Protein Structure Prediction:  Methods and Protocols Edited by David M. Webster (Southern Cross Molecular, Bath, UK). Methods in Molecular Biology 143. Humana Press:  Totowa, NJ. 2000. x + 422 pp. $89.50. ISBN 0-89603-637-5.

AbstractBackgroundContact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support the contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed.ResultsWe develop an improved contact-driven protein modeling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain top five models. CONFOLD2 is benchmarked on various datasets including CASP11 and 12 datasets with publicly available predicted contacts and yields better performance than the popular CONFOLD method.ConclusionCONFOLD2 allows to quickly generate top five structural models for a protein sequence, when its secondary structures and contacts predictions at hand. CONFOLD2 is publicly available at https://github.com/multicom-toolbox/CONFOLD2/.

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Protein structure search to support the development of protein structure prediction methods

10.1101/2020.06.03.131821 ◽

2020 ◽

Author(s):

Ronald Ayoub ◽

Yugyung Lee

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Unsolved Problem ◽

Protein Structures ◽

Prediction Methods ◽

Link Type ◽

Recent Success ◽

Structure Search ◽

Structure Similarity

AbstractProtein structure prediction is a long-standing unsolved problem in molecular biology that has seen renewed interest with the recent success of deep learning with AlphaFold at CASP13. While developing and evaluating protein structure prediction methods, researchers may want to identify the most similar known structures to their predicted structures. These predicted structures often have low sequence and structure similarity to known structures. We show how RUPEE, a purely geometric protein structure search, is able to identify the structures most similar to structure predictions, regardless of how they vary from known structures, something existing protein structure searches struggle with. RUPEE accomplishes this through the use of a novel linear encoding of protein structures as a sequence of residue descriptors. Using a fast Needleman-Wunsch algorithm, RUPEE is able to perform alignments on the sequences of residue descriptors for every available structure. This is followed by a series of increasingly accurate structure alignments from TM-align alignments initialized with the Needleman-Wunsch residue descriptor alignments to standard TM-align alignments of the final results. By using alignment normalization effectively at each stage, RUPEE also can execute containment searches in addition to full-length searches to identify structural motifs within proteins. We compare the results of RUPEE to mTM-align, SSM, CATHEDRAL and VAST using a benchmark derived from the protein structure predictions submitted to CASP13. RUPEE identifies better alignments on average with respect to RMSD and TM-score as well as Q-score and SSAP-score, scores specific to SSM and CATHEDRAL, respectively. Finally, we show a sample of the top-scoring alignments that RUPEE identified that none of the other protein structure searches we compared to were able to identify.The RUPEE protein structure search is available at https://ayoubresearch.com. Code and data are available at https://github.com/rayoub/rupee.

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