Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

Benchmark data sets of CDPred as described in <strong>Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks</strong> Zhiye Guo¹, Jian Liu¹, Jeffrey Skolnick², Jianlin Cheng^1* ¹Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211 ²School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332-2000 *Corresponding author ([e-mail]) There is four test dataset in this package, each test dataset contains four different folders and one list file. The <strong>afpred_pdb</strong> includes all the corresponding monomer structures predicted by alphafold. The <strong>cdpred_output </strong>includes the prediction results of our tool CDPred for each dataset. The <strong>pre_gen_a3m </strong>includes the multiple sequence alignments file used by CDPred to generate prediction results. And the <strong>true_pdb </strong>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