Computed structures of core eukaryotic protein complexes

Ian R. Humphreys; Jimin Pei; Minkyung Baek; Aditya Krishnakumar; Ivan Anishchenko; Sergey Ovchinnikov; Jing Zhang; Travis J. Ness; Sudeep Banjade; Saket R. Bagde; Viktoriya G. Stancheva; Xiao Han Li; Kaixian Liu; Zhi Zheng; Daniel J. Barrero; Upasana Roy; Jochen Kuper; Israel S. Fernández; Barnabas Szakal; Dana Branzei; Josep Rizo; Caroline Kisker; Eric C. Greene; Sue Biggins; Scott Keeney; Elizabeth A. Miller; J. Christopher Fromme; Tamara L. Hendrickson; Qian Cong; David Baker

doi:10.1126/science.abm4805

Computed structures of core eukaryotic protein complexes

Ian R. Humphreys, Jimin Pei, Minkyung Baek, Aditya Krishnakumar, Ivan Anishchenko, Sergey Ovchinnikov, Jing Zhang, Travis J. Ness, Sudeep Banjade, Saket R. Bagde, Viktoriya G. Stancheva, Xiao Han Li, Kaixian Liu, Zhi Zheng, Daniel J. Barrero, Upasana Roy, Jochen Kuper, Israel S. Fernández, Barnabas Szakal, Dana BranzeiJosep Rizo, Caroline Kisker, Eric C. Greene, Sue Biggins, Scott Keeney, Elizabeth A. Miller, J. Christopher Fromme, Tamara L. Hendrickson, Qian Cong (Lead / Corresponding author), David Baker (Lead / Corresponding author)

Molecular Cell and Developmental Biology

Research output: Contribution to journal › Article › peer-review

319 Citations (Scopus)

Abstract

Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coevolution analysis and deep-learning–based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes within the Saccharomyces cerevisiae proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.3 million pairs of yeast proteins, identify 1505 likely to interact, and build structure models for 106 previously unidentified assemblies and 806 that have not been structurally characterized. These complexes, which have as many as five subunits, play roles in almost all key processes in eukaryotic cells and provide broad insights into biological function.

Original language	English
Article number	1340
Number of pages	13
Journal	Science
Volume	374
Issue number	6573
Early online date	11 Nov 2021
DOIs	https://doi.org/10.1126/science.abm4805
Publication status	Published - 10 Dec 2021

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Humphreys, I. R., Pei, J., Baek, M., Krishnakumar, A., Anishchenko, I., Ovchinnikov, S., Zhang, J., Ness, T. J., Banjade, S., Bagde, S. R., Stancheva, V. G., Li, X. H., Liu, K., Zheng, Z., Barrero, D. J., Roy, U., Kuper, J., Fernández, I. S., Szakal, B., ... Baker, D. (2021). Computed structures of core eukaryotic protein complexes. Science, 374(6573), Article 1340. https://doi.org/10.1126/science.abm4805

@article{c9126878e2e44959bac9715684af916e,

title = "Computed structures of core eukaryotic protein complexes",

abstract = "Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coevolution analysis and deep-learning–based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes within the Saccharomyces cerevisiae proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.3 million pairs of yeast proteins, identify 1505 likely to interact, and build structure models for 106 previously unidentified assemblies and 806 that have not been structurally characterized. These complexes, which have as many as five subunits, play roles in almost all key processes in eukaryotic cells and provide broad insights into biological function.",

author = "Humphreys, {Ian R.} and Jimin Pei and Minkyung Baek and Aditya Krishnakumar and Ivan Anishchenko and Sergey Ovchinnikov and Jing Zhang and Ness, {Travis J.} and Sudeep Banjade and Bagde, {Saket R.} and Stancheva, {Viktoriya G.} and Li, {Xiao Han} and Kaixian Liu and Zhi Zheng and Barrero, {Daniel J.} and Upasana Roy and Jochen Kuper and Fern{\'a}ndez, {Israel S.} and Barnabas Szakal and Dana Branzei and Josep Rizo and Caroline Kisker and Greene, {Eric C.} and Sue Biggins and Scott Keeney and Miller, {Elizabeth A.} and Fromme, {J. Christopher} and Hendrickson, {Tamara L.} and Qian Cong and David Baker",

year = "2021",

month = dec,

day = "10",

doi = "10.1126/science.abm4805",

language = "English",

volume = "374",

journal = "Science",

issn = "0036-8075",

publisher = "American Association for the Advancement of Science",

number = "6573",

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Humphreys, IR, Pei, J, Baek, M, Krishnakumar, A, Anishchenko, I, Ovchinnikov, S, Zhang, J, Ness, TJ, Banjade, S, Bagde, SR, Stancheva, VG, Li, XH, Liu, K, Zheng, Z, Barrero, DJ, Roy, U, Kuper, J, Fernández, IS, Szakal, B, Branzei, D, Rizo, J, Kisker, C, Greene, EC, Biggins, S, Keeney, S, Miller, EA, Fromme, JC, Hendrickson, TL, Cong, Q & Baker, D 2021, 'Computed structures of core eukaryotic protein complexes', Science, vol. 374, no. 6573, 1340. https://doi.org/10.1126/science.abm4805

TY - JOUR

T1 - Computed structures of core eukaryotic protein complexes

AU - Humphreys, Ian R.

AU - Pei, Jimin

AU - Baek, Minkyung

AU - Krishnakumar, Aditya

AU - Anishchenko, Ivan

AU - Ovchinnikov, Sergey

AU - Zhang, Jing

AU - Ness, Travis J.

AU - Banjade, Sudeep

AU - Bagde, Saket R.

AU - Stancheva, Viktoriya G.

AU - Li, Xiao Han

AU - Liu, Kaixian

AU - Zheng, Zhi

AU - Barrero, Daniel J.

AU - Roy, Upasana

AU - Kuper, Jochen

AU - Fernández, Israel S.

AU - Szakal, Barnabas

AU - Branzei, Dana

AU - Rizo, Josep

AU - Kisker, Caroline

AU - Greene, Eric C.

AU - Biggins, Sue

AU - Keeney, Scott

AU - Miller, Elizabeth A.

AU - Fromme, J. Christopher

AU - Hendrickson, Tamara L.

AU - Cong, Qian

AU - Baker, David

PY - 2021/12/10

Y1 - 2021/12/10

N2 - Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coevolution analysis and deep-learning–based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes within the Saccharomyces cerevisiae proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.3 million pairs of yeast proteins, identify 1505 likely to interact, and build structure models for 106 previously unidentified assemblies and 806 that have not been structurally characterized. These complexes, which have as many as five subunits, play roles in almost all key processes in eukaryotic cells and provide broad insights into biological function.

AB - Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coevolution analysis and deep-learning–based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes within the Saccharomyces cerevisiae proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.3 million pairs of yeast proteins, identify 1505 likely to interact, and build structure models for 106 previously unidentified assemblies and 806 that have not been structurally characterized. These complexes, which have as many as five subunits, play roles in almost all key processes in eukaryotic cells and provide broad insights into biological function.

UR - https://europepmc.org/article/MED/34762488

U2 - 10.1126/science.abm4805

DO - 10.1126/science.abm4805

M3 - Article

C2 - 34762488

AN - SCOPUS:85121324733

SN - 0036-8075

VL - 374

JO - Science

JF - Science

IS - 6573

M1 - 1340

ER -

Computed structures of core eukaryotic protein complexes

Abstract

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