ISAMBARD: an open-source computational environment for biomolecular analysis, modelling and design

AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network

The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, while pursuing high thermodynamic stability and rigidity of proteins, inevitably sacrifices biological functions closely related to protein flexibility. The thermodynamic stability of proteins is not always optimal when they are highest to perfectly perform their biological functions. Extensive theoretical and experimental screening is often required to obtain stable protein structures. Thus, it becomes critically important to develop a stability prediction model based on the balance between protein stability and bioactivity. To design protein drugs with better functionality in a broader structural space, a novel protein structural stability predictor called PSSP has been developed in this study. PSSP is a mean pooled dual graph convolutional network (GCN) model based on sequence characteristics and secondary structure, distance matrix, graph, and residue properties of a nanoprotein to provide rapid prediction and judgment. This model exhibits excellent robustness in predicting the structural stability of nanoproteins. Comparing with previous artificial intelligence algorithms, the results indicate this model can provide a rapid and accurate assessment of the structural stability of artificially designed proteins, which shows the great promises for promoting the robust development of protein design.

Comparative Simulative Analysis and Design of Single-Chain Self-Assembled Protein Cages

Coiled-coil protein origami (CCPO) is a modular strategy for the de novo design of polypeptide nanostructures. It represents a type of modular design based on pairwise-interacting coiled-coil (CC) units with a single-chain protein programmed to fold into a polyhedral cage. However, the mechanisms underlying the self-assembly of the protein tetrahedron are still not fully understood. In the present study, 18 CCPO cages with three different topologies were modeled in silico. Then, molecular dynamics simulations and CC parameters were calculated to characterize the dynamic properties of protein tetrahedral cages at both the local and global levels. Furthermore, a deformed CC unit was redesigned, and the stability of the new cage was significantly improved.

Functionally Active Synthetic α-Helical Pores

ConspectusTransmembrane pores are currently at the forefront of nanobiotechnology, nanopore chemistry, and synthetic chemical biology research. Over the past few decades, significant studies in protein engineering have paved the way for redesigning membrane protein pores tailored for specific applications in nanobiotechnology. Most previous efforts predominantly centered on natural β-barrel pores designed with atomic precision for nucleic acid sequencing and sensing of biomacromolecules, including protein fragments. The requirement for a more efficient single-molecule detection system has driven the development of synthetic nanopores. For example, engineering channels to conduct ions and biomolecules selectively could lead to sophisticated nanopore sensors. Also, there has been an increased interest in synthetic pores, which can be fabricated to provide more control in designing architecture and diameter for single-molecule sensing of complex biomacromolecules. There have been impressive advancements in developing synthetic DNA-based pores, although their application in nanopore technology is limited. This has prompted a significant shift toward building synthetic transmembrane α-helical pores, a relatively underexplored field offering novel opportunities. Recently, computational tools have been employed to design and construct α-helical barrels of defined structure and functionality.We focus on building synthetic α-helical pores using naturally occurring transmembrane motifs of membrane protein pores. Our laboratory has developed synthetic α-helical transmembrane pores based on the natural porin PorACj (Porin A derived from Corynebacterium jeikeium) that function as nanopore sensors for single-molecule sensing of cationic cyclodextrins and polypeptides. Our breakthrough lies in being the first to create a functional and large stable synthetic transmembrane pore composed of short synthetic α-helical peptides. The key highlight of our work is that these pores can be synthesized using easy chemical synthesis, which permits its easy modification to include a variety of functional groups to build charge-selective sophisticated pores. Additionally, we have demonstrated that stable functional pores can be constructed from D-amino acid peptides. The analysis of pores composed of D- and L-amino acids in the presence of protease showed that only the D pores are highly functional and stable. The structural models of these pores revealed distinct surface charge conformation and geometry. These new classes of synthetic α-helical pores are highly original systems of general interest due to their unique architecture, functionality, and potential applications in nanopore technology and chemical biology. We emphasize that these simplified transmembrane pores have the potential to be components of functional nanodevices and therapeutic tools. We also suggest that such designed peptides might be valuable as antimicrobial agents and can be targeted to cancer cells. This article will focus on the evolutions in assembling α-helical transmembrane pores and highlight their advantages, including structural and functional versatility.

TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks

Sequence design is a crucial step in the process of designing or engineering proteins. Traditionally, physics-based methods have been used to solve for optimal sequences, with the main disadvantages being that they are computationally intensive for the end user. Deep learning-based methods offer an attractive alternative, outperforming physics-based methods at a significantly lower computational cost. In this paper, we explore the application of Convolutional Neural Networks (CNNs) for sequence design. We describe the development and benchmarking of a range of networks, as well as reimplementations of previously described CNNs. We demonstrate the flexibility of representing proteins in a three-dimensional voxel grid by encoding additional design constraints into the input data. Finally, we describe TIMED-Design, a web application and command line tool for exploring and applying the models described in this paper. The user interface will be available at the URL: https://pragmaticproteindesign.bio.ed.ac.uk/timed. The source code for TIMED-Design is available at https://github.com/wells-wood-research/timed-design.

Computational Protein Design - Where it goes?

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

An expandable, modular de novo protein platform for precision redox engineering

The electron-conducting circuitry of life represents an as-yet untapped resource of exquisite, nanoscale biomolecular engineering. Here, we report the characterization and structure of a de novo diheme "maquette" protein, 4D2, which we subsequently use to create an expanded, modular platform for heme protein design. A well-folded monoheme variant was created by computational redesign, which was then utilized for the experimental validation of continuum electrostatic redox potential calculations. This demonstrates how fundamental biophysical properties can be predicted and fine-tuned. 4D2 was then extended into a tetraheme helical bundle, representing a 7 nm molecular wire. Despite a molecular weight of only 24 kDa, electron cryomicroscopy illustrated a remarkable level of detail, indicating the positioning of the secondary structure and the heme cofactors. This robust, expressible, highly thermostable and readily designable modular platform presents a valuable resource for redox protein design and the future construction of artificial electron-conducting circuitry.

Understanding a protein fold: the physics, chemistry, and biology of α-helical coiled coils

Protein science is being transformed by powerful computational methods for structure prediction and design: AlphaFold2 can predict many natural protein structures from sequence, and other AI methods are enabling the de novo design of new structures. This raises a question: how much do we understand the underlying sequence-to-structure/function relationships being captured by these methods? This perspective presents our current understanding of one class of protein assembly, the α-helical coiled coils. At first sight, these are straightforward: sequence repeats of hydrophobic (h) and polar (p) residues, (hpphppp)_n, direct the folding and assembly of amphipathic α helices into bundles. However, many different bundles are possible: they can have two or more helices (different oligomers); the helices can have parallel, antiparallel or mixed arrangements (different topologies); and the helical sequences can be the same (homomers) or different (heteromers). Thus, sequence-to-structure relationships must be present within the hpphppp repeats to distinguish these states. I discuss the current understanding of this problem at three levels: First, physics gives a parametric framework to generate the many possible coiled-coil backbone structures. Second, chemistry provides a means to explore and deliver sequence-to-structure relationships. Third, biology shows how coiled coils are adapted and functionalized in nature, inspiring applications of coiled coils in synthetic biology. I argue that the chemistry is largely understood; the physics is partly solved, though the considerable challenge of predicting even relative stabilities of different coiled-coil states remains; but there is much more to explore in the biology and synthetic biology of coiled coils.

Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation

Computational de novo protein design involves iterative processes consisting of amino acid sequence design, structural modelling and scoring, and design validation by synthesis and experimental characterisation. Recent advances in protein structure prediction and modelling methods have enabled the highly efficient and accurate design of water-soluble proteins. However, the design of membrane proteins remains a major challenge. To advance membrane protein design, considering the higher complexity of membrane protein folding, stability, and dynamic interactions between water, ions, lipids, and proteins is an important task. For introducing explicit solvents and membranes to these design methods, all-atom molecular dynamics (MD) simulations of designed proteins provide useful information that cannot be obtained experimentally. In this review, we first describe two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design. We further illustrate recent MD studies of membrane protein folding related to protein design, as well as advanced treatments in molecular models and conformational sampling techniques in the simulations. Finally, we discuss the possibility to introduce MD simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.

PDBench: evaluating computational methods for protein-sequence design

SUMMARY

Ever increasing amounts of protein structure data, combined with advances in machine learning, have led to the rapid proliferation of methods available for protein-sequence design. In order to utilize a design method effectively, it is important to understand the nuances of its performance and how it varies by design target. Here, we present PDBench, a set of proteins and a number of standard tests for assessing the performance of sequence-design methods. PDBench aims to maximize the structural diversity of the benchmark, compared with previous benchmarking sets, in order to provide useful biological insight into the behaviour of sequence-design methods, which is essential for evaluating their performance and practical utility. We believe that these tools are useful for guiding the development of novel sequence design algorithms and will enable users to choose a method that best suits their design target.

AVAILABILITY AND IMPLEMENTATION

https://github.com/wells-wood-research/PDBench.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

Metal ion-regulated assembly of designed modular protein cages

Coiled-coil (CC) dimers are versatile, customizable building modules for the design of diverse protein architectures unknown in nature. Incorporation of dynamic self-assembly, regulated by a selected chemical signal, represents an important challenge in the construction of functional polypeptide nanostructures. Here, we engineered metal binding sites to render an orthogonal set of CC heterodimers Zn(II)-responsive as a generally applicable principle. The designed peptides assemble into CC heterodimers only in the presence of Zn(II) ions, reversibly dissociate by metal ion sequestration, and additionally act as pH switches, with low pH triggering disassembly. The developed Zn(II)-responsive CC set is used to construct programmable folding of CC-based nanostructures, from protein triangles to a two-chain bipyramidal protein cage that closes and opens depending on the metal ion. This demonstrates that dynamic self-assembly can be designed into CC-based protein cages by incorporation of metal ion-responsive CC building modules that act as conformational switches and that could also be used in other contexts.

Socket2: A Program for Locating, Visualising, and Analysing Coiled-coil Interfaces in Protein Structures

Motivation

Protein–protein interactions are central to all biological processes. One frequently observed mode of such interactions is the α-helical coiled coil (CC). Thus, an ability to extract, visualize and analyze CC interfaces quickly and without expert guidance would facilitate a wide range of biological research. In 2001, we reported Socket, which locates and characterizes CCs in protein structures based on the knobs-into-holes (KIH) packing between helices in CCs. Since then, studies of natural and de novo designed CCs have boomed, and the number of CCs in the RCSB PDB has increased rapidly. Therefore, we have updated Socket and made it accessible to expert and nonexpert users alike.

Results

The original Socket only classified CCs with up to six helices. Here, we report Socket2, which rectifies this oversight to identify CCs with any number of helices, and KIH interfaces with any of the 20 proteinogenic residues or incorporating nonnatural amino acids. In addition, we have developed a new and easy-to-use web server with additional features. These include the use of NGL Viewer for instantly visualizing CCs, and tabs for viewing the sequence repeats, helix-packing angles and core-packing geometries of CCs identified and calculated by Socket2.

Availability and implementation

Socket2 has been tested on all modern browsers. It can be accessed freely at http://coiledcoils.chm.bris.ac.uk/socket2/home.html. The source code is distributed using an MIT licence and available to download under the Downloads tab of the Socket2 home page.

A Brief History of De Novo Protein Design: Minimal, Rational, and Computational

Protein design has come of age, but how will it mature? In the 1980s and the 1990s, the primary motivation for de novo protein design was to test our understanding of the informational aspect of the protein-folding problem; i.e., how does protein sequence determine protein structure and function? This necessitated minimal and rational design approaches whereby the placement of each residue in a design was reasoned using chemical principles and/or biochemical knowledge. At that time, though with some notable exceptions, the use of computers to aid design was not widespread. Over the past two decades, the tables have turned and computational protein design is firmly established. Here, I illustrate this progress through a timeline of de novo protein structures that have been solved to atomic resolution and deposited in the Protein Data Bank. From this, it is clear that the impact of rational and computational design has been considerable: More-complex and more-sophisticated designs are being targeted with many being resolved to atomic resolution. Furthermore, our ability to generate and manipulate synthetic proteins has advanced to a point where they are providing realistic alternatives to natural protein functions for applications both in vitro and in cells. Also, and increasingly, computational protein design is becoming accessible to non-specialists. This all begs the questions: Is there still a place for minimal and rational design approaches? And, what challenges lie ahead for the burgeoning field of de novo protein design as a whole?

Coiled coils 9-to-5: rational de novo design of α-helical barrels with tunable oligomeric states

The rational design of linear peptides that assemble controllably and predictably in water is challenging. Short sequences must encode unique target structures and avoid alternative states. However, the non-covalent forces that stabilize and discriminate between states are weak. Nonetheless, for α-helical coiled-coil assemblies considerable progress has been made in rational de novo design. In these, sequence repeats of nominally hydrophobic (h) and polar (p) residues, hpphppp, direct the assembly of amphipathic helices into dimeric to tetrameric bundles. Expanding this pattern to hpphhph can produce larger α-helical barrels. Here, we show that pentameric to nonameric barrels are accessed by varying the residue at one of the h sites. In peptides with four L/I-K-E-I-A-x-Z repeats, decreasing the size of Z from threonine to serine to alanine to glycine gives progressively larger oligomers. X-ray crystal structures of the resulting α-helical barrels rationalize this: side chains at Z point directly into the helical interfaces, and smaller residues allow closer helix contacts and larger assemblies.

Pepblock Builder VR - An Open-Source Tool for Gaming-Based Bio-Edutainment in Interactive Protein Design

Proteins mediate and perform various fundamental functions of life. This versatility of protein function is an attribute of its 3D structure. In recent years, our understanding of protein 3D structure has been complemented with advances in computational and mathematical tools for protein modelling and protein design. 3D molecular visualisation is an essential part in every protein design and protein modelling workflow. Over the years, stand-alone and web-based molecular visualisation tools have been used to emulate three-dimensional view on computers. The advent of virtual reality provided the scope for immersive control of molecular visualisation. While these technologies have significantly improved our insights into protein modelling, designing new proteins with a defined function remains a complicated process. Current tools to design proteins lack user-interactivity and demand high computational skills. In this work, we present the Pepblock Builder VR, a gaming-based molecular visualisation tool for bio-edutainment and understanding protein design. Simulating the concepts of protein design and incorporating gaming principles into molecular visualisation promotes effective game-based learning. Unlike traditional sequence-based protein design and fragment-based stitching, the Pepblock Builder VR provides a building block style environment for complex structure building. This provides users a unique visual structure building experience. Furthermore, the inclusion of virtual reality to the Pepblock Builder VR brings immersive learning and provides users with "being there" experience in protein visualisation. The Pepblock Builder VR works both as a stand-alone and VR-based application, and with a gamified user interface, the Pepblock Builder VR aims to expand the horizons of scientific data generation to the masses.

Breakthroughs in computational design methods open up new frontiers for de novo protein engineering

Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5 years.

A potential interaction between the SARS-CoV-2 spike protein and nicotinic acetylcholine receptors

Changeux et al. (Changeux et al. C. R. Biol. 343:33-39.) recently suggested that the SARS-CoV-2 spike protein may interact with nicotinic acetylcholine receptors (nAChRs) and that such interactions may be involved in pathology and infectivity. This hypothesis is based on the fact that the SARS-CoV-2 spike protein contains a sequence motif similar to known nAChR antagonists. Here, we use molecular simulations of validated atomically detailed structures of nAChRs and of the spike to investigate the possible binding of the Y674-R685 region of the spike to nAChRs. We examine the binding of the Y674-R685 loop to three nAChRs, namely the human α4β2 and α7 subtypes and the muscle-like αβγδ receptor from Tetronarce californica. Our results predict that Y674-R685 has affinity for nAChRs. The region of the spike responsible for binding contains a PRRA motif, a four-residue insertion not found in other SARS-like coronaviruses. The conformational behavior of the bound Y674-R685 is highly dependent on the receptor subtype; it adopts extended conformations in the α4β2 and α7 complexes but is more compact when bound to the muscle-like receptor. In the α4β2 and αβγδ complexes, the interaction of Y674-R685 with the receptors forces the loop C region to adopt an open conformation, similar to other known nAChR antagonists. In contrast, in the α7 complex, Y674-R685 penetrates deeply into the binding pocket in which it forms interactions with the residues lining the aromatic box, namely with TrpB, TyrC1, and TyrC2. Estimates of binding energy suggest that Y674-R685 forms stable complexes with all three nAChR subtypes. Analyses of simulations of the glycosylated spike show that the Y674-R685 region is accessible for binding. We suggest a potential binding orientation of the spike protein with nAChRs, in which they are in a nonparallel arrangement to one another.

Repeat proteins: designing new shapes and functions for solenoid folds

The modular nature of repeat proteins has inspired the design of regular and completely novel sequences and structures. Research in the past years has provided a broad set of design approaches and new repeat proteins that have found applications in molecular recognition, taking advantage of the natural ability of some of these families to bind proteins, peptides and nucleic acids. Here, we provide an overview on the recent trends in design of repeat proteins, particularly solenoid folds, and their applications. By exploiting the intrinsic modularity of repeats, new architectures have been designed that combine different types of repeat, are easily scalable by changing the number of repeats and can be quickly generated by using existing modular building blocks.

DE-STRESS: a user-friendly web application for the evaluation of protein designs

De novo protein design is a rapidly growing field, and there are now many interesting and useful examples of designed proteins in the literature. However, most designs could be classed as failures when characterised in the lab, usually as a result of low expression, misfolding, aggregation or lack of function. This high attrition rate makes protein design unreliable and costly. It is possible that some of these failures could be caught earlier in the design process if it were quick and easy to generate information and a set of high-quality metrics regarding designs, which could be used to make reproducible and data-driven decisions about which designs to characterise experimentally. We present DE-STRESS (DEsigned STRucture Evaluation ServiceS), a web application for evaluating structural models of designed and engineered proteins. DE-STRESS has been designed to be simple, intuitive to use and responsive. It provides a wealth of information regarding designs, as well as tools to help contextualise the results and formally describe the properties that a design requires to be fit for purpose.

Self-assembly and regulation of protein cages from pre-organised coiled-coil modules

Coiled-coil protein origami (CCPO) is a modular strategy for the de novo design of polypeptide nanostructures. CCPO folds are defined by the sequential order of concatenated orthogonal coiled-coil (CC) dimer-forming peptides, where a single-chain protein is programmed to fold into a polyhedral cage. Self-assembly of CC-based nanostructures from several chains, similarly as in DNA nanotechnology, could facilitate the design of more complex assemblies and the introduction of functionalities. Here, we show the design of a de novo triangular bipyramid fold comprising 18 CC-forming segments and define the strategy for the two-chain self-assembly of the bipyramidal cage from asymmetric and pseudo-symmetric pre-organised structural modules. In addition, by introducing a protease cleavage site and masking the interfacial CC-forming segments in the two-chain bipyramidal cage, we devise a proteolysis-mediated conformational switch. This strategy could be extended to other modular protein folds, facilitating the construction of dynamic multi-chain CC-based complexes.

Coiled-coil protein origami is a strategy for the de novo design of polypeptide nanostructures based on coiled-coil dimer forming peptides, where a single chain protein folds into a polyhedral cage. Here, the authors design a single-chain triangular bipyramid and also demonstrate that the bipyramid can be self-assembled as a heterodimeric complex, comprising pre-defined subunits.

Arming Yourself for The In Silico Protein Design Revolution

Proteins mediate many essential processes of life to a degree of functional precision unmatched by any synthetic device. While engineered proteins are currently used in biotech, food, biomedicine, and material technology-based industries, the true potential of proteins is practically untapped. The emerging field of in silico protein design is predicted to provide the next quantum leap in the biotech industry. Having predictive control over protein function and the ability to redefine these functions have driven the field of protein engineering into an era of unprecedented development. This article provides a holistic analysis of protein design R&D (current state-of-the-art tools and knowhow) and commercial landscape, as well as a one-stop-shop profile of in silico protein design technology for biotechnology stakeholders.

Elfin UI: A Graphical Interface for Protein Design With Modular Building Blocks

Molecular models have enabled understanding of biological structures and functions and allowed design of novel macro-molecules. Graphical user interfaces (GUIs) in molecular modeling are generally focused on atomic representations, but, especially for proteins, do not usually address designs of complex and large architectures, from nanometers to microns. Therefore, we have developed Elfin UI as a Blender add-on for the interactive design of large protein architectures with custom shapes. Elfin UI relies on compatible building blocks to design single- and multiple-chain protein structures. The software can be used: (1) as an interactive environment to explore building blocks combinations; and (2) as a computer aided design (CAD) tool to define target shapes that guide automated design. Elfin UI allows users to rapidly build new protein shapes, without the need to focus on amino acid sequence, and aims to make design of proteins and protein-based materials intuitive and accessible to researchers and members of the general public with limited expertise in protein engineering.

BAlaS: fast, interactive and accessible computational alanine-scanning using BudeAlaScan

MOTIVATION

In experimental protein engineering, alanine-scanning mutagenesis involves the replacement of selected residues with alanine to determine the energetic contribution of each side chain to forming an interaction. For example, it is often used to study protein-protein interactions. However, such experiments can be time-consuming and costly, which has led to the development of programmes for performing computational alanine-scanning mutagenesis (CASM) to guide experiments. While programmes are available for this, there is a need for a real-time web application that is accessible to non-expert users.

RESULTS

Here, we present BAlaS, an interactive web application for performing CASM via BudeAlaScan and visualizing its results. BAlaS is interactive and intuitive to use. Results are displayed directly in the browser for the structure being interrogated enabling their rapid inspection. BAlaS has broad applications in areas, such as drug discovery and protein-interface design.

AVAILABILITY AND IMPLEMENTATION

BAlaS works on all modern browsers and is available through the following website: https://balas.app. The project is open source, distributed using an MIT license and is available on GitHub (https://github.com/wells-wood-research/balas).

KOBRA: a fluctuating elastic rod model for slender biological macromolecules

KOBRA (KirchOff Biological Rod Algorithm) is an algorithm and software package designed to perform dynamical simulations of elongated biomolecules such as those containing alpha-helices and coiled-coils. It represents these as coarsely-discretised Kirchoff rods, with linear elements that can stretch, bend and twist independently. These rods can have anisotropic and inhomogeneous parameters and bent or twisted equilibrium structures, allowing for a coarse-grained parameterisation of complex biological structures. Each element is non-inertial and subject to thermal fluctuations. The speed and simplicity of the algorithm allows KOBRA rods to easily access timescales from nanoseconds to seconds. To demonstrate this functionality, a KOBRA rod was parameterised using data from all-atom simulations of the Ndc80 protein complex, and compared against these simulations and negative-stain EM images. The distribution of bend angles and principal components were highly correlated between KOBRA, all-atom molecular dynamics, and experimental data. The properties of a hinge region, thought to be found at an unstructured loop, were studied. A C++ implementation of KOBRA is available under the GNU GPLv3 free software licence.

Applying graph theory to protein structures: an Atlas of coiled coils

Motivation

To understand protein structure, folding and function fully and to design proteins de novo reliably, we must learn from natural protein structures that have been characterized experimentally. The number of protein structures available is large and growing exponentially, which makes this task challenging. Indeed, computational resources are becoming increasingly important for classifying and analyzing this resource. Here, we use tools from graph theory to define an Atlas classification scheme for automatically categorizing certain protein substructures.

Results

Focusing on the α-helical coiled coils, which are ubiquitous protein-structure and protein-protein interaction motifs, we present a suite of computational resources designed for analyzing these assemblies. iSOCKET enables interactive analysis of side-chain packing within proteins to identify coiled coils automatically and with considerable user control. Applying a graph theory-based Atlas classification scheme to structures identified by iSOCKET gives the Atlas of Coiled Coils, a fully automated, updated overview of extant coiled coils. The utility of this approach is illustrated with the first formal classification of an emerging subclass of coiled coils called α-helical barrels. Furthermore, in the Atlas, the known coiled-coil universe is presented alongside a partial enumeration of the 'dark matter' of coiled-coil structures; i.e. those coiled-coil architectures that are theoretically possible but have not been observed to date, and thus present defined targets for protein design.

Availability and implementation

iSOCKET is available as part of the open-source GitHub repository associated with this work (https://github.com/woolfson-group/isocket). This repository also contains all the data generated when classifying the protein graphs. The Atlas of Coiled Coils is available at: http://coiledcoils.chm.bris.ac.uk/atlas/app.

Function2Form Bridge-Toward synthetic protein holistic performance prediction

Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple "parts"/functions and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F-Bridge) attempts to address this by combining the scores of different design parameters pertaining to the protein being analyzed into a single easily interpreted output describing overall performance. The strategy comprises of (a) a mathematical strategy combining data from a myriad of in silico tools into an OP-score (a singular score informing on a user-defined overall performance) and (b) the F2F Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built, and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user-friendly strategy represents a pivotal enabler in increasing the accessibility of synthetic protein building and de novo protein design.

Peptide Assembly Directed and Quantified Using Megadalton DNA Nanostructures

In nature, co-assembly of polypeptides, nucleic acids, and polysaccharides is used to create functional supramolecular structures. Here, we show that DNA nanostructures can be used to template interactions between peptides and to enable the quantification of multivalent interactions that would otherwise not be observable. Our functional building blocks are peptide-oligonucleotide conjugates comprising de novo designed dimeric coiled-coil peptides covalently linked to oligonucleotide tags. These conjugates are incorporated in megadalton DNA origami nanostructures and direct nanostructure association through peptide-peptide interactions. Free and bound nanostructures can be counted directly from electron micrographs, allowing estimation of the dissociation constants of the peptides linking them. Results for a single peptide-peptide interaction are consistent with the measured solution-phase free energy; DNA nanostructures displaying multiple peptides allow the effects of polyvalency to be probed. This use of DNA nanostructures as identifiers allows the binding strengths of homo- and heterodimeric peptide combinations to be measured in a single experiment and gives access to dissociation constants that are too low to be quantified by conventional techniques. The work also demonstrates that hybrid biomolecules can be programmed to achieve spatial organization of complex synthetic biomolecular assemblies.

Guiding Biomolecular Interactions in Cells Using de Novo Protein-Protein Interfaces

An improved ability to direct and control biomolecular interactions in living cells would have an impact on synthetic biology. A key issue is the need to introduce interacting components that act orthogonally to endogenous proteomes and interactomes. Here, we show that low-complexity, de novo designed protein-protein interaction (PPI) domains can substitute for natural PPIs and guide engineered protein-DNA interactions in Escherichia coli. Specifically, we use de novo homo- and heterodimeric coiled coils to reconstitute a cytoplasmic split adenylate cyclase, recruit RNA polymerase to a promoter and activate gene expression, and oligomerize both natural and designed DNA-binding domains to repress transcription. Moreover, the stabilities of the heterodimeric coiled coils can be modulated by rational design and, thus, adjust the levels of gene activation and repression in vivo. These experiments demonstrate the possibilities for using designed proteins and interactions to control biomolecular systems such as enzyme cascades and circuits in cells.

The de novo design of α-helical peptides for supramolecular self-assembly

One approach to designing de novo proteinaceous assemblies and materials is to develop simple, standardised building blocks and then to combine these symmetrically to construct more-complex higher-order structures. This has been done extensively using β-structured peptides to produce peptide fibres and hydrogels. Here, we focus on building with de novo α-helical peptides. Because of their self-contained, well-defined structures and clear sequence-to-structure relationships, α helices are highly programmable making them robust building blocks for biomolecular construction. The progress made with this approach over the past two decades is astonishing and has led to a variety of de novo assemblies, including discrete nanoscale objects, and fibrous, nanotube, sheet and colloidal materials. This body of work provides an exceptionally strong foundation for advancing the field beyond in vitro design and into in vivo applications including what we call protein design in cells.

SwitCCh: Metal-Site Design for Controlling the Assembly of a Coiled-Coil Homodimer

Conformational change of proteins in response to chemical or physical signals is the underlying principle of many regulatory and transport mechanisms in biological systems. The ability to design proteins the conformational state of which can be precisely and reversibly controlled would facilitate the development of molecular machines tailored for specific applications. Here we explore metal-binding site design to engineer a peptide-based conformational switch called SwitCCh that assembles into a homodimeric coiled-coil in response to the addition of Zn^II ions or low pH. Addition of Zn^II promoted formation of a parallel homodimer with an increase in thermal stability by more than 30 °C. The peptide could be reversibly cycled between the coiled-coil and random conformation. Furthermore, the SwitCCh peptide was orthogonal to the previously developed coiled-coil dimer set, indicating it could be used for regulated self-assembly of coiled-coil based nanostructures and materials.

Maintaining and breaking symmetry in homomeric coiled-coil assemblies

In coiled-coil (CC) protein structures α-helices wrap around one another to form rope-like assemblies. Most natural and designed CCs have two–four helices and cyclic (C_n) or dihedral (D_n) symmetry. Increasingly, CCs with five or more helices are being reported. A subset of these higher-order CCs is of interest as they have accessible central channels that can be functionalised; they are α-helical barrels. These extended cavities are surprising given the drive to maximise buried hydrophobic surfaces during protein folding and assembly in water. Here, we show that α-helical barrels can be maintained by the strategic placement of β-branched aliphatic residues lining the lumen. Otherwise, the structures collapse or adjust to give more-complex multi-helix assemblies without C_n or D_n symmetry. Nonetheless, the structural hallmark of CCs—namely, knobs-into-holes packing of side chains between helices—is maintained leading to classes of CCs hitherto unobserved in nature or accessed by design.

Higher order coiled coils with five or more helices can form α-helical barrels. Here the authors show that placing β-branched aliphatic residues along the lumen yields stable and open α-helical barrels, which is of interest for the rational design of functional proteins; whereas, the absence of β-branched side chains leads to unusual low-symmetry α-helical bundles.

Intraring allostery controls the function and assembly of a hetero-oligomeric class II chaperonin

Class II chaperonins are essential multisubunit complexes that aid the folding of nonnative proteins in the cytosol of archaea and eukarya. They use energy derived from ATP to drive a series of structural rearrangements that enable polypeptides to fold within their central cavity. These events are regulated by an elaborate allosteric mechanism in need of elucidation. We employed mutagenesis and experimental analysis in concert with in silico molecular dynamics simulations and interface-binding energy calculations to investigate the class II chaperonin from Thermoplasma acidophilum. Here we describe the effects on the asymmetric allosteric mechanism and on hetero-oligomeric complex formation in a panel of mutants in the ATP-binding pocket of the α and β subunits. Our observations reveal a potential model for a nonconcerted folding mechanism optimized for protecting and refolding a range of nonnative substrates under different environmental conditions, starting to unravel the role of subunit heterogeneity in this folding machine and establishing important links with the behavior of the most complex eukaryotic chaperonins.—Shoemark, D. K., Sessions, R. B., Brancaccio, A., Bigotti, M. G. Intraring allostery controls the function and assembly of a hetero-oligomeric class II chaperonin.

CCBuilder 2.0: Powerful and accessible coiled-coil modeling

The increased availability of user-friendly and accessible computational tools for biomolecular modeling would expand the reach and application of biomolecular engineering and design. For protein modeling, one key challenge is to reduce the complexities of 3D protein folds to sets of parametric equations that nonetheless capture the salient features of these structures accurately. At present, this is possible for a subset of proteins, namely, repeat proteins. The α-helical coiled coil provides one such example, which represents ≈ 3-5% of all known protein-encoding regions of DNA. Coiled coils are bundles of α helices that can be described by a small set of structural parameters. Here we describe how this parametric description can be implemented in an easy-to-use web application, called CCBuilder 2.0, for modeling and optimizing both α-helical coiled coils and polyproline-based collagen triple helices. This has many applications from providing models to aid molecular replacement for X-ray crystallography, in silico model building and engineering of natural and designed protein assemblies, and through to the creation of completely de novo "dark matter" protein structures. CCBuilder 2.0 is available as a web-based application, the code for which is open-source and can be downloaded freely. http://coiledcoils.chm.bris.ac.uk/ccbuilder2.

LAY SUMMARY

We have created CCBuilder 2.0, an easy to use web-based application that can model structures for a whole class of proteins, the α-helical coiled coil, which is estimated to account for 3-5% of all proteins in nature. CCBuilder 2.0 will be of use to a large number of protein scientists engaged in fundamental studies, such as protein structure determination, through to more-applied research including designing and engineering novel proteins that have potential applications in biotechnology.