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

Science ◽  
2020 ◽  
Vol 369 (6507) ◽  
pp. 1132-1136
Author(s):  
Xingjie Pan ◽  
Michael C. Thompson ◽  
Yang Zhang ◽  
Lin Liu ◽  
James S. Fraser ◽  
...  
Keyword(s):  
De Novo ◽  
Design Method ◽  
Computational Design ◽  
Structural Elements ◽  
New Paradigm ◽  
Biophysical Characterization ◽  
Structure Space ◽  
Naturally Occurring

Naturally occurring proteins vary the precise geometries of structural elements to create distinct shapes optimal for function. We present a computational design method, loop-helix-loop unit combinatorial sampling (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 showed that 17 (38%) of 45 tested LUCS designs encompassing two different structural topologies were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely matched the designs. LUCS greatly expands the designable structure space and offers a new paradigm for designing proteins with tunable geometries that may be customizable for novel functions.

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 De novo design of transmembrane β-barrels

2020 ◽  
Author(s):  
Anastassia A. Vorobieva ◽  
Paul White ◽  
Binyong Liang ◽  
Jim E Horne ◽  
Asim K. Bera ◽  
...  
Keyword(s):  
Lipid Bilayers ◽  
Single Molecule ◽  
First Principles ◽  
Lipid Membranes ◽  
De Novo ◽  
Computational Design ◽  
Protein Sequencing ◽  
Naturally Occurring ◽  
Transmembrane Pores ◽  
Almost All

AbstractThe ability of naturally occurring transmembrane β-barrel proteins (TMBs) to spontaneously insert into lipid bilayers and form stable transmembrane pores is a remarkable feat of protein evolution and has been exploited in biotechnology for applications ranging from single molecule DNA and protein sequencing to biomimetic filtration membranes. Because it has not been possible to design TMBs from first principles, these efforts have relied on re-engineering of naturally occurring TMBs that generally have a biological function very different from that desired. Here we leverage the power of de novo computational design coupled with a “hypothesis, design and test” approach to determine principles underlying TMB structure and folding, and find that, unlike almost all other classes of protein, locally destabilizing sequences in both the β-turns and β-strands facilitate TMB expression and global folding by modulating the kinetics of folding and the competition between soluble misfolding and proper folding into the lipid bilayer. We use these principles to design new eight stranded TMBs with sequences unrelated to any known TMB and show that they insert and fold into detergent micelles and synthetic lipid membranes. The designed proteins fold more rapidly and reversibly in lipid membranes than the TMB domain of the model native protein OmpA, and high resolution NMR and X-ray crystal structures of one of the designs are very close to the computational model. The ability to design TMBs from first principles opens the door to custom design of TMBs for biotechnology and demonstrates the value of de novo design to investigate basic protein folding problems that are otherwise hidden by evolutionary history.One sentence summarySuccess in de novo design of transmembrane β-barrels reveals geometric and sequence constraints on the fold and paves the way to design of custom pores for sequencing and other single-molecule analytical applications.

scholarly journals A generative algorithm for de novo design of proteins with diverse pocket structures

2020 ◽  
Author(s):  
Benjamin Basanta ◽  
Matthew J Bick ◽  
Asim K Bera ◽  
Christoffer Norn ◽  
Cameron M Chow ◽  
...  
Keyword(s):  
Small Molecule ◽  
Large Scale ◽  
De Novo ◽  
Experimental Testing ◽  
Biophysical Characterization ◽  
Synthetic Genes ◽  
Precise Control ◽  
Yeast Cell Surface ◽  
Naturally Occurring ◽  
Genes Encoding

AbstractTo create new enzymes and biosensors from scratch, precise control over the structure of small molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed a generative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The generative algorithm was tested and refined through feedback from two rounds of large scale experimental testing, involving in total, the assembly of synthetic genes encoding 7896 generated designs and assessment of their stability on the yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.

A Computational Design Method for a Shape Memory Alloy Wire Actuated Compliant Finger

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
Chao-Chieh Lan ◽  
You-Nien Yang
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Design Method ◽  
Three Dimensional ◽  
Computational Design ◽  
Computational Method ◽  
Output Force ◽  
Robotic Manipulations ◽  
Experimental Validations ◽  
High Work

This paper presents a computational method to design a compliant finger for robotic manipulations. As traditional mechanical fingers require bulky electromagnetic motors and numerous relative moving parts to achieve dexterous motion, we propose a class of fingers; the manipulation of which relies on finger deflections. These compliant fingers are actuated by shape memory alloy (SMA) wires that exhibit high work-density, frictionless, and quiet operations. The combination of compliant members with embedded SMA wires makes the finger more compact and lightweight. Various SMA wire layouts are investigated to reduce their response time while maintaining sufficient output force. The mathematical models of finger deflection caused by SMA contraction are then derived along with experimental validations. As finger shapes are essential to the range of deflected motion and output force, we find its optimal initial shapes through the use of a shape parametrization technique. We further illustrate our method by designing a humanoid finger that is capable of three-dimensional manipulation. Since compliant fingers can be fabricated monolithically, we expect the proposed design method to be utilized for applications of various scales.

scholarly journals An enumerative algorithm for de novo design of proteins with diverse pocket structures

2020 ◽  
Vol 117 (36) ◽  
pp. 22135-22145 ◽  
Author(s):  
Benjamin Basanta ◽  
Matthew J. Bick ◽  
Asim K. Bera ◽  
Christoffer Norn ◽  
Cameron M. Chow ◽  
...  
Keyword(s):  
Small Molecule ◽  
Large Scale ◽  
De Novo ◽  
Experimental Testing ◽  
Biophysical Characterization ◽  
Precise Control ◽  
Yeast Cell Surface ◽  
Naturally Occurring ◽  
Genes Encoding ◽  
Enumerative Algorithm

To create new enzymes and biosensors from scratch, precise control over the structure of small-molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed an enumerative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The enumerative algorithm was tested and refined through feedback from two rounds of large-scale experimental testing, involving in total the assembly of synthetic genes encoding 7,896 designs and assessment of their stability on yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small-molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.

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 Computational Design Method for Tucking Axisymmetric Origami Consisting of Triangular Facets

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 469
Author(s):  
Yan Zhao ◽  
Yuki Endo ◽  
Yoshihiro Kanamori ◽  
Jun Mitani
Keyword(s):  
Design Method ◽  
Three Dimensional ◽  
Computational Design ◽  
Interior Vertex ◽  
Computational Method ◽  
The Other ◽  
Load Bearing ◽  
3D Space ◽  
A Value ◽  
Flat Sheet

Three-dimensional (3D) origami, which can generate a structure through folding a crease pattern on a flat sheet of paper, has received considerable attention in art, mathematics, and engineering. With consideration of symmetry, the user can efficiently generate a rational crease pattern and make the fabricated shape stable. In this paper, we focus on a category of axisymmetric origami consisting of triangular facets and edit the origami in 3D space for expanding its variations. However, it is difficult to retain the developability, which requires the sum of the angles around each interior vertex needing to equal 360 degrees, for designing origami. Intersections occur between crease lines when such a value is larger than 360 degrees. On the other hand, blank spaces (unfolded areas) emerge in the crease pattern when the value is less than 360 degrees. The former case is difficult to generate a realizable shape due to the crease lines are intersected with each other. For the latter case, however, blank spaces can be filled with crease lines and become a part of the origami through tucking. Here, we propose a computational method to add flaps or tucks on the 3D shape, which contains non-developable interior vertices, for achieving the resulting origami. Finally, on the application side, we describe a load-bearing experiment on a stool shape-like origami to demonstrate the potential usage.

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

A Framework for Component Layout and Geometry Design of Mechanical Systems: Configuration Network and its Viewing Control

1995 ◽  
Author(s):  
Kikuo Fujita ◽  
Shinsuke Akagi
Keyword(s):  
Design Method ◽  
Mechanical Systems ◽  
Optimization Procedure ◽  
Computational Design ◽  
Object Oriented Programming ◽  
Design System ◽  
Air Conditioner ◽  
Programming Technique ◽  
Geometry Design ◽  
Component Layout

Abstract A Framework of computational design method and model is proposed for layout and geometry design of complicated mechanical systems, which is named “configuration network and its viewing control”. In the method, a design object is represented with a set of declarative relationships among various elements of a system, that is, configurations, which is gradually extended from schematic structure to exact layout and geometry through design process. Since a whole of such configurations forms a too complicated network to compute all together, how to view subparts is controlled based on levels of granularity and width of scope range. Such a configuration network is made to grow and refined through embodying geometry and layout corresponding to a focused subpart with a numerical optimization procedure. The framework has also an ability to flexibly integrate with engineering analysis. Moreover, a design system is implemented with an object-oriented programming technique, and it is applied to a design problem of air conditioner units in order to show the validity and effectiveness of the framework.

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