Learning structural motif representations for efficient protein structure search

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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Efficient protein structure search using indexing methods

BMC Medical Informatics and Decision Making ◽

10.1186/1472-6947-13-s1-s8 ◽

2013 ◽

Vol 13 (S1) ◽

Author(s):

Sungchul Kim ◽

Lee Sael ◽

Hwanjo Yu

Keyword(s):

Protein Structure ◽

Indexing Methods ◽

Structure Search

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Learning structural motif representations for efficient protein structure search

10.1101/137828 ◽

2017 ◽

Cited By ~ 2

Author(s):

Yang Liu ◽

Qing Ye ◽

Liwei Wang ◽

Jian Peng

Keyword(s):

Protein Structure ◽

Fundamental Problem ◽

Sequence Similarity ◽

Structural Alignment ◽

Protein Structures ◽

Computational Cost ◽

Data Bank ◽

Hierarchical Organization ◽

Structural Motif ◽

And Function

AbstractMotivationUnderstanding the relationship between protein structure and function is a fundamental problem in protein science. Given a protein of unknown function, fast identification of similar protein structures from the Protein Data Bank (PDB) is a critical step for inferring its biological function. Such structural neighbors can provide evolutionary insights into protein conformation, interfaces and binding sites that are not detectable from sequence similarity. However, the computational cost of performing pairwise structural alignment against all structures in PDB is prohibitively expensive. Alignment-free approaches have been introduced to enable fast but coarse comparisons by representing each protein as a vector of structure features or fingerprints and only computing similarity between vectors. As a notable example, FragBag represents each protein by a “bag of fragments”, which is a vector of frequencies of contiguous short backbone fragments from a predetermined library.ResultsHere we present a new approach to learning effective structural motif presentations using deep learning. We develop DeepFold, a deep convolutional neural network model to extract structural motif features of a protein structure. Similar to FragBag, DeepFold represents each protein structure or fold using a vector of learned structural motif features. We demonstrate that DeepFold substantially outperforms FragBag on protein structural search on a non-redundant protein structure database and a set of newly released structures. Remarkably, DeepFold not only extracts meaningful backbone segments but also finds important long-range interacting motifs for structural comparison. We expect that DeepFold will provide new insights into the evolution and hierarchical organization of protein structural motifs.Availabilityhttps://github.com/largelymfs/[email protected]

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

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.26048 ◽

2021 ◽

Author(s):

Ronald Ayoub ◽

Yugyung Lee

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Prediction Methods ◽

Structure Search

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RUPEE: A fast and accurate purely geometric protein structure search

10.1101/475301 ◽

2018 ◽

Author(s):

Ronald Ayoub ◽

Yugyung Lee

Keyword(s):

Big Data ◽

Protein Structure ◽

Response Times ◽

Protein Structures ◽

Fast Response ◽

Link Type ◽

Novel Approach ◽

Structure Search ◽

Close Relationship ◽

And Function

AbstractGiven the close relationship between protein structure and function, protein structure searches have long played an established role in bioinformatics. Despite their maturity, existing protein structure searches either use simplifying assumptions or compromise between fast response times and quality of results. These limitations can prevent the easy and efficient exploration of relationships between protein structures, which is the norm in other areas of inquiry. We have developed RUPEE, a fast, scalable, and purely geometric structure search combining techniques from information retrieval and big data with a novel approach to encoding sequences of torsion angles.Comparing our results to the output of mTM, SSM, and the CATHEDRAL structural scan, it is clear that RUPEE has set a new bar for purely geometric big data approaches to protein structure searches. RUPEE in top-aligned mode produces equal or better results than the best available protein structure searches, and RUPEE in fast mode demonstrates the fastest response times coupled with high quality results.The RUPEE protein structure search is available at http://www.ayoubresearch.com. Code and data are available at https://github.com/rayoub/rupee.

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Real-Time Structure Search and Structure Classification for AlphaFold Protein Models

10.1101/2021.10.21.465371 ◽

2021 ◽

Author(s):

Tunde Aderinwale ◽

Vijay Bharadwaj ◽

Charles Christoffer ◽

Genki Terashi ◽

Zicong Zhang ◽

...

Keyword(s):

Neural Networks ◽

Protein Structure ◽

Real Time ◽

Structure Prediction ◽

Deep Neural Networks ◽

Substantial Improvement ◽

Time Structure ◽

Molecular Surface ◽

Structure Search ◽

Protein Models

AlphaFold2 showed a substantial improvement in the accuracy of protein structure prediction. Following the release of the software, whole-proteome protein structure predictions by AlphaFold2 for 21 organisms were made publicly available. Here, we developed the infrastructure, 3D-AF-Surfer, to enable real-time structure-based search for the AlphaFold2 models by combining molecular surface representation with 3D Zernike descriptors and deep neural networks.

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