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Article ; Online: Deep Learning for Subtypes Identification of Pure Seminoma of the Testis.

Medvedev, Kirill E / Acosta, Paul H / Jia, Liwei / Grishin, Nick V

Clinical pathology (Thousand Oaks, Ventura County, Calif.)

2024 Volume 17, Page(s) 2632010X241232302

Abstract: The most critical step in the clinical diagnosis workflow is the pathological evaluation of each tumor sample. Deep learning is a powerful approach that is widely used to enhance diagnostic accuracy and streamline the diagnosis process. In our previous ... ...

Abstract	The most critical step in the clinical diagnosis workflow is the pathological evaluation of each tumor sample. Deep learning is a powerful approach that is widely used to enhance diagnostic accuracy and streamline the diagnosis process. In our previous study using omics data, we identified 2 distinct subtypes of pure seminoma. Seminoma is the most common histological type of testicular germ cell tumors (TGCTs). Here we developed a deep learning decision making tool for the identification of seminoma subtypes using histopathological slides. We used all available slides for pure seminoma samples from The Cancer Genome Atlas (TCGA). The developed model showed an area under the ROC curve of 0.896. Our model not only confirms the presence of 2 distinct subtypes within pure seminoma but also unveils the presence of morphological differences between them that are imperceptible to the human eye.
Language	English
Publishing date	2024-02-18
Publishing country	United States
Document type	Journal Article
ISSN	2632-010X
ISSN (online)	2632-010X
DOI	10.1177/2632010X241232302
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Article: Orange fringes, crenulate hindwings and genomic DNA identify a new species of

Gallardo, Robert J / Grishin, Nick V

Tropical lepidoptera research

2021 Volume 31, Issue 1, Page(s) 48–52

Abstract: Jonaspyge ... ...

Abstract	Jonaspyge elizabethae
Language	English
Publishing date	2021-07-02
Publishing country	United States
Document type	Journal Article
ISSN	1941-7659
ISSN	1941-7659
DOI	10.5281/zenodo.4966725
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Article ; Online: The DBSAV Database: Predicting Deleteriousness of Single Amino Acid Variations in the Human Proteome.

Pei, Jimin / Grishin, Nick V

Journal of molecular biology

2021 Volume 433, Issue 11, Page(s) 166915

Abstract: Deleterious single amino acid variation (SAV) is one of the leading causes of human diseases. Evaluating the functional impact of SAVs is crucial for diagnosis of genetic disorders. We previously developed a deep convolutional neural network predictor, ... ...

Abstract	Deleterious single amino acid variation (SAV) is one of the leading causes of human diseases. Evaluating the functional impact of SAVs is crucial for diagnosis of genetic disorders. We previously developed a deep convolutional neural network predictor, DeepSAV, to evaluate the deleterious effects of SAVs on protein function based on various sequence, structural, and functional properties. DeepSAV scores of rare SAVs observed in the human population are aggregated into a gene-level score called GTS (Gene Tolerance of rare SAVs) that reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. In this study, we aim to enhance the performance of DeepSAV by using expanded datasets of pathogenic and benign variants, more features, and neural network optimization. We found that multiple sequence alignments built from vertebrate-level orthologs yield better prediction results compared to those built from mammalian-level orthologs. For multiple sequence alignments built from BLAST searches, optimal performance was achieved with a sequence identify cutoff of 50% to remove distant homologs. The new version of DeepSAV exhibits the best performance among standalone predictors of deleterious effects of SAVs. We developed the DBSAV database (http://prodata.swmed.edu/DBSAV) that reports GTS scores of human genes and DeepSAV scores of SAVs in the human proteome, including pathogenic and benign SAVs, population-level SAVs, and all possible SAVs by single nucleotide variations. This database serves as a useful resource for research of human SAVs and their relationships with protein functions and human diseases.
MeSH term(s)	Amino Acids/genetics ; Area Under Curve ; Databases, Protein ; Genetic Variation ; Humans ; Neural Networks, Computer ; Proteome/genetics ; ROC Curve
Chemical Substances	Amino Acids ; Proteome
Language	English
Publishing date	2021-03-04
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2021.166915
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Article ; Online: ECOD domain classification of 48 whole proteomes from AlphaFold Structure Database using DPAM2.

Schaeffer, R Dustin / Zhang, Jing / Medvedev, Kirill E / Kinch, Lisa N / Cong, Qian / Grishin, Nick V

PLoS computational biology

2024 Volume 20, Issue 2, Page(s) e1011586

Abstract: Protein structure prediction has now been deployed widely across several different large protein sets. Large-scale domain annotation of these predictions can aid in the development of biological insights. Using our Evolutionary Classification of Protein ... ...

Abstract	Protein structure prediction has now been deployed widely across several different large protein sets. Large-scale domain annotation of these predictions can aid in the development of biological insights. Using our Evolutionary Classification of Protein Domains (ECOD) from experimental structures as a basis for classification, we describe the detection and cataloging of domains from 48 whole proteomes deposited in the AlphaFold Database. On average, we can provide positive classification (either of domains or other identifiable non-domain regions) for 90% of residues in all proteomes. We classified 746,349 domains from 536,808 proteins comprised of over 226,424,000 amino acid residues. We examine the varying populations of homologous groups in both eukaryotes and bacteria. In addition to containing a higher fraction of disordered regions and unassigned domains, eukaryotes show a higher proportion of repeated proteins, both globular and small repeats. We enumerate those highly populated domains that are shared in both eukaryotes and bacteria, such as the Rossmann domains, TIM barrels, and P-loop domains. Additionally, we compare the sampling of homologous groups from this whole proteome set against our stable ECOD reference and discuss groups that have been enriched by structure predictions. Finally, we discuss the implication of these results for protein target selection for future classification strategies for very large protein sets.
MeSH term(s)	Protein Domains ; Proteome ; Biological Evolution ; Evolution, Molecular ; Bacteria ; Databases, Protein
Chemical Substances	Proteome
Language	English
Publishing date	2024-02-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1011586
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Article: The DBSAV Database: Predicting Deleteriousness of Single Amino Acid Variations in the Human Proteome

Pei, Jimin / Grishin, Nick V

Journal of molecular biology. 2021 May 28, v. 433, no. 11

2021

Abstract	Deleterious single amino acid variation (SAV) is one of the leading causes of human diseases. Evaluating the functional impact of SAVs is crucial for diagnosis of genetic disorders. We previously developed a deep convolutional neural network predictor, DeepSAV, to evaluate the deleterious effects of SAVs on protein function based on various sequence, structural, and functional properties. DeepSAV scores of rare SAVs observed in the human population are aggregated into a gene-level score called GTS (Gene Tolerance of rare SAVs) that reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. In this study, we aim to enhance the performance of DeepSAV by using expanded datasets of pathogenic and benign variants, more features, and neural network optimization. We found that multiple sequence alignments built from vertebrate-level orthologs yield better prediction results compared to those built from mammalian-level orthologs. For multiple sequence alignments built from BLAST searches, optimal performance was achieved with a sequence identify cutoff of 50% to remove distant homologs. The new version of DeepSAV exhibits the best performance among standalone predictors of deleterious effects of SAVs. We developed the DBSAV database (http://prodata.swmed.edu/DBSAV) that reports GTS scores of human genes and DeepSAV scores of SAVs in the human proteome, including pathogenic and benign SAVs, population-level SAVs, and all possible SAVs by single nucleotide variations. This database serves as a useful resource for research of human SAVs and their relationships with protein functions and human diseases.
Keywords	amino acids ; data collection ; databases ; genes ; human population ; humans ; molecular biology ; neural networks ; prediction ; proteome
Language	English
Dates of publication	2021-0528
Publishing place	Elsevier Ltd
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2021.166915
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Pathogenic mutation hotspots in protein kinase domain structure.

Medvedev, Kirill E / Schaeffer, R Dustin / Pei, Jimin / Grishin, Nick V

Protein science : a publication of the Protein Society

2023 Volume 32, Issue 9, Page(s) e4750

Abstract: Control of eukaryotic cellular function is heavily reliant on the phosphorylation of proteins at specific amino acid residues, such as serine, threonine, tyrosine, and histidine. Protein kinases that are responsible for this process comprise one of the ... ...

Abstract	Control of eukaryotic cellular function is heavily reliant on the phosphorylation of proteins at specific amino acid residues, such as serine, threonine, tyrosine, and histidine. Protein kinases that are responsible for this process comprise one of the largest families of evolutionarily related proteins. Dysregulation of protein kinase signaling pathways is a frequent cause of a large variety of human diseases including cancer, autoimmune, neurodegenerative, and cardiovascular disorders. In this study, we mapped all pathogenic mutations in 497 human protein kinase domains from the ClinVar database to the reference structure of Aurora kinase A (AURKA) and grouped them by the relevance to the disease type. Our study revealed that the majority of mutation hotspots associated with cancer are situated within the catalytic and activation loops of the kinase domain, whereas non-cancer-related hotspots tend to be located outside of these regions. Additionally, we identified a hotspot at residue R371 of the AURKA structure that has the highest number of exclusively non-cancer-related pathogenic mutations (21) and has not been previously discussed.
MeSH term(s)	Humans ; Protein Kinases/chemistry ; Protein Serine-Threonine Kinases/chemistry ; Aurora Kinase A/genetics ; Aurora Kinase A/chemistry ; Aurora Kinase A/metabolism ; Models, Molecular ; Phosphorylation ; Mutation
Chemical Substances	Protein Kinases (EC 2.7.-) ; Protein Serine-Threonine Kinases (EC 2.7.11.1) ; Aurora Kinase A (EC 2.7.11.1)
Language	English
Publishing date	2023-08-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	1106283-6
ISSN	1469-896X ; 0961-8368
ISSN (online)	1469-896X
ISSN	0961-8368
DOI	10.1002/pro.4750
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Article ; Online: Pan-cancer structurome reveals overrepresentation of beta sandwiches and underrepresentation of alpha helical domains.

Medvedev, Kirill E / Schaeffer, R Dustin / Chen, Kenneth S / Grishin, Nick V

Scientific reports

2023 Volume 13, Issue 1, Page(s) 11988

Abstract: The recent progress in the prediction of protein structures marked a historical milestone. AlphaFold predicted 200 million protein models with an accuracy comparable to experimental methods. Protein structures are widely used to understand evolution and ... ...

Abstract	The recent progress in the prediction of protein structures marked a historical milestone. AlphaFold predicted 200 million protein models with an accuracy comparable to experimental methods. Protein structures are widely used to understand evolution and to identify potential drug targets for the treatment of various diseases, including cancer. Thus, these recently predicted structures might convey previously unavailable information about cancer biology. Evolutionary classification of protein domains is challenging and different approaches exist. Recently our team presented a classification of domains from human protein models released by AlphaFold. Here we evaluated the pan-cancer structurome, domains from over and under expressed proteins in 21 cancer types, using the broadest levels of the ECOD classification: the architecture (A-groups) and possible homology (X-groups) levels. Our analysis reveals that AlphaFold has greatly increased the three-dimensional structural landscape for proteins that are differentially expressed in these 21 cancer types. We show that beta sandwich domains are significantly overrepresented and alpha helical domains are significantly underrepresented in the majority of cancer types. Our data suggest that the prevalence of the beta sandwiches is due to the high levels of immunoglobulins and immunoglobulin-like domains that arise during tumor development-related inflammation. On the other hand, proteins with exclusively alpha domains are important elements of homeostasis, apoptosis and transmembrane transport. Therefore cancer cells tend to reduce representation of these proteins to promote successful oncogeneses.
MeSH term(s)	Humans ; Proteins/chemistry ; Protein Domains ; Protein Conformation, alpha-Helical ; Neoplasms
Chemical Substances	Proteins
Language	English
Publishing date	2023-07-25
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-39273-5
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Article: Thirteen new species of butterflies (Lepidoptera: Hesperiidae) from Texas.

Zhang, Jing / Cong, Qian / Grishin, Nick V

Insecta mundi

2022 Volume 2023

Abstract: Analyses of whole genomic shotgun datasets, COI barcodes, morphology, and historical literature suggest that the following 13 butterfly species from the family Hesperiidae (Lepidoptera: Papilionoidea) in Texas, USA are distinct from their closest named ... ...

Abstract	Analyses of whole genomic shotgun datasets, COI barcodes, morphology, and historical literature suggest that the following 13 butterfly species from the family Hesperiidae (Lepidoptera: Papilionoidea) in Texas, USA are distinct from their closest named relatives and therefore are described as new (type localities are given in parenthesis):
Language	English
Publishing date	2022-12-31
Publishing country	United States
Document type	Journal Article
ZDB-ID	2433661-0
ISSN	1942-1354 ; 0749-6737
ISSN (online)	1942-1354
ISSN	0749-6737
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Article ; Online: DisEnrich: database of enriched regions in human dark proteome.

Medvedev, Kirill E / Pei, Jimin / Grishin, Nick V

Bioinformatics (Oxford, England)

2022 Volume 38, Issue 7, Page(s) 1870–1876

Abstract: Motivation: Intrinsically disordered proteins (IDPs) are involved in numerous processes crucial for living organisms. Bias in amino acid composition of these proteins determines their unique biophysical and functional features. Distinct intrinsically ... ...

Abstract	Motivation: Intrinsically disordered proteins (IDPs) are involved in numerous processes crucial for living organisms. Bias in amino acid composition of these proteins determines their unique biophysical and functional features. Distinct intrinsically disordered regions (IDRs) with compositional bias play different important roles in various biological processes. IDRs enriched in particular amino acids in human proteome have not been described consistently. Results: We developed DisEnrich-the database of human proteome IDRs that are significantly enriched in particular amino acids. Each human protein is described using Gene Ontology (GO) function terms, disorder prediction for the full-length sequence using three methods, enriched IDR composition and ranks of human proteins with similar enriched IDRs. Distribution analysis of enriched IDRs among broad functional categories revealed significant overrepresentation of R- and Y-enriched IDRs in metabolic and enzymatic activities and F-enriched IDRs in transport. About 75% of functional categories contain IDPs with IDRs significantly enriched in hydrophobic residues that are important for protein-protein interactions. Availability and implementation: The database is available at http://prodata.swmed.edu/DisEnrichDB/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.
MeSH term(s)	Humans ; Proteome ; Intrinsically Disordered Proteins/chemistry ; Computational Biology ; Amino Acids ; Protein Conformation
Chemical Substances	Proteome ; Intrinsically Disordered Proteins ; Amino Acids
Language	English
Publishing date	2022-01-29
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btac051
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Article ; Online: Welcome back Mr. Rudkin: differentiating Papilio zelicaon and Papilio polyxenes in Southern California (Lepidoptera: Papilionidae).

Shiraiwa, Kojiro / Grishin, Nick V

Zootaxa

2020 Volume 4877, Issue 3, Page(s) zootaxa.4877.3.3

Abstract: We studied wing pattern characters to distinguish closely related sympatric species Papilio zelicaon Lucas, 1852 and Papilio polyxenes Fabricius, 1775 in Southern California, and developed a morphometric method based on the ventral black postmedian band. ...

Abstract	We studied wing pattern characters to distinguish closely related sympatric species Papilio zelicaon Lucas, 1852 and Papilio polyxenes Fabricius, 1775 in Southern California, and developed a morphometric method based on the ventral black postmedian band. Application of this method to the holotype of Papilio [Zolicaon variety] Coloro W. G. Wright, 1905, the name currently applied to the P. polyxenes populations, revealed that it is a P. zelicaon specimen. The name for western US polyxenes subspecies thus becomes Papilio polyxenes rudkini (F. R. Chermock, 1981), reinstated status, and we place coloro as a junior subjective synonym of P. zelicaon. Furthermore, we sequenced mitochondrial DNA COI barcodes of rudkini and coloro holotypes and compared them with those of polyxenes and zelicaon specimens, confirming rudkini as polyxenes and coloro as zelicaon.
MeSH term(s)	Animals ; Base Sequence ; Butterflies/genetics ; California ; DNA, Mitochondrial ; Wings, Animal
Chemical Substances	DNA, Mitochondrial
Language	English
Publishing date	2020-11-11
Publishing country	New Zealand
Document type	Journal Article
ISSN	1175-5334
ISSN (online)	1175-5334
DOI	10.11646/zootaxa.4877.3.3
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