Solving the mystery of protein surface interactions with geometric fingerprints nature
was used as the loss function. MaSIF-search was trained for 40 h in a GPU, after which it was automatically killed, resulting in 260,000 iterations of the data. The MaSIF-search neural network implementation contained a total of 66,080 trainable parameters and was implemented in Tensorflow.In the MaSIF-search pipeline, surfaces are computed for each protein of interest, and both a MaSIF-search fingerprint and a MaSIF-site prediction are computed for each surface vertex.
Once a candidate patch is aligned, the IPA neural network is used to score the alignment with a score between 0 and 1 on the prediction of whether the alignment corresponds to a real interaction or not.
Once fingerprints are matched, a second-stage alignment and scoring method uses the RANSAC algorithm as described above. After RANSAC produces an alignment, the IPA neural network classifies true binders versus non-binders and outputs an IPA score . Those candidate binders with an IPA score of more than 0.90–0.97 in the neural network score were accepted.A set of transient interactions from PDBBind was scanned to identify proteins that bind to helical motifs. A binding motif was determined to be a helix if 80% of residues are helical and the total number of residues does not exceed 60.
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