Flexibility-aware graph-based algorithm improves antigen epitopes identification

Abstract

Epitopes of an antigen are the surface residues in the spatial proximity that can be recognized by antibodies. Identifying such residues has shown promising potentiality in vaccine design, drug development and chemotherapy, thus attracting extensive endeavors. Although great efforts have been made, the epitope prediction performance is still unsatisfactory. One possible issue accounting to this poor performance could be the ignorance of structural flexibility of antigens. Flexibility is a natural characteristic of antigens, which has been widely reported. However, this property has never been used by existing models. To this end, we propose a novel flexibility-aware graph-based computational model to identify epitopes. Unlike existing graph-based approaches that take the static structures of antigens as input, we consider all possible variations of the side chains in graph construction. These flexibility-aware graphs, of which the edges are highly enriched, are further partitioned into subgraphs by using a graph clustering algorithm. These clusters are subsequently expanded into larger graphs for detecting overlapping residues between epitopes if exist. Finally, the expanded graphs are classified as epitopes or non-epitopes via a newly designed graph convolutional network. Experimental results show that our flexibility-aware model markedly outperforms existing approaches and promotes the F1-score to 0.656. Comparing to the state-of-the-art, our approach makes an increment of F1-score by 16.3%. Further in-depth analysis demonstrates that the flexibility-aware strategy contributes the most to the improvement.

A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

“…1. Build a graph from the input data following the graph formation procedure in Gao et al [7] 2. Calculate the edge weights following the procedure in Gao [7] 3.…”

“…Build a graph from the input data following the graph formation procedure in Gao et al [7] 2. Calculate the edge weights following the procedure in Gao [7] 3. Calculate the ASA, RSA, PSAIA, and AAIndex features of the residues in the graph and store them as attributes of each node of the graph 4.…”

“…The dataset was collected and preprocessed using the procedure described in Solihah et al [25]. Raw data in the form of 3d chain antigen structures containing information on residues exposed at the atomic level and labeled epitope and non-epitope was taken from Gao et al [7]. These residues are enriched with features of structural analysis with PSAIA, which include ASA, RSA, and Protrusion Index [26], Contact Number [27], QSE [28], HSE [29], AAIndex [30], B Factor [31] and Log Odd Ratio [32].…”