Robust folding of a de novo designed ideal protein even with most of the core mutated to valine

Hydrophobic core evolution of major histocompatibility complex class I chain-related protein A for dramatic enhancing binding affinity

Interface residues at sites of protein-protein interaction (PPI) are the focus for affinity optimisation. However, protein hydrophobic cores (HCs) play critical roles and shape the protein surface. We hypothesise that manipulating protein HCs can enhance PPI interaction affinities. A cell stress molecule, major histocompatibility complex class I chain-related protein A (MICA), binds to the natural killer group 2D (NKG2D) homodimer to form three molecule interactions. MICA was used as a study subject to support our hypothesis. We redesigned MICA HCs by directed mutagenesis and isolated high-affinity variants through a newly designed partial-denature panning (PDP) method. A few mutations in MICA HCs increased the NKG2D-MICA interaction affinity by 325-5613-fold. Crystal structures of the NKG2D-MICA variant complexes indicated that mutagenesis of MICA HCs stabilised helical elements for decreasing intermolecular interactive free energy (ΔG) of the NKG2D-MICA heterotrimer. The repacking of MICA HC mutants maintained overall surface residues and the authentic binding specificity of MICA. In conclusion, this study provides a new method for MICA redesign and affinity optimisation through HC manipulation without mutating PPI interface residues. Our study introduces a novel approach to protein manipulation, potentially expanding the toolkit for protein affinity optimisation.

Sparks of function by de novo protein design

Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.

Design of stimulus-responsive two-state hinge proteins

In nature, proteins that switch between two conformations in response to environmental stimuli structurally transduce biochemical information in a manner analogous to how transistors control information flow in computing devices. Designing proteins with two distinct but fully structured conformations is a challenge for protein design as it requires sculpting an energy landscape with two distinct minima. Here we describe the design of "hinge" proteins that populate one designed state in the absence of ligand and a second designed state in the presence of ligand. X-ray crystallography, electron microscopy, double electron-electron resonance spectroscopy, and binding measurements demonstrate that despite the significant structural differences the two states are designed with atomic level accuracy and that the conformational and binding equilibria are closely coupled.

Why we are made of proteins and nucleic acids: Structural biology views on extraterrestrial life

Is it a miracle that life exists on the Earth, or is it a common phenomenon in the universe? If extraterrestrial organisms exist, what are they like? To answer these questions, we must understand what kinds of molecules could evolve into life, or in other words, what properties are generally required to perform biological functions and store genetic information. This review summarizes recent findings on simple ancestral proteins, outlines the basic knowledge in textbooks, and discusses the generally required properties for biological molecules from structural biology viewpoints (e.g., restriction of shapes, and types of intra- and intermolecular interactions), leading to the conclusion that proteins and nucleic acids are at least one of the simplest (and perhaps very common) forms of catalytic and genetic biopolymers in the universe. This review article is an extended version of the Japanese article, On the Origin of Life: Coevolution between RNA and Peptide, published in SEIBUTSU BUTSURI Vol. 61, p. 232-235 (2021).

New insights on the catalytic center of proteins from peptidylprolyl isomerase group based on the FOD-M model

Generating the structure of the hydrophobic core is based on the orientation of hydrophobic residues towards the central part of the protein molecule with the simultaneous exposure of polar residues. Such a course of the protein folding process takes place with the active participation of the polar water environment. While the self-assembly process leading to the formation of micelles concerns freely moving bi-polar molecules, bipolar amino acids in polypeptide chain have limited mobility due to the covalent bonds. Therefore, proteins form a more or less perfect micelle-like structure. The criterion is the hydrophobicity distribution, which to a greater or lesser extent reproduces the distribution expressed by the 3D Gaussian function on the protein body. The vast majority of proteins must ensure solubility, so a certain part of it-as it is expected-should reproduce the structuring of micelles. The biological activity of proteins is encoded in the part that does not reproduce the micelle-like system. The location and quantitative assessment of the contribution of orderliness to disorder is of critical importance for the determination of biological activity. The form of maladjustment to the 3D Gauss function may be varied-hence the obtained high diversity of specific interactions with strictly defined molecules: ligands or substrates. The correctness of this interpretation was verified on the basis of the group of enzymes Peptidylprolyl isomerase-E.C.5.2.1.8. In proteins representing this class of enzymes, zones responsible for solubility-micelle-like hydrophobicity system-the location and specificity of the incompatible part in which the specific activity of the enzyme is located and coded were identified. The present study showed that the enzymes of the discussed group show two different schemes of the structure of catalytic center (taking into account the status as defined by the fuzzy oil drop model).

Ab Initio Modelling of the Structure of ToxA-like and MAX Fungal Effector Proteins

Pathogenic fungal diseases in crops are mediated by the release of effector proteins that facilitate infection. Characterising the structure of these fungal effectors is vital to understanding their virulence mechanisms and interactions with their hosts, which is crucial in the breeding of plant cultivars for disease resistance. Several effectors have been identified and validated experimentally; however, their lack of sequence conservation often impedes the identification and prediction of their structure using sequence similarity approaches. Structural similarity has, nonetheless, been observed within fungal effector protein families, creating interest in validating the use of computational methods to predict their tertiary structure from their sequence. We used Rosetta ab initio modelling to predict the structures of members of the ToxA-like and MAX effector families for which experimental structures are known to validate this method. An optimised approach was then used to predict the structures of phenotypically validated effectors lacking known structures. Rosetta was found to successfully predict the structure of fungal effectors in the ToxA-like and MAX families, as well as phenotypically validated but structurally unconfirmed effector sequences. Interestingly, potential new effector structural families were identified on the basis of comparisons with structural homologues and the identification of associated protein domains.

Origins of life: first came evolutionary dynamics

When life arose from prebiotic molecules 3.5 billion years ago, what came first? Informational molecules (RNA, DNA), functional ones (proteins), or something else? We argue here for a different logic: rather than seeking a molecule type, we seek a dynamical process. Biology required an ability to evolve before it could choose and optimise materials. We hypothesise that the evolution process was rooted in the peptide folding process. Modelling shows how short random peptides can collapse in water and catalyse the elongation of others, powering both increased folding stability and emergent autocatalysis through a disorder-to-order process.

Engineering the kinetic stability of a β-trefoil protein by tuning its topological complexity

Kinetic stability, defined as the rate of protein unfolding, is central to determining the functional lifetime of proteins, both in nature and in wide-ranging medical and biotechnological applications. Further, high kinetic stability is generally correlated with high resistance against chemical and thermal denaturation, as well as proteolytic degradation. Despite its significance, specific mechanisms governing kinetic stability remain largely unknown, and few studies address the rational design of kinetic stability. Here, we describe a method for designing protein kinetic stability that uses protein long-range order, absolute contact order, and simulated free energy barriers of unfolding to quantitatively analyze and predict unfolding kinetics. We analyze two β-trefoil proteins: hisactophilin, a quasi-three-fold symmetric natural protein with moderate stability, and ThreeFoil, a designed three-fold symmetric protein with extremely high kinetic stability. The quantitative analysis identifies marked differences in long-range interactions across the protein hydrophobic cores that partially account for the differences in kinetic stability. Swapping the core interactions of ThreeFoil into hisactophilin increases kinetic stability with close agreement between predicted and experimentally measured unfolding rates. These results demonstrate the predictive power of readily applied measures of protein topology for altering kinetic stability and recommend core engineering as a tractable target for rationally designing kinetic stability that may be widely applicable.

A generic framework for hierarchical de novo protein design

De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test for our understanding of the underlying physical principles of protein folding and may lead to the development of proteins with unmatched functional characteristics. The first fundamental challenge of de novo design is to devise "designable" structural templates leading to sequences that will adopt the predicted fold. Here, we built on the TopoBuilder (TB) de novo design method, to automatically assemble structural templates with native-like features starting from string descriptors that capture the overall topology of proteins. Our framework eliminates the dependency of hand-crafted and fold-specific rules through an iterative, data-driven approach that extracts geometrical parameters from structural tertiary motifs. We evaluated the TopoBuilder framework by designing sequences for a set of five protein folds and experimental characterization revealed that several sequences were folded and stable in solution. The TopoBuilder de novo design framework will be broadly useful to guide the generation of artificial proteins with customized geometries, enabling the exploration of the protein universe.

De Novo Design of a Highly Stable Ovoid TIM Barrel: Unlocking Pocket Shape towards Functional Design

The ability to finely control the structure of protein folds is an important prerequisite to functional protein design. The TIM barrel fold is an important target for these efforts as it is highly enriched for diverse functions in nature. Although a TIM barrel protein has been designed de novo, the ability to finely alter the curvature of the central beta barrel and the overall architecture of the fold remains elusive, limiting its utility for functional design. Here, we report the de novo design of a TIM barrel with ovoid (twofold) symmetry, drawing inspiration from natural beta and TIM barrels with ovoid curvature. We use an autoregressive backbone sampling strategy to implement our hypothesis for elongated barrel curvature, followed by an iterative enrichment sequence design protocol to obtain sequences which yield a high proportion of successfully folding designs. Designed sequences are highly stable and fold to the designed barrel curvature as determined by a 2.1 Å resolution crystal structure. The designs show robustness to drastic mutations, retaining high melting temperatures even when multiple charged residues are buried in the hydrophobic core or when the hydrophobic core is ablated to alanine. As a scaffold with a greater capacity for hosting diverse hydrogen bonding networks and installation of binding pockets or active sites, the ovoid TIM barrel represents a major step towards the de novo design of functional TIM barrels.

Active site center redesign increases protein stability preserving catalysis in thioredoxin

The stabilization of natural proteins is a long‐standing desired goal in protein engineering. Optimizing the hydrophobicity of the protein core often results in extensive stability enhancements. However, the presence of totally or partially buried catalytic charged residues, essential for protein function, has limited the applicability of this strategy. Here, focusing on the thioredoxin, we aimed to augment protein stability by removing buried charged residues in the active site without loss of catalytic activity. To this end, we performed a charged‐to‐hydrophobic substitution of a buried and functional group, resulting in a significant stability increase yet abolishing catalytic activity. Then, to simulate the catalytic role of the buried ionizable group, we designed a combinatorial library of variants targeting a set of seven surface residues adjacent to the active site. Notably, more than 50% of the library variants restored, to some extent, the catalytic activity. The combination of experimental study of 2% of the library with the prediction of the whole mutational space by partial least squares regression revealed that a single point mutation at the protein surface is sufficient to fully restore the catalytic activity without thermostability cost. As a result, we engineered one of the highest thermal stabilities reported for a protein with a natural occurring fold (137°C). Further, our hyperstable variant preserves the catalytic activity both in vitro and in vivo.

A Method for Assessing the Robustness of Protein Structures by Randomizing Packing Interactions

Many single-domain proteins are not only stable and water-soluble, but they also populate few to no intermediates during folding. This reduces interactions between partially folded proteins, misfolding, and aggregation, and makes the proteins tractable in biotechnological applications. Natural proteins fold thus, not necessarily only because their structures are well-suited for folding, but because their sequences optimize packing and fit their structures well. In contrast, folding experiments on the de novo designed Top7 suggest that it populates several intermediates. Additionally, in de novo protein design, where sequences are designed for natural and new non-natural structures, tens of sequences still need to be tested before success is achieved. Both these issues may be caused by the specific scaffolds used in design, i.e., some protein scaffolds may be more tolerant to packing perturbations and varied sequences. Here, we report a computational method for assessing the response of protein structures to packing perturbations. We then benchmark this method using designed proteins and find that it can identify scaffolds whose folding gets disrupted upon perturbing packing, leading to the population of intermediates. The method can also isolate regions of both natural and designed scaffolds that are sensitive to such perturbations and identify contacts which when present can rescue folding. Overall, this method can be used to identify protein scaffolds that are more amenable to whole protein design as well as to identify protein regions which are sensitive to perturbations and where further mutations should be avoided during protein engineering.

Seven Amino Acid Types Suffice to Create the Core Fold of RNA Polymerase

The extant complex proteins must have evolved from ancient short and simple ancestors. The double-ψ β-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its plausible evolutionary pathway started by "interlacing homodimerization" of a half-size peptide, followed by gene duplication and fusion. Furthermore, by simplifying the amino acid repertoire of the peptide, we successfully created the DPBB fold with only seven amino acid types (Ala, Asp, Glu, Gly, Lys, Arg, and Val), which can be coded by only GNN and ARR (R = A or G) codons in the modern translation system. Thus, the DPBB fold could have been materialized by the early translation system and genetic code.

The Protein Folding Problem: The Role of Theory

The protein folding problem was first articulated as question of how order arose from disorder in proteins: How did the various native structures of proteins arise from interatomic driving forces encoded within their amino acid sequences, and how did they fold so fast? These matters have now been largely resolved by theory and statistical mechanics combined with experiments. There are general principles. Chain randomness is overcome by solvation-based codes. And in the needle-in-a-haystack metaphor, native states are found efficiently because protein haystacks (conformational ensembles) are funnel-shaped. Order-disorder theory has now grown to encompass a large swath of protein physical science across biology.

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.

Recent Progress Using De Novo Design to Study Protein Structure, Design and Binding Interactions

De novo protein design is a powerful methodology used to study natural functions in an artificial-protein context. Since its inception, it has been used to reproduce a plethora of reactions and uncover biophysical principles that are often difficult to extract from direct studies of natural proteins. Natural proteins are capable of assuming a variety of different structures and subsequently binding ligands at impressively high levels of both specificity and affinity. Here, we will review recent examples of de novo design studies on binding reactions for small molecules, nucleic acids, and the formation of protein-protein interactions. We will then discuss some new structural advances in the field. Finally, we will discuss some advancements in computational modeling and design approaches and provide an overview of some modern algorithmic tools being used to design these proteins.