Implicit model to capture electrostatic features of membrane environment.

Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.

Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design.

Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.

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.

The Human Ganglioside Interactome in Live Cells Revealed Using Clickable Photoaffinity Ganglioside Probes.

Gangliosides, sialic acid bearing glycosphingolipids, are components of the outer leaflet of plasma membranes of all vertebrate cells. They contribute to cell regulation by interacting with proteins in their own membranes (cis) or their extracellular milieu (trans). As amphipathic membrane constituents, gangliosides present challenges for identifying their ganglioside protein interactome. To meet these challenges, we synthesized bifunctional clickable photoaffinity gangliosides, delivered them to plasma membranes of cultured cells, then captured and identified their interactomes using proteomic mass spectrometry. Installing probes on ganglioside lipid and glycan moieties, we captured cis and trans ganglioside-protein interactions. Ganglioside interactomes varied with the ganglioside structure, cell type, and site of the probe (lipid or glycan). Gene ontology revealed that gangliosides engage with transmembrane transporters and cell adhesion proteins including integrins, cadherins, and laminins. The approach developed is applicable to other gangliosides and cell types, promising to provide insights into molecular and cellular regulation by gangliosides.

Shotgun scanning glycomutagenesis: A simple and efficient strategy for constructing and characterizing neoglycoproteins.

As a common protein modification, asparagine-linked (N-linked) glycosylation has the capacity to greatly influence the biological and biophysical properties of proteins. However, the routine use of glycosylation as a strategy for engineering proteins with advantageous properties is limited by our inability to construct and screen large collections of glycoproteins for cataloguing the consequences of glycan installation. To address this challenge, we describe a combinatorial strategy termed shotgun scanning glycomutagenesis in which DNA libraries encoding all possible glycosylation site variants of a given protein are constructed and subsequently expressed in glycosylation-competent bacteria, thereby enabling rapid determination of glycosylatable sites in the protein. The resulting neoglycoproteins can be readily subjected to available high-throughput assays, making it possible to systematically investigate the structural and functional consequences of glycan conjugation along a protein backbone. The utility of this approach was demonstrated with three different acceptor proteins, namely bacterial immunity protein Im7, bovine pancreatic ribonuclease A, and human anti-HER2 single-chain Fv antibody, all of which were found to tolerate N-glycan attachment at a large number of positions and with relatively high efficiency. The stability and activity of many glycovariants was measurably altered by N-linked glycans in a manner that critically depended on the precise location of the modification. Structural models suggested that affinity was improved by creating novel interfacial contacts with a glycan at the periphery of a protein-protein interface. Importantly, we anticipate that our glycomutagenesis workflow should provide access to unexplored regions of glycoprotein structural space and to custom-made neoglycoproteins with desirable properties.

Career Advancement Insights from Certified Pharmacy Technicians: Enhanced Opportunities for the Pharmacy Technician Workforce.

BACKGROUND: Sustainable career advancement opportunities for pharmacy technicians will be a critical part of patient-centered community pharmacy environments as the role of the pharmacist provider expands. OBJECTIVES: (1) To determine the impact of a Pharmacy Technician Certification Board pharmacy (PTCB) certification on career advancement and professional growth metrics; (2) To assess technicians' role in advanced pharmacy services before and after certification; and (3) To identify changes in pharmacist services when a certified pharmacy technician (CPhT) was added to the provider team. METHODS: A 73-question web-based survey was distributed to all PTCB certified pharmacy technicians (CPhT) in the United States, Washing D.C., Puerto Rico, Guam, and the US Virgin Islands. The survey was distributed by PTCB in April 2021 with a 28-day collection period. The survey included multiple choice, rating scale, and free text questions centered on five domains: Practice experience, Career aspirations, Compensation, Pharmacy practice motivations, and Impact of COVID-19 pandemic. RESULTS: 23,007 CPhTs completed the survey. Respondents were primarily female (85.5%), age 30-39 (32.8%), and ≥ 10 years CPhT experience (42.8%). The majority of respondents cited improvement of patient health (77.4%), career advancement opportunities (53.5%), the ability to expand their role during emergencies (e.g., COVID-19) (52.6%), and future career advancement opportunities (51.7%) as benefits of CPhT certification. Increases in job responsibility after certification included changes occurring in roles related to clinical pharmacy services, patient education, preventive health services, provider communication, and staff training. Respondents agreed that PTCB-certification allowed for the expansion of pharmacists' services where they practiced, including clinical services (18.5%), patient education (18.3%), and preventive health services (18.1%). CONCLUSION: CPhT's value certification for its benefits on career advancement, personal growth, and salary enhancement. Affirmation of skill and training through certification is also recognized to positively influence patient care and the pharmacy's ability to provide advanced patient care and services.

De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD).

The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

Macromolecular modeling and design in Rosetta: recent methods and frameworks.

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Induced fit with replica exchange improves protein complex structure prediction.

Despite the progress in prediction of protein complexes over the last decade, recent blind protein complex structure prediction challenges revealed limited success rates (less than 20% models with DockQ score > 0.4) on targets that exhibit significant conformational change upon binding. To overcome limitations in capturing backbone motions, we developed a new, aggressive sampling method that incorporates temperature replica exchange Monte Carlo (T-REMC) and conformational sampling techniques within docking protocols in Rosetta. Our method, ReplicaDock 2.0, mimics induced-fit mechanism of protein binding to sample backbone motions across putative interface residues on-the-fly, thereby recapitulating binding-partner induced conformational changes. Furthermore, ReplicaDock 2.0 clocks in at 150-500 CPU hours per target (protein-size dependent); a runtime that is significantly faster than Molecular Dynamics based approaches. For a benchmark set of 88 proteins with moderate to high flexibility (unbound-to-bound iRMSD over 1.2 Å), ReplicaDock 2.0 successfully docks 61% of moderately flexible complexes and 35% of highly flexible complexes. Additionally, we demonstrate that by biasing backbone sampling particularly towards residues comprising flexible loops or hinge domains, highly flexible targets can be predicted to under 2 Å accuracy. This indicates that additional gains are possible when mobile protein segments are known.

The structure of the colorectal cancer-associated enzyme GalNAc-T12 reveals how nonconserved residues dictate its function.

Polypeptide N-acetylgalactosaminyl transferases (GalNAc-Ts) initiate mucin type O-glycosylation by catalyzing the transfer of N-acetylgalactosamine (GalNAc) to Ser or Thr on a protein substrate. Inactive and partially active variants of the isoenzyme GalNAc-T12 are present in subsets of patients with colorectal cancer, and several of these variants alter nonconserved residues with unknown functions. While previous biochemical studies have demonstrated that GalNAc-T12 selects for peptide and glycopeptide substrates through unique interactions with its catalytic and lectin domains, the molecular basis for this distinct substrate selectivity remains elusive. Here we examine the molecular basis of the activity and substrate selectivity of GalNAc-T12. The X-ray crystal structure of GalNAc-T12 in complex with a di-glycosylated peptide substrate reveals how a nonconserved GalNAc binding pocket in the GalNAc-T12 catalytic domain dictates its unique substrate selectivity. In addition, the structure provides insight into how colorectal cancer mutations disrupt the activity of GalNAc-T12 and illustrates how the rules dictating GalNAc-T12 function are distinct from those for other GalNAc-Ts.

Caulobacter crescentus Hfq structure reveals a conserved mechanism of RNA annealing regulation.

We have solved the X-ray crystal structure of the RNA chaperone protein Hfq from the alpha-proteobacterium Caulobacter crescentus to 2.15-Å resolution, resolving the conserved core of the protein and the entire C-terminal domain (CTD). The structure reveals that the CTD of neighboring hexamers pack in crystal contacts, and that the acidic residues at the C-terminal tip of the protein interact with positive residues on the rim of Hfq, as has been recently proposed for a mechanism of modulating RNA binding. De novo computational models predict a similar docking of the acidic tip residues against the core of Hfq. We also show that C. crescentus Hfq has sRNA binding and RNA annealing activities and is capable of facilitating the annealing of certain Escherichia coli sRNA:mRNA pairs in vivo. Finally, we describe how the Hfq CTD and its acidic tip residues provide a mechanism to modulate annealing activity and substrate specificity in various bacteria.

Protein docking and steered molecular dynamics suggest alternative phospholamban-binding sites on the SERCA calcium transporter.

The transport activity of the sarco(endo)plasmic reticulum calcium ATPase (SERCA) in cardiac myocytes is modulated by an inhibitory interaction with a transmembrane peptide, phospholamban (PLB). Previous biochemical studies have revealed that PLB interacts with a specific inhibitory site on SERCA, and low-resolution structural evidence suggests that PLB interacts with distinct alternative sites on SERCA. High-resolution details of the structural determinants of SERCA regulation have been elusive because of the dynamic nature of the regulatory complex. In this study, we used computational approaches to develop a structural model of SERCA-PLB interactions to gain a mechanistic understanding of PLB-mediated SERCA transport regulation. We combined steered molecular dynamics and membrane protein-protein docking experiments to achieve both a global search and all-atom force calculations to determine the relative affinities of PLB for candidate sites on SERCA. We modeled the binding of PLB to several SERCA conformations, representing different enzymatic states sampled during the calcium transport catalytic cycle. The results of the steered molecular dynamics and docking experiments indicated that the canonical PLB-binding site (comprising transmembrane helices M2, M4, and M9) is the preferred site. This preference was even more stringent for a superinhibitory PLB variant. Interestingly, PLB-binding specificity became more ambivalent for other SERCA conformers. These results provide evidence for polymorphic PLB interactions with novel sites on M3 and with the outside of the SERCA helix M9. Our findings are compatible with previous physical measurements that suggest that PLB interacts with multiple binding sites, conferring dynamic responsiveness to changing physiological conditions.

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment.

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.

Geometric potentials from deep learning improve prediction of CDR H3 loop structures.

MOTIVATION: Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody and predict new CDR H3 loop structures de novo. RESULTS: When evaluated on the Rosetta antibody benchmark dataset of 49 targets, DeepH3-predicted potentials identified better, same and worse structures [measured by root-mean-squared distance (RMSD) from the experimental CDR H3 loop structure] than the standard Rosetta energy function for 33, 6 and 10 targets, respectively, and improved the average RMSD of predictions by 32.1% (1.4 Å). Analysis of individual geometric potentials revealed that inter-residue orientations were more effective than inter-residue distances for discriminating near-native CDR H3 loops. When applied to de novo prediction of CDR H3 loop structures, DeepH3 achieves an average RMSD of 2.2 ± 1.1 Å on the Rosetta antibody benchmark. AVAILABILITY AND IMPLEMENTATION: DeepH3 source code and pre-trained model parameters are freely available at https://github.com/Graylab/deepH3-distances-orientations. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Better together: Elements of successful scientific software development in a distributed collaborative community.

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.

Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane.

Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. Although soluble protein design has advanced, membrane protein design remains challenging because of difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational benchmarks against experimental targets, including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure discrimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Furthermore, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI rounds 37-45.

Critical Assessment of PRediction of Interactions (CAPRI) rounds 37 through 45 introduced larger complexes, new macromolecules, and multistage assemblies. For these rounds, we used and expanded docking methods in Rosetta to model 23 target complexes. We successfully predicted 14 target complexes and recognized and refined near-native models generated by other groups for two further targets. Notably, for targets T110 and T136, we achieved the closest prediction of any CAPRI participant. We created several innovative approaches during these rounds. Since round 39 (target 122), we have used the new RosettaDock 4.0, which has a revamped coarse-grained energy function and the ability to perform conformer selection during docking with hundreds of pregenerated protein backbones. Ten of the complexes had some degree of symmetry in their interactions, so we tested Rosetta SymDock, realized its shortcomings, and developed the next-generation symmetric docking protocol, SymDock2, which includes docking of multiple backbones and induced-fit refinement. Since the last CAPRI assessment, we also developed methods for modeling and designing carbohydrates in Rosetta, and we used them to successfully model oligosaccharide-protein complexes in round 41. Although the results were broadly encouraging, they also highlighted the pressing need to invest in (a) flexible docking algorithms with the ability to model loop and linker motions and in (b) new sampling and scoring methods for oligosaccharide-protein interactions.

Plasma Cells Are the Most Abundant Gluten Peptide MHC-expressing Cells in Inflamed Intestinal Tissues From Patients With Celiac Disease.

BACKGROUND & AIMS: Development of celiac disease is believed to involve the transglutaminase-dependent response of CD4+ T cells toward deamidated gluten peptides in the intestinal mucosa of individuals with specific HLA-DQ haplotypes. We investigated the antigen presentation process during this mucosal immune response. METHODS: We generated monoclonal antibodies (mAbs) specific for the peptide-MHC (pMHC) complex of HLA-DQ2.5 and the immunodominant gluten epitope DQ2.5-glia-α1a using phage display. We used these mAbs to assess gluten peptide presentation and phenotypes of presenting cells by flow cytometry and enzyme-linked immune absorbent spot (ELISPOT) in freshly prepared single-cell suspensions from intestinal biopsies from 40 patients with celiac disease (35 untreated and 5 on a gluten-free diet) as well as 18 subjects with confirmed noninflamed gut mucosa (controls, 12 presumed healthy, 5 undergoing pancreatoduodenectomy, and 1 with potential celiac disease). RESULTS: Using the mAbs, we detected MHC complexes on cells from intestinal biopsies from patients with celiac disease who consume gluten, but not from patients on gluten-free diets. We found B cells and plasma cells to be the most abundant cells that present DQ2.5-glia-α1a in the inflamed mucosa. We identified a subset of plasma cells that expresses B-cell receptors (BCR) specific for gluten peptides or the autoantigen transglutaminase 2 (TG2). Expression of MHC class II (MHCII) was not restricted to these specific plasma cells in patients with celiac disease but was observed in an average 30% of gut plasma cells from patients and controls. CONCLUSIONS: A population of plasma cells from intestinal biopsies of patients with celiac disease express MHCII; this is the most abundant cell type presenting the immunodominant gluten peptide DQ2.5-glia-α1a in the tissues from these patients. These results indicate that plasma cells in the gut can function as antigen-presenting cells and might promote and maintain intestinal inflammation in patients with celiac disease or other inflammatory disorders.

A humanized yeast system to analyze cleavage of prelamin A by ZMPSTE24.

The nuclear lamins A, B, and C are intermediate filament proteins that form a nuclear scaffold adjacent to the inner nuclear membrane in higher eukaryotes, providing structural support for the nucleus. In the past two decades it has become evident that the final step in the biogenesis of the mature lamin A from its precursor prelamin A by the zinc metalloprotease ZMPSTE24 plays a critical role in human health. Defects in prelamin A processing by ZMPSTE24 result in premature aging disorders including Hutchinson Gilford Progeria Syndrome (HGPS) and related progeroid diseases. Additional evidence suggests that defects in prelamin A processing, due to diminished ZMPSTE24 expression or activity, may also drive normal physiological aging. Because of the important connection between prelamin A processing and human aging, there is increasing interest in how ZMPSTE24 specifically recognizes and cleaves its substrate prelamin A, encoded by LMNA. Here, we describe two humanized yeast systems we have recently developed to examine ZMPSTE24 processing of prelamin A. These systems differ from one another slightly. Version 1.0 is optimized to analyze ZMPSTE24 mutations, including disease alleles that may affect the function or stability of the protease. Using this system, we previously showed that some ZMPSTE24 disease alleles that affect stability can be rescued by the proteasome inhibitor bortezomib, which may have therapeutic implications. Version 2.0 is designed to analyze LMNA mutations at or near the ZMPSTE24 processing site to assess whether they permit or impede prelamin A processing. Together these systems offer powerful methodology to study ZMPSTE24 disease alleles and to dissect the specific residues and features of the lamin A tail that are required for recognition and cleavage by the ZMPSTE24 protease.