Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
Year: 2022
Type: dataset
Abstract: Benchmark data sets of CDPred as described in Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks Zhiye Guo1, Jian Liu1, Jeffrey Skolnick2, Jianlin Cheng1* 1 Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211 2 School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332-2000 *Corresponding author ([email protected]) There is four test dataset in this package, each test dataset contains four different folders and one list file. The afpred_pdb includes all the corresponding monomer structures predicted by alphafold. The cdpred_output includes the prediction results of our tool CDPred for each dataset. The pre_gen_a3m includes the multiple sequence alignments file used by CDPred to generate prediction results. And the true_pdb includes the fasta file for the test dataset and its heavy atom distance map (h_dist) and carbon alpha distance map (real_dist) that extract from the native structure. HomoTest1: The homodimer test dataset contains 28 targets collect from CASP_CAPRI 10-13 HomoTest2: The homodimer test dataset contains 23 targets collect from CASP_CAPRI 13-14 HeteroTest1: The heterodimer test dataset contains 9 targets collect from CASP_CAPRI13-14 HeteroTest2: The heterodimer test dataset contains 55 targets collect from PDB bank 09-2021 to 11-2021
Institutions: University of Missouri, Georgia Institute of Technology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4394050033