Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Kurciński, Mateusz; Kmiecik, Sebastian; Zalewski, M.; Koliński, Andrzej

doi:10.3390/ijms22147341

Cited by 5 publications

(9 citation statements)

References 63 publications

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“…To determine the generality of Upside ’s decrease in performance when backbone movement is allowed, we examined two other recent forcefield-guided methods, the CABS coarse grain model ,, and a computationally heavy all-atom explicit solvent approach . Both approaches let the dynamics guide the association of the protein partners, unlike the search process used in the Upside docking pipeline to start with up to 1000 pre-docked poses.…”

Section: Resultsmentioning

confidence: 99%

Challenges and Advantages of Accounting for Backbone Flexibility in Prediction of Protein–Protein Complexes

Faruk

Peng

Freed

et al. 2022

J. Chem. Theory Comput.

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Predicting protein binding is a core problem of computational biophysics. That this objective can be partly achieved with some amount of success using docking algorithms based on rigid protein models is remarkable, although going further requires allowing for protein flexibility. However, accurately capturing the conformational changes upon binding remains an enduring challenge for docking algorithms. Here, we adapt our Upside folding model, where side chains are represented as multi-position beads, to explore how flexibility may impact predictions of protein–protein complexes. Specifically, the Upside model is used to investigate where backbone flexibility helps, which types of interactions are important, and what is the impact of coarse graining. These efforts also shed light on the relative challenges posed by folding and docking. After training the Upside energy function for docking, the model is competitive with the established all-atom methods. However, allowing for backbone flexibility during docking is generally detrimental, as the presence of comparatively minor (3–5 Å) deviations relative to the docked structure has a large negative effect on performance. While this issue appears to be inherent to current forcefield-guided flexible docking methods, systems involving the co-folding of flexible loops such as antibody–antigen complexes represent an interesting exception. In this case, binding is improved when backbone flexibility is allowed using the Upside model.

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Section: Resultsmentioning

confidence: 99%

Challenges and Advantages of Accounting for Backbone Flexibility in Prediction of Protein–Protein Complexes

Faruk

Peng

Freed

et al. 2022

J. Chem. Theory Comput.

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show abstract

“…To reduce this cost and enable the conformational sampling of backbone flexibility, coarse grain models are used [ 61 ]. In this perspective, Kurcinski et al [ 62 ] recently combined the CABS CG protein model with Replica Exchange Monte Carlo (REMC) simulations, thus allowing the sampling of large structural backbone conformational changes across a protein–protein complex. The PACSAB coarse-grained force field [ 63 ] of Emperador et al was specifically developed to include conformational variations in many protein systems.…”

Section: Tools For Calculating Ppi Dynamicsmentioning

confidence: 99%

Modeling the Dynamics of Protein–Protein Interfaces, How and Why?

2022

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Protein–protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein–protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein–protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein–protein interface.

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“…Such a knowledge-based force field has been derived by statistical analysis of structures accessible in properly sorted PDB [19] entries to avoid overrepresentation of the most frequent folds. The CABS method has proven to be an efficient tool for de novo structure prediction [20][21][22], comparative modeling, simulation of protein dynamics, and protein-peptide docking [23,24], including free docking of small proteins to protein receptors [25].…”

Section: Modeling Peptide Aggregation Using Cabs-dockmentioning

confidence: 99%

Multiscale Modeling of Amyloid Fibrils Formed by Aggregating Peptides Derived from the Amyloidogenic Fragment of the A-Chain of Insulin

Koliński

Dec

Dzwolak

2021

IJMS

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Computational prediction of molecular structures of amyloid fibrils remains an exceedingly challenging task. In this work, we propose a multi-scale modeling procedure for the structure prediction of amyloid fibrils formed by the association of ACC1-13 aggregation-prone peptides derived from the N-terminal region of insulin’s A-chain. First, a large number of protofilament models composed of five copies of interacting ACC1-13 peptides were predicted by application of CABS-dock coarse-grained (CG) docking simulations. Next, the models were reconstructed to all-atom (AA) representations and refined during molecular dynamics (MD) simulations in explicit solvent. The top-scored protofilament models, selected using symmetry criteria, were used for the assembly of long fibril structures. Finally, the amyloid fibril models resulting from the AA MD simulations were compared with atomic force microscopy (AFM) imaging experimental data. The obtained results indicate that the proposed multi-scale modeling procedure is capable of predicting protofilaments with high accuracy and may be applied for structure prediction and analysis of other amyloid fibrils.

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Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Cited by 5 publications

References 63 publications

Challenges and Advantages of Accounting for Backbone Flexibility in Prediction of Protein–Protein Complexes

Challenges and Advantages of Accounting for Backbone Flexibility in Prediction of Protein–Protein Complexes

Modeling the Dynamics of Protein–Protein Interfaces, How and Why?

Multiscale Modeling of Amyloid Fibrils Formed by Aggregating Peptides Derived from the Amyloidogenic Fragment of the A-Chain of Insulin

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