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Article: Fine tuned personalized machine learning models to detect insomnia risk based on data from a smart bed platform.

Winger, Trevor / Chellamuthu, Vidhya / Guzenko, Dmytro / Aloia, Mark / Barr, Shawn / DeFranco, Susan / Gorski, Brandon / Mushtaq, Faisal / Garcia-Molina, Gary

Frontiers in neurology

2024 Volume 15, Page(s) 1303978

Abstract: Introduction: Insomnia causes serious adverse health effects and is estimated to affect 10-30% of the worldwide population. This study leverages personalized fine-tuned machine learning algorithms to detect insomnia risk based on questionnaire and ... ...

Abstract	Introduction: Insomnia causes serious adverse health effects and is estimated to affect 10-30% of the worldwide population. This study leverages personalized fine-tuned machine learning algorithms to detect insomnia risk based on questionnaire and longitudinal objective sleep data collected by a smart bed platform. Methods: Users of the Sleep Number smart bed were invited to participate in an IRB approved study which required them to respond to four questionnaires (which included the Insomnia Severity Index; ISI) administered 6 weeks apart from each other in the period from November 2021 to March 2022. For 1,489 participants who completed at least 3 questionnaires, objective data (which includes sleep/wake and cardio-respiratory metrics) collected by the platform were queried for analysis. An incremental, passive-aggressive machine learning model was used to detect insomnia risk which was defined by the ISI exceeding a given threshold. Three ISI thresholds (8, 10, and 15) were considered. The incremental model is advantageous because it allows personalized fine-tuning by adding individual training data to a generic model. Results: The generic model, without personalizing, resulted in an area under the receiving-operating curve (AUC) of about 0.5 for each ISI threshold. The personalized fine-tuning with the data of just five sleep sessions from the individual for whom the model is being personalized resulted in AUCs exceeding 0.8 for all ISI thresholds. Interestingly, no further AUC enhancements resulted by adding personalized data exceeding ten sessions. Discussion: These are encouraging results motivating further investigation into the application of personalized fine tuning machine learning to detect insomnia risk based on longitudinal sleep data and the extension of this paradigm to sleep medicine.
Language	English
Publishing date	2024-02-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564214-5
ISSN	1664-2295
ISSN	1664-2295
DOI	10.3389/fneur.2024.1303978
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Article ; Online: Optimal data-driven parameterization of coiled coils.

Guzenko, Dmytro / Strelkov, Sergei V

Journal of structural biology

2018 Volume 204, Issue 1, Page(s) 125–129

Abstract: α-Helical coiled coils (CCs) represent an important, highly regular protein folding motif. To date, many thousands of CC structures have been determined experimentally. Their geometry is usually modelled by theoretical equations introduced by F. Crick ... ...

Abstract	α-Helical coiled coils (CCs) represent an important, highly regular protein folding motif. To date, many thousands of CC structures have been determined experimentally. Their geometry is usually modelled by theoretical equations introduced by F. Crick that involve a predefined set of parameters. Here we have addressed the problem of efficient CC parameterization from scratch by performing a statistical evaluation of all available CC structures. The procedure is based on the principal component analysis and yields a minimal set of independent parameters that provide for the reconstruction of the complete CC structure at a required precision. The approach is successfully validated on a set of canonical parallel CC dimers. Its applications include all cases where an efficient sampling of the CC geometry is important, such as for solving the phase problem in crystallography.
MeSH term(s)	Protein Conformation ; Protein Folding ; Protein Structure, Secondary ; Proteins/chemistry
Chemical Substances	Proteins
Language	English
Publishing date	2018-07-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1032718-6
ISSN	1095-8657 ; 1047-8477
ISSN (online)	1095-8657
ISSN	1047-8477
DOI	10.1016/j.jsb.2018.07.001
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Article ; Online: Real time structural search of the Protein Data Bank.

Guzenko, Dmytro / Burley, Stephen K / Duarte, Jose M

PLoS computational biology

2020 Volume 16, Issue 7, Page(s) e1007970

Abstract: Detection of protein structure similarity is a central challenge in structural bioinformatics. Comparisons are usually performed at the polypeptide chain level, however the functional form of a protein within the cell is often an oligomer. This fact, ... ...

Abstract	Detection of protein structure similarity is a central challenge in structural bioinformatics. Comparisons are usually performed at the polypeptide chain level, however the functional form of a protein within the cell is often an oligomer. This fact, together with recent growth of oligomeric structures in the Protein Data Bank (PDB), demands more efficient approaches to oligomeric assembly alignment/retrieval. Traditional methods use atom level information, which can be complicated by the presence of topological permutations within a polypeptide chain and/or subunit rearrangements. These challenges can be overcome by comparing electron density volumes directly. But, brute force alignment of 3D data is a compute intensive search problem. We developed a 3D Zernike moment normalization procedure to orient electron density volumes and assess similarity with unprecedented speed. Similarity searching with this approach enables real-time retrieval of proteins/protein assemblies resembling a target, from PDB or user input, together with resulting alignments (http://shape.rcsb.org).
MeSH term(s)	Algorithms ; Computational Biology/methods ; Databases, Protein ; Internet ; Models, Molecular ; Models, Statistical ; Normal Distribution ; Peptides/chemistry ; Protein Conformation ; Proteins/chemistry ; Software
Chemical Substances	Peptides ; Proteins
Language	English
Publishing date	2020-07-08
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1007970
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Article ; Online: CCFold: rapid and accurate prediction of coiled-coil structures and application to modelling intermediate filaments.

Guzenko, Dmytro / Strelkov, Sergei V

Bioinformatics (Oxford, England)

2017 Volume 34, Issue 2, Page(s) 215–222

Abstract: Motivation: Accurate molecular structure of the protein dimer representing the elementary building block of intermediate filaments (IFs) is essential towards the understanding of the filament assembly, rationalizing their mechanical properties and ... ...

Abstract	Motivation: Accurate molecular structure of the protein dimer representing the elementary building block of intermediate filaments (IFs) is essential towards the understanding of the filament assembly, rationalizing their mechanical properties and explaining the effect of disease-related IF mutations. The dimer contains a ∼300-residue long α-helical coiled coil which cannot be assessed by either direct experimental structure determination or modelling using standard approaches. At the same time, coiled coils are well-represented in structural databases. Results: Here we present CCFold, a generally applicable threading-based algorithm which produces coiled-coil models from protein sequence only. The algorithm is based on a statistical analysis of experimentally determined structures and can handle any hydrophobic repeat patterns in addition to the most common heptads. We demonstrate that CCFold outperforms general-purpose computational folding in terms of accuracy, while being faster by orders of magnitude. By combining the CCFold algorithm and Rosetta folding we generate representative dimer models for all IF protein classes. Availability and implementation: The source code is freely available at https://github.com/biocryst/IF; a web server to run the program is at http://pharm.kuleuven.be/Biocrystallography/cc. Supplementary information: Supplementary data are available at Bioinformatics online.
Language	English
Publishing date	2017-10-11
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btx551
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Article ; Online: Granular clustering of de novo protein models.

Guzenko, Dmytro / Strelkov, Sergei V

Bioinformatics (Oxford, England)

2017 Volume 33, Issue 3, Page(s) 390–396

Abstract: Motivation: Modern algorithms for de novo prediction of protein structures typically output multiple full-length models (decoys) rather than a single solution. Subsequent clustering of such decoys is used both to gauge the success of the modelling and ... ...

Abstract	Motivation: Modern algorithms for de novo prediction of protein structures typically output multiple full-length models (decoys) rather than a single solution. Subsequent clustering of such decoys is used both to gauge the success of the modelling and to decide on the most native-like conformation. At the same time, partial protein models are sufficient for some applications such as crystallographic phasing by molecular replacement (MR) in particular, provided these models represent a certain part of the target structure with reasonable accuracy. Results: Here we propose a novel clustering algorithm that natively operates in the space of partial models through an approach known as granular clustering (GC). The algorithm is based on growing local similarities found in a pool of initial decoys. We demonstrate that the resulting clusters of partial models provide a substantially more accurate structural detail on the target protein than those obtained upon a global alignment of decoys. As the result, the partial models output by our GC algorithm are also much more effective towards the MR procedure, compared to the models produced by existing software. Availability and implementation: The source code is freely available at https://github.com/biocryst/gc Contact: sergei.strelkov@kuleuven.be Suplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Cluster Analysis ; Computational Biology/methods ; Models, Molecular ; Protein Conformation ; Sequence Analysis, Protein/methods ; Software
Language	English
Publishing date	2017-04-10
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btw628
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Article: Granular clustering of de novo protein models

Guzenko, Dmytro / Strelkov, Sergei V

Bioinformatics. 2017 Feb. 01, v. 33, no. 3

2017

Abstract	Motivation: Modern algorithms for de novo prediction of protein structures typically output multiple full-length models (decoys) rather than a single solution. Subsequent clustering of such decoys is used both to gauge the success of the modelling and to decide on the most native-like conformation. At the same time, partial protein models are sufficient for some applications such as crystallographic phasing by molecular replacement (MR) in particular, provided these models represent a certain part of the target structure with reasonable accuracy. Results: Here we propose a novel clustering algorithm that natively operates in the space of partial models through an approach known as granular clustering (GC). The algorithm is based on growing local similarities found in a pool of initial decoys. We demonstrate that the resulting clusters of partial models provide a substantially more accurate structural detail on the target protein than those obtained upon a global alignment of decoys. As the result, the partial models output by our GC algorithm are also much more effective towards the MR procedure, compared to the models produced by existing software. Availability and Implementation: The source code is freely available at https://github.com/biocryst/gc Contact: sergei.strelkov@kuleuven.be Supplementary information: Supplementary data are available at Bioinformatics online.
Keywords	algorithms ; bioinformatics ; computer software ; crystallization ; models ; prediction ; protein structure
Language	English
Dates of publication	2017-0201
Size	p. 390-396.
Publishing place	Oxford University Press
Document type	Article
ZDB-ID	1468345-3
ISSN	1460-2059 ; 1367-4803
ISSN (online)	1460-2059
ISSN	1367-4803
DOI	10.1093/bioinformatics/btw628
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Article: CCFold: rapid and accurate prediction of coiled-coil structures and application to modelling intermediate filaments

Guzenko, Dmytro / Strelkov, Sergei V / Valencia, Alfonso

Bioinformatics. 2018 Jan. 15, v. 34, no. 2

2018

Abstract: Accurate molecular structure of the protein dimer representing the elementary building block of intermediate filaments (IFs) is essential towards the understanding of the filament assembly, rationalizing their mechanical properties and explaining the ... ...

Abstract	Accurate molecular structure of the protein dimer representing the elementary building block of intermediate filaments (IFs) is essential towards the understanding of the filament assembly, rationalizing their mechanical properties and explaining the effect of disease-related IF mutations. The dimer contains a ∼300-residue long α-helical coiled coil which cannot be assessed by either direct experimental structure determination or modelling using standard approaches. At the same time, coiled coils are well-represented in structural databases. Here we present CCFold, a generally applicable threading-based algorithm which produces coiled-coil models from protein sequence only. The algorithm is based on a statistical analysis of experimentally determined structures and can handle any hydrophobic repeat patterns in addition to the most common heptads. We demonstrate that CCFold outperforms general-purpose computational folding in terms of accuracy, while being faster by orders of magnitude. By combining the CCFold algorithm and Rosetta folding we generate representative dimer models for all IF protein classes. The source code is freely available at https://github.com/biocryst/IF; a web server to run the program is at http://pharm.kuleuven.be/Biocrystallography/cc. Supplementary data are available at Bioinformatics online.
Keywords	algorithms ; amino acid sequences ; bioinformatics ; databases ; hydrophobicity ; intermediate filaments ; Internet ; mechanical properties ; models ; mutation ; prediction ; statistical analysis
Language	English
Dates of publication	2018-0115
Size	p. 215-222.
Publishing place	Oxford University Press
Document type	Article
ZDB-ID	1468345-3
ISSN	1460-2059 ; 1367-4811 ; 1367-4803
ISSN (online)	1460-2059 ; 1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btx551
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Article ; Online: Lateral A

Lilina, Anastasia V / Chernyatina, Anastasia A / Guzenko, Dmytro / Strelkov, Sergei V

Journal of structural biology

2019 Volume 209, Issue 1, Page(s) 107404

Abstract: The assembly of intermediate filaments (IFs) including nuclear lamins is driven by specific interactions of the elementary coiled-coil dimers in both lateral and longitudinal direction. The assembly mode ... ...

Abstract	The assembly of intermediate filaments (IFs) including nuclear lamins is driven by specific interactions of the elementary coiled-coil dimers in both lateral and longitudinal direction. The assembly mode A
MeSH term(s)	Amino Acid Sequence/genetics ; Crystallography, X-Ray ; Cytoskeleton/chemistry ; Cytoskeleton/genetics ; Humans ; Intermediate Filaments/genetics ; Lamins/chemistry ; Lamins/genetics ; Lamins/ultrastructure ; Nuclear Lamina/genetics ; Nuclear Lamina/ultrastructure ; Protein Conformation ; Protein Conformation, alpha-Helical ; Protein Domains/genetics ; Protein Multimerization/genetics ; Vimentin
Chemical Substances	Lamins ; Vimentin
Language	English
Publishing date	2019-10-11
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1032718-6
ISSN	1095-8657 ; 1047-8477
ISSN (online)	1095-8657
ISSN	1047-8477
DOI	10.1016/j.jsb.2019.10.006
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Article: Crystallographic Studies of Intermediate Filament Proteins.

Guzenko, Dmytro / Chernyatina, Anastasia A / Strelkov, Sergei V

Sub-cellular biochemistry

2017 Volume 82, Page(s) 151–170

Abstract: Intermediate filaments (IFs), together with microtubules and actin microfilaments, are the three main cytoskeletal components in metazoan cells. IFs are formed by a distinct protein family, which is made up of 70 members in humans. Most IF proteins are ... ...

Abstract	Intermediate filaments (IFs), together with microtubules and actin microfilaments, are the three main cytoskeletal components in metazoan cells. IFs are formed by a distinct protein family, which is made up of 70 members in humans. Most IF proteins are tissue- or organelle-specific, which includes lamins, the IF proteins of the nucleus. The building block of IFs is an elongated dimer, which consists of a central α-helical 'rod' domain flanked by flexible N- and C-terminal domains. The conserved rod domain is the 'signature feature' of the IF family. Bioinformatics analysis reveals that the rod domain of all IF proteins contains three α-helical segments of largely conserved length, interconnected by linkers. Moreover, there is a conserved pattern of hydrophobic repeats within each segment, which includes heptads and hendecads. This defines the presence of both left-handed and almost parallel coiled-coil regions along the rod length. Using X-ray crystallography on multiple overlapping fragments of IF proteins, the atomic structure of the nearly complete rod domain has been determined. Here, we discuss some specific challenges of this procedure, such as crystallization and diffraction data phasing by molecular replacement. Further insights into the structure of the coiled coil and the terminal domains have been obtained using electron paramagnetic resonance measurements on the full-length protein, with spin labels attached at specific positions. This atomic resolution information, as well as further interesting findings, such as the variation of the coiled-coil stability along the rod length, provide clues towards interpreting the data on IF assembly, collected by a range of methods. However, a full description of this process at the molecular level is not yet at hand.
MeSH term(s)	Amino Acid Sequence ; Animals ; Crystallography, X-Ray ; Humans ; Intermediate Filament Proteins/chemistry ; Models, Molecular ; Protein Conformation, alpha-Helical
Chemical Substances	Intermediate Filament Proteins
Language	English
Publishing date	2017
Publishing country	United States
Document type	Journal Article ; Review
ISSN	0306-0225 ; 0096-8757
ISSN	0306-0225 ; 0096-8757
DOI	10.1007/978-3-319-49674-0_6
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Article ; Online: Assessment of protein assembly prediction in CASP13.

Guzenko, Dmytro / Lafita, Aleix / Monastyrskyy, Bohdan / Kryshtafovych, Andriy / Duarte, Jose M

Proteins

2019 Volume 87, Issue 12, Page(s) 1190–1199

Abstract: We present the assembly category assessment in the 13th edition of the CASP community-wide experiment. For the second time, protein assemblies constitute an independent assessment category. Compared to the last edition we see a clear uptake in ... ...

Abstract	We present the assembly category assessment in the 13th edition of the CASP community-wide experiment. For the second time, protein assemblies constitute an independent assessment category. Compared to the last edition we see a clear uptake in participation, more oligomeric targets released, and consistent, albeit modest, improvement of the predictions quality. Looking at the tertiary structure predictions, we observe that ignoring the oligomeric state of the targets hinders modeling success. We also note that some contact prediction groups successfully predicted homomeric interfacial contacts, though it appears that these predictions were not used for assembly modeling. Homology modeling with sizeable human intervention appears to form the basis of the assembly prediction techniques in this round of CASP. Future developments should see more integrated approaches where subunits are modeled in the context of the assemblies they form.
MeSH term(s)	Algorithms ; Computational Biology ; Humans ; Molecular Dynamics Simulation ; Protein Conformation ; Proteins/chemistry ; Proteins/genetics ; Proteins/ultrastructure ; Sequence Analysis, Protein ; Software
Chemical Substances	Proteins
Language	English
Publishing date	2019-08-27
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	806683-8
ISSN	1097-0134 ; 0887-3585
ISSN (online)	1097-0134
ISSN	0887-3585
DOI	10.1002/prot.25795
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