Literature DB >> 20685957

SPRINT: side-chain prediction inference toolbox for multistate protein design.

Menachem Fromer1, Chen Yanover, Amir Harel, Ori Shachar, Yair Weiss, Michal Linial.   

Abstract

UNLABELLED: SPRINT is a software package that performs computational multistate protein design using state-of-the-art inference on probabilistic graphical models. The input to SPRINT is a list of protein structures, the rotamers modeled for each structure and the pre-calculated rotamer energies. Probabilistic inference is performed using the belief propagation or A* algorithms, and dead-end elimination can be applied as pre-processing. The output can either be a list of amino acid sequences simultaneously compatible with these structures, or probabilistic amino acid profiles compatible with the structures. In addition, higher order (e.g. pairwise) amino acid probabilities can also be predicted. Finally, SPRINT also has a module for protein side-chain prediction and single-state design. AVAILABILITY: The full C++ source code for SPRINT can be freely downloaded from http://www.protonet.cs.huji.ac.il/sprint.

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Year:  2010        PMID: 20685957     DOI: 10.1093/bioinformatics/btq445

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  5 in total

1.  comets (Constrained Optimization of Multistate Energies by Tree Search): A Provable and Efficient Protein Design Algorithm to Optimize Binding Affinity and Specificity with Respect to Sequence.

Authors:  Mark A Hallen; Bruce R Donald
Journal:  J Comput Biol       Date:  2016-01-13       Impact factor: 1.479

Review 2.  Algorithms for protein design.

Authors:  Pablo Gainza; Hunter M Nisonoff; Bruce R Donald
Journal:  Curr Opin Struct Biol       Date:  2016-04-14       Impact factor: 6.809

3.  Design of Protein Multi-specificity Using an Independent Sequence Search Reduces the Barrier to Low Energy Sequences.

Authors:  Alexander M Sevy; Tim M Jacobs; James E Crowe; Jens Meiler
Journal:  PLoS Comput Biol       Date:  2015-07-06       Impact factor: 4.475

4.  Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network.

Authors:  Julian Nazet; Elmar Lang; Rainer Merkl
Journal:  PLoS One       Date:  2021-08-26       Impact factor: 3.240

Review 5.  Computational reconstruction of atomistic protein structures from coarse-grained models.

Authors:  Aleksandra E Badaczewska-Dawid; Andrzej Kolinski; Sebastian Kmiecik
Journal:  Comput Struct Biotechnol J       Date:  2019-12-26       Impact factor: 7.271

  5 in total

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