J Lamb1,2, A Elofsson1,2. 1. Science for Life Laboratory, Stockholm University, Solna, SE-171 21, Sweden. 2. Department of Biochemistry and Biophysics, Stockholm University, Stockholm, SE-106 91, Sweden.
Abstract
MOTIVATION: Contact predictions within a protein has recently become a viable method for accurate prediction of protein structure. Using predicted distance distributions has been shown in many cases to be superior to only using a binary contact annotation. Using predicted inter-protein distances has also been shown to be able to dock some protein dimers. RESULTS: Here, we present pyconsFold. Using CNS as its underlying folding mechanism and predicted contact distance it outperforms regular contact prediction based modelling on our dataset of 210 proteins. It performs marginally worse than the state of the art pyRosetta folding pipeline but is on average about 20 times faster per model. More importantly pyconsFold can also be used as a fold-and-dock protocol by using predicted inter-protein contacts/distances to simultaneously fold and dock two protein chains. AVAILABILITY AND IMPLEMENTATION: pyconsFold is implemented in Python 3 with a strong focus on using as few dependencies as possible for longevity. It is available both as a pip package in Python 3 and as source code on GitHub and is published under the GPLv3 license. SUPPLEMENTAL MATERIAL: Install instructions, examples and parameters can be found in the supplemental notes. AVAILABILITY OF DATA: The data underlying this article together with source code are available on github, at https://github.com/johnlamb/pyconsfold.
MOTIVATION: Contact predictions within a protein has recently become a viable method for accurate prediction of protein structure. Using predicted distance distributions has been shown in many cases to be superior to only using a binary contact annotation. Using predicted inter-protein distances has also been shown to be able to dock some protein dimers. RESULTS: Here, we present pyconsFold. Using CNS as its underlying folding mechanism and predicted contact distance it outperforms regular contact prediction based modelling on our dataset of 210 proteins. It performs marginally worse than the state of the art pyRosetta folding pipeline but is on average about 20 times faster per model. More importantly pyconsFold can also be used as a fold-and-dock protocol by using predicted inter-protein contacts/distances to simultaneously fold and dock two protein chains. AVAILABILITY AND IMPLEMENTATION: pyconsFold is implemented in Python 3 with a strong focus on using as few dependencies as possible for longevity. It is available both as a pip package in Python 3 and as source code on GitHub and is published under the GPLv3 license. SUPPLEMENTAL MATERIAL: Install instructions, examples and parameters can be found in the supplemental notes. AVAILABILITY OF DATA: The data underlying this article together with source code are available on github, at https://github.com/johnlamb/pyconsfold.