Fast procedure for reconstruction of full-atom protein models from reduced representations

By Piotr Rotkiewicz, Jeffrey Skolnick. 2008

389 citations recorded by the source corpus.

We introduce PULCHRA, a fast and robust method for the reconstruction of full‐atom protein models starting from a reduced protein representation. The algorithm is particularly suitable as an intermediate step between coarse‐grained model‐based structure prediction and applications requiring an all‐atom structure, such as molecular dynamics, protein‐ligand docking, structure‐based function prediction, or assessment of quality of the predicted structure. The accuracy of the method was tested on a set of high‐resolution crystallographic structures as well as on a set of low‐resolution protein decoys generated by a protein structure prediction algorithm TASSER. The method is implemented as a standalone program that is available for download from http://cssb.biology.gatech.edu/skolnick/files/PULCHRA. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008

Subfields: Materials Chemistry, Molecular Biology

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In Materials Chemistry

1. Paul D. Adams
2. Pavel V. Afonine
3. Yang Zhang
4. Randy J. Read
5. T. C. Terwilliger
6. Nigel W. Moriarty
7. Jianlin Cheng
8. T. Croll

In Molecular Biology

1. Yang Zhang
2. Robin Pearce
3. Jianlin Cheng
4. David T. Jones
5. Jinbo Xu
6. Paul D. Adams
7. Randy J. Read
8. David Baker

Citations in the corpus

• Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix (2019, methodology): “When coupled with Phenix tools, such as phenix.pulchra (Rotkiewicz & Skolnick, 2008) and real-space refinement, complete atomistic models can be generated within a few minutes for individual proteins or complexes.”
• Deep learning-based prediction of protein structure using learned representations of multiple sequence alignments (2020, methodology): “The current version of the DMPfold2 end-to-end model predicts Cα coordinates only, however the missing main-chain and Cβ atom positions can very quickly be reconstructed using either PULCHRA [29] or the ‘catomain’ routine from the DRAGON method [30].”
• Fast and accurate protein structure search with Foldseek (2022, methodology): “Because we only store C α traces of the database proteins, we use PULCHRA 30 to complete the backbone of these sequences, and also of the query if necessary, to enable a ribbon visualization 45 of the proteins.”
• Genomics-aided structure prediction (2012, methodology): “To reconstruct all heavy atoms in predicted protein conformations, we used PULCHRA software (39).”
• Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement (2011, methodology): “26,48–50 In Table IV, we show a comparison between the quality of the final models constructed from SPICKER cluster centroids by three different procedures, i.e., PULCHRA,50 REMO,26 and FG-MD,27 where FG-MD started the atomic-level refinement from the REMO models.”
• Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. (2011, methodology): “The overall results were better than those obtained with other state-of-the-art programs, including PULCHRA (8), REMO (9), Modeller (25), Rosetta (26), and Scwrl (27).”
• UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling (2016, methodology): “Since both ProQ2 and Qprob require full-atom representation of decoys, we convert united-residue decoy pools produced by UniCon3D into all-atom level using PULCHRA software (Rotkiewicz and Skolnick, 2008).”
• Clustering predicted structures at the scale of the known protein universe (2023, methodology): “Similar to the Foldseek webserver, we used NGL 50 to visualize structures and WebAssembly-based versions of PULCHRA 51 to restore full protein structures from our stored C-alpha traces and TM-align for pairwise structure alignments of cluster members to their representatives.”
• The Phyre2 web portal for protein modelling, prediction and analysis (2015, methodology): “PSIPRED is one of the most widely used methods for secondary structure prediction, and it uses neural networks The new protein is ‘synthesized’ from a virtual ribosome in the context of these forces, and the final C α structure is used to construct a full backbone using Pulchra followed by side chai”