Modelling peptide–protein complexes: docking, simulations and machine learning

Mondal, Arup; Chang, Liwei; Pérez, Alberto

doi:10.1017/qrd.2022.14

Cited by 15 publications

(20 citation statements)

References 153 publications

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“…They are not only able to maintain the stability of native states in micro- to millisecond scales but are also able to capture and identify protein folding processes, distinguish protein folding pathways, and capture disorder-to-order transitions of peptides as they bind their protein partners. There are still several limitations, such as the ability to describe intrinsically disordered peptides (IDPs), typically due to bias toward compact states, which has led to a plethora of new force fields to describe IDP systems …”

Section: Results and Discussionmentioning

confidence: 99%

“…There are still several limitations, such as the ability to describe intrinsically disordered peptides (IDPs), typically due to bias toward compact states, 42 which has led to a plethora of new force fields to describe IDP systems. 48 For processes such as binding, the amount of sampling needed to sample multiple binding/unbinding events is the computational bottleneck. To alleviate computational cost, we used the same implicit solvent model with MELD that has previously captured the folding of IDP peptides upon binding their protein receptors.…”

Section: ■ Introductionmentioning

confidence: 99%

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Molecular Modeling of Self-Assembling Peptides

Jones

Pérez

2023

ACS Appl. Bio Mater.

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Peptide epitopes mediate as many as 40% of protein− protein interactions and fulfill signaling, inhibition, and activation roles within the cell. Beyond protein recognition, some peptides can self-or coassemble into stable hydrogels, making them a readily available source of biomaterials. While these 3D assemblies are routinely characterized at the fiber level, there are missing atomistic details about the assembly scaffold. Such atomistic detail can be useful in the rational design of more stable scaffold structures and with improved accessibility to functional motifs. Computational approaches can in principle reduce the experimental cost of such an endeavor by predicting the assembly scaffold and identifying novel sequences that adopt said structure. Yet, inaccuracies in physical models and inefficient sampling have limited atomistic studies to short (two or three amino acid) peptides. Given recent developments in machine learning and advances in sampling strategies, we revisit the suitability of physical models for this task. We use the MELD (Modeling Employing Limited Data) approach to drive self-assembly in combination with generic data in cases where conventional MD is unsuccessful. Finally, despite recent developments in machine learning algorithms for protein structure and sequence predictions, we find the algorithms are not yet suited for studying the assembly of short peptides.

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Section: Results and Discussionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Molecular Modeling of Self-Assembling Peptides

Jones

Pérez

2023

ACS Appl. Bio Mater.

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“…AF's success rate in predicting native-like protein-peptide complex structures starting from sequences hovers around $50%-similar to the recent PatchMAN methodology. 30,40 Furthermore, AF remains unable to predict the impact of point mutations, modified amino acids, and non-standard residues. 47 In such scenarios, traditional docking tools retain their value.…”

Section: Post-af Opens New Opportunities For Developing Peptide Vs Pi...mentioning

confidence: 99%

“…In cases involving larger peptides, methods such as AutoDock CrankPep (ADCP), 31 Rosetta PIPER FlexPepDock (PFPD), 32 InterPep2, 33 and PatchMAN 34 have made notable strides, outperforming traditional tools within benchmark sets. However, their success in sampling diverse binding conformations often contrasts with a lower success rate in selecting the optimal docked models 30 . Despite these challenges, the presence of virtual screening (VS) peptide pipelines in prominent toolkits like Schrodinger Modeling Suites and OpenEye Docking toolkits attests to the interest in the field 35 …”

Section: Computational Drug Discovery Pipelinesmentioning

confidence: 99%

Revolutionizing peptide‐based drug discovery: Advances in the post‐AlphaFold era

Chang,

Mondal,

Singh

et al. 2023

WIREs Comput Mol Sci

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Peptide‐based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide‐based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide‐protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post‐AF era, we are witnessing rapid progress with the potential to revolutionize peptide‐based drug discovery such as the ability to rank peptide binders or classify them as binders/non‐binders and the ability to design novel peptide sequences. However, AI‐based methods are struggling due to the lack of well‐curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics‐based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD‐based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics‐based methods holds the promise of reshaping the landscape of peptide‐based drug discovery.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions Software > Molecular Modeling

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“…[10] Accurate modeling of these interactions, especially those with less defined secondary structure, is often tedious because of the flexibility of one of the partners. Different strategies have been developed over the years to address this challenge, [11] including the use of AlphaFold2. [3] This is crucial, since the above-mentioned protocols for distinguishing between binding and non-binding partners predominantly rely on an available structure (crystal or model) for at least one of the peptides bound to the peptide binding domain.…”

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confidence: 99%

Who Binds Better? Let Alphafold2 Decide!

Schueler‐Furman

Varga

2023

Angewandte Chemie

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Deep learning is revolutionizing structural biology to an unprecedented extent. Spearheaded by DeepMind's Alphafold2, structural models of high quality can be generated, and are now available for most known proteins and many protein interactions. The next challenge will be to leverage this rich structural corpus to learn about binding: which protein can contact which partner(s), and at what affinity? In a recent study, Chang and Perez have presented an elegant approach towards this challenging goal for interactions that involve a short peptide binding to its receptor. The basic idea is straightforward: given a receptor that binds to two peptides, if the receptor sequence is presented with both peptides together at the same time, AlphaFold2 should model the tighter binding peptide into the binding site, while excluding the second. A simple idea that works!

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Modelling peptide–protein complexes: docking, simulations and machine learning

Cited by 15 publications

References 153 publications

Molecular Modeling of Self-Assembling Peptides

Molecular Modeling of Self-Assembling Peptides

Revolutionizing peptide‐based drug discovery: Advances in the post‐AlphaFold era

Who Binds Better? Let Alphafold2 Decide!

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