New opportunities in integrative structural modeling

Ziemianowicz, Daniel S.; Kosiński, Jan

doi:10.1016/j.sbi.2022.102488

Cited by 7 publications

(4 citation statements)

References 43 publications

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“…In addition, several methods have been proposed to dock and fit predicted structures into density maps [ 62 , 66 , 67 ]. In cases of multi-domain and multi-component complexes, one approach is to first use AI-prediction tools to generate structures of sub-complexes and then assemble them into higher-order structures [ 168 , 179 , 180 ]. For example, the protein structure prediction and modeling tool I-TASSER-MTD [ 168 ] predicts structures of individual domains and then uses the EM density to guide the construction of a complete atomic model.…”

Section: Applications Of Ai-based Protein Structure Prediction To Ato...mentioning

confidence: 99%

Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy

DiIorio,

Kulczyk

2023

Micromachines

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Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for near-atomic structure determination, shedding light on the important molecular mechanisms of biological macromolecules. However, the inherent dynamics and structural variability of biological complexes coupled with the large number of experimental images generated by a cryo-EM experiment make data processing nontrivial. In particular, ab initio reconstruction and atomic model building remain major bottlenecks that demand substantial computational resources and manual intervention. Approaches utilizing recent innovations in artificial intelligence (AI) technology, particularly deep learning, have the potential to overcome the limitations that cannot be adequately addressed by traditional image processing approaches. Here, we review newly proposed AI-based methods for ab initio volume generation, heterogeneous 3D reconstruction, and atomic model building. We highlight the advancements made by the implementation of AI methods, as well as discuss remaining limitations and areas for future development.

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Section: Applications Of Ai-based Protein Structure Prediction To Ato...mentioning

confidence: 99%

Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy

DiIorio,

Kulczyk

2023

Micromachines

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“…By detecting very close amino acid neighbors, XL/MS has the added power of providing structural insights, since the measured cross-links constrain the relative 3D orientation and packing of the interaction partners. Thus, XLs can be combined with protein structure prediction or cryo-electron tomography to build detailed 3D models of protein complexes [33][34][35][36] .…”

Section: Introductionmentioning

confidence: 99%

An amino acid-resolution interactome for motile cilia illuminates the structure and function of ciliopathy protein complexes

McCafferty¹,

Papoulas

Lee

et al. 2023

Preprint

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Motile cilia are ancient, evolutionarily conserved organelles whose dysfunction underlies motile ciliopathies, a broad class of human diseases. Motile cilia contain myriad different proteins that assemble into an array of distinct machines, so understanding the interactions and functional hierarchies among them presents an important challenge. Here, we defined the protein interactome of motile axonemes using cross-linking mass spectrometry (XL/MS) in Tetrahymena thermophila. From over 27,000 XLs, we identified 9,208 unique amino acid interactions among 1,368 distinct proteins, providing both macromolecular and atomic-scale insights into diverse ciliary machines, including the Intraflagellar Transport system, axonemal dynein arms, radial spokes, the 96 nm ruler, and microtubule inner proteins, among others. Guided by this dataset, we used vertebrate multiciliated cells to reveal novel functional interactions among several poorly-defined human ciliopathy proteins. The dataset therefore provides a powerful resource for studying the basic biology of an ancient organelle and the molecular etiology of human genetic disease.

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“…As cross-linkers have known spacer lengths, the distance constraints they provide has made XL-MS a valuable resource for structural characterization of proteins and their assemblies, including highly dynamic systems with many interactions. − XL-MS data is frequently used in conjunction with data from other structural techniques, notably X-ray crystallography and cryo-electron microscopy to provide complementary information, , such as additional protein states, filling in structural resolution gaps, and assist in protein identification of unknown densities . Cross-linking data is also used in computational modeling ,− to either improve on existing proteins models or through de novo modeling if a structure from the PDB or a suitable model is not available, with new tools emerging to aid in more realistic protein modeling, taking into consideration protein energy landscapes. − Another major application of XL-MS is identifying protein–protein interactions (PPIs), − especially as the field has quickly moved toward complex systems and in vivo (or in-cell) cross-linking. In vivo cross-linking allows for a global overview of protein interactions without prior assumptions as it preserves potential transient interactions and offers the possibility of detecting novel PPIs that may be lost during purification.…”

mentioning

confidence: 99%

“… 4 − 6 XL-MS data is frequently used in conjunction with data from other structural techniques, notably X-ray crystallography and cryo-electron microscopy to provide complementary information, 7 , 8 such as additional protein states, 9 filling in structural resolution gaps, 10 and assist in protein identification of unknown densities. 11 Cross-linking data is also used in computational modeling 2 , 12 − 14 to either improve on existing proteins models or through de novo modeling if a structure from the PDB or a suitable model is not available, with new tools emerging to aid in more realistic protein modeling, taking into consideration protein energy landscapes. 15 − 17 Another major application of XL-MS is identifying protein–protein interactions (PPIs), 18 − 20 especially as the field has quickly moved toward complex systems and in vivo (or in-cell) cross-linking.…”

mentioning

confidence: 99%

Do Not Waste Time─Ensure Success in Your Cross-Linking Mass Spectrometry Experiments before You Begin

Nouchikian,

Fernandez-Martinez,

Renard

et al. 2024

Anal. Chem.

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Cross-linking mass spectrometry (XL-MS) has become a very useful tool for studying protein complexes and interactions in living systems. It enables the investigation of many large and dynamic assemblies in their native state, providing an unbiased view of their protein interactions and restraints for integrative modeling. More researchers are turning toward trying XL-MS to probe their complexes of interest, especially in their native environments. However, due to the presence of other potentially higher abundant proteins, sufficient cross-links on a system of interest may not be reached to achieve satisfactory structural and interaction information. There are currently no rules for predicting whether XL-MS experiments are likely to work or not; in other words, if a protein complex of interest will lead to useful XL-MS data. Here, we show that a simple iBAQ (intensity-based absolute quantification) analysis performed from trypsin digest data can provide a good understanding of whether proteins of interest are abundant enough to achieve successful cross-linking data. Comparing our findings to large-scale data on diverse systems from several other groups, we show that proteins of interest should be at least in the top 20% abundance range to expect more than one cross-link found per protein. We foresee that this guideline is a good starting point for researchers who would like to use XL-MS to study their protein of interest and help ensure a successful cross-linking experiment from the beginning. Data are available via ProteomeXchange with identifier PXD045792.

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New opportunities in integrative structural modeling

Cited by 7 publications

References 43 publications

Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy

Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy

An amino acid-resolution interactome for motile cilia illuminates the structure and function of ciliopathy protein complexes

Do Not Waste Time─Ensure Success in Your Cross-Linking Mass Spectrometry Experiments before You Begin

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