Ranking Peptide Binders by Affinity with AlphaFold**

Chang, Liwei; Pérez, Alberto

doi:10.1002/anie.202213362

Cited by 34 publications

(53 citation statements)

References 27 publications

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“…Two years later, AlphaFold presented a novel strategy based on attention networks with an impressive performance in CASP ( Jumper et al, 2021 ). Making the network available to the community and the appearance of collaborative notebooks ( Mirdita et al, 2022 ) rapidly allowed groups to apply it to a myriad of problems: for molecular recognition (protein-protein and protein-peptide) ( Humphreys et al, 2021 ; Tsaban et al, 2022 ), for predicting multiple biological states ( Wayment-Steele et al, 2022 ), relative binding affinities ( Chang and Perez, 2022 ), or even for designing new proteins via deep network hallucination ( Anishchenko et al, 2021 ). As these networks learn from data deposited in the protein data Bank, they also implicitly learn about the position of ions or ligands in active sites.…”

Section: Discussionmentioning

confidence: 99%

“…This was possible thanks to a large database of peptides known to be either binders/non-binders to MHC. Such type of initiatives could soon provide accurate results for predicting complexes involving integrins, which combined with competitive binding strategies ( Chang and Perez, 2022 ) could lead to rapid identification of functional motifs.…”

Section: Discussionmentioning

confidence: 99%

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Assessing a computational pipeline to identify binding motifs to the α2β1 integrin

Liu

Pérez²

2023

Front. Chem.

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Integrins in the cell surface interact with functional motifs found in the extracellular matrix (ECM) that queue the cell for biological actions such as migration, adhesion, or growth. Multiple fibrous proteins such as collagen or fibronectin compose the ECM. The field of biomechanical engineering often deals with the design of biomaterials compatible with the ECM that will trigger cellular response (e.g., in tissue regeneration). However, there are a relative few number of known integrin binding motifs compared to all the possible peptide epitope sequences available. Computational tools could help identify novel motifs, but have been limited by the challenges in modeling the binding to integrin domains. We revisit a series of traditional and novel computational tools to assess their performance in identifying novel binding motifs for the I-domain of the α2β1 integrin.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Assessing a computational pipeline to identify binding motifs to the α2β1 integrin

Liu

Pérez²

2023

Front. Chem.

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“…One of the most recently developed computational methods, AlphaFold, has revolutionized structural biology by predicting highly accurate structures of proteins and their complexes with peptides, antibodies and proteins [ 92 , 93 , 94 ]. In addition to that, AlphaFold can also be useful for protein–peptide systems and drug discovery, as it can help identify the highest affinity binder among a set of peptides.…”

Section: Computational Methods For Predicting Checkpoint Inhibitorsmentioning

confidence: 99%

“…In addition to that, AlphaFold can also be useful for protein–peptide systems and drug discovery, as it can help identify the highest affinity binder among a set of peptides. Chang and Perez [ 92 ] presented a new competitive binding assay using AlphaFold to predict the structures of the receptor in the presence of peptides. The authors tested the application on six protein receptors for which they possessed the experimental binding affinities to several peptides and obtained the predicted structures (bound form) for the higher affinity peptides [ 92 ].…”

Section: Computational Methods For Predicting Checkpoint Inhibitorsmentioning

confidence: 99%

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Sobral

Luz

Almeida

et al. 2023

IJMS

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Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein–protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

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“…Alternatively, recent improvements in machine learning for structure prediction and sequence design , offer possible new pipelines for exploring the sequence space of peptide self-assembly. Although the initial emphasis of structural machine learning methods was on folded structure prediction, they have consistently shown promise in the prediction of protein assemblies. − Their potential for the study of self-assembling peptides is currently unknown and can lead to retraining strategies for improved self-assembly predictions …”

Section: Introductionmentioning

confidence: 99%

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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Ranking Peptide Binders by Affinity with AlphaFold**

Cited by 34 publications

References 27 publications

Assessing a computational pipeline to identify binding motifs to the α2β1 integrin

Assessing a computational pipeline to identify binding motifs to the α2β1 integrin

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Molecular Modeling of Self-Assembling Peptides

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