Generalized Fragment Picking in Rosetta: Design, Protocols and Applications

By Dominik Gront, Daniel W. Kulp, Robert M. Vernon, Charles E.M. Strauss, David Baker. 2011

191 citations recorded by the source corpus.

The Rosetta de novo structure prediction and loop modeling protocols begin with coarse grained Monte Carlo searches in which the moves are based on short fragments extracted from a database of known structures. Here we describe a new object oriented program for picking fragments that greatly extends the functionality of the previous program (nnmake) and opens the door for new approaches to structure modeling. We provide a detailed description of the code design and architecture, highlighting its modularity, and new features such as extensibility, total control over the fragment picking workflow and scoring system customization. We demonstrate that the program provides at least as good building blocks for ab-initio structure prediction as the previous program, and provide examples of the wide range of applications that are now accessible.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Protein sequence design with a learned potential (2022, methodology): “We obtained Psipred predictions, picked 200 fragments per residue position 70 , and ran 10 4 trajectories per design.”
One contact for every twelve residues allows robust and accurate topology-level protein structure modeling (2013, positive): “Fragments were selected using the Rosetta fragment picker24 with an additional score term favoring fragments with Schematic representation of the protocol used for contact assisted structure prediction.”
High-accuracy refinement using Rosetta in CASP13 (2019, methodology): “An initial pool of 50 selected low energy structures were then subjected to iterative refinement; at each iteration new models were built from the current pool through recombining secondary structure chunks and replacing torsional angles with values from a generic fragment library.(11) Next generati”
Improvements to Robotics-Inspired Conformational Sampling in Rosetta (2013, methodology): “CCD relies on fragments extracted from PDB structures [35] and thus performs well whenever sufficiently close fragments are available (here, to ensure suitability of the benchmark for reconstruction of segments in proteins of unknown structure, fragments from homologs are excluded).”
Chemical shift-based methods in NMR structure determination (2018, methodology): “In the earlier implementation of this protocol, the fragment selection was carried out using the MFR approach [62], which was later superseded by a modular algorithm that incorporates various experimental data terms and/or other prior biases during the fragment selection process [73].”
Better together: Elements of successful scientific software development in a distributed collaborative community (2020, methodology): “The Robetta server[54] was deployed in 2003 and is commonly used by scientists inside and outside of our community for fragment generation[55], domain parsing and structure prediction[56,57], alanine scanning[58], and DNA interface scanning[59].”
CS-ROSETTA (2018, unknown): “To select such fragments, Robetta server (Kim, Chivian, & Baker, 2004) or CS-Rosetta’s fragment picker (Gront, Kulp, Vernon, Strauss, & Baker, 2011) (see Section 9) can be used.”
Harnessing protein folding neural networks for peptide–protein docking (2021, methodology): “Based on this finding, we developed the first high resolution, global blind peptide docking protocol, PIPER-FlexPepDock (PFPD) (21), in which a fragment conformer ensemble extracted from monomer structures (using the Rosetta Fragment Picker, (22)) is used to represent the peptide conformation, exhau”