Computational Protein Design Research Articles

De novo design of beta hairpin peptides with custom installed halogen-bonding groups.

Much of the success of computational protein design relies on the huge number of experimental crystal structures available for training and testing. Such information is simply not available for designing solution-stable hairpin peptides, much less ones that incorporate non-canonical amino acids. We tackle this problem using a two-step physics-based approach. First, we use a simple statistical mechanical model, trained on a number of designed hairpin sequences known to be stable in solution, to rank a number of potential designs with desired properties. Then, we perform all-atom folding simulations of the highest-ranked sequences using large-scale distributed computing. To validate our predictions, a selection of designed sequences predicted to have sensitive halogen bond donor-acceptor pairs are synthesized and experimentally characterized using solution-state NMR spectroscopy. Our results show the utility of simple statistical mechanical models, coupled with massively parallel simulations for de novo molecular design of non-natural foldamers. Such designed sequences will help develop model systems to understand the role of halogen bonding in peptide stability.

Peptides from human BNIP5 and PXT1 and non-native binders of pro-apoptotic BAK can directly activate or inhibit BAK-mediated membrane permeabilization

Apoptosis is important for development and tissue homeostasis, and its dysregulation can lead to diseases, including cancer. As an apoptotic effector, BAK undergoes conformational changes that promote mitochondrial outer membrane disruption, leading to cell death. This is termed "activation" and can be induced by peptides from the human proteins BID, BIM, and PUMA. To identify additional peptides that can regulate BAK, we used computational protein design, yeast surface display screening, and structure-based energy scoring to identify 10 diverse new binders. We discovered peptides from the human proteins BNIP5 and PXT1 and three non-native peptides that activate BAK in liposome assays and induce cytochrome c release from mitochondria. Crystal structures and binding studies reveal a high degree of similarity among peptide activators and inhibitors, ruling out a simple function-determining property. Our results shed light on the vast peptide sequence space that can regulate BAK function and will guide the design of BAK-modulating tools and therapeutics.

Rational design of enzyme activity and enantioselectivity.

The strategy of rational design to engineer enzymes is to predict the potential mutants based on the understanding of the relationships between protein structure and function, and subsequently introduce the mutations using the site-directed mutagenesis. Rational design methods are universal, relatively fast and have the potential to be developed into algorithms that can quantitatively predict the performance of the designed sequences. Compared to the protein stability, it was more challenging to design an enzyme with improved activity or selectivity, due to the complexity of enzyme molecular structure and inadequate understanding of the relationships between enzyme structures and functions. However, with the development of computational force, advanced algorithm and a deeper understanding of enzyme catalytic mechanisms, rational design could significantly simplify the process of engineering enzyme functions and the number of studies applying rational design strategy has been increasing. Here, we reviewed the recent advances of applying the rational design strategy to engineer enzyme functions including activity and enantioselectivity. Five strategies including multiple sequence alignment, strategy based on steric hindrance, strategy based on remodeling interaction network, strategy based on dynamics modification and computational protein design are discussed and the successful cases using these strategies are introduced.

Physics-based approach to extend a de novo TIM barrel with rationally designed helix-loop-helix motifs.

Computational protein design promises the ability to build tailor-made proteins de novo. While a range of de novo proteins have been constructed so far, the majority of these designs have idealized topologies that lack larger cavities which are necessary for the incorporation of small molecule binding sites or enzymatic functions. One attractive target for enzyme design is the TIM-barrel fold, due to its ubiquity in nature and capability to host versatile functions. With the successful de novo design of a 4-fold symmetric TIM barrel, sTIM11, an idealized, minimalistic scaffold was created. In this work, we attempted to extend this de novo TIM barrel by incorporating a helix-loop-helix motif into its βα-loops by applying a physics-based modular design approach using Rosetta. Further diversification was performed by exploiting the symmetry of the scaffold to integrate two helix-loop-helix motifs into the scaffold. Analysis with AlphaFold2 and biochemical characterization demonstrate the formation of additional α-helical secondary structure elements supporting the successful extension as intended.

Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance.

In response to antibiotics that inhibit a bacterial enzyme, resistance mutations inevitably arise. Predicting them ahead of time would aid target selection and drug design. The simplest resistance mechanism would be to reduce antibiotic binding without sacrificing too much substrate binding. The property that reflects this is the enzyme "vitality", defined here as the difference between the inhibitor and substrate binding free energies. To predict such mutations, we borrow methodology from computational protein design. We use a Monte Carlo exploration of mutation space and vitality changes, allowing us to rank thousands of mutations and identify ones that might provide resistance through the simple mechanism considered. As an illustration, we chose dihydrofolate reductase, an essential enzyme targeted by several antibiotics. We simulated its complexes with the inhibitor trimethoprim and the substrate dihydrofolate. 20 active site positions were mutated, or "redesigned" individually, then in pairs or quartets. We computed the resulting binding free energy and vitality changes. Out of seven known resistance mutations involving active site positions, five were correctly recovered. Ten positions exhibited mutations with significant predicted vitality gains. Direct couplings between designed positions were predicted to be small, which reduces the combinatorial complexity of the mutation space to be explored. It also suggests that over the course of evolution, resistance mutations involving several positions do not need the underlying point mutations to arise all at once: they can appear and become fixed one after the other.

PocketOptimizer 2.0: A modular framework for computer-aided ligand-binding design.

The ability to design customized proteins to perform specific tasks is of great interest. We are particularly interested in the design of sensitive and specific small molecule ligand-binding proteins for biotechnological or biomedical applications. Computational methods can narrow down the immense combinatorial space to find the best solution and thus provide starting points for experimental procedures. However, success rates strongly depend on accurate modeling and energetic evaluation. Not only intra- but also intermolecular interactions have to be considered. To address this problem, we developed PocketOptimizer, a modular computational protein design pipeline, that predicts mutations in the binding pockets of proteins to increase affinity for a specific ligand. Its modularity enables users to compare different combinations of force fields, rotamer libraries, and scoring functions. Here, we present a much-improved version--PocketOptimizer 2.0. We implemented a cleaner user interface, an extended architecture with more supported tools, such as force fields and scoring functions, a backbone-dependent rotamer library, as well as different improvements in the underlying algorithms. Version 2.0 was tested against a benchmark of design cases and assessed in comparison to the first version. Our results show how newly implemented features such as the new rotamer library can lead to improved prediction accuracy. Therefore, we believe that PocketOptimizer 2.0, with its many new and improved functionalities, provides a robust and versatile environment for the design of small molecule-binding pockets in proteins. It is widely applicable and extendible due to its modular framework. PocketOptimizer 2.0 can be downloaded at https://github.com/Hoecker-Lab/pocketoptimizer.

Improved interface packing and design opportunities revealed by CryoEM analysis of a designed protein nanocage

Symmetric protein assemblies play important roles in nature which makes them an attractive target for engineering. De novo symmetric protein complexes can be created through computational protein design to tailor their properties from first principles, and recently several protein nanocages have been created by bringing together protein components through hydrophobic interactions. Accurate experimental structures of newly-developed proteins are essential to validate their design, improve assembly stability, and tailor downstream applications. We describe the CryoEM structure of the nanocage I3-01, at an overall resolution of 3.5 Å. I3-01, comprising 60 aldolase subunits arranged with icosahedral symmetry, has resisted high-resolution characterization. Some key differences between the refined structure and the original design are identified, such as improved packing of hydrophobic sidechains, providing insight to the resistance of I3-01 to high-resolution averaging. Based on our analysis, we suggest factors important in the design and structural processing of new assemblies.

Computational protein design with data‐driven approaches: Recent developments and perspectives

AbstractA fundamental and challenging task of computational protein studies is to design proteins of desired structures and functions on demand. Data‐driven approaches to protein design have been gaining tremendous momentum, with recent developments concentrated on protein sequence representation and generation by using deep learning language models, structure‐based sequence design or inverse protein folding, and the de novo generation of new protein backbones. Currently, design methods have been assessed mainly by several useful computational metrics. However, these metrics are still highly insufficient for predicting the performance of design methods in wet experiments. Nevertheless, some methods have been verified experimentally, which showed that proteins of novel sequences and structures can be designed with data‐driven models learned from natural proteins. Despite the progress, an important current limitation is the lack of accurate data‐driven approaches to model or design protein dynamics.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Artificial Intelligence/Machine Learning

Computationally Guided Redesign of a Heme-free Cytochrome with Native-like Structure and Stability.

Metals can play key roles in stabilizing protein structures, but ensuring their proper incorporation is a challenge when a metalloprotein is overexpressed in a non-native cellular environment. Here, we have used computational protein design tools to redesign cytochrome b562 (cyt b562), which relies on the binding of its heme cofactor to achieve its proper fold, into a stable, heme-free protein. The resulting protein, ApoCyt, features only four mutations and no metal-ligand or covalent bonds, yet displays improved stability over cyt b562. Mutagenesis studies and X-ray crystal structures reveal that the increase in stability is due to the computationally prescribed mutations, which stabilize the protein fold through a combination of hydrophobic packing interactions, hydrogen bonds, and cation-π interactions. Upon installation of the relevant mutations, ApoCyt is capable of assembling into previously reported, cytochrome-based trimeric and tetrameric assemblies, demonstrating that ApoCyt retains the structure and assembly properties of cyt b562. The successful design of ApoCyt therefore enables further functional diversification of cytochrome-based assemblies and demonstrates that structural metal cofactors can be replaced by a small number of well-designed, non-covalent interactions.

RESISTOR: A New OSPREY Module to Predict Resistance Mutations.

Computational, in silico prediction of resistance-conferring escape mutations could accelerate the design of therapeutics less prone to resistance. This article describes how to use the Resistor algorithm to predict escape mutations. Resistor employs Pareto optimization on four resistance-conferring criteria-positive and negative design, mutational probability, and hotspot cardinality-to assign a Pareto rank to each prospective mutant. It also predicts the mechanism of resistance, that is, whether a mutant ablates binding to a drug, strengthens binding to the endogenous ligand, or a combination of these two factors, and provides structural models of the mutants. Resistor is part of the free and open-source computational protein design software OSPREY.

Increasing Computational Protein Design Literacy through Cohort-Based Learning for Undergraduate Students

Increasing Computational Protein Design Literacy through Cohort-Based Learning for Undergraduate Students

A comprehensive protein design protocol to identify resistance mutations and signatures of adaptation in pathogens.

Most pathogens mutate and evolve over time to escape immune and drug pressure. To achieve this, they alter specific hotspot residues in their intracellular proteins to render the targeted drug(s) ineffective and develop resistance. Such hotspot residues may be located as a cluster or uniformly as a signature of adaptation in a protein. Identifying the hotspots and signatures is extremely important to comprehensively understand the disease pathogenesis and rapidly develop next-generation therapeutics. As experimental methods are time-consuming and often cumbersome, there is a need to develop efficient computational protocols and adequately utilize them. To address this issue, we present a unique computational protein design protocol that identifies hotspot residues, resistance mutations and signatures of adaptation in a pathogen's protein against a bound drug. Using the protocol, the binding affinity between the designed mutants and drug is computed quickly, which offers predictions for comparison with biophysical experiments. The applicability and accuracy of the protocol are shown using case studies of a few protein-drug complexes. As a validation, resistance mutations in severe acute respiratory syndrome coronavirus 2 main protease (Mpro) against narlaprevir (an inhibitor of hepatitis C NS3/4A serine protease) are identified. Notably, a detailed methodology and description of the working principles of the protocol are presented. In conclusion, our protocol will assist in providing a first-hand explanation of adaptation, hotspot-residue variations and surveillance of evolving resistance mutations in a pathogenic protein.

Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation.

While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation—an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used with Rosetta to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model’s generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.

Improved Repeat Protein Stability by Combined Consensus and Computational Protein Design.

High protein stability is an important feature for proteins used as therapeutics, as diagnostics, and in basic research. We have previously employed consensus design to engineer optimized Armadillo repeat proteins (ArmRPs) for sequence-specific recognition of linear epitopes with a modular binding mode. These designed ArmRPs (dArmRPs) feature high stability and are composed of M-type internal repeats that are flanked by N- and C-terminal capping repeats that protect the hydrophobic core from solvent exposure. While the overall stability of the designed ArmRPs is remarkably high, subsequent biochemical and biophysical experiments revealed that the N-capping repeat assumes a partially unfolded, solvent-accessible conformation for a small fraction of time that renders it vulnerable to proteolysis and aggregation. To overcome this problem, we have designed new N-caps starting from an M-type internal repeat using the Rosetta software. The superior stability of the computationally refined models was experimentally verified by circular dichroism and nuclear magnetic resonance spectroscopy. A crystal structure of a dArmRP containing the novel N-cap revealed that the enhanced stability correlates with an improved packing of this N-cap onto the hydrophobic core of the dArmRP. Hydrogen exchange experiments further show that the level of local unfolding of the N-cap is reduced by several orders of magnitude, resulting in increased resistance to proteolysis and weakened aggregation. As a first application of the novel N-cap, we determined the solution structure of a dArmRP with four internal repeats, which was previously impeded by the instability of the original N-cap.

Computational design of Lactobacillus Acidophilus α-L-rhamnosidase to increase its structural stability.

α-L-rhamnosidase catalyzes hydrolysis of the terminal α-L-rhamnose from various natural rhamnoglycosides, including naringin and hesperidin, and has various applications such as debittering of citrus juices in the food industry and flavonoid derhamnosylation in the pharmaceutical industry. However, its activity is lost at high temperatures, limiting its usage. To improve Lactobacillus acidophilus α-L-rhamnosidase stability, we employed molecular dynamics (MD) to identify a highly flexible region, as evaluated by its root mean square fluctuation (RMSF) value, and computational protein design (Rosetta) to increase rigidity and favorable interactions of residues in highly flexible regions. MD results show that five regions have the highest flexibilities and were selected for design by Rosetta. Twenty-one designed mutants with the best ΔΔG at each position and ΔΔG < 0 REU were simulated at high temperature. Eight designed mutants with ΔRMSF of highly flexible regions lower than -10.0% were further simulated at the optimum temperature of the wild type. N88Q, N202V, G207D, Q209M, N211T and Y213K mutants were predicted to be more stable and could maintain their native structures better than the wild type due to increased hydrogen bond interactions of designed residues and their neighboring residues. These designed mutants are promising enzymes with high potential for stability improvement.

Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology.

Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input-output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit's application. In this article, we review recent computational approaches to design protein-based sensors for small-molecule inputs with particular focus on those based on the widely used Rosetta software suite. Furthermore, we review mechanisms that have been harnessed to couple ligand inputs to functional outputs. Based on recent literature, we illustrate how the combination of protein design and synthetic biology enables new sensors for diverse applications ranging from biomedicine to metabolic engineering. We conclude with a perspective on how strategies to address frontiers in protein design and cellular circuit design may enable the next generation of sense-response networks, which may increasingly be assembled from de novo components to display diverse and engineerable input-output behaviors.

Computational design of mechanically coupled axle-rotor protein assemblies.

Natural molecular machines contain protein components that undergo motion relative to each other. Designing such mechanically constrained nanoscale protein architectures with internal degrees of freedom is an outstanding challenge for computational protein design. Here we explore the de novo construction of protein machinery from designed axle and rotor components with internal cyclic or dihedral symmetry. We find that the axle-rotor systems assemble in vitro and in vivo as designed. Using cryo-electron microscopy, we find that these systems populate conformationally variable relative orientations reflecting the symmetry of the coupled components and the computationally designed interface energy landscape. These mechanical systems with internal degrees of freedom are a step toward the design of genetically encodable nanomachines.

Prediction of Fluorophore Brightness in Designed Mini Fluorescence Activating Proteins

The de novo computational design of proteins with predefined three-dimensional structure is becoming much more routine due to advancements both in force fields and algorithms. However, creating designs with functions beyond folding is more challenging. In that regard, the recent design of small beta barrel proteins that activate the fluorescence of an exogenous small molecule chromophore (DFHBI) is noteworthy. These proteins, termed mini fluorescence activating proteins (mFAPs), have been shown to increase the brightness of the chromophore more than 100-fold upon binding to the designed ligand pocket. The design process created a large library of variants with different brightness levels but gave no rational explanation for why one variant was brighter than another. Here, we use quantum mechanics and molecular dynamics simulations to investigate how molecular flexibility in the ground and excited states influences brightness. We show that the ability of the protein to resist dihedral angle rotation of the chromophore is critical for predicting brightness. Our simulations suggest that the mFAP/DFHBI complex has a rough energy landscape, requiring extensive ground-state sampling to achieve converged predictions of excited-state kinetics. While computationally demanding, this roughness suggests that mFAP protein function can be enhanced by reshaping the energy landscape toward conformations that better resist DFHBI bond rotation.

Addressing Intersite Coupling Unlocks Large Combinatorial Chemical Spaces for Alchemical Free Energy Methods.

Alchemical free energy methods are playing a growing role in molecular design, both for computer-aided drug design of small molecules and for computational protein design. Multisite λ dynamics (MSλD) is a uniquely scalable alchemical free energy method that enables more efficient exploration of combinatorial alchemical spaces encountered in molecular design, but simulations have typically been limited to a few hundred ligands or sequences. Here, we focus on coupling between sites to enable scaling to larger alchemical spaces. We first discuss updates to the biasing potentials that facilitate MSλD sampling to include coupling terms and show that this can provide more thorough sampling of alchemical states. We then harness coupling between sites by developing a new free energy estimator based on the Potts models underlying direct coupling analysis, a method for predicting contacts from sequence coevolution, and find it yields more accurate free energies than previous estimators. The sampling requirements of the Potts model estimator scale with the square of the number of sites, a substantial improvement over the exponential scaling of the standard estimator. This opens up exploration of much larger alchemical spaces with MSλD for molecular design.

Accurate positioning of functional residues with robotics-inspired computational protein design

SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.