Perturbing the energy landscape for improved packing during computational protein design

Maguire, Jack; Haddox, Hugh K.; Strickland, Devin; Halabiya, Samer; Coventry, Brian; Cummins, Matthew C.; Thieker, David F.; Klavins, Eric; DiMaio, Frank; Baker, David; Kuhlman, Brian

doi:10.22541/au.158986804.41133682

Cited by 5 publications

(3 citation statements)

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“…We used trRosetta to predict stability on a dataset composed of the designs from Rocklin et al (25) ( N = 16,174, four different topologies: HHH, HEEH, EHEE, EEHEE) and 13,985 small β-barrels designs (four different topologies: OB, SH3, Barrel_5, Barrel_6). Backbones were constructed using blueprints (26) , followed by sequence design with Rosetta (FastDesign (24,27) in conjunction with LayerDesign (18) , and using the Rosetta all-atom energy function (28) with beta_nov16 weights). We performed the analysis across the entire dataset, as well as within each topological group, and compared the prediction to Rosetta energy (all designs were re-scored with beta16_nostab weights to enable comparisons).…”

Section: Methodsmentioning

confidence: 99%

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Protein sequence design by explicit energy landscape optimization

Norn

Wicky

Juergens

et al. 2020

Preprint

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The protein design problem is to identify an amino acid sequence which folds to a desired structure. Given Anfinsen’s thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the lowest energy conformation is that structure. As this calculation involves not only all possible amino acid sequences but also all possible structures, most current approaches focus instead on the more tractable problem of finding the lowest energy amino acid sequence for the desired structure, often checking by protein structure prediction in a second step that the desired structure is indeed the lowest energy conformation for the designed sequence, and discarding the in many cases large fraction of designed sequences for which this is not the case. Here we show that by backpropagating gradients through the trRosetta structure prediction network from the desired structure to the input amino acid sequence, we can directly optimize over all possible amino acid sequences and all possible structures, and in one calculation explicitly design amino acid sequences predicted to fold into the desired structure and not any other. We find that trRosetta calculations, which consider the full conformational landscape, can be more effective than Rosetta single point energy estimations in predicting folding and stability of de novo designed proteins. We compare sequence design by landscape optimization to the standard fixed backbone sequence design methodology in Rosetta, and show that the results of the former, but not the latter, are sensitive to the presence of competing low-lying states. We show further that more funneled energy landscapes can be designed by combining the strengths of the two approaches: the low resolution trRosetta model serves to disfavor alternative states, and the high resolution Rosetta model, to create a deep energy minimum at the design target structure.SignificanceComputational protein design has primarily focused on finding sequences which have very low energy in the target designed structure. However, what is most relevant during folding is not the absolute energy of the folded state, but the energy difference between the folded state and the lowest lying alternative states. We describe a deep learning approach which captures the entire folding landscape, and show that it can enhance current protein design methods.

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Section: Methodsmentioning

confidence: 99%

“…Sequence design with Rosetta was performed with FastDesign (24,27), using the Rosetta all-atom energy function (28) with beta16_nostab weights. We applied three different methods for restricting and biasing amino acid choices during FastDesign.…”

Section: Restricting and Biasing Rosetta Sequence Designmentioning

confidence: 99%

Protein sequence design by explicit energy landscape optimization

Norn

Wicky

Juergens

et al. 2020

Preprint

Self Cite

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“…Early in method development, we observed that hydrophobic amino acids tended to be favored during design owing to Lennard-Jones score terms 37 and the default FastDesign ramping protocol 38 . As the fragment database utilized for pose identification contains motifs found in interfaces from native complexes, and those complexes typically demonstrate greater polar characteristics 39 , we hypothesized that explicitly sampling polar amino acids could reduce the prevalence of hydrophobic atomic interactions while retaining well-packed and complementarity tertiary motifs 40 .…”

Section: Interface Design Using Fragments and Knowledge-based Hydroge...mentioning

confidence: 99%

A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols

Meador,

Castells-Graells,

Aguirre

et al. 2023

Preprint

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Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, while methods for their creation remain challenging and unpredictable. In the present study, we apply new computational approaches to design a suite of new tetrahedrally symmetric, self-assembling protein cages. For the generation of docked poses, we emphasize a protein fragment-based approach, while forde novointerface design, a comparison of computational protocols highlights the power and increased experimental success achieved using the machine learning program ProteinMPNN. In relating information from docking and design, we observe that agreement between fragment-based sequence preferences and ProteinMPNN sequence inference correlates with experimental success. Additional insights for designing polar interactions are highlighted by experimentally testing larger and more polar interfaces. In all, using X-ray crystallography and cryo-EM, we report five structures for seven protein cages, with atomic resolution in the best case reaching 2.0 Å. We also report structures of two incompletely assembled protein cages, providing unique insights into one type of assembly failure. The new set of designed cages and their structures add substantially to the body of available protein nanoparticles, and to methodologies for their creation.

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XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers

Maguire¹,

Grattarola

Klyshko

et al. 2021

Preprint

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Graph representations are traditionally used to represent protein structures in sequence design protocols where the folding pattern is known. This infrequently extends to machine learning projects: existing graph convolution algorithms have shortcomings when representing protein environments. One reason for this is the lack of emphasis on edge attributes during massage-passing operations. Another reason is the traditionally shallow nature of graph neural network architectures. Here we introduce an improved message-passing operation that is better equipped to model local kinematics problems such as protein design. Our approach, XENet, pays special attention to both incoming and outgoing edge attributes.We compare XENet against existing graph convolutions in an attempt to decrease rotamer sample counts in Rosetta’s rotamer substitution protocol. This use case is motivating because it allows larger protein design problems to fit onto near-term quantum computers. XENet outperformed competing models while also displaying a greater tolerance for deeper architectures. We found that XENet was able to decrease rotamer counts by 40% without loss in quality. This decreased the problem size of our use case by more than a factor of 3. Additionally, XENet displayed an ability to handle deeper architectures than competing convolutions.Author summaryGraphs data structures are ubiquitous in the field of protein design and are at the core of the recent advances in artificial intelligence brought forth by graph neural networks (GNNs). GNNs have led to some impressive results in modeling protein interactions, but are not as common as other tensor representations.Most GNN architectures tend to put little to no emphasis on the information stored on edges; however, protein modeling tools often use edges to represent vital geometric relationships about residue pair interactions. In this paper, we show that a more advanced processing of edge attributes can lead to considerable benefits when modeling chemical data.We introduce XENet, a new member of the GNN family that is shown to have improved ability to model protein residue environments based on chemical and geometric data. We use XENet to intelligently simplify the optimization problem that is solved when designing proteins. This task is important to us and others because it allows larger proteins to be designed on near-term quantum computers. We show that XENet is able to train on our protein modeling data better than existing methods, successfully resulting in a dramatic decrease in protein design sample space with no loss in quality.

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Perturbing the energy landscape for improved packing during computational protein design

Cited by 5 publications

References 0 publications

Protein sequence design by explicit energy landscape optimization

Protein sequence design by explicit energy landscape optimization

A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols

XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers

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