PreprintCited by 18Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

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Do Newly Born Orphan Proteins Resemble Never Born Proteins? A Study Using Three Deep Learning Algorithms

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Liu, Jing, Yuan, Rongqing, Shao, Wei, Wang, Jitong, Silman, Israel, Sussman, Joel L.·2022·Proteins Structure Function and Bioinformatics

Preprint — published as 10.1002/prot.26496 · Paper page

Abstract

‘Newly Born’ proteins, devoid of detectable homology to any other proteins, known as orphan proteins, occur in a single species or within a taxonomically restricted gene family. They are generated by expression of novel Open Reading Frames, and appear throughout evolution. We were curious if the three recently developed programs for predicting protein structures, viz., AlphaFold2, RoseTTAFold, and ESMFold, might be of value for comparison of such ‘ Newly Born’ proteins to random polypeptides with amino acid content similar to that of native proteins, which have been called ‘ Never Born ’ proteins. The programs were used to compare the structures of two sets of ‘ Never Born’ proteins that had been expressed – Group 1, which had been shown experimentally to possess substantial secondary structure, and Group 3, which had been shown to be intrinsically disordered. Overall, the models generated were scored as being of low quality but revealed some general principles. Specifically, all four members of Group 1 were predicted to be compact by all three algorithms. The members of Group 3 were predicted to be very extended, as would be expected for intrinsically disordered proteins. The three programs were then used to predict the structures of three orphan proteins whose crystal structures had been solved, two of which display novel folds. Finally, they were used to predict the structures of seven orphan proteins with well-identified biological functions, whose 3D structures are not known. Two proteins, which were predicted to be disordered based on their sequences, are predicted by all three structure algorithms to be extended structures. The other five were predicted to be compact structures with two exceptions in the case of AlphaFold2. All three prediction algorithms make remarkably similar and high-quality predictions for one large protein, HCO_11565, from a nematode. It is conjectured that this is due to many homologs in the taxonomically restricted family of which it is a member and to the fact that the Dali server revealed several non-related proteins with similar folds. Overall, orphan and taxonomically restricted proteins are often predicted to have compact 3D structures, sometimes with a novel fold that is a consequence of their novel sequences, which are associated with the appearance of new biological functions.

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Metadata

Authors
Liu, J., Yuan, R., Shao, W., Wang, J., Silman, I., & Sussman, J. L.
Affiliations
  • Technion – Israel Institute of Technology (IL)
  • Tsinghua University (CN)
  • Shanghai Jiao Tong University (CN)
  • Weizmann Institute of Science (IL)
Corresponding author
  • Silman, Israel
  • Sussman, Joel L.
Venue
Proteins Structure Function and Bioinformatics
Publisher
openRxiv
Published
2022-08-02
Volume
91
Issue
8
Pages
1097-1115
ISSN
0887-3585 (ISSN-L)
OpenAlex
W4366819635
Cited by
18
References
2
Citations by year
2026: 4 · 2025: 3 · 2024: 8 · 2023: 2 · 2022: 1
FWCI
1.51
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Proteins Structure Function and Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
orphan proteins, protein structure prediction, AlphaFold2, RoseTTAFold, ESMFold, intrinsically disordered proteins, novel protein fold, solvent accessible surface area, protein compactness
MeSH terms
Deep Learning, Algorithms, Amino Acid Sequence, Amino Acids, Proteins

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