scholarly journals Computational design of closely related proteins that adopt two well-defined but structurally divergent folds

2020 ◽  
Vol 117 (13) ◽  
pp. 7208-7215 ◽  
Author(s):  
Kathy Y. Wei ◽  
Danai Moschidi ◽  
Matthew J. Bick ◽  
Santrupti Nerli ◽  
Andrew C. McShan ◽  
...  
Keyword(s):  
Large Scale ◽  
De Novo ◽  
Conformational Transition ◽  
Protein Structures ◽  
Computational Design ◽  
Spectroscopic Characterization ◽  
De Novo Design ◽  
Viral Fusion ◽  
Naturally Occurring ◽  
Related Proteins

The plasticity of naturally occurring protein structures, which can change shape considerably in response to changes in environmental conditions, is critical to biological function. While computational methods have been used for de novo design of proteins that fold to a single state with a deep free-energy minimum [P.-S. Huang, S. E. Boyken, D. Baker, Nature 537, 320–327 (2016)], and to reengineer natural proteins to alter their dynamics [J. A. Davey, A. M. Damry, N. K. Goto, R. A. Chica, Nat. Chem. Biol. 13, 1280–1285 (2017)] or fold [P. A. Alexander, Y. He, Y. Chen, J. Orban, P. N. Bryan, Proc. Natl. Acad. Sci. U.S.A. 106, 21149–21154 (2009)], the de novo design of closely related sequences which adopt well-defined but structurally divergent structures remains an outstanding challenge. We designed closely related sequences (over 94% identity) that can adopt two very different homotrimeric helical bundle conformations—one short (∼66 Å height) and the other long (∼100 Å height)—reminiscent of the conformational transition of viral fusion proteins. Crystallographic and NMR spectroscopic characterization shows that both the short- and long-state sequences fold as designed. We sought to design bistable sequences for which both states are accessible, and obtained a single designed protein sequence that populates either the short state or the long state depending on the measurement conditions. The design of sequences which are poised to adopt two very different conformations sets the stage for creating large-scale conformational switches between structurally divergent forms.

scholarly journals Computational design of a protein family that adopts two well-defined and structurally divergent de novo folds

2019 ◽  
Author(s):  
Kathy Y. Wei ◽  
Danai Moschidi ◽  
Matthew J. Bick ◽  
Santrupti Nerli ◽  
Andrew C. McShan ◽  
...  
Keyword(s):  
Large Scale ◽  
De Novo ◽  
Conformational Transition ◽  
Protein Structures ◽  
Computational Design ◽  
Spectroscopic Characterization ◽  
De Novo Design ◽  
Viral Fusion ◽  
Naturally Occurring ◽  
Viral Fusion Proteins

AbstractThe plasticity of naturally occurring protein structures, which can change shape considerably in response to changes in environmental conditions, is critical to biological function. While computational methods have been used to de novo design proteins that fold to a single state with a deep free energy minima (Huang et al., 2016), and to reengineer natural proteins to alter their dynamics (Davey et al., 2017) or fold (Alexander et al., 2009), the de novo design of closely related sequences which adopt well-defined, but structurally divergent structures remains an outstanding challenge. Here, we design closely related sequences (over 94% identity) that can adopt two very different homotrimeric helical bundle conformations -- one short (∼66 Å height) and the other long (∼100 Å height) -- reminiscent of the conformational transition of viral fusion proteins (Ivanovic et al., 2013; Podbilewicz, 2014; Skehel and Wiley, 2000). Crystallographic and NMR spectroscopic characterization show that both the short and long state sequences fold as designed. We sought to design bistable sequences for which both states are accessible, and obtained a single designed protein sequence that populates either the short state or the long state depending on the measurement conditions. The design of sequences which are poised to adopt two very different conformations sets the stage for creating large scale conformational switches between structurally divergent forms.

scholarly journals A defined structural unit enables de novo design of small-molecule–binding proteins

Science ◽  
2020 ◽  
Vol 369 (6508) ◽  
pp. 1227-1233 ◽  
Author(s):  
Nicholas F. Polizzi ◽  
William F. DeGrado
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Small Molecules ◽  
Structural Unit ◽  
De Novo ◽  
Computational Design ◽  
De Novo Design ◽  
X Ray ◽  
X Ray Crystallography ◽  
Chemical Groups

The de novo design of proteins that bind highly functionalized small molecules represents a great challenge. To enable computational design of binders, we developed a unit of protein structure—a van der Mer (vdM)—that maps the backbone of each amino acid to statistically preferred positions of interacting chemical groups. Using vdMs, we designed six de novo proteins to bind the drug apixaban; two bound with low and submicromolar affinity. X-ray crystallography and mutagenesis confirmed a structure with a precisely designed cavity that forms favorable interactions in the drug–protein complex. vdMs may enable design of functional proteins for applications in sensing, medicine, and catalysis.

scholarly journals De novo design of tunable, pH-driven conformational changes

Science ◽  
2019 ◽  
Vol 364 (6441) ◽  
pp. 658-664 ◽  
Author(s):  
Scott E. Boyken ◽  
Mark A. Benhaim ◽  
Florian Busch ◽  
Mengxuan Jia ◽  
Matthew J. Bick ◽  
...  
Keyword(s):  
Conformational Changes ◽  
Mammalian Cells ◽  
Large Scale ◽  
Environmental Changes ◽  
De Novo ◽  
Hydrophobic Interactions ◽  
De Novo Design ◽  
General Strategy ◽  
Histidine Residues ◽  
Hydrogen Bond Networks

The ability of naturally occurring proteins to change conformation in response to environmental changes is critical to biological function. Although there have been advances in the de novo design of stable proteins with a single, deep free-energy minimum, the design of conformational switches remains challenging. We present a general strategy to design pH-responsive protein conformational changes by precisely preorganizing histidine residues in buried hydrogen-bond networks. We design homotrimers and heterodimers that are stable above pH 6.5 but undergo cooperative, large-scale conformational changes when the pH is lowered and electrostatic and steric repulsion builds up as the network histidine residues become protonated. The transition pH and cooperativity can be controlled through the number of histidine-containing networks and the strength of the surrounding hydrophobic interactions. Upon disassembly, the designed proteins disrupt lipid membranes both in vitro and after being endocytosed in mammalian cells. Our results demonstrate that environmentally triggered conformational changes can now be programmed by de novo protein design.

scholarly journals Computational protein design with backbone plasticity

2016 ◽  
Vol 44 (5) ◽  
pp. 1523-1529 ◽  
Author(s):  
James T. MacDonald ◽  
Paul S. Freemont
Keyword(s):  
Protein Design ◽  
De Novo ◽  
Protein Structures ◽  
Search Space ◽  
Computational Protein Design ◽  
Artificial Enzymes ◽  
Backbone Flexibility ◽  
Artificial Proteins ◽  
Naturally Occurring ◽  
Backbone Structure

The computational algorithms used in the design of artificial proteins have become increasingly sophisticated in recent years, producing a series of remarkable successes. The most dramatic of these is the de novo design of artificial enzymes. The majority of these designs have reused naturally occurring protein structures as ‘scaffolds’ onto which novel functionality can be grafted without having to redesign the backbone structure. The incorporation of backbone flexibility into protein design is a much more computationally challenging problem due to the greatly increased search space, but promises to remove the limitations of reusing natural protein scaffolds. In this review, we outline the principles of computational protein design methods and discuss recent efforts to consider backbone plasticity in the design process.

scholarly journals Multi-Scale Structural Analysis of Proteins by Deep Semantic Segmentation

2018 ◽  
Author(s):  
Raphael R. Eguchi ◽  
Po-Ssu Huang
Keyword(s):  
Image Classification ◽  
Protein Design ◽  
Large Scale ◽  
De Novo ◽  
Protein Structures ◽  
Semantic Segmentation ◽  
Amino Acid Sequences ◽  
Structural Quality ◽  
Small Subset ◽  
Structural Prediction

AbstractRecent advancements in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds, and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation — a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structural quality assessment. We represent protein structures as 2D α-carbon distance matrices (“contact maps”), and train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model performs exceptionally well, achieving a per-residue accuracy of 90.8% on the test set (95.0% average accuracy over all classes; 87.8% average within-structure accuracy). The unique aspect of our classifier is that it encodes sequence agnostic residue environments from the PDB and can assess structural quality as quantitative probabilities. We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design.SignificanceRecent computational advances have allowed researchers to predict the structure of many proteins from their amino acid sequences, as well as designing new sequences that fold into predefined structures. However, these tasks are often challenging because they require selection of a small subset of promising structural models from a large pool of stochastically generated ones. Here, we describe a novel approach to protein model selection that uses 2D image classification techniques to evaluate 3D protein models. Our method can be used to select structures based on the fold that they adopt, and can also be used to identify regions of low structural quality. These capabilities yield a powerful tool for both protein design and structure prediction.

scholarly journals Expanding the space of protein geometries by computational design of de novo fold families

2020 ◽  
Author(s):  
Xingjie Pan ◽  
Michael Thompson ◽  
Yang Zhang ◽  
Lin Liu ◽  
James S. Fraser ◽  
...  
Keyword(s):  
De Novo ◽  
Design Method ◽  
Computational Design ◽  
Computational Method ◽  
Structural Elements ◽  
New Paradigm ◽  
Biophysical Characterization ◽  
Structure Space ◽  
Naturally Occurring ◽  
Designer Proteins

AbstractNaturally occurring proteins use a limited set of fold topologies, but vary the precise geometries of structural elements to create distinct shapes optimal for function. Here we present a computational design method termed LUCS that mimics nature’s ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization shows that 17 (38%) out of 45 tested LUCS designs were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely match the designs. LUCS greatly expands the designable structure space and provides a new paradigm for designing proteins with tunable geometries customizable for novel functions.One Sentence SummaryA computational method to systematically sample loop-helix-loop geometries expands the structure space of designer proteins.

scholarly journals Design of complicated all-α protein structures

2021 ◽  
Author(s):  
Koya Sakuma ◽  
Naohiro Kobayashi ◽  
Toshihiko Sugiki ◽  
Toshio Nagashima ◽  
Toshimichi Fujiwara ◽  
...  
Keyword(s):  
De Novo ◽  
Protein Structures ◽  
Building Blocks ◽  
Helical Structures ◽  
Helix Loop Helix ◽  
Naturally Occurring ◽  
Wide Range ◽  
Helical Protein ◽  
Helical Proteins ◽  
The Universe

A wide range of de novo protein structure designs have been achieved, but the complexity of naturally occurring protein structures is still far beyond these designs. To expand the diversity and complexity of de novo designed protein structures, we sought to develop a method for designing 'difficult-to-describe' α-helical protein structures composed of irregularly aligned α-helices, such as globins. Backbone structure libraries consisting of a myriad of α-helical structures with 5- or 6- helices were generated by combining 18 helix-loop-helix motifs and canonical α-helices, and five distinct topologies were selected for de novo design. The designs were found to be monomeric with high thermal stability in solution and fold into the target topologies with atomic accuracy. This study demonstrated that complicated α-helical proteins are created using typical building blocks. The method we developed would enable us to explore the universe of protein structures for designing novel functional proteins.

scholarly journals Role of backbone strain in de novo design of complex α/β protein structures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nobuyasu Koga ◽  
Rie Koga ◽  
Gaohua Liu ◽  
Javier Castellanos ◽  
Gaetano T. Montelione ◽  
...  
Keyword(s):  
De Novo ◽  
Protein Structures ◽  
De Novo Design ◽  
Structure Packing ◽  
Nmr Structures ◽  
Β Protein ◽  
Wide Range ◽  
Explicit Consideration ◽  
Non Local

AbstractWe previously elucidated principles for designing ideal proteins with completely consistent local and non-local interactions which have enabled the design of a wide range of new αβ-proteins with four or fewer β-strands. The principles relate local backbone structures to supersecondary-structure packing arrangements of α-helices and β-strands. Here, we test the generality of the principles by employing them to design larger proteins with five- and six- stranded β-sheets flanked by α-helices. The initial designs were monomeric in solution with high thermal stability, and the nuclear magnetic resonance (NMR) structure of one was close to the design model, but for two others the order of strands in the β-sheet was swapped. Investigation into the origins of this strand swapping suggested that the global structures of the design models were more strained than the NMR structures. We incorporated explicit consideration of global backbone strain into the design methodology, and succeeded in designing proteins with the intended unswapped strand arrangements. These results illustrate the value of experimental structure determination in guiding improvement of de novo design, and the importance of consistency between local, supersecondary, and global tertiary interactions in determining protein topology. The augmented set of principles should inform the design of larger functional proteins.

scholarly journals Extreme stability in de novo-designed repeat arrays is determined by unusually stable short-range interactions

2018 ◽  
Vol 115 (29) ◽  
pp. 7539-7544 ◽  
Author(s):  
Kathryn Geiger-Schuller ◽  
Kevin Sforza ◽  
Max Yuhas ◽  
Fabio Parmeggiani ◽  
David Baker ◽  
...  
Keyword(s):  
Protein Design ◽  
Nearest Neighbor ◽  
De Novo ◽  
Protein Structures ◽  
Free Energies ◽  
Intrinsic Stability ◽  
Repeat Proteins ◽  
Naturally Occurring ◽  
Wide Range ◽  
The Individual

Designed helical repeats (DHRs) are modular helix–loop–helix–loop protein structures that are tandemly repeated to form a superhelical array. Structures combining tandem DHRs demonstrate a wide range of molecular geometries, many of which are not observed in nature. Understanding cooperativity of DHR proteins provides insight into the molecular origins of Rosetta-based protein design hyperstability and facilitates comparison of energy distributions in artificial and naturally occurring protein folds. Here, we use a nearest-neighbor Ising model to quantify the intrinsic and interfacial free energies of four different DHRs. We measure the folding free energies of constructs with varying numbers of internal and terminal capping repeats for four different DHR folds, using guanidine-HCl and glycerol as destabilizing and solubilizing cosolvents. One-dimensional Ising analysis of these series reveals that, although interrepeat coupling energies are within the range seen for naturally occurring repeat proteins, the individual repeats of DHR proteins are intrinsically stable. This favorable intrinsic stability, which has not been observed for naturally occurring repeat proteins, adds to stabilizing interfaces, resulting in extraordinarily high stability. Stable repeats also impart a downhill shape to the energy landscape for DHR folding. These intrinsic stability differences suggest that part of the success of Rosetta-based design results from capturing favorable local interactions.

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