doubleQ2018/Deep-learning-for-contact_map_v2

Prediction of protein contact map

Deep-learning-for-contact_map_v2

This is an unofficial implementation of Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model, which is a deep learning method for prediction of pretein contact map, with predicted contact map by other software(for example CCMpred, PSICOV, and so on) as input.

Requirements

• python2.7
• Tensorflow (>= 1.0.0)
• Numpy

Introduction

• Network structure: 2 networks(residual network and highway network) were implemented;
• Batch normalization and L2 regulation were implemented for optimization.

Need to do

1. Get protein structure 1D features(for example sequence, sse, ACA, and so on), and 2D features(for example predicted CCMpred, PSICOV and other pairwise features)
2. Modify ./read_into_tfrecord.py, and used it to transfer your data to tfrecord
3. set your own config in ./libs/config/config.py
4. run python train.py