Computed structures of core eukaryotic protein complexes

Humphreys, Ian R.; Pei, Jimin; Baek, Minkyung; Krishnakumar, Aditya; Anishchenko, Ivan; Овчинников, С. Г.; Zhang, Jing; Ness, Travis J.; Banjade, Sudeep; Bagde, Saket R.; Stancheva, Viktoriya; Li, Xiaohan; Liu, Kaixian; Zheng, Zhi; Barrero, Daniel J.; Roy, Upasana; Kuper, Jochen; Fernandez, I.S.; Szakál, Barnabás; Branzei, Dana; Rizo, Josep; Kisker, Caroline; Greene, Eric C.; Biggins, Sue; Keeney, Scott; Miller, Elizabeth A.; Fromme, J. Christopher; Hendrickson, Tamara L.; Cong, Qian; Baker, David

doi:10.1126/science.abm4805

Cited by 424 publications

(395 citation statements)

References 118 publications

Supporting

Mentioning

388

Contrasting

Unclassified

Order By: Relevance

“…Very recent studies indicate that the AlphaFold2 deep learning system [66][67][68], or a modified version trained specifically for multimeric inputs [69], outperforms all ab initio docking algorithms and template-based docking approaches. It predicts acceptable conformations for about two thirds of the tested dimers, and estimates prediction quality with a very small error rate.…”

Section: Discussionmentioning

confidence: 99%

From complete cross-docking to partners identification and binding sites predictions

et al. 2022

View full text Add to dashboard Cite

Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a “blind” protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. The docking algorithm uses a coarse-grained representation of the protein structures and treats them as rigid bodies. We applied the approach to a few hundred of proteins, in the unbound conformations, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces, and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. We provide a readout of the contributions of shape and physico-chemical complementarity, interface matching, and specificity, in the predictions. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.

show abstract

Section: Discussionmentioning

confidence: 99%

From complete cross-docking to partners identification and binding sites predictions

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Given the flexibility and low sample requirements of HR-HRPF compared with traditional high-resolution structural biology techniques, this methodology can play a significant role in the validation of computational structures, as well as in the generation of accurate and reliable structural models when computational methods fail. The application of HR-HRPF constraints could also support modern computational methods for predicting structures of protein complexes 50 , which currently have suspected issues regarding comprehensiveness and accuracy in certain cases that could be greatly remedied through the application of experimental HR-HRPF results. Future work examining the ability of HR-HRPF combined with conical neighbor count to correctly identify domain-domain contacts and orientation will be important for developing the application of HR-HRPF combined with conical neighbor count to address challenging problems in multi-domain protein structural biology.…”

Section: Discussionmentioning

confidence: 99%

Validated Determination of NRG1 Ig-like Domain Structure by Mass Spectrometry Coupled with Computational Modeling

Khaje

Eletsky

Biehn

et al. 2021

Preprint

View full text Add to dashboard Cite

High resolution hydroxyl radical protein footprinting (HR-HRPF) is a mass spectrometry-based method that measures the solvent exposure of multiple amino acids in a single experiment, offering constraints for experimentally-informed computational modeling. HR-HRPF-based modeling has previously been used to accurately model the structure of proteins of known structure, but the technique has never been used to determine the structure of a protein of unknown structure leaving questions of unintentional bias and applicability to unknown structures unresolved. Here, we present the use of HR-HRPF-based modeling to determine the structure of the Ig-like domain of NRG1, a protein with no close homolog of known structure. Independent determination of the protein structure by both HR-HRPF-based modeling and heteronuclear NMR was carried out, with results compared only after both processes were complete. The HR-HRPF-based model was highly similar to the lowest energy NMR model, with a backbone RMSD of 1.6 Å. To our knowledge, this is the first use of HR-HRPF-based modeling to determine a previously uncharacterized protein structure.

show abstract

“…RoseTTAFold provided early evidence that this technology can model protein complexes in addition to individual proteins 60 . Recently, the respective strengths of RoseTTAFold and AlphaFold were combined to not only model but also identify protein complexes 61 . The high speed of RoseTTAFold was leveraged to examine more than 4 million paired multiple sequence alignments to generate a set of approximately 5,500 potential PPIs in Saccharomyces cerevisiae (budding yeast).…”

Section: Coevolution and Deep Learning Approachesmentioning

confidence: 99%

“…The high speed of RoseTTAFold was leveraged to examine more than 4 million paired multiple sequence alignments to generate a set of approximately 5,500 potential PPIs in Saccharomyces cerevisiae (budding yeast). AlphaFold was then applied to this smaller set to identify higher-confidence candidate protein complexes and model their structures 61 . Importantly, like all technologies discussed in this Review, these methods rely on data generated from experimental approaches and should be viewed as powerful complements to these 62 , rather than as replacements.…”

Section: Coevolution and Deep Learning Approachesmentioning

confidence: 99%

From systems to structure — using genetic data to model protein structures

et al. 2022

View full text Add to dashboard Cite

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical–genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

show abstract

Computed structures of core eukaryotic protein complexes

Cited by 424 publications

References 118 publications

From complete cross-docking to partners identification and binding sites predictions

From complete cross-docking to partners identification and binding sites predictions

Validated Determination of NRG1 Ig-like Domain Structure by Mass Spectrometry Coupled with Computational Modeling

From systems to structure — using genetic data to model protein structures

Contact Info

Product

Resources

About