Revealing protein-protein interactions at the transcriptome scale by sequencing

Johnson, Kara; Qi, Zhijie; Yan, Zhangming; Wen, Xingzhao; Nguyen, Tri C.; Zaleta-Rivera, Kathia; Chen, Chien-Ju; Fan, Xiaotang; Sriram, Kiran; Wan, Xueyi; Chen, Zhen Bouman; Su, Zhong

doi:10.1016/j.molcel.2021.08.035

Cited by 9 publications

(10 citation statements)

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“…The question is whether it will ever be possible to develop, for example, a technique that allows researchers “to follow the kinetics and structure of each of the intermediates involved in the many fascinating transport reactions that occur deep within the lipid bilayer membrane”. Alberts wrote about this in 1998; almost a quarter of a century has passed, but the situation has not changed considerably (see, for example [ 79 , 80 , 81 ]).…”

Section: Complete Interactomes—an (Unreachable?) Dream Of Systems Bio...mentioning

confidence: 99%

“…The incomplete nature of the available data is exacerbated by the fact that we do not really know how far we are from completeness. Although attempts are being made with the help of computer analysis to predict completeness (see, for example, [ 79 , 80 , 81 ]), it is not clear how realistic these predictions are given the poor quality of the data on which they are based.…”

Section: Conclusion: Is Data Completeness One More Unsolvable Problem...mentioning

confidence: 99%

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Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

Kondratyeva

Alekseenko

Чернов

2022

Biology

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In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

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Section: Complete Interactomes—an (Unreachable?) Dream Of Systems Bio...mentioning

confidence: 99%

Section: Conclusion: Is Data Completeness One More Unsolvable Problem...mentioning

confidence: 99%

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

Kondratyeva

Alekseenko

Чернов

2022

Biology

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“…In particular, the comparison of such networks derived from different cell types or tissues can indicate those interactions that are specific to a biological context. We thus considered experimentally determined PPI networks in three human cell lines (HUVEC, HEK293T, and JURKAT) [17], and extracted the contrast subgraphs for each of the 6 possible compar- isons. Each contrast subgraph thus contains proteins with higher density of interactions in one cell line compared with the other.…”

Section: Protein Interaction Network In Human Cell Linesmentioning

confidence: 99%

“…Analysis of PPI constrast subgraphs. PPI networks obtained in HEK293T, HUVEC, and JURKAT cells were obtained from the Supplementary Material of the original publication [17]. PPI networks were filtered to include only proteins described in all cell lines.…”

Section: B Construction Of Coexpression Network Normalizedmentioning

confidence: 99%

Contrast Subgraphs Allow Comparing Homogeneous and Heterogeneous Networks Derived from Omics Data

Lanciano

Savino

Porcu

et al. 2022

Preprint

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Biological networks are often used to describe the relationships between relevant entities, in particular genes and proteins, and are a powerful tool for functional genomics. Many important biological problems can be investigated by comparing biological networks between different conditions, or networks obtained with different techniques. We show that contrast subgraphs, a recently introduced technique to identify the most important structural differences between two networks, provide a versatile tool for comparing gene and protein networks of diverse origin. We show in three concrete examples how contrast subgraphs can provide new insight in functional genomics by extracting the gene/protein modules whose connectivity is most altered between two conditions or experimental techniques.

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“…Protein complexes are then purified from cell extracts on the beads by applying extensive washing protocols that reduce non-specific background step-wise while retaining the bound specific interaction partners 4 5 . Other HTP methods used to discover protein complexes are based on two-hybrid screens, the co-fractionation of molecular assemblies, display technologies, or the irreversible modification of proteins found in close proximity, and are often coupled to a pulldown-based approach 6 7 8 9 10 . While all of these methods are powerful for discovering interactions, they are qualitative and do not shed light on the biophysical attributes underpinning the observed interactions, such as dissociation constants ( K d ) or its negative logarithm (p K d ).…”

Section: Introductionmentioning

confidence: 99%

Native holdup (nHU) to measure binding affinities from cell extracts

Zámbó

Morlet

Négroni

et al. 2022

Preprint

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Characterizing macromolecular interactions is essential for understanding cellular processes, yet nearly all methods used to detect protein interactions from cells are qualitative. Here, we introduce the native holdup (nHU) approach to quantify equilibrium binding constants and explore binding mechanisms of protein interactions from cell extracts. Compared to other pulldown-based assays, nHU requires less sample preparation and can be coupled to any analytical methods, such as western blotting (nHU-WB) or mass spectrometry (nHU-MS) as readouts. We use nHU to explore interactions of SNX27, a cargo adaptor of the retromer complex and find good agreement between in vitro affinities and those measured directly from cell extracts using nHU. This challenges the unwritten paradigm stating that biophysical parameters like binding constants cannot be accurately determined from cells or cellular extracts. We discuss the strengths and limitations of nHU and provide simple protocols that can be implemented in most laboratories.

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Revealing protein-protein interactions at the transcriptome scale by sequencing

Abstract: In the originally published version of this article, the submission date was incorrect. It has been corrected online to March 10, 2021. The editor would like to apologize for any confusion this may have caused.

Cited by 9 publications

References 0 publications

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

Contrast Subgraphs Allow Comparing Homogeneous and Heterogeneous Networks Derived from Omics Data

Native holdup (nHU) to measure binding affinities from cell extracts

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