sequence-order independent protein structure comparison algorithm.
