Computational approaches to macromolecular interactions in the cell

Vakser, Ilya A.; Deeds, Eric J.

doi:10.1016/j.sbi.2019.03.012

Cited by 13 publications

(13 citation statements)

References 73 publications

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“…They have focused almost exclusively on validating predictions of the diffusion coefficient of a protein in a crowded cellular environment by measurements of fluorescent proteins diffusion in cells. 10, 22 These results showed that effects like transient interactions and excluded volume significantly decrease the rate of diffusion of proteins in cells. 10…”

Section: Introductionmentioning

confidence: 84%

“…10, 22 These results showed that effects like transient interactions and excluded volume significantly decrease the rate of diffusion of proteins in cells. 10…”

Section: Introductionmentioning

confidence: 84%

“…In the context of the spectacular advances in experimental and computational structural biology, structure-based modeling of protein interactions in the living cell is becoming more central than ever before. [10][11][12] Traditional simulation protocols (such as MD and BD) are either relatively slow, if carried out at the all-atom representation, 13 or significantly coarse-grained, with one particle representing a protein. 14 Thus, there are only a few examples of structure-based simulations at the scale of the whole cell.…”

Section: Introductionmentioning

confidence: 99%

“…11,13,15 Cell modeling is important for a variety of reasons, including integration of data into a unified representation of knowledge about an organism, prediction of multi-network phenotypes, filling the gaps in our knowledge of cellular processes, and development of our ability to modulate them. 10,[16][17][18] Early approaches to cell modeling represented proteins by hard spheres. 14,19 BD simulations of a part of the E. coli cytoplasm were run for 20 μs in rigid body all-atom representation, 20 coarse grained in a subsequent study.…”

Section: Introductionmentioning

confidence: 99%

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Docking-based long timescale simulation of cell-size protein systems at atomic resolution

Vakser

Grudinin

Jenkins

et al. 2022

Preprint

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Computational methodologies are increasingly addressing modeling of the whole cell at the molecular level. Proteins and their interactions are the key component of cellular processes. Techniques for modeling protein interactions, so far, have included protein docking and molecular simulation. The latter approaches account for the dynamics of the interactions, but are relatively slow, if carried out at all-atom resolution, or are significantly coarse-grained. Protein docking algorithms are far more efficient in sampling spatial coordinates. However, they do not account for the kinetics of the association (i.e., they do not involve the time coordinate). Our proof-of-concept study bridges the two modeling approaches, developing an approach that can reach unprecedented simulation timescales at all-atom resolution. The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise Fast Fourier Transform docking and sampled in space and time by Monte Carlo simulations. The simulation protocol was parametrized on existing data and validated on a number of observations from experiments and molecular dynamics simulations. The simulation protocol performed consistently across very different systems of proteins at different protein concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells, at atomic resolution.

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Section: Introductionmentioning

confidence: 84%

“…10, 22 These results showed that effects like transient interactions and excluded volume significantly decrease the rate of diffusion of proteins in cells. 10…”

Section: Introductionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Docking-based long timescale simulation of cell-size protein systems at atomic resolution

Vakser

Grudinin

Jenkins

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years molecular simulation techniques have been gaining traction as alternative methods for the elucidation of structural details underpinning cellular mechanisms 1 . Methods like molecular docking can complement experimental techniques such as X-RAY crystallography, NMR and cryo-electron microscopy and allow us to gain insight into the structural machinery of the cell.…”

Section: Introductionmentioning

confidence: 99%

An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

Koukos

Roel‐Touris

Ambrosetti

et al. 2019

Preprint

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Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modelling process, and to the robustness of its scoring function. We participated in CAPRI both as server and as manual predictors.In CAPRI rounds 38-45, we have used various strategies depending on the information at hand. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement / CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall in the current rounds, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces.Our server performance (acceptable models) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.

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Prediction of protein interactions is essential for studying biomolecular mechanisms

Vakser

2023

Proteomics

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Structural characterization of protein interactions is essential for our ability to understand and modulate physiological processes. Computational approaches to modeling of protein complexes provide structural information that far exceeds capabilities of the existing experimental techniques. Protein structure prediction in general, and prediction of protein interactions in particular, has been revolutionized by the rapid progress in Deep Learning techniques. The work of Schweke et al. (Proteomics 2023, 23, 2200323) presents a community‐wide study of an important problem of distinguishing physiological protein–protein complexes/interfaces (experimentally determined or modeled) from non‐physiological ones. The authors designed and generated a large benchmark set of physiological and non‐physiological homodimeric complexes, and evaluated a large set of scoring functions, as well as AlphaFold predictions, on their ability to discriminate the non‐physiological interfaces. The problem of separating physiological interfaces from non‐physiological ones is very difficult, largely due to the lack of a clear distinction between the two categories in a crowded environment inside a living cell. Still, the ability to identify key physiologically significant interfaces in the variety of possible configurations of a protein–protein complex is important. The study presents a major data resource and methodological development in this important direction for molecular and cellular biology.

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Computational approaches to macromolecular interactions in the cell

Cited by 13 publications

References 73 publications

Docking-based long timescale simulation of cell-size protein systems at atomic resolution

Docking-based long timescale simulation of cell-size protein systems at atomic resolution

An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

Prediction of protein interactions is essential for studying biomolecular mechanisms

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