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Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs. Author summary: Machine learning (ML) is changing the world of protein design, from structure prediction methods like AlphaFold to fixed-backbone design methods like ProteinMPNN. ML methods have made much progress in various aspects of protein computational biology, both complementing and, in some cases, surpassing traditional macromolecular modeling methods such as those combined in libraries like the Rosetta software suite. However, a lack of compatibility and flexibility can hinder interoperability with existing methods, preventing the full potential of these new solutions from being realized. Here, we first present a new machine learning tool for predicting post-translational modifications (PTMs), which play an important role in the stability and function of proteins, and then highlight how the implementation of this tool in the existing Rosetta toolbox can facilitate new applications. To this end, we combine PTM prediction with protein design, maximizing or minimizing the predicted probability of a post-translational modification occurring at a specific site. As one example, we predict the N-linked glycosylation of influenza hemagglutinin, which has applications in both understanding the evolution of viral strains over time, and engineering additional glycosylation sites to mask unwanted epitopes of vaccine candidates. [ABSTRACT FROM AUTHOR]

The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures of L- and D-amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC50 values across seven designed NDM-1-inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, bindingcompetent macrocycles should have broad applicability to a wide range of therapeutic targets. [ABSTRACT FROM AUTHOR]

Abstract: Among the diseases of protein misfolding, amyotrophic lateral sclerosis (ALS) is unusual in that the proteinaceous neuronal inclusions that are the hallmark of the disease have neither the classic fibrillar appearance of amyloid by transmission electron microscopy nor the affinity for the dye Congo red that is a defining feature of amyloid. Mutations in the Cu, Zn superoxide dismutase (SOD1) cause the largest subset of inherited ALS cases. The mechanism by which this highly stable enzyme misfolds to form non-amyloid aggregates is currently poorly understood, as are the stresses that initiate misfolding. The oxidative damage hypothesis proposes that SOD1''s normal free radical scavenger role puts it at risk of oxidative damage and that it is this damage that triggers the misfolding primed by mutation. Here, we present evidence that hydrogen peroxide treatment, which generates free radical species at the SOD1 active site, causes oxidative damage to active-site histidine residues, leading to major structural changes and non-amyloid aggregation similar to that seen in ALS. Time-resolved measurements of release of bound metal ligands, exposure of hydrophobic surface area, and alterations in the SOD1 proton NMR spectrum have allowed us to model the early structural changes occurring as SOD1 misfolds, prior to aggregation. ALS-causing SOD1 mutations apparently alter this pathway by increasing exposure of buried epitopes in misfolded species populated at endpoint. We have identified a well-populated early misfolding intermediate that could serve as a target for therapies designed to block downstream misfolding and aggregation events and thereby treat SOD1-associated ALS. [Copyright &y& Elsevier]

Abstract: Kinetic experiments provide much information about protein folding mechanisms. Time-resolved signals are often best described by expressions with many exponential terms, but this hinders the extraction of rate constants by nonlinear least squares (NLS) fitting. Numerical inverse Laplace transformation, which converts a time-resolved dataset into a spectrum of amplitudes as a function of rate constant, allows easy estimation of the rate constants, amplitudes, and number of processes underlying the data. Here, we present a Tikhonov regularization-based method that converts a dataset into a rate spectrum, subject to regularization constraints, without requiring an iterative search of parameter space. This allows more rapid generation of rate spectra as well as analysis of datasets too noisy to process by existing iterative search algorithms. This method’s simplicity also permits highly objective, largely automatic analysis with minimal human guidance. We show that this regularization method reproduces results previously obtained by NLS fitting and that it is effective for analyzing datasets too complex for traditional fitting methods. This method’s reliability and speed, as well as its potential for objective, model-free analysis, make it extremely useful as a first step in analysis of complicated noisy datasets and an excellent guide for subsequent NLS analysis. [Copyright &y& Elsevier]

Abstract: Point mutations scattered throughout the sequence of Cu,Zn superoxide dismutase (SOD1) cause a subset of amyotrophic lateral sclerosis (ALS) cases. SOD1 is a homodimer in which each subunit binds one copper atom and one zinc atom. Inclusions containing misfolded SOD1 are seen in motor neurons of SOD1-associated ALS cases. The mechanism by which these diverse mutations cause misfolding and converge on the same disease is still not well understood. Previously, we developed several time-resolved techniques to monitor structural changes in SOD1 as it unfolds in guanidine hydrochloride. By measuring the rates of Cu and Zn release using an absorbance-based assay, dimer dissociation through chemical cross-linking, and β-barrel conformation changes by tryptophan fluorescence, we established that wild-type SOD1 unfolds by a branched pathway involving a Zn-deficient monomer as the dominant intermediate of the major pathway, and with various metal-loaded and Cu-deficient dimers populated along the minor pathway. We have now compared the unfolding pathway of wild-type SOD1 with those of A4V, G37R, G85R, G93A, and I113T ALS-associated mutant SOD1. The kinetics of unfolding of the mutants were generally much faster than those of wild type. However, all of the mutants utilize the minority pathway to a greater extent than the wild-type protein, leading to greater populations of Cu-deficient intermediates and decreases in Zn-deficient intermediates relative to the wild-type protein. The greater propensity of the mutants to populate Cu-deficient states potentially implicates these species as a pathogenic form of SOD1 in SOD1-associated ALS and provides a novel target for therapeutic intervention. [Copyright &y& Elsevier]

Abstract: The amyloid cascade model hypothesizes that neurotoxic oligomers or aggregates formed by the Alzheimer amyloid peptide (Aβ) cause disease pathology in Alzheimer''s disease. Attempted treatment strategies for Alzheimer''s disease have involved either inhibiting Aβ oligomerization or aggregation, or dissolving existing aggregates. Blocking such downhill processes, however, has proved daunting. We have used a different approach that targets Aβ before the oligomerization cascade begins. We predicted that an amphipathic helix could convert Aβ into a native-like protein and inhibit initiation of oligomerization and aggregation. This idea was tested with a designed library and genetic screen. We exhaustively screened a library of semi-randomized amphipathic helical sequences, each expressed as a fusion protein with an Aβ42-yellow fluorescent protein sequence serving as a reporter for folding and solubilization. This yielded an amphipathic helix capable of initiating native-like folding in Aβ42 and preventing aggregation. This amphipathic helix has direct application to Alzheimer''s disease therapy development. [Copyright &y& Elsevier]

Abstract: Mutations in the Cu,Zn superoxide dismutase (SOD1) cause a subset of amyotrophic lateral sclerosis cases. SOD1 is a homodimer in which each monomer binds one copper atom and one zinc atom. Mutation is believed to increase the conformational flexibility of SOD1, giving rise to a misfolded SOD1 population with novel cytotoxic properties. While SOD1''s metal ligands affect its stability greatly, little is known about the role these metals play in the folding, unfolding, and misfolding processes. Here, we present a method by which we were able to measure the rates of metal release during SOD1 unfolding in guanidine hydrochloride. Rates of dimer dissociation, measured by a time-resolved cross-linking assay, and conformational changes in SOD1''s β-barrel core, monitored by tryptophan fluorescence intensity, were compared with the rates of copper release and zinc release. Correlations were observed across a range of denaturant concentrations, giving rise to a more detailed model of the SOD1 unfolding process than was previously available. According to this model, the major unfolding pathway involves simultaneous dimer dissociation and zinc release as an early step that is followed by a slow conformational change in the protein''s core, which, in turn, is followed by rapid copper release. This model establishes a zinc-deficient, copper-loaded SOD1 monomer as a well-populated SOD1 unfolding intermediate and a species likely to be populated under conditions of denaturational stress. Because the cytotoxicity of zinc-deficient SOD1 has been demonstrated previously, this species is a good candidate for the cytotoxic species in SOD1-associated amyotrophic lateral sclerosis. [Copyright &y& Elsevier]

Graph representations are traditionally used to represent protein structures in sequence design protocols in which the protein backbone conformation is known. This infrequently extends to machine learning projects: existing graph convolution algorithms have shortcomings when representing protein environments. One reason for this is the lack of emphasis on edge attributes during massage-passing operations. Another reason is the traditionally shallow nature of graph neural network architectures. Here we introduce an improved message-passing operation that is better equipped to model local kinematics problems such as protein design. Our approach, XENet, pays special attention to both incoming and outgoing edge attributes. We compare XENet against existing graph convolutions in an attempt to decrease rotamer sample counts in Rosetta's rotamer substitution protocol, used for protein side-chain optimization and sequence design. This use case is motivating because it both reduces the size of the search space for classical side-chain optimization algorithms, and allows larger protein design problems to be solved with quantum algorithms on near-term quantum computers with limited qubit counts. XENet outperformed competing models while also displaying a greater tolerance for deeper architectures. We found that XENet was able to decrease rotamer counts by 40% without loss in quality. This decreased the memory consumption for classical pre-computation of rotamer energies in our use case by more than a factor of 3, the qubit consumption for an existing sequence design quantum algorithm by 40%, and the size of the solution space by a factor of 165. Additionally, XENet displayed an ability to handle deeper architectures than competing convolutions. Author summary: Graph data structures are ubiquitous in the field of protein design, and are at the core of the recent advances in machine learning brought forth by graph neural networks (GNNs). GNNs have led to some impressive results in modeling protein interactions, but are still not as common as other tensor representations. Most GNN architectures tend to put minimal emphasis on information stored on edges; however, protein modeling tools often use edges to represent vital geometric relationships about residue pair interactions. We show that a more advanced processing of edge attributes can lead to considerable benefits when modeling chemical data. We introduce XENet and show it to have improved ability to represent protein structural data while allowing information about amino acid interactions to be stored on graph edges. We use XENet to intelligently simplify the optimization problem that is solved when designing proteins. This task is important to us and others because it allows larger proteins to be designed on near-term quantum computers. We show that XENet is able to train on our protein modeling data better than existing methods, successfully resulting in a dramatic decrease in protein design sample space with negligible loss in quality. [ABSTRACT FROM AUTHOR]

The alanine dipeptide is a standard system to model dihedral angles in proteins. It is shown that obtaining the Ramachandran plot accurately is a hard problem because of many local minima; depending on the details of geometry optimizations, different Ramachandran plots can be obtained. To locate all energy minima, starting from geometries from MD simulations, 250,000 geometry optimizations were performed at the level of RHF/6‐31G*, followed by re‐optimizations of the located 827 minima at the level of MP2/6–311++G**, yielding 30 unique minima, most of which were not previously reported in literature. Both in vacuo and solvated structures are discussed. The minima are systematically categorized based on four backbone dihedral angles. The Gibbs energies are evaluated and the structural factors determining the relative stabilities of conformers are discussed. © 2018 Wiley Periodicals, Inc. Using molecular dynamics to obtain starting geometries for geometry optimizations, minima on the energy surface of alanine dipeptide as a function of two dihedral angles are systematically located, and their energetics are analyzed both in vacuo and in aqueous solution. It is found that two other dihedral angles are useful to label the minima. The proposed approach can be used to locate stationary points in other systems. [ABSTRACT FROM AUTHOR]

Mixed-chirality peptide macrocycles such as cyclosporine are among the most potent therapeutics identified to date, but there is currently no way to systematically search the structural space spanned by such compounds. Natural proteins do not provide a useful guide: Peptide macrocycles lack regular secondary structures and hydrophobic cores, and can contain local structures not accessible with L-amino acids. Here, we enumerate the stable structures that can be adopted by macrocyclic peptides composed of L- and D-amino acids by near-exhaustive backbone sampling followed by sequence design and energy landscape calculations. We identify more than 200 designs predicted to fold into single stable structures, many times more than the number of currently available unbound peptide macrocycle structures. Nuclear magnetic resonance structures of 9 of 12 designed 7- to 10-residue macrocycles, and three 11- to 14-residue bicyclic designs, are close to the computational models. Our results provide a nearly complete coverage of the rich space of structures possible for short peptide macrocycles and vastly increase the available starting scaffolds for both rational drug design and library selection methods. [ABSTRACT FROM AUTHOR]

The folding of natural proteins typically relies on hydrophobic packing, metal binding, or disulfide bond formation in the protein core. Alternatively, a 3D structure can be defined by incorporating a multivalent cross-linking agent, and this approach has been successfully developed for the selection of bicyclic peptides from large random-sequence libraries. By contrast, there is no general method for the de novo computational design of multicross-linked proteins with predictable and well-defined folds, including ones not found in nature. Here we use Rosetta and Tertiary Motifs (TERMs) to design small proteins that fold around multivalent cross-linkers. The hydrophobic cross-linkers stabilize the fold by macrocyclic restraints, and they also form an integral part of a small apolar core. The designed CovCore proteins were prepared by chemical synthesis, and their structures were determined by solution NMR or X-ray crystallography. These mesosized proteins, lying between conventional proteins and small peptides, are easily accessible either through biosynthetic precursors or chemical synthesis. The unique tertiary structures and ease of synthesis of CovCore proteins indicate that they should provide versatile templates for developing inhibitors of protein-protein interactions. [ABSTRACT FROM AUTHOR]

CCM3 mutations give rise to cerebral cavernous malformations (CCMs) of the vasculature through a mechanism that remains unclear. Interaction of CCM3 with the germinal center kinase III (GCKIII) subfamily of Sterile 20 protein kinases, MST4, STK24, and STK25, has been implicated in cardiovascular development in the zebrafish, raising the possibility that dysregulated GCKLII function may contribute to the etiology of CCM disease. Here, we show that the amino-terminal region of CCM3 is necessary and sufficient to bind directly to the C-terminal tail region of GCKIII proteins. This same region of CCM3 was shown previously to mediate homodimerization through the formation of an interdigitated α-helical domain. Sequence conservation and binding studies suggest that CCM3 may preferentially heterodimerize with GCKIII proteins through a manner structurally analogous to that employed for CCM3 homodimerization. [ABSTRACT FROM AUTHOR]

Prion diseases occur when the normally α-helical prion protein (PrP) converts to a pathological β-structured state with prion infectivity (PrPSc). Exposure to PrPSc from other mammals can catalyze this conversion. Evidence from experimental and accidental transmis- sion of prions suggests that mammals vary in their prion disease susceptibility: Hamsters and mice show relatively high susceptibility, whereas rabbits, horses, and dogs show low susceptibility. Using a novel approach to quantify conformational states of PrP by circular dichroism (CD), we find that prion susceptibility tracks with the intrinsic propensity of mammalian PrP to convert from the native, α-helical state to a cytotoxic β-structured state, which exists in a monomer-octarner equilibrium. It has been controversial whether β-structured monomers exist at acidic pH; sedimentation equilibrium and dual-wavelength CD evidence is presented for an equilibrium between a β-structured monomer and octamerin some acidic pH conditions. Our X-ray crystallographic structure of rabbit PrP has identified a key helix-capping motif implicated in the low prion disease susceptibility of rabbits. Removal of. this capping motif increases the β-structure folding propensity of rabbit PrP to match that of PrP from mouse, a species more susceptible to prion disease. [ABSTRACT FROM AUTHOR]

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities. [ABSTRACT FROM AUTHOR]

We use computational design coupled with experimental characterization to systematically investigate the design principles for macrocycle membrane permeability and oral bioavailability. We designed 184 6–12 residue macrocycles with a wide range of predicted structures containing noncanonical backbone modifications and experimentally determined structures of 35; 29 are very close to the computational models. With such control, we show that membrane permeability can be systematically achieved by ensuring all amide (NH) groups are engaged in internal hydrogen bonding interactions. 84 designs over the 6–12 residue size range cross membranes with an apparent permeability greater than 1 × 10−6 cm/s. Designs with exposed NH groups can be made membrane permeable through the design of an alternative isoenergetic fully hydrogen-bonded state favored in the lipid membrane. The ability to robustly design membrane-permeable and orally bioavailable peptides with high structural accuracy should contribute to the next generation of designed macrocycle therapeutics. [Display omitted] • Computational design of diverse permeable macrocycles beyond the "rule-of-five" space • X-ray and NMR structures of designed macrocycles match their computational models • Designed macrocycles are permeable in vitro and orally bioavailable in vivo • Designed chameleonic peptides show solvent-dependent conformational switching An investigation of the design principles of macrocyclic peptide membrane permeability and oral bioavailability enables the generation of synthetic macrocycles that fold into the predicted conformation, can cross membranes, and even adopt different conformations depending on polar versus nonpolar contexts. [ABSTRACT FROM AUTHOR]