Method for finding universal peptide linkers

Abstract

The invention provides a method for finding universal peptide linkers, comprising following steps. By aligning peptides from a database back to a protein sequence, a required length of a flanking linker protein is increased to generate positive samples. Afterwards, non-cutting point positions outside peptide intervals are determined as possible negative samples, and the possible negative sample interval is cut into a same length as the positive sample. After defining positive samples and negative samples, the negative samples that are identical to positive samples are removed. Next, the negative samples are grouped through a clustering algorithm, and a number of clusters is determined by an elbow method. Then, a difference or distance between the positive samples and the negative samples is calculated using a group center or an average within each group. Based on an average distance ranking and a proportion ranking, a weighted average ranking is taken as a selection of peptide linkers.

Claims

1. A method for identifying universal peptide linkers, comprising: By aligning peptides back to protein sequences from a database, the flanking length of the linker protein is increased to generate positive samples. Non-cutting points outside the peptide region are identified as potential negative samples, and the potential negative sample regions are cut to the same length as the positive samples. After defining positive and negative samples, negative samples that overlap with positive samples are removed. Negative samples are grouped using a clustering algorithm, and the number of clusters is determined using the elbow method. The distance between positive and negative samples is calculated using the cluster center or the average within each cluster. A weighted average ranking is then taken based on the average distance ranking and the proportion ranking, and used as the selection criterion for peptide linkers. The average distance ranking utilizes the characteristics of positive samples and their distance from the cluster center, ranking them from farthest to closest within each cluster.

2. The method for identifying universal peptide linkers as described in claim 1, wherein the characteristics of the clusters include physicochemical properties or structural features.

3. The method for finding universal peptide linkers as described in claim 1, wherein the ratio ranking is based on the ratio of positive to negative samples before removing duplicates, and the positive and negative sample ratios are divided and ranked from largest to smallest.

4. The method for finding universal peptide linkers as described in claim 1, wherein the formula for obtaining the weighted average ranking is as follows: Weighted average ranking = W1 * distance ranking + W2 * proportion ranking, where W1 and W2 are weights defined by the user.

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