Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques

Oľha, Jaroslav; Slanináková, Terézia; Martin, Gendiar,; Antol, Matej; Dohnal, Vlastislav

doi:10.1007/978-3-031-17849-8_22

Cited by 4 publications

(6 citation statements)

References 12 publications

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“…The distance expresses complex similarity beyond mere comparison of two integers, as shown in (9, 12). The latter two methods in conjunction with (10) establish the basis of the indexing solution presented in here.…”

Section: Description Of the Web Servermentioning

confidence: 95%

“…We utilize the compact data embedding method described in (10) in conjunction with data clustering and machine learning. This approach captures the semantic relationships between protein structures and quickly identifies the most relevant groups of data for a given query.…”

Section: Description Of the Web Servermentioning

confidence: 99%

“…Therefore, it is not limited by the occurrence of specific fold patterns. AlphaFind takes advantage of the Learned Metric Index approach (9) as well as a 3D feature extraction method designed for protein structures (10),both of which have demonstrated remarkable scalability to large datasets and to large protein structures.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

AlphaFind: Discover structure similarity across the entire known proteome

Prochazka,

Slaninakova,

Olha

et al. 2024

Preprint

Self Cite

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AlphaFind is a web-based search engine that provides fast structure-based retrieval in the entire set of AlphaFold DB structures. Unlike other protein processing tools, AlphaFind is focused entirely on tertiary structure, automatically extracting the main 3D features of each protein chain and using a machine learning model to find the most similar structures. This indexing approach and the 3D feature extraction method used by AlphaFind have both demonstrated remarkable scalability to large datasets as well as to large protein structures. The web application itself has been designed with a focus on clarity and ease of use. The searcher accepts any valid Uniprot ID, PDB ID or gene symbol as input, and returns a set of similar protein chains from AlphaFold DB, including various similarity metrics between the query and each of the retrieved results. In addition to the main search functionality, the application provides 3D visualizations of protein structure superpositions in order to allow researchers to instantly analyze the structural similarity of the retrieved results. The AlphaFind web application is available online for free and without any registration at https://alphafind.fi.muni.cz.

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Section: Description Of the Web Servermentioning

confidence: 95%

Section: Description Of the Web Servermentioning

confidence: 99%

See 1 more Smart Citation

AlphaFind: Discover structure similarity across the entire known proteome

Prochazka,

Slaninakova,

Olha

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…We utilize the compact data embedding method described in ( 9 ) in conjunction with data clustering and machine learning. This approach captures the semantic relationships between protein structures and quickly identifies the most relevant groups of data for a given query.…”

Section: Description Of the Web Servermentioning

confidence: 99%

AlphaFind: discover structure similarity across the proteome in AlphaFold DB

Procházka,

Slanináková,

Olha

et al. 2024

Nucleic Acids Research

Self Cite

View full text Add to dashboard Cite

AlphaFind is a web-based search engine that provides fast structure-based retrieval in the entire set of AlphaFold DB structures. Unlike other protein processing tools, AlphaFind is focused entirely on tertiary structure, automatically extracting the main 3D features of each protein chain and using a machine learning model to find the most similar structures. This indexing approach and the 3D feature extraction method used by AlphaFind have both demonstrated remarkable scalability to large datasets as well as to large protein structures. The web application itself has been designed with a focus on clarity and ease of use. The searcher accepts any valid UniProt ID, Protein Data Bank ID or gene symbol as input, and returns a set of similar protein chains from AlphaFold DB, including various similarity metrics between the query and each of the retrieved results. In addition to the main search functionality, the application provides 3D visualizations of protein structure superpositions in order to allow researchers to instantly analyze the structural similarity of the retrieved results. The AlphaFind web application is available online for free and without any registration at https://alphafind.fi.muni.cz.

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“…In particular, they are widely used for databases, providing new challenges and opportunities [24], such as the development of the so-called Learned Databases [25]. They have also been applied in specific kinds of databases, such as spatial [26,27] and biological [28]. Finally, another very recent application is the development of frameworks for optimizing database queries [29][30][31][32].…”

Section: Applicationsmentioning

confidence: 99%

Learned Sorted Table Search and Static Indexes in Small-Space Data Models

Amato

Giancarlo

Bosco

2023

Data

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Machine-learning techniques, properly combined with data structures, have resulted in Learned Static Indexes, innovative and powerful tools that speed up Binary Searches with the use of additional space with respect to the table being searched into. Such space is devoted to the machine-learning models. Although in their infancy, these are methodologically and practically important, due to the pervasiveness of Sorted Table Search procedures. In modern applications, model space is a key factor, and a major open question concerning this area is to assess to what extent one can enjoy the speeding up of Binary Searches achieved by Learned Indexes while using constant or nearly constant-space models. In this paper, we investigate the mentioned question by (a) introducing two new models, i.e., the Learned k-ary Search Model and the Synoptic Recursive Model Index; and (b) systematically exploring the time–space trade-offs of a hierarchy of existing models, i.e., the ones in the reference software platform Searching on Sorted Data, together with the new ones proposed here. We document a novel and rather complex time–space trade-off picture, which is informative for users as well as designers of Learned Indexing data structures. By adhering to and extending the current benchmarking methodology, we experimentally show that the Learned k-ary Search Model is competitive in time with respect to Binary Search in constant additional space. Our second model, together with the bi-criteria Piece-wise Geometric Model Index, can achieve speeding up of Binary Search with a model space of 0.05% more than the one taken by the table, thereby, being competitive in terms of the time–space trade-off with existing proposals. The Synoptic Recursive Model Index and the bi-criteria Piece-wise Geometric Model complement each other quite well across the various levels of the internal memory hierarchy. Finally, our findings stimulate research in this area since they highlight the need for further studies regarding the time–space relation in Learned Indexes.

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Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques

Cited by 4 publications

References 12 publications

AlphaFind: Discover structure similarity across the entire known proteome

AlphaFind: Discover structure similarity across the entire known proteome

AlphaFind: discover structure similarity across the proteome in AlphaFold DB

Learned Sorted Table Search and Static Indexes in Small-Space Data Models

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