Synthetic beta-solenoid proteins with the fragment-free computational design of a beta-hairpin extension

Parametrically guided design of beta barrels and transmembrane nanopores using deep learning

Francis Crick's global parameterization of coiled coil geometry has been widely useful for guiding design of new protein structures and functions. However, design guided by similar global parameterization of beta barrel structures has been less successful, likely due to the deviations required from ideal beta barrel geometry to maintain extensive inter-strand hydrogen bonding without introducing considerable backbone strain. Instead, beta barrels and other protein folds have been designed guided by 2D structural blueprints; while this approach has successfully generated new fluorescent proteins, transmembrane nanopores, and other structures, it requires considerable expert knowledge and provides only indirect control over the global barrel shape. Here we show that the simplicity and control over shape and structure provided by global parametric representations can be generalized beyond coiled coils by taking advantage of the rich sequence-structure relationships implicit in RoseTTAFold based inpainting and diffusion design methods. Starting from parametrically generated idealized barrel backbones, both RFjoint inpainting and RFdiffusion readily incorporate the backbone irregularities necessary for proper folding with minimal deviation from the idealized barrel geometries. We show that for beta barrels across a broad range of global beta sheet parameterizations, these methods achieve high in silico and experimental success rates, with atomic accuracy confirmed by an X-ray crystal structure of a novel beta barrel topology, and de novo designed 12, 14, and 16 stranded transmembrane nanopores with conductances ranging from 200 to 500 pS. By combining the simplicity and control of parametric generation with the high success rates of deep learning based protein design methods, our approach makes the design of proteins where global shape confers function, such as beta barrel nanopores, more precisely specifiable and accessible.

A Closer Look at Type I Left-Handed β-Helices Provides a Better Understanding in Their Sequence-Structure Relationship: Toward Their Rational Design

Understanding the sequence-structure relationship in protein is of fundamental interest, but has practical applications such as the rational design of peptides and proteins. This relationship in the Type I left-handed β-helix containing proteins is updated and revisited in this study. Analyzing the available experimental structures in the Protein Data Bank, we could describe, further in detail, the structural features that are important for the stability of this fold, as well as its nucleation and termination. This study is meant to complete previous work, as it provides a separate analysis of the N-terminal and C-terminal rungs of the helix. Particular sequence motifs of these rungs are described along with the structural element they form.

Deep Learning-based structure modelling illuminates structure and function in uncharted regions of β-solenoid fold space

Repeat proteins are common in all domains of life and exhibit a wide range of functions. One class of repeat protein contains solenoid folds where the repeating unit consists of β-strands separated by tight turns. β-solenoids have distinguishing structural features such as handedness, twist, oligomerisation state, coil shape and size which give rise to their diversity. Characterised β-solenoid repeat proteins are known to form regions in bacterial and viral virulence factors, antifreeze proteins and functional amyloids. For many of these proteins, the experimental structure has not been solved, as they are difficult to crystallise or model. Here we use various deep learning-based structure-modelling methods to discover novel predicted β-solenoids, perform structural database searches to mine further structural neighbours and relate their predicted structure to possible functions. We find both eukaryotic and prokaryotic adhesins, confirming a known functional linkage between adhesin function and the β-solenoid fold. We further identify exceptionally long, flat β-solenoid folds as possible structures of mucin tandem repeat regions and unprecedentedly small β-solenoid structures. Additionally, we characterise a novel β-solenoid coil shape, the FapC Greek key β-solenoid as well as plausible complexes between it and other proteins involved in Pseudomonas functional amyloid fibres.

Designed Loop Extension Followed by Combinatorial Screening Confers High Specificity to a Broad Matrix MetalloproteinaseInhibitor

Matrix metalloproteinases (MMPs) are key drivers of various diseases, including cancer. Development of probes and drugs capable of selectively inhibiting the individual members of the large MMP family remains a persistent challenge. The inhibitory N-terminal domain of tissue inhibitor of metalloproteinases-2 (N-TIMP2), a natural broad MMP inhibitor, can provide a scaffold for protein engineering to create more selective MMP inhibitors. Here, we pursued a unique approach harnessing both computational design and combinatorial screening to confer high binding specificity toward a target MMP in preference to an anti-target MMP. We designed a loop extension of N-TIMP2 to allow new interactions with the non-conserved MMP surface and generated an efficient focused library for yeast surface display, which was then screened for high binding to the target MMP-14 and low binding to anti-target MMP-3. Deep sequencing analysis identified the most promising variants, which were expressed, purified, and tested for selectivity of inhibition. Our best N-TIMP2 variant exhibited 29 pM binding affinity to MMP-14 and 2.4 µM affinity to MMP-3, revealing 7500-fold greater specificity than WT N-TIMP2. High-confidence structural models were obtained by including NGS data in the AlphaFold multiple sequence alignment. The modeling together with experimental mutagenesis validated our design predictions, demonstrating that the loop extension packs tightly against non-conserved residues on MMP-14 and clashes with MMP-3. This study demonstrates how introduction of loop extensions in a manner guided by target protein conservation data and loop design can offer an attractive strategy to achieve specificity in design of protein ligands.

Algorithms for protein design

Computational Protein Design has the potential to contribute to major advances in enzyme technology, vaccine design, receptor-ligand engineering, biomaterials, nanosensors, and synthetic biology. Although Protein Design is a challenging problem, proteins can be designed by experts in Protein Design, as well as by non-experts whose primary interests are in the applications of Protein Design. The increased accessibility of Protein Design technology is attributable to the accumulated knowledge and experience with Protein Design as well as to the availability of software and online resources. The objective of this review is to serve as a guide to the relevant literature with a focus on the novel methods and algorithms that have been developed or applied for Protein Design, and to assist in the selection of algorithms for Protein Design. Novel algorithms and models that have been introduced to utilize the enormous amount of experimental data and novel computational hardware have the potential for producing substantial increases in the accuracy, reliability and range of applications of designed proteins.

A backbone-centred energy function of neural networks for protein design

A protein backbone structure is designable if a substantial number of amino acid sequences exist that autonomously fold into it^1,2. It has been suggested that the designability of backbones is governed mainly by side chain-independent or side chain type-insensitive molecular interactions^3-5, indicating an approach for designing new backbones (ready for amino acid selection) based on continuous sampling and optimization of the backbone-centred energy surface. However, a sufficiently comprehensive and precise energy function has yet to be established for this purpose. Here we show that this goal is met by a statistical model named SCUBA (for Side Chain-Unknown Backbone Arrangement) that uses neural network-form energy terms. These terms are learned with a two-step approach that comprises kernel density estimation followed by neural network training and can analytically represent multidimensional, high-order correlations in known protein structures. We report the crystal structures of nine de novo proteins whose backbones were designed to high precision using SCUBA, four of which have novel, non-natural overall architectures. By eschewing use of fragments from existing protein structures, SCUBA-driven structure design facilitates far-reaching exploration of the designable backbone space, thus extending the novelty and diversity of the proteins amenable to de novo design.

Step-by-step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.

De Novo-Designed β-Sheet Heme Proteins

The field of de novo protein design has met with considerable success over the past few decades. Heme, a cofactor, has often been introduced to impart a diverse array of functions to a protein, ranging from electron transport to respiration. In nature, heme is found to occur predominantly in α-helical structures over β-sheets, which has resulted in significant designs of heme proteins utilizing coiled-coil helices. By contrast, there are only a few known β-sheet proteins that bind heme and designs of β-sheets frequently result in amyloid-like aggregates. This review reflects on our success in designing a series of multistranded β-sheet heme binding peptides that are well folded in both aqueous and membrane-like environments. Initially, we designed a β-hairpin peptide that self-assembles to bind heme and performs peroxidase activity in membrane. The β-hairpin was optimized further to accommodate a heme binding pocket within multistranded β-sheets for catalysis and electron transfer in membranes. Furthermore, we de novo designed and characterized β-sheet peptides and miniproteins that are soluble in an aqueous environment capable of binding single and multiple hemes with high affinity and stability. Collectively, these studies highlight the substantial progress made toward the design of functional β-sheets.

Decoupling a tandem-repeat protein: Impact of multiple loop insertions on a modular scaffold

The simple topology and modular architecture of tandem-repeat proteins such as tetratricopeptide repeats (TPRs) and ankyrin repeats makes them straightforward to dissect and redesign. Repeat-protein stability can be manipulated in a predictable way using site-specific mutations. Here we explore a different type of modification - loop insertion - that will enable a simple route to functionalisation of this versatile scaffold. We previously showed that a single loop insertion has a dramatically different effect on stability depending on its location in the repeat array. Here we dissect this effect by a combination of multiple and alternated loop insertions to understand the origins of the context-dependent loss in stability. We find that the scaffold is remarkably robust in that its overall structure is maintained. However, adjacent repeats are now only weakly coupled, and consequently the increase in solvent protection, and thus stability, with increasing repeat number that defines the tandem-repeat protein class is lost. Our results also provide us with a rulebook with which we can apply these principles to the design of artificial repeat proteins with precisely tuned folding landscapes and functional capabilities, thereby paving the way for their exploitation as a versatile and truly modular platform in synthetic biology.

Designed for life: biocompatible de novo designed proteins and components

A principal goal of synthetic biology is the de novo design or redesign of biomolecular components. In addition to revealing fundamentally important information regarding natural biomolecular engineering and biochemistry, functional building blocks will ultimately be provided for applications including the manufacture of valuable products and therapeutics. To fully realize this ambitious goal, the designed components must be biocompatible, working in concert with natural biochemical processes and pathways, while not adversely affecting cellular function. For example, de novo protein design has provided us with a wide repertoire of structures and functions, including those that can be assembled and function in vivo. Here we discuss such biocompatible designs, as well as others that have the potential to become biocompatible, including non-protein molecules, and routes to achieving full biological integration.

Folding cooperativity and allosteric function in the tandem-repeat protein class

The term allostery was originally developed to describe structural changes in one binding site induced by the interaction of a partner molecule with a distant binding site, and it has been studied in depth in the field of enzymology. Here, we discuss the concept of action at a distance in relation to the folding and function of the solenoid class of tandem-repeat proteins such as tetratricopeptide repeats (TPRs) and ankyrin repeats. Distantly located repeats fold cooperatively, even though only nearest-neighbour interactions exist in these proteins. A number of repeat-protein scaffolds have been reported to display allosteric effects, transferred through the repeat array, that enable them to direct the activity of the multi-subunit enzymes within which they reside. We also highlight a recently identified group of tandem-repeat proteins, the RRPNN subclass of TPRs, recent crystal structures of which indicate that they function as allosteric switches to modulate multiple bacterial quorum-sensing mechanisms. We believe that the folding cooperativity of tandem-repeat proteins and the biophysical mechanisms that transform them into allosteric switches are intimately intertwined. This opinion piece aims to combine our understanding of the two areas and develop ideas on their common underlying principles.This article is part of a discussion meeting issue 'Allostery and molecular machines'.

Computational design of structured loops for new protein functions

The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.

De novo design of a non-local β-sheet protein with high stability and accuracy

β-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein design. Despite this potential, de novo design of all-β-sheet proteins from first principles lags far behind the design of all-α or mixed-αβ domains owing to their non-local nature and the tendency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have identified a series of structural relationships between loop geometry, side chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to de novo design jellyroll structures with double-stranded β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable design closely matched the computational model, demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the design of a broad range of non-local β-sheet protein structures.

In silico stress-strain measurements on self-assembled protein lattices

Due to their large mechanical strength and potential for functionalization, beta-solenoid proteins show promise as building blocks in biomaterials applications such as two- and three-dimensional scaffolds. We have designed simulation models of two-dimensional square and honeycomb protein lattices by covalently linking a beta-solenoid protein, the spruce budworm antifreeze protein (SBAFP), to symmetric protein multimers. Periodic boundary conditions applied to the simulation cell allow for the simulation of an infinite lattice. We use molecular dynamics to strain the lattice by deforming the simulation cell and measuring the resulting stress tensor. We evaluate the linear portion of stress-strain curves to extract the corresponding bulk and shear elastic moduli. When strained at a rate of 0.3 nm ps-1, the lattices yield a bulk modulus of approximately 3 GPa. This large elastic modulus demonstrates that 2-dimensional structures designed from beta-solenoid proteins can be expected to retain the exceptional material strength of their building blocks.

The ascent of man(made oxidoreductases)

Though established 40 years ago, the field of de novo protein design has recently come of age, with new designs exhibiting an unprecedented level of sophistication in structure and function. With respect to catalysis, de novo enzymes promise to revolutionise the industrial production of useful chemicals and materials, while providing new biomolecules as plug-and-play components in the metabolic pathways of living cells. To this end, there are now de novo metalloenzymes that are assembled in vivo, including the recently reported C45 maquette, which can catalyse a variety of substrate oxidations with efficiencies rivalling those of closely related natural enzymes. Here we explore the successful design of this de novo enzyme, which was designed to minimise the undesirable complexity of natural proteins using a minimalistic bottom-up approach.

Hexapeptide Tandem Repeats Dictate the Formation of Silkmoth Chorion, a Natural Protective Amyloid

Silkmoth chorion is a fibrous structure composed mainly of two major protein classes, families A and B. Both families of silkmoth chorion proteins present a highly conserved, in sequence and in length, central domain, consisting of Gly-rich tandem hexapeptide repetitive segments, flanked by two more variable N-terminal and C-terminal arms. Primary studies identified silkmoth chorion as a functional protective amyloid by unveiling the amyloidogenic properties of the central domain of both protein families. In this work, we attempt to detect the principal source of amyloidogenicity of the central domain by focusing on the role of the tandem hexapeptide sequence repeats. Concurrently, we discuss a possible mechanism for the self-assembly of class A protofilaments, suggesting that the aggregation-prone hexapeptide building blocks may fold into a triangle-shaped β-helical structure.

TetraBASE: A Side Chain-Independent Statistical Energy for Designing Realistically Packed Protein Backbones

To construct backbone structures of high designability is a primary aspect of computational protein design. We report here a side chain-independent statistical energy that aims at realistic modeling of through-space packing of polypeptide backbones. To mitigate the lack of explicit amino acid side chains, the model treats the interbackbone site packing as being dependent on peptide local conformation. In addition, new variables suitable for statistical analysis, one for relative orientation and another for distance, have been introduced to represent the intersite geometry based on the asymmetrical tetrahedron organization of distinct chemical groups surrounding the Cα-carbon atoms. The resulting tetrahedron-based backbone statistical energy (tetraBASE) model has been used to optimize the tertiary organizations of secondary structure elements (SSEs) of designated types with Monte Caro simulated annealing, starting from artificial initial configurations. The tetraBASE minimum energy structures can reproduce SSE packing frequently observed in native proteins with atomic root-mean-square deviations of 1-2 Å. The model has also been tested by examining the stability of native SSE arrangements under tetraBASE. The results suggest that tetraBASE model can be used to effectively represent interbackbone packing when designing backbone structures without explicitly knowing side chain types.

Structural and Biochemical Characterization of BdsA from Bacillus subtilis WU-S2B, a Key Enzyme in the "4S" Desulfurization Pathway

Dibenzothiophene (DBT) and their derivatives, accounting for the major part of the sulfur components in crude oil, make one of the most significant pollution sources. The DBT sulfone monooxygenase BdsA, one of the key enzymes in the “4S” desulfurization pathway, catalyzes the oxidation of DBT sulfone to 2′-hydroxybiphenyl 2-sulfonic acid (HBPSi). Here, we determined the crystal structure of BdsA from Bacillus subtilis WU-S2B, at the resolution of 2.2 Å, and the structure of the BdsA-FMN complex at 2.4 Å. BdsA and the BdsA-FMN complex exist as tetramers. DBT sulfone was placed into the active site by molecular docking. Seven residues (Phe12, His20, Phe56, Phe246, Val248, His316, and Val372) are found to be involved in the binding of DBT sulfone. The importance of these residues is supported by the study of the catalytic activity of the active site variants. Structural analysis and enzyme activity assay confirmed the importance of the right position and orientation of FMN and DBT sulfone, as well as the involvement of Ser139 as a nucleophile in catalysis. This work combined with our previous structure of DszC provides a systematic structural basis for the development of engineered desulfurization enzymes with higher efficiency and stability.

Propagation of Fibrillar Structural Forms in Proteins Stopped by Naturally Occurring Short Polypeptide Chain Fragments

Amyloids characterized by unbounded growth of fibrillar structures cause many pathological processes. Such unbounded propagation is due to the presence of a propagating hydrophobicity field around the fibril's main axis, preventing its closure (unlike in globular proteins). Interestingly, similar fragments, commonly referred to as solenoids, are present in many naturally occurring proteins, where their propagation is arrested by suitably located "stopper" fragments. In this work, we analyze the distribution of hydrophobicity in solenoids and in their corresponding "stoppers" from the point of view of the fuzzy oil drop model (called FOD in this paper). This model characterizes the unique linear propagation of local hydrophobicity in the solenoid fragment and allows us to pinpoint "stopper" sequences, where local hydrophobicity quite closely resembles conditions encountered in globular proteins. Consequently, such fragments perform their function by mediating entropically advantageous contact with the water environment. We discuss examples of amyloid-like structures in solenoids, with particular attention to "stop" segments present in properly folded proteins found in living organisms.

Geometrical frustration as a potential design principle for peptide-based assemblies

Two-dimensional peptide and protein assemblies have been the focus of increased scientific research as they display significant potential for the creation of functional nanomaterials. Soluble subunits derived from a variety of protein motifs have been demonstrated to self-assemble into structurally defined nanosheets under environmentally benign conditions in which the components often retain their native structure and function. These types of two-dimensional assemblies may have an advantage for nanofabrication in that their extended planar shapes can be more straightforwardly incorporated into the current formats of nanoscale devices. However, significant challenges remain in the fabrication of these materials, particularly in devising methods to control the size, shape and internal structure of the resultant materials. Geometrical frustration may be envisioned as a possible mechanism to exert control over these structural parameters through rational design. While this objective has yet to be realized in practice, we discuss in this article the potential role of geometrical frustration as a principle to rationalize unusual self-assembly behaviour in several examples of two-dimensional peptide assemblies.

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.

Computational protein design with backbone plasticity

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.