multicom-toolbox Goto Github PK

chromosome3d

Reconstructing Three-Dimensional Chromosomal Structures from Hi- C Interaction Frequency Data using Distance Geometry Simulated Annealing

cnnqa

Deep convolutional neural networks for protein model quality assessment

coneva

Toolbox for Assessment of Protein Contacts

confold

Contact-based protein structure prediction

confold2

Improved ab initio protein structure reconstruction

deepcovqa

Deep convolutional neural networks for protein model quality assessment

deepdist

Deep learning prediction of protein residue-residue distances

deepqa

Deep Belief Networks for Single Protein Structural Model Quality Assessment

deepsf

Deep convolutional neural network for mapping protein sequences to folds

dncon2

Deep convolutional neural networks for protein contact map prediction

dnss

Deep learning networks for protein secondary structure prediction

dnss2

Deep learning architectures for protein secondary structure prediction (version 2)

dntor

Deep learning networks for protein torsion angle prediction

dobo

emap

The system of estimating model accuracy for protein structures

multicom

The MULTICOM protein structure system. This repository includes the source code and documents of both template-based and template-free modeling of the MULTICOM protein structure prediction system. 1D feature prediction, contact prediction, and clustering-based model ranking programs are also included.

pdcn

protein function prediction using protein domain co-occurrence network (pdcn - ranked as one of the best methods in CAFA1 competition

profun

Profun - protein function prediction tool (one of the top methods in CAFA2 competition)

unicon3d

UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling. The tool was developed in Bioinformatics, Data Mining and Machine Learning (BDM) Lab at the University of Missouri, Columbia, USA.