Hybrid computational methods combining experimental information with molecular dynamics

Mondal, Arup; Lenz, Stefan; MacCallum, Justin L.; Pérez, Alberto

doi:10.1016/j.sbi.2023.102609

Cited by 8 publications

(6 citation statements)

References 70 publications

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“…The current literature provides limited application examples for peptides due to computational costs, and their folding upon binding natures. However, the continuous development of more efficient enhanced sampling methods, 76,77 the increase in computational power, and development of integrative approaches 78 bring optimism to the field.…”

Section: Do Physics-based Methods Still Play a Role In A Post-af World?mentioning

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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Section: Do Physics-based Methods Still Play a Role In A Post-af World?mentioning

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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“…Some of the challenges in this field are the heterogeneity in experimental data reporting originating from different systems and laboratories, heterogeneity in how different modeler groups handle the data (e.g., some data might be ignored), and how the final models are reported (e.g., a structure, an ensemble, interpretation of the data, modeling of the errors, ...). Such heterogeneity ultimately leads to a diverse range of protocols and techniques, typically associated with different computational laboratories, using similar probabilistic integration techniques (e.g., Bayesian inference , ) to answer a diverse set of biological questions. ,,,, Every new project requires a deep understanding of the data, how to interpret it and apply it to simulations, or how to use it to reweight MD ensembles. Users who access MD data need to understand exactly how the experimental information was modeled, which might be hard to obtain from a paper.…”

Section: Potential Of MD Simulations In Integrative Structural Biologymentioning

confidence: 99%

“…When using biases (top), the system directly samples a distribution that aligns with the experimental data. Alternatively (bottom), unbiased simulations lead to a population distribution that is later reweighted against experimental data …”

Section: Potential Of MD Simulations In Integrative Structural Biologymentioning

confidence: 99%

Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology

Caparotta,

Perez

2024

J. Phys. Chem. B

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Molecular dynamics (MD) simulations have become a valuable tool in structural biology, offering insights into complex biological systems that are difficult to obtain through experimental techniques alone. The lack of available data sets and structures in most published computational work has limited other researchers' use of these models. In recent years, the emergence of online sharing platforms and MD database initiatives favor the deposition of ensembles and structures to accompany publications, favoring reuse of the data sets. However, the lack of uniform metadata collection, formats, and what data are deposited limits the impact and its use by different communities that are not necessarily experts in MD. This Perspective highlights the need for standardization and better resource sharing for processing and interpreting MD simulation results, akin to efforts in other areas of structural biology. As the field moves forward, we will see an increase in popularity and benefits of MD-based integrative approaches combining experimental data and simulations through probabilistic reasoning, but these too are limited by uniformity in experimental data availability and choices on how the data are modeled that are not trivial to decipher from papers. Other fields have addressed similar challenges comprehensively by establishing task forces with different degrees of success. The large scope and number of communities to represent the breadth of types of MD simulations complicates a parallel approach that would fit all. Thus, each group typically decides what data and which format to upload on servers like Zenodo. Uploading data with FAIR (findable, accessible, interoperable, reusable) principles in mind including optimal metadata collection will make the data more accessible and actionable by the community. Such a wealth of simulation data will foster method development and infrastructure advancements, thus propelling the field forward.

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“…Other classic ways to improve the activity and performance of AMPs are the insertion of unusual amino acids, tricyclic groups, and modifications on the scaffold to generate peptidomimetics ( Petri et al, 2022 ). Hybrid computational methods, such as the combination of different experimental data with molecular dynamics simulations ( Mondal et al, 2023 ), can be employed to predict information to improve secondary structures in peptides. Additionally, artificial intelligence algorithms, such as machine and deep learning ( Jiang et al, 2023 ; Yue et al, 2024 ), as well as geometric deep learning ( Fernandes et al, 2023 ), contribute to this advancement.…”

Section: Improving the Structure Of Amps To Enhance Their Activitymentioning

confidence: 99%

Capping motifs in antimicrobial peptides and their relevance for improved biological activities

Brango-Vanegas,

Leite,

Macedo

et al. 2024

Front. Chem.

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N-capping (N-cap) and C-capping (C-cap) in biologically active peptides, including specific amino acids or unconventional group motifs, have been shown to modulate activity against pharmacological targets by interfering with the peptide’s secondary structure, thus generating unusual scaffolds. The insertion of capping motifs in linear peptides has been shown to prevent peptide degradation by reducing its susceptibility to proteolytic cleavage, and the replacement of some functional groups by unusual groups in N- or C-capping regions in linear peptides has led to optimized peptide variants with improved secondary structure and enhanced activity. Furthermore, some essential amino acid residues that, when placed in antimicrobial peptide (AMP) capping regions, are capable of complexing metals such as Cu2+, Ni2+, and Zn2+, give rise to the family known as metallo-AMPs, which are capable of boosting antimicrobial efficacy, as well as other activities. Therefore, this review presents and discusses the different strategies for creating N- and C-cap motifs in AMPs, aiming at fine-tuning this class of antimicrobials.

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Hybrid computational methods combining experimental information with molecular dynamics

Cited by 8 publications

References 70 publications

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

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

Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology

Capping motifs in antimicrobial peptides and their relevance for improved biological activities

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