Mental health effects associated with COVID-19 financial assistance in South Korea: a comparison of employment status.

BACKGROUND: COVID-19 has created tensions across different sectors of the society, but the impact has been unequal. Vulnerable people have been most affected, especially those with insecure employment and who have experienced economic hardships due to unemployment and lost wages. The combination of social change and economic hardships due to the pandemic increases the risk of poor mental health. Some countries have utilized financial assistance to alleviate economic hardships caused by COVID-19, and in South Korea, the central and local governments have implemented COVID-19 financial assistance. This study analysed the impact of financial assistance on mental health associated with working status during the COVID-19 pandemic in South Korea. METHODS: The participants of this study were randomly selected from residents of Gyeonggi-do after being proportionally allocated by resident registration population status. A total of 1,000 adult males and females aged 19 years or older in Gyeonggi-do who received financial assistance from the central and local governments were selected. A retrospective pre-post-study design was applied, and mental health surveys including the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7-item scale (GAD-7) were applied. RESULTS: The results show that depression scores averaged 5.5 and anxiety scores averaged 4.4 before COVID-19 Financial Assistance. It is similar to the national average of 5.1 and 4.5 respectively at that time. After the assistance, depression scores dropped to 4.5, and anxiety scores dropped to 3.2. Before the assistance, depression and anxiety were higher among temporary day labourers with less job security, and they showed the most significant improvement in mental health. For full-time workers, there was no significant change in anxiety or depression after receiving the assistance. CONCLUSIONS: Financial assistance can provide material resources and also positively affect mental health. In particular, it had a greater impact on the relatively vulnerable groups, such as those in unstable employment.

A conformation-selective protein binder for a KRAS mutant inhibits the interaction between RAS and RAF.

Small GTPases are key signaling nodes that regulate the cellular processes and subcellular events, and their abnormal activities and dysregulations are closely linked with diverse cancers. Here, we report the development of conformation-selective protein binders for a KRAS mutant. The conformation-specific protein binders were selected from a repebody scaffold composed of LRR (Leucine-rich repeat) modules through phage display and modular engineering against constitute active conformation of KRAS. Epitope of the selected binders was mapped to be located close to switch I of KRAS. The conformation-selective protein binders were shown to effectively block the interaction between active KRAS and RAS-binding domain of BRAF, suppressing the KRAS-mediated downstream signaling.

Unemployment risk of all employed working-age cancer survivors after cancer diagnosis in South Korea: a retrospective cohort analysis of population-based administrative data.

PURPOSE: As the cancer survival rate increases, discussions on the employment status of cancer survivors should be actively carried out worldwide. This study examined patients' working status changes after cancer diagnosis to provide the basis for cancer survivors' return to work. METHODS: We established a nationwide cohort to determine long-term work changes after cancer diagnosis. All patients aged 19 to 50 years who were newly diagnosed with cancer while working for the previous 3 years were considered as the case group in 2010. Using propensity score matching (PSM), the cases were matched by sex and age at a ratio of 1:3 with the control group. Kaplan-Meier curve and Cox hazard model analyses were performed to determine the occurrence of unemployment and return to work in the case and control groups on a yearly basis. RESULTS: According to the 6-year follow-up after cancer diagnosis, 26.5% of cancer patients and 23.2% of controls had lost their jobs by the end of the follow-up (P < .0001). Meanwhile, 50.5% of cancer patients and 57.4% of controls had returned to work after unemployment (P < .0001). Subsequently, based on the Cox hazard model, the unemployment risk of cancer patients was 1.42 times higher than that of the general population, while the proportion of those who returned to work was 1.15 times lower. CONCLUSION: Employment is very closely related to the quality of life of cancer survivors. These results highlight the need for a system that can support cancer survivors' work maintenance and return to work after unemployment during the treatment period and the fact that awareness of this must be improved.

Computer-guided binding mode identification and affinity improvement of an LRR protein binder without structure determination.

Precise binding mode identification and subsequent affinity improvement without structure determination remain a challenge in the development of therapeutic proteins. However, relevant experimental techniques are generally quite costly, and purely computational methods have been unreliable. Here, we show that integrated computational and experimental epitope localization followed by full-atom energy minimization can yield an accurate complex model structure which ultimately enables effective affinity improvement and redesign of binding specificity. As proof-of-concept, we used a leucine-rich repeat (LRR) protein binder, called a repebody (Rb), that specifically recognizes human IgG1 (hIgG1). We performed computationally-guided identification of the Rb:hIgG1 binding mode and leveraged the resulting model to reengineer the Rb so as to significantly increase its binding affinity for hIgG1 as well as redesign its specificity toward multiple IgGs from other species. Experimental structure determination verified that our Rb:hIgG1 model closely matched the co-crystal structure. Using a benchmark of other LRR protein complexes, we further demonstrated that the present approach may be broadly applicable to proteins undergoing relatively small conformational changes upon target binding.

Epitopes of Protein Binders Are Related to the Structural Flexibility of a Target Protein Surface.

Protein binders including antibodies are known not to bind to random sites of target proteins, and their functional effectiveness mainly depends on the binding region, called the epitope. For the development of protein binders with desired functions, it is thus critical to understand which surface region protein binders prefer (or do not prefer) to bind. The current methods for epitope prediction focus on static indicators such as structural geometry or amino acid propensity, whereas protein binding events are in fact a consequence of dynamic interactions. Here, we demonstrate that the preference for a binding site by protein binders is strongly related to the structural flexibility of a target protein surface. Molecular dynamics simulations on unbound forms of antigen structures revealed that the antigen surface in direct contact with antibodies is less flexible than the rest of the surface. This tendency was shown to be similar in other non-antibody protein binders such as affibody, DARPin, monobody, and repebody. We also found that the relatedness of epitopes to the structural flexibility of a target protein surface is dependent on the secondary structure elements of paratopes. Monobody and repebody, whose binding sites are composed of ß-strands, distinctively prefer to bind to a relatively more rigid region of a target protein. These observations enabled us to develop a simple epitope prediction method which shows a comparable performance to the commonly used ones.

A regulatory SH2 domain-targeting protein binder effectively inhibits the activity of Bruton's tyrosine kinase and its drug-resistant variants.

Human Bruton's tyrosine kinase (hBtk) plays a key role in growth and metabolism of B cells, but its dysfunctions cause various B-cell malignancies. Inhibitors targeting the ATP-binding pocket of hBtk have been developed, but they have several drawbacks such as adverse side effects and occurrence of drug-resistant mutations. Here, we present a protein binder which specifically binds to an allosteric regulatory SH2 domain of hBtk. The protein binder effectively inhibited the hBtk activity, indicating a critical role of the SH2 domain in allosteric regulation of the hBtk activity. Cytosolic delivery of the protein binder led to a significant inhibition on the BCR-mediated signaling and viability of B lymphoma cells. The utility of our approach was demonstrated by effective inhibition of drug-resistant hBtk variants by the protein binder. Based on the computationally predicted binding mode, the protein binder is likely to inhibit the hBtk activity by disrupting the interaction between the SH2 domain and kinase domain. The present approach can be used for developing therapeutic agents with improved efficacy for B-cell lymphoma.

Effective inhibition of C3a-mediated pro-inflammatory response by a human C3a-specific protein binder.

Complement component 3a (C3a) plays a crucial role in the immune response and host defense, but it is also involved in pro-inflammatory responses, causing many inflammatory disorders. Blockade of C3a has been regarded as a potent therapeutic strategy for inflammatory diseases. Here, we present the development of a human C3a (hC3a)-specific protein binder, which effectively inhibits pro-inflammatory responses. The protein binder, which is composed of leucine-rich repeat modules, was selected against hC3a through phage display, and its binding affinity was matured up to 600 pM by further expanding the binding interface in a module-by-module manner. The developed protein binder was shown to have more than 10-fold higher specificity to hC3a compared with human C5a, exhibiting a remarkable suppression effect on pro-inflammatory response in monocyte, by blocking the interaction between hC3a and its receptor. The hC3a-specific protein binder is likely to have a therapeutic potential for C3a-mediated inflammatory diseases.

GradDock: rapid simulation and tailored ranking functions for peptide-MHC Class I docking.

Motivation: The identification of T-cell epitopes has many profound translational applications in the areas of transplantation, disease diagnosis, vaccine/therapeutic protein development and personalized immunotherapy. While data-driven methods have been widely used for the prediction of peptide binders with notable successes, the structural modeling of peptide binding to MHC molecules is crucial for understanding the underlying molecular mechanism of the immunological processes. Results: We developed GradDock, a structure-based method for the rapid and accurate modeling of peptide binding to MHC Class I (pMHC-I). GradDock explicitly models diverse unbound peptides in vacuo and inserts them into the MHC-I groove through a steered gradient descent with a topological correction process. The simulation process yields diverse structural conformations including native-like peptides. We completely revised the Rosetta score terms and developed a new ranking function specifically for pMHC-I. Using the diverse peptides, a linear programming approach is applied to find the optimal weights for the individual Rosetta score terms. Our examination revealed that a refinement of the dihedral angles and a modification of the repulsion can dramatically improve the modeling quality. GradDock is five-times faster than a Rosetta-based docking approach for pMHC-I. We also demonstrate that the predictive capability of GradDock with the re-weighted Rosetta ranking function is consistently more accurate than the Rosetta-based method with the standard Rosetta score (approximately three-times better for a cross-docking set). Availability and implementation: GradDock is freely available for academic purposes. The program and the ranking score weights for Rosetta are available at http://bel.kaist.ac.kr/research/GradDock. Contact: hskim76@kaist.ac.kr. Supplementary information: Supplementary data are available at Bioinformatics online.

DisruPPI: structure-based computational redesign algorithm for protein binding disruption.

Motivation: Disruption of protein-protein interactions can mitigate antibody recognition of therapeutic proteins, yield monomeric forms of oligomeric proteins, and elucidate signaling mechanisms, among other applications. While designing affinity-enhancing mutations remains generally quite challenging, both statistically and physically based computational methods can precisely identify affinity-reducing mutations. In order to leverage this ability to design variants of a target protein with disrupted interactions, we developed the DisruPPI protein design method (DISRUpting Protein-Protein Interactions) to optimize combinations of mutations simultaneously for both disruption and stability, so that incorporated disruptive mutations do not inadvertently affect the target protein adversely. Results: Two existing methods for predicting mutational effects on binding, FoldX and INT5, were demonstrated to be quite precise in selecting disruptive mutations from the SKEMPI and AB-Bind databases of experimentally determined changes in binding free energy. DisruPPI was implemented to use an INT5-based disruption score integrated with an AMBER-based stability assessment and was applied to disrupt protein interactions in a set of different targets representing diverse applications. In retrospective evaluation with three different case studies, comparison of DisruPPI-designed variants to published experimental data showed that DisruPPI was able to identify more diverse interaction-disrupting and stability-preserving variants more efficiently and effectively than previous approaches. In prospective application to an interaction between enhanced green fluorescent protein (EGFP) and a nanobody, DisruPPI was used to design five EGFP variants, all of which were shown to have significantly reduced nanobody binding while maintaining function and thermostability. This demonstrates that DisruPPI may be readily utilized for effective removal of known epitopes of therapeutically relevant proteins. Availability and implementation: DisruPPI is implemented in the EpiSweep package, freely available under an academic use license. Supplementary information: Supplementary data are available at Bioinformatics online.

B-SIDER: Computational Algorithm for the Design of Complementary ß-Sheet Sequences.

The ß-sheet is an element of protein secondary structure, and intra-/intermolecular ß-sheet interactions play pivotal roles in biological regulatory processes including scaffolding, transporting, and oligomerization. In nature, a ß-sheet formation is tightly regulated because dysregulated ß-stacking often leads to severe diseases such as Alzheimer's, Parkinson's, systemic amyloidosis, or diabetes. Thus, the identification of intrinsic ß-sheet-forming propensities can provide valuable insight into protein designs for the development of novel therapeutics. However, structure-based design methods may not be generally applicable to such amyloidogenic peptides mainly owing to high structural plasticity and complexity. Therefore, an alternative design strategy based on complementary sequence information is of significant importance. Herein, we developed a database search method called ß-Stacking Interaction DEsign for Reciprocity (B-SIDER) for the design of complementary ß-strands. This method makes use of the structural database information and generates target-specific score matrices. The discriminatory power of the B-SIDER score function was tested on representative amyloidogenic peptide substructures against a sequence-based score matrix (PASTA 2.0) and two popular ab initio protein design score functions (Rosetta and FoldX). B-SIDER is able to distinguish wild-type amyloidogenic ß-strands as favored interactions in a more consistent manner than other methods. B-SIDER was prospectively applied to the design of complementary ß-strands for a splitGFP scaffold. Three variants were identified to have stronger interactions than the original sequence selected through a directed evolution, emitting higher fluorescence intensities. Our results indicate that B-SIDER can be applicable to the design of other ß-strands, assisting in the development of therapeutics against disease-related amyloidogenic peptides.

Repeat Module-Based Rational Design of a Photoswitchable Protein for Light-Driven Control of Biological Processes.

Light-driven control of biological processes using photoswitchable proteins allows high spatiotemporal interrogation or manipulation of such processes, assisting in understanding their functions. Despite considerable advances, however, the wide spread use of optical control has been hampered by a limited repertoire of photoswitchable proteins and a lack of generalized design strategy. Herein, we present a repeat module-based rational design of a photoswitchable protein composed of LRR (Leucine-rich repeat) modules using azobenzene as a photochromic ligand. Our design approach involves the rational selection of a Cß pair between two nearby modules within a convex region and subsequent cross-linking with a photochromic ligand. We demonstrate the general utility and potential of our strategy by showing the design of three target-specific photoswitchable proteins and a light-driven modulation of the cell signaling. With an abundance of LRR proteins in nature, our approach can expand the repertoire of photoswitchable proteins for light-driven control of biological processes.

Mapping the Pareto optimal design space for a functionally deimmunized biotherapeutic candidate.

The immunogenicity of biotherapeutics can bottleneck development pipelines and poses a barrier to widespread clinical application. As a result, there is a growing need for improved deimmunization technologies. We have recently described algorithms that simultaneously optimize proteins for both reduced T cell epitope content and high-level function. In silico analysis of this dual objective design space reveals that there is no single global optimum with respect to protein deimmunization. Instead, mutagenic epitope deletion yields a spectrum of designs that exhibit tradeoffs between immunogenic potential and molecular function. The leading edge of this design space is the Pareto frontier, i.e. the undominated variants for which no other single design exhibits better performance in both criteria. Here, the Pareto frontier of a therapeutic enzyme has been designed, constructed, and evaluated experimentally. Various measures of protein performance were found to map a functional sequence space that correlated well with computational predictions. These results represent the first systematic and rigorous assessment of the functional penalty that must be paid for pursuing progressively more deimmunized biotherapeutic candidates. Given this capacity to rapidly assess and design for tradeoffs between protein immunogenicity and functionality, these algorithms may prove useful in augmenting, accelerating, and de-risking experimental deimmunization efforts.

Protein deimmunization via structure-based design enables efficient epitope deletion at high mutational loads.

Anti-drug immune responses are a unique risk factor for biotherapeutics, and undesired immunogenicity can alter pharmacokinetics, compromise drug efficacy, and in some cases even threaten patient safety. To fully capitalize on the promise of biotherapeutics, more efficient and generally applicable protein deimmunization tools are needed. Mutagenic deletion of a protein's T cell epitopes is one powerful strategy to engineer immunotolerance, but deimmunizing mutations must maintain protein structure and function. Here, EpiSweep, a structure-based protein design and deimmunization algorithm, has been used to produce a panel of seven beta-lactamase drug candidates having 27-47% reductions in predicted epitope content. Despite bearing eight mutations each, all seven engineered enzymes maintained good stability and activity. At the same time, the variants exhibited dramatically reduced interaction with human class II major histocompatibility complex proteins, key regulators of anti-drug immune responses. When compared to 8-mutation designs generated with a sequence-based deimmunization algorithm, the structure-based designs retained greater thermostability and possessed fewer high affinity epitopes, the dominant drivers of anti-biotherapeutic immune responses. These experimental results validate the first structure-based deimmunization algorithm capable of mapping optimal biotherapeutic design space. By designing optimal mutations that reduce immunogenic potential while imparting favorable intramolecular interactions, broadly distributed epitopes may be simultaneously targeted using high mutational loads.

Computationally driven deletion of broadly distributed T cell epitopes in a biotherapeutic candidate.

Biotherapeutics are subject to immune surveillance within the body, and anti-biotherapeutic immune responses can compromise drug efficacy and patient safety. Initial development of targeted antidrug immune memory is coordinated by T cell recognition of immunogenic subsequences, termed "T cell epitopes." Biotherapeutics may therefore be deimmunized by mutating key residues within cognate epitopes, but there exist complex trade-offs between immunogenicity, mutational load, and protein structure-function. Here, a protein deimmunization algorithm has been applied to P99 beta-lactamase, a component of antibody-directed enzyme prodrug therapies. The algorithm, integer programming for immunogenic proteins, seamlessly integrates computational prediction of T cell epitopes with both 1- and 2-body sequence potentials that assess protein tolerance to epitope-deleting mutations. Compared to previously deimmunized P99 variants, which bore only one or two mutations, the enzymes designed here contain 4-5 widely distributed substitutions. As a result, they exhibit broad reductions in major histocompatibility complex recognition. Despite their high mutational loads and markedly reduced immunoreactivity, all eight engineered variants possessed wild-type or better catalytic activity. Thus, the protein design algorithm is able to disrupt broadly distributed epitopes while maintaining protein function. As a result, this computational tool may prove useful in expanding the repertoire of next-generation biotherapeutics.

Fragment-based modeling of membrane protein loops: successes, failures, and prospects for the future.

Membrane proteins (MPs) have become a major focus in structure prediction, due to their medical importance. There is, however, a lack of fast and reliable methods that specialize in the modeling of MP loops. Often methods designed for soluble proteins (SPs) are applied directly to MPs. In this article, we investigate the validity of such an approach in the realm of fragment-based methods. We also examined the differences in membrane and soluble protein loops that might affect accuracy. We test our ability to predict soluble and MP loops with the previously published method FREAD. We show that it is possible to predict accurately the structure of MP loops using a database of MP fragments (0.5-1 Å median root-mean-square deviation). The presence of homologous proteins in the database helps prediction accuracy. However, even when homologues are removed better results are still achieved using fragments of MPs (0.8-1.6 Å) rather than SPs (1-4 Å) to model MP loops. We find that many fragments of SPs have shapes similar to their MP counterparts but have very different sequences; however, they do not appear to differ in their substitution patterns. Our findings may allow further improvements to fragment-based loop modeling algorithms for MPs. The current version of our proof-of-concept loop modeling protocol produces high-accuracy loop models for MPs and is available as a web server at http://medeller.info/fread.

Gene and protein sequence optimization for high-level production of fully active and aglycosylated lysostaphin in Pichia pastoris.

Lysostaphin represents a promising therapeutic agent for the treatment of staphylococcal infections, in particular those of methicillin-resistant Staphylococcus aureus (MRSA). However, conventional expression systems for the enzyme suffer from various limitations, and there remains a need for an efficient and cost-effective production process to facilitate clinical translation and the development of nonmedical applications. While Pichia pastoris is widely used for high-level production of recombinant proteins, there are two major barriers to the production of lysostaphin in this industrially relevant host: lack of expression from the wild-type lysostaphin gene and aberrant glycosylation of the wild-type protein sequence. The first barrier can be overcome with a synthetic gene incorporating improved codon usage and balanced A+T/G+C content, and the second barrier can be overcome by disrupting an N-linked glycosylation sequon using a broadened choice of mutations that yield aglyscosylated and fully active lysostaphin. The optimized lysostaphin variants could be produced at approximately 500 mg/liter in a small-scale bioreactor, and 50% of that material could be recovered at high purity with a simple 2-step purification. It is anticipated that this novel high-level expression system will bring down one of the major barriers to future development of biomedical, veterinary, and research applications of lysostaphin and its engineered variants.

Comparative Whole-Genome Sequencing Analysis of In-situ and Invasive Acral Lentiginous Melanoma: Markedly Increased Copy Number Gains of GAB2, PAK1, UCP2, and CCND1 are Associated with Melanoma Invasion.

Acral lentiginous melanoma (ALM) is the most common subtype of acral melanoma. Even though recent genetic studies are reported in acral melanomas, the genetic differences between in-situ and invasive ALM remain unclear. We aimed to analyze specific genetic changes in ALM and compare genetic differences between in-situ and invasive lesions to identify genetic changes associated with the pathogenesis and progression of ALM. We performed whole genome sequencing of 71 tissue samples from 29 patients with ALM. Comparative analyses were performed, pairing in-situ ALMs with normal tissues and, furthermore, invasive ALMs with normal and in-situ tissues. Among 21 patients with in-situ ALMs, 3 patients (14.3%) had SMIM14, SLC9B1, FRG1, FAM205A, ESRRA, and ESPN mutations, and copy number (CN) gains were identified in only 2 patients (9.5%). Comparing 13 invasive ALMs with in-situ tissues, CN gains were identified in GAB2 in 8 patients (61.5%), PAK1 in 6 patients (46.2%), and UCP2 and CCND1 in 5 patients (38.5%). Structural variants were frequent in in-situ and invasive ALM lesions. Both in-situ and invasive ALMs had very low frequencies of common driver mutations. Structural variants were common in both in-situ and invasive ALMs. Invasive ALMs had markedly increased CN gains, such as GAB2, PAK1, UCP2, and CCND1, compared with in-situ lesions. These results suggest that they are associated with melanoma invasion.

Positive Effects of Adiponectin, BDNF, and GLP-1 on Cortical Neurons Counteracting Palmitic Acid Induced Neurotoxicity.

The prevalence of metabolic syndrome caused by diets containing excessive fatty acids is increasing worldwide. Patients with metabolic syndrome exhibit abnormal lipid profiles, chronic inflammation, increased levels of saturated fatty acids, impaired insulin sensitivity, excessive fat accumulation, and neuropathological issues such as memory deficits. In particular, palmitic acid (PA) in saturated fatty acids aggravates inflammation, insulin resistance, impaired glucose tolerance, and synaptic failure. Recently, adiponectin, brain-derived neurotrophic factor (BDNF), and glucose-like peptide-1 (GLP-1) have been investigated to find therapeutic solutions for metabolic syndrome, with findings suggesting that they are involved in insulin sensitivity, enhanced lipid profiles, increased neuronal survival, and improved synaptic plasticity. We investigated the effects of adiponectin, BDNF, and GLP-1 on neurite outgrowth, length, and complexity in PA-treated primary cortical neurons using Sholl analysis. Our findings demonstrate the therapeutic potential of adiponectin, BDNF, and GLP-1 in enhancing synaptic plasticity within brains affected by metabolic imbalance. We underscore the need for additional research into the mechanisms by which adiponectin, BDNF, and GLP-1 influence neural complexity in brains with metabolic imbalances.

The identification of effective tumor-suppressing neoantigens using a tumor-reactive TIL TCR-pMHC ternary complex.

Neoantigens are ideal targets for cancer immunotherapy because they are expressed de novo in tumor tissue but not in healthy tissue and are therefore recognized as foreign by the immune system. Advances in next-generation sequencing and bioinformatics technologies have enabled the quick identification and prediction of tumor-specific neoantigens; however, only a small fraction of predicted neoantigens are immunogenic. To improve the predictability of immunogenic neoantigens, we developed the in silico neoantigen prediction workflows VACINUSpMHC and VACINUSTCR: VACINUSpMHC incorporates physical binding between peptides and MHCs (pMHCs), and VACINUSTCR integrates T cell reactivity to the pMHC complex through deep learning-based pairing with T cell receptors (TCRs) of putative tumor-reactive CD8 tumor-infiltrating lymphocytes (TILs). We then validated our neoantigen prediction workflows both in vitro and in vivo in patients with hepatocellular carcinoma (HCC) and in a B16F10 mouse melanoma model. The predictive abilities of VACINUSpMHC and VACINUSTCR were confirmed in a validation cohort of 8 patients with HCC. Of a total of 118 neoantigen candidates predicted by VACINUSpMHC, 48 peptides were ultimately selected using VACINUSTCR. In vitro validation revealed that among the 48 predicted neoantigen candidates, 13 peptides were immunogenic. Assessment of the antitumor efficacy of the candidate neoepitopes using a VACINUSTCR in vivo mouse model suggested that vaccination with the predicted neoepitopes induced neoantigen-specific T cell responses and enabled the trafficking of neoantigen-specific CD8 + T cell clones into the tumor tissue, leading to tumor suppression. This study showed that the prediction of immunogenic neoantigens can be improved by integrating a tumor-reactive TIL TCR-pMHC ternary complex.

Structure-based redesign of proteins for minimal T-cell epitope content.

The protein universe displays a wealth of therapeutically relevant activities, but T-cell driven immune responses to non-"self" biological agents present a major impediment to harnessing the full diversity of these molecular functions. Mutagenic T-cell epitope deletion seeks to mitigate the immune response, but can typically address only a small number of epitopes. Here, we pursue a "bottom-up" approach that redesigns an entire protein to remain native-like but contain few if any immunogenic epitopes. We do so by extending the Rosetta flexible-backbone protein design software with an epitope scoring mechanism and appropriate constraints. The method is benchmarked with a diverse panel of proteins and applied to three targets of therapeutic interest. We show that the deimmunized designs indeed have minimal predicted epitope content and are native-like in terms of various quality measures, and moreover that they display levels of native sequence recovery comparable to those of non-deimmunized designs.