Protein structure prediction is very heated research area in computational bioinformatics field. A Faster and more accurate protein secondary structure prediction tool can provide important support for ab initio and homology modelling protein 3D structure prediction. The protein secondary structure is acting as a bridge that link the sequence and 3D structure.
MUFOLD-SS version1 stand-alone executable can be downloaded here This version is using psiblast profile.
MUFOLD-SS version2 stand-alone executable can be downloaded here This version is using both psiblast profile and hhblits profiles for more accuracte prediction
Please note that version 1 is using just psiblast profile, which means the prediction speed is faster than version 2. Version 2 use a larger and more recent CullPDB to train the network, and the hhblits profiles are used for more accurate prediction.
MUFOLD-SS version 3 stand-alone executable can be downloaded here This version is for fast prediction if you already have the profiles generated by yourself.
Citation: Fang, Chao, Yi Shang, and Dong Xu. MUFold-SS: Protein Secondary Structure Prediction Using Deep Inception-Inside-Inception Networks. PROTEINS: Structure, Function, and Bioinformatics (2018).
Please note that MUFold-SS is free for academic use purpose only, for commercial usage, please contact authors(See contact page)
1. You need to have a Psiblast database to search for profile. The MUFold-SS model was trained using profiles which were generated by Psiblast using the UniRef50 database. This database is still large, so when we benchmark the tool using test sets. We use a smaller database, UniRef50_Shrinked. We filtered the database so that it contains proteins whose length falls into range[70,3000]. For more database, please see Database Menu
2. You need to download the Psiblast toolkit from NCBI Psiblast offical website
3. You need to download the HHBlits from here On their github webpage, please follow their detail instruction to install HHBlits
5. Our driver program in MUFOLD-SS runs on tsch. Most Mac or Linux system can run tsch. If your machine doesn't have tsch. Please install it by typing: yum install tcsh or other commands. Please see this webpage for more reference here
1. Download the MUFold-SS stand alone package
2. Download and install Psiblast and HHBlits
3. Install Tensorflow and Keras
4. Follow the configuration file in the stand alone package and run