Disrupting the α-synuclein-ESCRT interaction with a peptide inhibitor mitigates neurodegeneration in preclinical models of Parkinson's disease.

Accumulation of α-synuclein into toxic oligomers or fibrils is implicated in dopaminergic neurodegeneration in Parkinson's disease. Here we performed a high-throughput, proteome-wide peptide screen to identify protein-protein interaction inhibitors that reduce α-synuclein oligomer levels and their associated cytotoxicity. We find that the most potent peptide inhibitor disrupts the direct interaction between the C-terminal region of α-synuclein and CHarged Multivesicular body Protein 2B (CHMP2B), a component of the Endosomal Sorting Complex Required for Transport-III (ESCRT-III). We show that α-synuclein impedes endolysosomal activity via this interaction, thereby inhibiting its own degradation. Conversely, the peptide inhibitor restores endolysosomal function and thereby decreases α-synuclein levels in multiple models, including female and male human cells harboring disease-causing α-synuclein mutations. Furthermore, the peptide inhibitor protects dopaminergic neurons from α-synuclein-mediated degeneration in hermaphroditic C. elegans and preclinical Parkinson's disease models using female rats. Thus, the α-synuclein-CHMP2B interaction is a potential therapeutic target for neurodegenerative disorders.

Unraveling the Mechanics of a Repeat-Protein Nanospring: From Folding of Individual Repeats to Fluctuations of the Superhelix.

Tandem-repeat proteins comprise small secondary structure motifs that stack to form one-dimensional arrays with distinctive mechanical properties that are proposed to direct their cellular functions. Here, we use single-molecule optical tweezers to study the folding of consensus-designed tetratricopeptide repeats (CTPRs), superhelical arrays of short helix-turn-helix motifs. We find that CTPRs display a spring-like mechanical response in which individual repeats undergo rapid equilibrium fluctuations between partially folded and unfolded conformations. We rationalize the force response using Ising models and dissect the folding pathway of CTPRs under mechanical load, revealing how the repeat arrays form from the center toward both termini simultaneously. Most strikingly, we also directly observe the protein's superhelical tertiary structure in the force signal. Using protein engineering, crystallography, and single-molecule experiments, we show that the superhelical geometry can be altered by carefully placed amino acid substitutions, and we examine how these sequence changes affect intrinsic repeat stability and inter-repeat coupling. Our findings provide the means to dissect and modulate repeat-protein stability and dynamics, which will be essential for researchers to understand the function of natural repeat proteins and to exploit artificial repeats proteins in nanotechnology and biomedical applications.

Computational Design of Potent D-Peptide Inhibitors of SARS-CoV-2.

Blocking the association between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor-binding domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) is an attractive therapeutic approach to prevent the virus from entering human cells. While antibodies and other modalities have been developed to this end, d-amino acid peptides offer unique advantages, including serum stability, low immunogenicity, and low cost of production. Here, we designed potent novel D-peptide inhibitors that mimic the ACE2 α1-binding helix by searching a mirror-image version of the PDB. The two best designs bound the RBD with affinities of 29 and 31 nM and blocked the infection of Vero cells by SARS-CoV-2 with IC50 values of 5.76 and 6.56 µM, respectively. Notably, both D-peptides neutralized with a similar potency the infection of two variants of concern: B.1.1.7 and B.1.351 in vitro. These potent D-peptide inhibitors are promising lead candidates for developing SARS-CoV-2 prophylactic or therapeutic treatments.

Testing the length limit of loop grafting in a helical repeat protein.

Alpha-helical repeat proteins such as consensus-designed tetratricopeptide repeats (CTPRs) are exceptionally stable molecules that are able to tolerate destabilizing sequence alterations and are therefore becoming increasingly valued as a modular platform for biotechnology and biotherapeutic applications. A simple approach to functionalize the CTPR scaffold that we are pioneering is the insertion of short linear motifs (SLiMs) into the loops between adjacent repeats. Here, we test the limits of the scaffold by inserting 17 highly diverse amino acid sequences of up to 58 amino acids in length into a two-repeat protein and examine the impact on protein folding, stability and solubility. The sequences include three SLiMs that bind oncoproteins and eleven naturally occurring linker sequences all predicted to be intrinsically disordered but with conformational preferences ranging from compact globules to expanded coils. We show that the loop-grafted proteins retain the native CTPR structure and are thermally stable with melting temperatures above 60 â€‹°C, despite the longest loop sequence being almost the same size as the CTPR scaffold itself (68 amino acids). Although the main determinant of the effect of stability was found to be loop length and was relatively insensitive to amino acid composition, the relationship between protein solubility and the loop sequences was more complex, with the presence of negatively charged amino acids enhancing the solubility. Our findings will help us to fully realize the potential of the repeat-protein scaffold, allowing a rational design approach to create artificial modular proteins with customized functional capabilities.

Computational generation of proteins with predetermined three-dimensional shapes using ProteinSolver.

Computational generation of new proteins with a predetermined three-dimensional shape and computational optimization of existing proteins while maintaining their shape are challenging problems in structural biology. Here, we present a protocol that uses ProteinSolver, a pre-trained graph convolutional neural network, to quickly generate thousands of sequences matching a specific protein topology. We describe computational approaches that can be used to evaluate the generated sequences, and we show how select sequences can be validated experimentally. For complete details on the use and execution of this protocol, please refer to Strokach et al. (2020).

Engineering mono- and multi-valent inhibitors on a modular scaffold.

Here we exploit the simple, ultra-stable, modular architecture of consensus-designed tetratricopeptide repeat proteins (CTPRs) to create a platform capable of displaying both single as well as multiple functions and with diverse programmable geometrical arrangements by grafting non-helical short linear binding motifs (SLiMs) onto the loops between adjacent repeats. As proof of concept, we built synthetic CTPRs to bind and inhibit the human tankyrase proteins (hTNKS), which play a key role in Wnt signaling and are upregulated in cancer. A series of mono-valent and multi-valent hTNKS binders was assembled. To fully exploit the modular scaffold and to further diversify the multi-valent geometry, we engineered the binding modules with two different formats, one monomeric and the other trimeric. We show that the designed proteins are stable, correctly folded and capable of binding to and inhibiting the cellular activity of hTNKS leading to downregulation of the Wnt pathway. Multivalency in both the CTPR protein arrays and the hTNKS target results in the formation of large macromolecular assemblies, which can be visualized both in vitro and in the cell. When delivered into the cell by nanoparticle encapsulation, the multivalent CTPR proteins displayed exceptional activity. They are able to inhibit Wnt signaling where small molecule inhibitors have failed to date. Our results point to the tremendous potential of the CTPR platform to exploit a range of SLiMs and assemble synthetic binding molecules with built-in multivalent capabilities and precise, pre-programmed geometries.

Fast and Flexible Protein Design Using Deep Graph Neural Networks.

Protein structure and function is determined by the arrangement of the linear sequence of amino acids in 3D space. We show that a deep graph neural network, ProteinSolver, can precisely design sequences that fold into a predetermined shape by phrasing this challenge as a constraint satisfaction problem (CSP), akin to Sudoku puzzles. We trained ProteinSolver on over 70,000,000 real protein sequences corresponding to over 80,000 structures. We show that our method rapidly designs new protein sequences and benchmark them in silico using energy-based scores, molecular dynamics, and structure prediction methods. As a proof-of-principle validation, we use ProteinSolver to generate sequences that match the structure of serum albumin, then synthesize the top-scoring design and validate it in vitro using circular dichroism. ProteinSolver is freely available at http://design.proteinsolver.org and https://gitlab.com/ostrokach/proteinsolver. A record of this paper's transparent peer review process is included in the Supplemental Information.

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.

The tetratricopeptide-repeat motif is a versatile platform that enables diverse modes of molecular recognition.

Tetratricopeptide repeat (TPR) domains and TPR-like domains are widespread across nature. They are involved in varied cellular processes and have been traditionally associated with binding to short linear peptide motifs. However, examples of a much more diverse range of molecular recognition modes are increasing year by year. The Protein Data Bank has an ever-expanding collection of TPR proteins in complex with a myriad of different partners, ranging from short linear peptide motifs to large globular protein domains. In this review, we explore these varied binding modes. Additionally, we hope to highlight an emerging property of this simple, malleable fold-the potential for programmable complexity that can be achieved by acting as a scaffold for multiple binding partners.

Exploring new strategies for grafting binding peptides onto protein loops using a consensus-designed tetratricopeptide repeat scaffold.

Peptide display approaches, in which peptide epitopes of known binding activities are grafted onto stable protein scaffolds, have been developed to constrain the peptide in its bioactive conformation and to enhance its stability. However, peptide grafting can be a lengthy process requiring extensive computational modeling and/or optimisation by directed evolution techniques. In this study, we show that ultra-stable consensus-designed tetratricopeptide repeat (CTPR) proteins are amenable to the grafting of peptides that bind the Kelch-like ECH-associated protein 1 (Keap1) onto the loop between adjacent repeats. We explore simple strategies to optimize the grafting process and show that modest improvements in Keap1-binding affinity can be obtained by changing the composition of the linker sequence flanking either side of the binding peptide.

Context-Dependent Energetics of Loop Extensions in a Family of Tandem-Repeat Proteins.

Consensus-designed tetratricopeptide repeat proteins are highly stable, modular proteins that are strikingly amenable to rational engineering. They therefore have tremendous potential as building blocks for biomaterials and biomedicine. Here, we explore the possibility of extending the loops between repeats to enable further diversification, and we investigate how this modification affects stability and folding cooperativity. We find that extending a single loop by up to 25 residues does not disrupt the overall protein structure, but, strikingly, the effect on stability is highly context-dependent: in a two-repeat array, destabilization is relatively small and can be accounted for purely in entropic terms, whereas extending a loop in the middle of a large array is much more costly because of weakening of the interaction between the repeats. Our findings provide important and, to our knowledge, new insights that increase our understanding of the structure, folding, and function of natural repeat proteins and the design of artificial repeat proteins in biotechnology.

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'.

PyFolding: Open-Source Graphing, Simulation, and Analysis of the Biophysical Properties of Proteins.

For many years, curve-fitting software has been heavily utilized to fit simple models to various types of biophysical data. Although such software packages are easy to use for simple functions, they are often expensive and present substantial impediments to applying more complex models or for the analysis of large data sets. One field that is reliant on such data analysis is the thermodynamics and kinetics of protein folding. Over the past decade, increasingly sophisticated analytical models have been generated, but without simple tools to enable routine analysis. Consequently, users have needed to generate their own tools or otherwise find willing collaborators. Here we present PyFolding, a free, open-source, and extensible Python framework for graphing, analysis, and simulation of the biophysical properties of proteins. To demonstrate the utility of PyFolding, we have used it to analyze and model experimental protein folding and thermodynamic data. Examples include: 1) multiphase kinetic folding fitted to linked equations, 2) global fitting of multiple data sets, and 3) analysis of repeat protein thermodynamics with Ising model variants. Moreover, we demonstrate how PyFolding is easily extensible to novel functionality beyond applications in protein folding via the addition of new models. Example scripts to perform these and other operations are supplied with the software, and we encourage users to contribute notebooks and models to create a community resource. Finally, we show that PyFolding can be used in conjunction with Jupyter notebooks as an easy way to share methods and analysis for publication and among research teams.

A method for rapid high-throughput biophysical analysis of proteins.

Quantitative determination of protein thermodynamic stability is fundamental to many research areas, both basic and applied. Although chemical-induced denaturation is the gold-standard method, it has been replaced in many settings by semi-quantitative approaches such as thermal stability measurements. The reason for this shift is that chemical denaturation experiments are labour-intensive, sample-costly and time-consuming, and it has been assumed that miniaturisation to a high-throughput format would not be possible without concomitantly comprising data quality. Here we exploit current technologies to create a high-throughput label-free chemical denaturation method that is capable of generating replicate datasets on multiple proteins in parallel on a timescale that is at least ten times faster, much more economical on sample, and with the potential for superior data quality, than the conventional methods used in most research labs currently.

Dissecting and reprogramming the folding and assembly of tandem-repeat proteins.

Studying protein folding and protein design in globular proteins presents significant challenges because of the two related features, topological complexity and co-operativity. In contrast, tandem-repeat proteins have regular and modular structures composed of linearly arrayed motifs. This means that the biophysics of even giant repeat proteins is highly amenable to dissection and to rational design. Here we discuss what has been learnt about the folding mechanisms of tandem-repeat proteins. The defining features that have emerged are: (i) accessibility of multiple distinct routes between denatured and native states, both at equilibrium and under kinetic conditions; (ii) different routes are favoured for folding compared with unfolding; (iii) unfolding energy barriers are broad, reflecting stepwise unravelling of an array repeat by repeat; (iv) highly co-operative unfolding at equilibrium and the potential for exceptionally high thermodynamic stabilities by introducing consensus residues; (v) under force, helical-repeat structures are very weak with non-co-operative unfolding leading to elasticity and buffering effects. This level of understanding should enable us to create repeat proteins with made-to-measure folding mechanisms, in which one can dial into the sequence the order of repeat folding, number of pathways taken, step size (co-operativity) and fine-structure of the kinetic energy barriers.