The design of functional proteins using tensorized energy calculations

Maksymenko, K; Maurer, Andreas; Aghaallaei, Narges; Barry, Caroline; Ullrich, Timo; Dijkstra, Tjeerd M. H.; Alvarez, Birte Hernandez; Müller, Patrick; Lupas, Andrei N.; Skokowa, Julia; ElGamacy, Mohammad

doi:10.1016/j.crmeth.2023.100560

Cited by 4 publications

(10 citation statements)

References 56 publications

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“…1C) using all conformations from our previously compiled NMR ensemble (PDB: 7NY0). These models were used as input for the multi-model, single-point mutagenesis application from the Damietta protein design software [43]. We set six amino acid positions on the Boskar4 surface as mutation target sites at the binding interface with G-CSFR (Fig.…”

Section: Computationally-guided Screening Identifies Higher-affinity ...mentioning

confidence: 99%

“…3A). The templates named ori0 through ori4 (orientation-rigging designs) were initially sculpted with poly-alanine splicing helical segments that were sequence-designed using the combinatorial sampler application of Damietta [43]. We then ran molecular dynamics simulations of the lowest energy candidates in order to validate the putative conformation for each ori design, and estimate the degree of conformational deviation from the AlphaFold2-predicted models (Fig.…”

Section: Design Of Agonists Altering the Relative Orientation Of G-csfrmentioning

confidence: 99%

“…The obtained AF2 structures were used as input to optimize the sequence of the helix-linker region with Damietta. Depending on the construct, up to 14 positions (compare Supplementary Methods) were mutated utilizing the few-to-many-to-few combinatorial sampler of Damietta (v0.32, [43]). Analysis of the designs' structural stability was done using a tempering molecular dynamics routine, and candidates ranked by their conformational homogeneity score, as previously described in [42].…”

Section: Generation Of Orientation-rigging Designsmentioning

confidence: 99%

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Tuning of granulopoietic signaling byde novodesigned agonists

Ullrich,

Pollmann,

Ritter

et al. 2023

Preprint

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Enhancing cytokine-based therapies by systematically tuning how an agonist associates its receptor is emerging as a powerful new concept in drug discovery. Here, we report the design and characterization of agonists that tune the granulocyte-colony stimulating factor receptor (G-CSFR) activity, which is central for the proliferation and granulocytic differentiation of hematopoietic stem cells. Using design agonists, we study the impact of varying the receptor-binding affinity and dimerization geometry on receptor association, downstream signaling, and cellular response. Hence, we achieved agonists with altered signaling specificities that are hyper-thermostable, can outcompete the native ligand (G-CSF), and bias granulopoietic differentiation over triggering proliferation. Furthermore, the design agonists differentially modulate the kinetics and amplitudes of signal transduction pathways, and gene expression patterns. Unlike G-CSF, they achieve selective activation of hematopoietic functions with minimal undesired immunomodulatory effects. These findings demonstrate the potential of dissecting the complex G-CSFR signaling, and open ways for new therapeutic applications.Graphical abstract

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Section: Computationally-guided Screening Identifies Higher-affinity ...mentioning

confidence: 99%

Section: Design Of Agonists Altering the Relative Orientation Of G-csfrmentioning

confidence: 99%

Section: Generation Of Orientation-rigging Designsmentioning

confidence: 99%

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Tuning of granulopoietic signaling byde novodesigned agonists

Ullrich,

Pollmann,

Ritter

et al. 2023

Preprint

Self Cite

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“…Recently, we have developed Damietta, a framework that introduces a new approach to protein design computations ( 13 ). Unlike previous methods which evaluate interaction energies by looping over all interacting atom pairs, our framework calculates the interaction energies between two groups of atoms through a single tensor operation.…”

Section: Introductionmentioning

confidence: 99%

“…This has the two-fold advantage of relying on self-consistent scoring terms, and avoiding any training procedure for parametrizing them. Using the Damietta design software, we successfully designed copper-binding and EGFR-inhibiting proteins, and could improve the affinity and tune the activity of G-CSFR-binding designs ( 13 , 14 ).…”

Section: Introductionmentioning

confidence: 99%

The Damietta Server: a comprehensive protein design toolkit

Grin,

Maksymenko,

Wörtwein

et al. 2024

Nucleic Acids Research

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The growing importance of protein design to various research disciplines motivates the development of integrative computational platforms that enhance the accessibility and interoperability of different design tools. To this end, we describe a web-based toolkit that builds on the Damietta protein design engine, which deploys a tensorized energy calculation framework. The Damietta Server seamlessly integrates different design tools, in addition to other tools such as message-passing neural networks and molecular dynamics routines, allowing the user to perform multiple operations on structural models and forward them across tools. The toolkit can be used for tasks such as core or interface design, symmetric design, mutagenic scanning, or conformational sampling, through an intuitive user interface. With the envisioned integration of more tools, the Damietta Server will provide a central resource for protein design and analysis, benefiting basic and applied biomedical research communities. The toolkit is available with no login requirement through https://damietta.de/.

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Protein engineering using variational free energy approximation

Lobzaev,

Herrera,

Kasprzyk

et al. 2024

Nat Commun

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Engineering proteins is a challenging task requiring the exploration of a vast design space. Traditionally, this is achieved using Directed Evolution (DE), which is a laborious process. Generative deep learning, instead, can learn biological features of functional proteins from sequence and structural datasets and return novel variants. However, most models do not generate thermodynamically stable proteins, thus leading to many non-functional variants. Here we propose a model called PRotein Engineering by Variational frEe eNergy approximaTion (PREVENT), which generates stable and functional variants by learning the sequence and thermodynamic landscape of a protein. We evaluate PREVENT by designing 40 variants of the conditionally essential E. coli phosphotransferase N-acetyl-L-glutamate kinase (EcNAGK). We find 85% of the variants to be functional, with 55% of them showing similar growth rate compared to the wildtype enzyme, despite harbouring up to 9 mutations. Our results support a new approach that can significantly accelerate protein engineering.

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The design of functional proteins using tensorized energy calculations

Cited by 4 publications

References 56 publications

Tuning of granulopoietic signaling byde novodesigned agonists

Tuning of granulopoietic signaling byde novodesigned agonists

The Damietta Server: a comprehensive protein design toolkit

Protein engineering using variational free energy approximation

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