A common binding motif in the ET domain of BRD3 forms polymorphic structural interfaces with host and viral proteins

Aiyer, Sriram; Swapna, G.V.T.; Liang, Ma; Liu, Gaohua; Hao, Jingzhou; Chalmers, Gordon; Jacobs, Brian C.; Montelione, Gaetano T.; Roth, Monica J.

doi:10.1016/j.str.2021.01.010

Cited by 24 publications

(74 citation statements)

References 76 publications

(143 reference statements)

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“…Knowing the bound conformation of a protein-peptide system does not imply that other peptide sequences can adopt the same bound conformation. This binding plasticity is best exemplified by: 1) receptors that bind different peptide sequences in different conformations (Aiyer et al, 2021) and 2) peptide sequences that can adopt different structures when binding different receptors (Huart and Hupp, 2013). This amount of binding plasticity is a challenge for predicting peptide-protein interactions and for computing binding affinities.…”

Section: Computational Methods Used To Study Protein-peptide Interactionsmentioning

confidence: 99%

“…This includes the conformational changes of the protein receptor as well as the ability to accommodate multiple binding conformations. This is especially characteristic of protein interaction hubs such as those involved in gene regulation (Aiyer et al, 2021). Many peptides are intrinsically disordered and fold upon binding, hence optimization of peptide design will often try to maximize interactions in the complex as well as the propensity of the peptide to adopt bound structures (e.g.…”

Section: Structural Features Of Protein-peptide Interactionsmentioning

confidence: 99%

“…Approaches to model protein flexibility include the use of soft potentials (Fernández et al, 2002;Ferrari et al, 2004), explore rotameric states (Leach, 1994), using different protein receptor structures (Amaro et al, 2018;Falcon et al, 2019) or refining with molecular dynamics (Alonso et al, 2006). The challenges in modeling peptides arises from: 1) peptides can have different conformations in their free/bound states; 2) the same peptide sequence might bind different proteins in different conformations (Huart and Hupp, 2013), and 3) different peptide sequences can bind the same receptor in different conformations (Aiyer et al, 2021). Most docking methods use a flexible strategy for the peptide conformation.…”

Section: Docking-based Approachesmentioning

confidence: 99%

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Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Pérez

2021

Front. Mol. Biosci.

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Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

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Section: Computational Methods Used To Study Protein-peptide Interactionsmentioning

confidence: 99%

Section: Structural Features Of Protein-peptide Interactionsmentioning

confidence: 99%

Section: Docking-based Approachesmentioning

confidence: 99%

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Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

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2021

Front. Mol. Biosci.

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“…Here we describe an integrative approach to structure determination for peptide-protein complexes combining NMR chemical shift data and molecular simulations. High information-content NMR studies rely on extracting many distance and orientation restraints to solve the structure of the peptide-protein complex 7 . At the other extreme, lower information-content NMR studies, such as backbone chemical shift data which is prerequisite to more extensive studies, provide valuable information about the binding epitope and (in some cases) the bound-state conformation of the peptide, but do not usually provide enough data to reliably characterize the binding mode and structure of the resulting complex.…”

Section: Introductionmentioning

confidence: 99%

Structure determination of protein-peptide complexes from NMR chemical shift data using MELD

Mondal

Swapna

Hao

et al. 2022

Preprint

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Intrinsically disordered regions of proteins often mediate important protein-protein interactions. However, the folding upon binding nature of many polypeptide-protein interactions limits the ability of modeling tools to predict structures of such complexes. To address this problem, we have taken a tandem approach combining NMR chemical shift data and molecular simulations to determine structures of peptide-protein complexes. Here, we demonstrate this approach for polypeptide com-plexes formed with the extraterminal (ET) domain of bromo and extraterminal domain (BET) proteins, which exhibit a high degree of binding plasticity. This system is particularly challenging as the binding process includes allosteric changes across the ET receptor upon binding, and the polypeptide binding partners can form different conformations (e.g., helices and hair-pins) in the complex. In a blind study, the new approach successfully modeled bound-state conformations and binding pos-es, using only backbone chemical shift data, in excellent agreement with experimentally-determined structures. The approach also predicts relative binding affinities of different peptides. This hybrid MELD-NMR approach provides a powerful new tool for structural analysis of protein-polypeptide complexes in the low NMR information content regime, which can be used successfully for flexible systems where one polypeptide binding partner folds upon complex formation.

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“…Historically, molecular mechanics strategies such as simulated annealing approaches have been combined with NMR data to produce restrained conformational ensembles ( Clore and Gronenborn, 1998 ). For a typical protein solved by NMR, we need ∼20 restraints per amino acid ( Aiyer et al, 2021 ). In this scenario, the protein force field and sampling techniques have limited contribution to the overall structure but are useful to get the right stereochemical properties and reduced numbers of steric clashes.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Assignment and Structure Determination of Proteins From Sparsely Labeled NMR Datasets

Mondal

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2021

Front. Mol. Biosci.

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Sparsely labeled NMR samples provide opportunities to study larger biomolecular assemblies than is traditionally done by NMR. This requires new computational tools that can handle the sparsity and ambiguity in the NMR datasets. The MELD (modeling employing limited data) Bayesian approach was assessed to be the best performing in predicting structures from sparsely labeled NMR data in the 13th edition of the Critical Assessment of Structure Prediction (CASP) event—and limitations of the methodology were also noted. In this report, we evaluate the nature and difficulty in modeling unassigned sparsely labeled NMR datasets and report on an improved methodological pipeline leading to higher-accuracy predictions. We benchmark our methodology against the NMR datasets provided by CASP 13.

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A common binding motif in the ET domain of BRD3 forms polymorphic structural interfaces with host and viral proteins

Cited by 24 publications

References 76 publications

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Structure determination of protein-peptide complexes from NMR chemical shift data using MELD

Simultaneous Assignment and Structure Determination of Proteins From Sparsely Labeled NMR Datasets

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