Diffusing protein binders to intrinsically disordered proteins.

Proteins which bind intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) with high affinity and specificity could have considerable utility for therapeutic and diagnostic applications. However, a general methodology for targeting IDPs/IDRs has yet to be developed. Here, we show that starting only from the target sequence of the input, and freely sampling both target and binding protein conformation, RFdiffusion can generate binders to IDPs and IDRs in a wide range of conformations. We use this approach to generate binders to the IDPs Amylin, C-peptide and VP48 in a range of conformations with Kds in the 3 -100nM range. The Amylin binder inhibits amyloid fibril formation and dissociates existing fibers, and enables enrichment of amylin for mass spectrometry-based detection. For the IDRs G3bp1, common gamma chain (IL2RG) and prion, we diffused binders to beta strand conformations of the targets, obtaining 10 to 100 nM affinity. The IL2RG binder colocalizes with the receptor in cells, enabling new approaches to modulating IL2 signaling. Our approach should be widely useful for creating binders to flexible IDPs/IDRs spanning a wide range of intrinsic conformational preferences.

δ-Conotoxin Structure Prediction and Analysis through Large-Scale Comparative and Deep Learning Modeling Approaches.

The δ-conotoxins, a class of peptides produced in the venom of cone snails, are of interest due to their ability to inhibit the inactivation of voltage-gated sodium channels causing paralysis and other neurological responses, but difficulties in their isolation and synthesis have made structural characterization challenging. Taking advantage of recent breakthroughs in computational algorithms for structure prediction that have made modeling especially useful when experimental data is sparse, this work uses both the deep-learning-based algorithm AlphaFold and comparative modeling method RosettaCM to model and analyze 18 previously uncharacterized δ-conotoxins derived from piscivorous, vermivorous, and molluscivorous cone snails. The models provide useful insights into the structural aspects of these peptides and suggest features likely to be significant in influencing their binding and different pharmacological activities against their targets, with implications for drug development. Additionally, the described protocol provides a roadmap for the modeling of similar disulfide-rich peptides by these complementary methods.

Predicting success using response after lead implantation with sacral neuromodulation for urgency incontinence.

IMPORTANCE: Many women report inadequate symptom control after sacral neuromodulation (SNM), despite 50% reduction in urgency incontinence episodes (UUIE) after test stimulation. OBJECTIVE: To determine the ideal percent UUIE reduction after test stimulation that predicts 24-month success. STUDY DESIGN: Using data from a multicenter SNM trial, we constructed receiver operating characteristic curves to identify an ideal threshold of percent UUIE reduction after test stimulation. We defined 24-month success as Patient Global Impression of Improvement of "very much better" to "better." We compared predictive accuracy of two models predicting success: (1) percent UUIE reduction alone and (2) with baseline characteristics. RESULTS: Of 149 women (median [IQR] baseline daily UUIE 4.7 [3.7, 6.0]), the ideal threshold for 24-month success was 72% (95% confidence interval 64,76%) UUIE reduction with accuracy 0.54 (0.42, 0.66), sensitivity 0.71 (0.56, 0.86) and specificity 0.27 (0.05, 0.55). The accuracy of the 50% reduction threshold was 0.60 (0.49, 0.71), sensitivity 0.95 (0.88, 1.0) and specificity 0.04 (0.0, 0.12). Percent reduction in UUIE was not better than chance in predicting 24-month success (concordance index [c-index] 0.47 [0.46, 0.62]); adding age, body mass index, diabetes mellitus and visual or hearing impairment the c-index was 0.68 (0.61, 0.78). CONCLUSIONS: Among women who received an internal pulse generator (IPG) due to ≥50% UUIE reduction after test stimulation, we found no ideal threshold that better predicted 24-month success. Percent reduction in UUIE after test stimulation poorly predicts 24-month success with or without clinical factors. Given this, re-evaluating how we determine who should receive an IPG is needed.

Rational redesign of the loop dynamics of carbonyl reductase LfSDR1 to improve the stereoselectivity for asymmetric synthesis of bulky chiral alcohols.

Engineering biocatalysts with enhanced stereoselectivity is highly desirable, and active-site loop dynamics play an important role in its regulation. However, knowledge of their precise roles in catalysis and evolution is limited. Here, we used the strategy of Rosetta enzyme design combined molecular dynamic simulations (MDs) to reprogram the landscapes of the key active-site loop dynamics of the carbonyl reductase LfSDR1 to improve stereoselectivity. The key flexible loop in the active site showed the potential to regulate the catalytic properties. A library of virtual variants was produced using the Rosetta design and assessed dynamic effect of the loop with the aid of MDs. A potential candidate was obtained with significant stereoselectivity (ee > 99 %) compared to the wild-type (ee = 42 %) without loss of catalytic activity or thermostability. The molecular basis of the catalytic property enhancement was flanked by MDs, which revealed the role of the G92L mutation in regulating loop dynamics to stabilize the environment of the active site. Finally, a series of the challenge bulky substrate derivatives were assessed using the G92L variant, and all showed improved stereoselectivity ee > 99 %. This study provides novel insights for improving stereoselectivity through rational engineering of the loop dynamics of biocatalysts.

Toward high-resolution modeling of small molecule-ion channel interactions.

Ion channels are critical drug targets for a range of pathologies, such as epilepsy, pain, itch, autoimmunity, and cardiac arrhythmias. To develop effective and safe therapeutics, it is necessary to design small molecules with high potency and selectivity for specific ion channel subtypes. There has been increasing implementation of structure-guided drug design for the development of small molecules targeting ion channels. We evaluated the performance of two RosettaLigand docking methods, RosettaLigand and GALigandDock, on the structures of known ligand-cation channel complexes. Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium (CaV), and transient receptor potential vanilloid (TRPV) channel families. For each test case, RosettaLigand and GALigandDock methods frequently sampled a ligand-binding pose within a root mean square deviation (RMSD) of 1-2 Å relative to the experimental ligand coordinates. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify experimental ligand coordinates as top-scoring models. Our study reveals that the proper scoring criteria for RosettaLigand and GALigandDock modeling of ligand-ion channel complexes should be assessed on a case-by-case basis using sufficient ligand and receptor interface sampling, knowledge about state-specific interactions of the ion channel, and inherent receptor site flexibility that could influence ligand binding.

LidPose: Real-Time 3D Human Pose Estimation in Sparse Lidar Point Clouds with Non-Repetitive Circular Scanning Pattern.

In this paper, we propose a novel, vision-transformer-based end-to-end pose estimation method, LidPose, for real-time human skeleton estimation in non-repetitive circular scanning (NRCS) lidar point clouds. Building on the ViTPose architecture, we introduce novel adaptations to address the unique properties of NRCS lidars, namely, the sparsity and unusual rosetta-like scanning pattern. The proposed method addresses a common issue of NRCS lidar-based perception, namely, the sparsity of the measurement, which needs balancing between the spatial and temporal resolution of the recorded data for efficient analysis of various phenomena. LidPose utilizes foreground and background segmentation techniques for the NRCS lidar sensor to select a region of interest (RoI), making LidPose a complete end-to-end approach to moving pedestrian detection and skeleton fitting from raw NRCS lidar measurement sequences captured by a static sensor for surveillance scenarios. To evaluate the method, we have created a novel, real-world, multi-modal dataset, containing camera images and lidar point clouds from a Livox Avia sensor, with annotated 2D and 3D human skeleton ground truth.

Cloning, Expression, Purification, and Characterization of Lactate Dehydrogenase from Plasmodium knowlesi: A Zoonotic Malaria Parasite.

Plasmodium knowlesi is the only Plasmodium that causes zoonotic disease among the Plasmodium that cause infection in humans. It is fatal due to its short asexual growth cycle within 24 h. Lactate dehydrogenase (LDH), an enzyme that catalyzes the final step of glycolysis, is a biomarker for diagnosing infection by Plasmodium spp. parasite. Therefore, this study aimed to efficiently produce the soluble form of P. knowlesi LDH (PkLDH) using a bacterial expression system for studying malaria caused by P. knowlesi. Recombinant pET-21a(+)-PkLDH plasmid was constructed by inserting the PkLDH gene into a pET-21a(+) expression vector. Subsequently, the recombinant plasmid was inserted into the protein-expressing Escherichia coli Rosetta(DE3) strain, and the optimal conditions for overexpression of the PkLDH protein were established using this strain. We obtained a yield of 52.0 mg/L PkLDH from the Rosetta(DE3) strain and confirmed an activity of 483.9 U/mg through experiments. This methodology for high-efficiency PkLDH production can be utilized for the development of diagnostic methods and drug candidates for distinguishing malaria caused by P. knowlesi.

Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint.

Computational protein sequence design has the ambitious goal of modifying existing or creating new proteins; however, designing stable and functional proteins is challenging without predictability of protein dynamics and allostery. Informing protein design methods with evolutionary information limits the mutational space to more native-like sequences and results in increased stability while maintaining functions. Recently, language models, trained on millions of protein sequences, have shown impressive performance in predicting the effects of mutations. Assessing Rosetta-designed sequences with a language model showed scores that were worse than those of their original sequence. To inform Rosetta design protocols with language model predictions, we added a new metric to restrain the energy function during design using the Evolutionary Scale Modeling (ESM) model. The resulting sequences have better language model scores and similar sequence recovery, with only a minor decrease in the fitness as assessed by Rosetta energy. In conclusion, our work combines the strength of recent machine learning approaches with the Rosetta protein design toolbox.

Computational and experimental identification of keystone interactions in Ebola virus matrix protein VP40 dimer formation.

The Ebola virus (EBOV) is a lipid-enveloped virus with a negative sense RNA genome that can cause severe and often fatal viral hemorrhagic fever. The assembly and budding of EBOV is regulated by the matrix protein, VP40, which is a peripheral protein that associates with anionic lipids at the inner leaflet of the plasma membrane. VP40 is sufficient to form virus-like particles (VLPs) from cells, which are nearly indistinguishable from authentic virions. Due to the restrictions of studying EBOV in BSL-4 facilities, VP40 has served as a surrogate in cellular studies to examine the EBOV assembly and budding process from the host cell plasma membrane. VP40 is a dimer where inhibition of dimer formation halts budding and formation of new VLPs as well as VP40 localization to the plasma membrane inner leaflet. To better understand VP40 dimer stability and critical amino acids to VP40 dimer formation, we integrated computational approaches with experimental validation. Site saturation/alanine scanning calculation, combined with molecular mechanics-based generalized Born with Poisson-Boltzmann surface area (MM-GB/PBSA) method and molecular dynamics simulations were used to predict the energetic contribution of amino acids to VP40 dimer stability and the hydrogen bonding network across the dimer interface. These studies revealed several previously unknown interactions and critical residues predicted to impact VP40 dimer formation. In vitro and cellular studies were then pursued for a subset of VP40 mutations demonstrating reduction in dimer formation (in vitro) or plasma membrane localization (in cells). Together, the computational and experimental approaches revealed critical residues for VP40 dimer stability in an alpha-helical interface (between residues 106-117) as well as in a loop region (between residues 52-61) below this alpha-helical region. This study sheds light on the structural origins of VP40 dimer formation and may inform the design of a small molecule that can disrupt VP40 dimer stability.

The Mutagenic Plasticity of the Cholera Toxin B-Subunit Surface Residues: Stability and Affinity.

Mastering selective molecule trafficking across human cell membranes poses a formidable challenge in healthcare biotechnology while offering the prospect of breakthroughs in drug delivery, gene therapy, and diagnostic imaging. The cholera toxin B-subunit (CTB) has the potential to be a useful cargo transporter for these applications. CTB is a robust protein that is amenable to reengineering for diverse applications; however, protein redesign has mostly focused on modifications of the N- and C-termini of the protein. Exploiting the full power of rational redesign requires a detailed understanding of the contributions of the surface residues to protein stability and binding activity. Here, we employed Rosetta-based computational saturation scans on 58 surface residues of CTB, including the GM1 binding site, to analyze both ligand-bound and ligand-free structures to decipher mutational effects on protein stability and GM1 affinity. Complimentary experimental results from differential scanning fluorimetry and isothermal titration calorimetry provided melting temperatures and GM1 binding affinities for 40 alanine mutants among these positions. The results showed that CTB can accommodate diverse mutations while maintaining its stability and ligand binding affinity. These mutations could potentially allow modification of the oligosaccharide binding specificity to change its cellular targeting, alter the B-subunit intracellular routing, or impact its shelf-life and in vivo half-life through changes to protein stability. We anticipate that the mutational space maps presented here will serve as a cornerstone for future CTB redesigns, paving the way for the development of innovative biotechnological tools.

The structural landscape of the immunoglobulin fold by large-scale de novo design.

De novo designing immunoglobulin-like frameworks that allow for functional loop diversification shows great potential for crafting antibody-like scaffolds with fully customizable structures and functions. In this work, we combined de novo parametric design with deep-learning methods for protein structure prediction and design to explore the structural landscape of 7-stranded immunoglobulin domains. After screening folding of nearly 4 million designs, we have assembled a structurally diverse library of ~50,000 immunoglobulin domains with high-confidence AlphaFold2 predictions and structures diverging from naturally occurring ones. The designed dataset enabled us to identify structural requirements for the correct folding of immunoglobulin domains, shed light on ß-sheet-ß-sheet rotational preferences and how these are linked to functional properties. Our approach eliminates the need for preset loop conformations and opens the route to large-scale de novo design of immunoglobulin-like frameworks.

Rational design of soluble expressed human aldehyde dehydrogenase 2 with high stability and activity in pepsin and trypsin.

Acetaldehyde dehydrogenase 2 (ALDH2) is a crucial enzyme in alcohol metabolism, and oral administration of ALDH2 is a promising method for alcohol detoxification. However, recombinant ALDH2 is susceptible to hydrolysis by digestive enzymes in the gastrointestinal tract and is expressed as inactive inclusion bodies in E. coli. In this study, we performed three rounds of rational design to address these issues. Specifically, the surface digestive sites of pepsin and trypsin were replaced with other polar amino acids, while hydrophobic amino acids were incorporated to reshape the catalytic cavity of ALDH2. The resulting mutant DE2-852 exhibited a 45-fold increase in soluble expression levels, while its stability against trypsin and pepsin increased by eightfold and twofold, respectively. Its catalytic efficiency (kcat/Km) at pH 7.2 and 3.2 improved by more than four and five times, respectively, with increased Vmax and decreased Km values. The enhanced properties of DE2-852 were attributed to the D457Y mutation, which created a more compact protein structure and facilitated a faster collision between the substrate and catalytic residues. These results laid the foundation for the oral administration and mass preparation of highly active ALDH2 and offered insights into the oral application of other proteins.

Rationale in Custom Design of Transmembrane ß-Barrel Pores.

Biological nanopores incorporated into synthetic membranes are widely used for single-molecule analytical applications such as DNA sequencing. The ability to engineer custom membrane proteins with a pore would allow the generation of a multitude of nanopores for the sensing/sequencing of small molecules and (bio)polymers. The de novo design of transmembrane ß-barrel pores has recently enabled the generation of nanopores with custom size, shape, and properties. In this chapter, I describe the rationale of transmembrane ß-barrel design and computational methods to assemble the backbones, design sequences, and select the designs for experimental validation.

Synthesis of R-GABA Derivatives via Pd(II) Catalyzed Enantioselective C(sp3)-H Arylation and Virtual Validation with GABAB1 Receptor for Potential leads.

GABA (γ-amino butyric acid) analogues like baclofen, tolibut, phenibut, etc., are well-known GABAB1 inhibitors and pharmaceutically important drugs. However, there is a huge demand for more chiral GABA aryl analogues with promising pharmacological actions. Here, we demonstrate the chiral ligand acetyl-protected amino quinoline (APAQ) mediated enantioselective synthesis of GABAB1 inhibitor drug scaffolds from easily accessible GABA via Pd-catalyzed C(sp3)-H activation. The synthetic methodology shows moderate to good yields, up to 74% of ee. We have successfully demonstrated the deprotection and removal of the directing group to synthesize R-tolibut in 86% yield. Further, we employed computation to probe the binding of R-GABA analogues to the extracellular domain of the human GABAB1 receptor. Our Rosetta-based molecular docking calculations show better binding for four R-enantiomers of GABA analogues than R-baclofen and R-phenibut. In addition, we employed GROMACS MD simulations and MMPB(GB)SA calculations to identify per-residue contribution to binding free energy. Our computational results suggest analogues (3R)-4-amino-3-(3,4-dimethylphenyl) butanoic acid, (3R)-4-amino-3-(3-fluorophenyl) butanoic acid, (3R)-3-(4-acetylphenyl)-4-aminobutanoic acid, (3R)-4-amino-3-(4-methoxyphenyl) butanoic acid, and (3R)-4-amino-3-phenylbutanoic acid are potential leads which could be synthesized from our methodology reported here.

In silico design of multipoint mutants for enhanced performance of Thermomyces lanuginosus lipase for efficient biodiesel production.

BACKGROUND: Biodiesel, an emerging sustainable and renewable clean energy, has garnered considerable attention as an alternative to fossil fuels. Although lipases are promising catalysts for biodiesel production, their efficiency in industrial-scale application still requires improvement. RESULTS: In this study, a novel strategy for multi-site mutagenesis in the binding pocket was developed via FuncLib (for mutant enzyme design) and Rosetta Cartesian_ddg (for free energy calculation) to improve the reaction rate and yield of lipase-catalyzed biodiesel production. Thermomyces lanuginosus lipase (TLL) with high activity and thermostability was obtained using the Pichia pastoris expression system. The specific activities of the mutants M11 and M21 (each with 5 and 4 mutations) were 1.50- and 3.10-fold higher, respectively, than those of the wild-type (wt-TLL). Their corresponding melting temperature profiles increased by 10.53 and 6.01 °C, [Formula: see text] (the temperature at which the activity is reduced to 50% after 15 min incubation) increased from 60.88 to 68.46 °C and 66.30 °C, and the optimum temperatures shifted from 45 to 50 °C. After incubation in 60% methanol for 1 h, the mutants M11 and M21 retained more than 60% activity, and 45% higher activity than that of wt-TLL. Molecular dynamics simulations indicated that the increase in thermostability could be explained by reduced atomic fluctuation, and the improved catalytic properties were attributed to a reduced binding free energy and newly formed hydrophobic interaction. Yields of biodiesel production catalyzed by mutants M11 and M21 for 48 h at an elevated temperature (50 °C) were 94.03% and 98.56%, respectively, markedly higher than that of the wt-TLL (88.56%) at its optimal temperature (45 °C) by transesterification of soybean oil. CONCLUSIONS: An integrating strategy was first adopted to realize the co-evolution of catalytic efficiency and thermostability of lipase. Two promising mutants M11 and M21 with excellent properties exhibited great potential for practical applications for in biodiesel production.

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

Modeling membrane geometries implicitly in Rosetta.

Interactions between membrane proteins (MPs) and lipid bilayers are critical for many cellular functions. In the Rosetta molecular modeling suite, the implicit membrane energy function is based on a "slab" model, which represent the membrane as a flat bilayer. However, in nature membranes often have a curvature that is important for function and/or stability. Even more prevalent, in structural biology research MPs are reconstituted in model membrane systems such as micelles, bicelles, nanodiscs, or liposomes. Thus, we have modified the existing membrane energy potentials within the RosettaMP framework to allow users to model MPs in different membrane geometries. We show that these modifications can be utilized in core applications within Rosetta such as structure refinement, protein-protein docking, and protein design. For MP structures found in curved membranes, refining these structures in curved, implicit membranes produces higher quality models with structures closer to experimentally determined structures. For MP systems embedded in multiple membranes, representing both membranes results in more favorable scores compared to only representing one of the membranes. Modeling MPs in geometries mimicking the membrane model system used in structure determination can improve model quality and model discrimination.

Integrative analysis of pathogenic variants in glucose-6-phosphatase based on an AlphaFold2 model.

Mediating the terminal reaction of gluconeogenesis and glycogenolysis, the integral membrane protein glucose-6-phosphate catalytic subunit 1 (G6PC1) regulates hepatic glucose production by catalyzing hydrolysis of glucose-6-phosphate (G6P) within the lumen of the endoplasmic reticulum. Consistent with its vital contribution to glucose homeostasis, inactivating mutations in G6PC1 causes glycogen storage disease (GSD) type 1a characterized by hepatomegaly and severe hypoglycemia. Despite its physiological importance, the structural basis of G6P binding to G6PC1 and the molecular disruptions induced by missense mutations within the active site that give rise to GSD type 1a are unknown. In this study, we determine the atomic interactions governing G6P binding as well as explore the perturbations imposed by disease-linked missense variants by subjecting an AlphaFold2 G6PC1 structural model to molecular dynamics simulations and in silico predictions of thermodynamic stability validated with robust in vitro and in situ biochemical assays. We identify a collection of side chains, including conserved residues from the signature phosphatidic acid phosphatase motif, that contribute to a hydrogen bonding and van der Waals network stabilizing G6P in the active site. The introduction of GSD type 1a mutations modified the thermodynamic landscape, altered side chain packing and substrate-binding interactions, and induced trapping of catalytic intermediates. Our results, which corroborate the high quality of the AF2 model as a guide for experimental design and to interpret outcomes, not only confirm the active-site structural organization but also identify previously unobserved mechanistic contributions of catalytic and noncatalytic side chains.

Next generation of multispecific antibody engineering.

Multispecific antibodies recognize two or more epitopes located on the same or distinct targets. This added capability through protein design allows these man-made molecules to address unmet medical needs that are no longer possible with single targeting such as with monoclonal antibodies or cytokines alone. However, the approach to the development of these multispecific molecules has been met with numerous road bumps, which suggests that a new workflow for multispecific molecules is required. The investigation of the molecular basis that mediates the successful assembly of the building blocks into non-native quaternary structures will lead to the writing of a playbook for multispecifics. This is a must do if we are to design workflows that we can control and in turn predict success. Here, we reflect on the current state-of-the-art of therapeutic biologics and look at the building blocks, in terms of proteins, and tools that can be used to build the foundations of such a next-generation workflow.

Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds.

The design of protein interaction inhibitors is a promising approach to address aberrant protein interactions that cause disease. One strategy in designing inhibitors is to use peptidomimetic scaffolds that mimic the natural interaction interface. A central challenge in using peptidomimetics as protein interaction inhibitors, however, is determining how best the molecular scaffold aligns to the residues of the interface it is attempting to mimic. Here we present the Scaffold Matcher algorithm that aligns a given molecular scaffold onto hotspot residues from a protein interaction interface. To optimize the degrees of freedom of the molecular scaffold we implement the covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm in Rosetta. To evaluate the performance of the CMA-ES, we used 26 peptides from the FlexPepDock Benchmark and compared with three other algorithms in Rosetta, specifically, Rosetta's default minimizer, a Monte Carlo protocol of small backbone perturbations, and a Genetic algorithm. We test the algorithms' performance on their ability to align a molecular scaffold to a series of hotspot residues (i.e., constraints) along native peptides. Of the 4 methods, CMA-ES was able to find the lowest energy conformation for all 26 benchmark peptides. Additionally, as a proof of concept, we apply the Scaffold Match algorithm with CMA-ES to align a peptidomimetic oligooxopiperazine scaffold to the hotspot residues of the substrate of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our implementation of CMA-ES into Rosetta allows for an alternative optimization method to be used on macromolecular modeling problems with rough energy landscapes. Finally, our Scaffold Matcher algorithm allows for the identification of initial conformations of interaction inhibitors that can be further designed and optimized as high-affinity reagents.