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Age-related declines in cardiorespiratory fitness and physical function are mitigated by regular endurance exercise in older adults. This may be due, in part, to changes in the transcriptional program of skeletal muscle following repeated bouts of exercise. However, the impact of chronic exercise training on the transcriptional response to an acute bout of endurance exercise has not been clearly determined. Here, we characterized baseline differences in muscle transcriptome and exercise-induced response in older adults who were active/endurance trained or sedentary. RNA-sequencing was performed on vastus lateralis biopsy specimens obtained before, immediately after, and 3 h following a bout of endurance exercise (40 min of cycling at 60%–70% of heart rate reserve). Using a recently developed bioinformatics approach, we found that transcript signatures related to type I myofibers, mitochondria, and endothelial cells were higher in active/endurance-trained adults and were associated with key phenotypic features including V̇o(2peak), ATP(max), and muscle fiber proportion. Immune cell signatures were elevated in the sedentary group and linked to visceral and intermuscular adipose tissue mass. Following acute exercise, we observed distinct temporal transcriptional signatures that were largely similar among groups. Enrichment analysis revealed catabolic processes were uniquely enriched in the sedentary group at the 3-h postexercise timepoint. In summary, this study revealed key transcriptional signatures that distinguished active and sedentary adults, which were associated with difference in oxidative capacity and depot-specific adiposity. The acute response signatures were consistent with beneficial effects of endurance exercise to improve muscle health in older adults irrespective of exercise history and adiposity. NEW & NOTEWORTHY Muscle transcript signatures associated with oxidative capacity and immune cells underlie important phenotypic and clinical characteristics of older adults who are endurance trained or sedentary. Despite divergent phenotypes, the temporal transcriptional signatures in response to an acute bout of endurance exercise were largely similar among groups. These data provide new insight into the transcriptional programs of aging muscle and the beneficial effects of endurance exercise to promote healthy aging in older adults.

The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

The mRNA-1273 vaccine was recently determined to be effective against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from interim Phase 3 results. Human studies, however, cannot provide the controlled response to infection and complex immunological insight that are only possible with preclinical studies. Hamsters are the only model that reliably exhibit more severe SARS-CoV-2 disease similar to hospitalized patients, making them pertinent for vaccine evaluation. We demonstrate that prime or prime-boost administration of mRNA-1273 in hamsters elicited robust neutralizing antibodies, ameliorated weight loss, suppressed SARS-CoV-2 replication in the airways, and better protected against disease at the highest prime-boost dose. Unlike in mice and non-human primates, mRNA-1273- mediated immunity was non-sterilizing and coincided with an anamnestic response. Single-cell RNA sequencing of lung tissue permitted high resolution analysis which is not possible in vaccinated humans. mRNA-1273 prevented inflammatory cell infiltration and the reduction of lymphocyte proportions, but enabled antiviral responses conducive to lung homeostasis. Surprisingly, infection triggered transcriptome programs in some types of immune cells from vaccinated hamsters that were shared, albeit attenuated, with mock-vaccinated hamsters. Our results support the use of mRNA-1273 in a two-dose schedule and provides insight into the potential responses within the lungs of vaccinated humans who are exposed to SARS-CoV-2.

The mRNA-1273 vaccine is effective against SARS-CoV-2 and was granted emergency use authorization by the FDA. Clinical studies, however, cannot provide the controlled response to infection and complex immunological insight that are only possible with preclinical studies. Hamsters are the only model that reliably exhibits severe SARS-CoV-2 disease similar to that in hospitalized patients, making them pertinent for vaccine evaluation. We demonstrate that prime or prime-boost administration of mRNA-1273 in hamsters elicited robust neutralizing antibodies, ameliorated weight loss, suppressed SARS-CoV-2 replication in the airways, and better protected against disease at the highest prime-boost dose. Unlike in mice and nonhuman primates, low-level virus replication in mRNA-1273-vaccinated hamsters coincided with an anamnestic response. Single-cell RNA sequencing of lung tissue permitted high-resolution analysis that is not possible in vaccinated humans. mRNA-1273 prevented inflammatory cell infiltration and the reduction of lymphocyte proportions, but enabled antiviral responses conducive to lung homeostasis. Surprisingly, infection triggered transcriptome programs in some types of immune cells from vaccinated hamsters that were shared, albeit attenuated, with mock-vaccinated hamsters. Our results support the use of mRNA-1273 in a 2-dose schedule and provide insight into the potential responses within the lungs of vaccinated humans who are exposed to SARS-CoV-2.

Skeletal muscle is a heterogeneous tissue comprised of muscle fiber and mononuclear cell types that, in addition to movement, influences immunity, metabolism and cognition. We investigated the gene expression patterns of skeletal muscle cells using RNA-seq of subtype-pooled single human muscle fibers and single cell RNA-seq of mononuclear cells from human vastus lateralis, mouse quadriceps, and mouse diaphragm. We identified 11 human skeletal muscle mononuclear cell types, including two fibro-adipogenic progenitor (FAP) cell subtypes. The human FBN1+ FAP cell subtype is novel and a corresponding FBN1+ FAP cell type was also found in single cell RNA-seq analysis in mouse. Transcriptome exercise studies using bulk tissue analysis do not resolve changes in individual cell-type proportion or gene expression. The cell-type gene signatures provide the means to use computational methods to identify cell-type level changes in bulk studies. As an example, we analyzed public transcriptome data from an exercise training study and revealed significant changes in specific mononuclear cell-type proportions related to age, sex, acute exercise and training. Our single-cell expression map of skeletal muscle cell types will further the understanding of the diverse effects of exercise and the pathophysiology of muscle disease.

Characterizing the substrate specificity of protease enzymes is critical for illuminating the molecular basis of their diverse and complex roles in a wide array of biological processes. Rapid and accurate prediction of their extended substrate specificity would also aid in the design of custom proteases capable of selectively and controllably cleaving biotechnologically or therapeutically relevant targets. However, current in silico approaches for protease specificity prediction, rely on, and are therefore limited by, machine learning of sequence patterns in known experimental data. Here, we describe a general approach for predicting peptidase substrates de novo using protein structure modeling and biophysical evaluation of enzyme-substrate complexes. We construct atomic resolution models of thousands of candidate substrate-enzyme complexes for each of five model proteases belonging to the four major protease mechanistic classes-serine, cysteine, aspartyl, and metallo-proteases-and develop a discriminatory scoring function using enzyme design modules from Rosetta and AMBER's MMPBSA. We rank putative substrates based on calculated interaction energy with a modeled near-attack conformation of the enzyme active site. We show that the energetic patterns obtained from these simulations can be used to robustly rank and classify known cleaved and uncleaved peptides and that these structural-energetic patterns have greater discriminatory power compared to purely sequence-based statistical inference. Combining sequence and energetic patterns using machine-learning algorithms further improves classification performance, and analysis of structural models provides physical insight into the structural basis for the observed specificities. We further tested the predictive capability of the model by designing and experimentally characterizing the cleavage of four novel substrate motifs for the hepatitis C virus NS3/4 protease using an in vivo assay. The presented structure-based approach is generalizable to other protease enzymes with known or modeled structures, and complements existing experimental methods for specificity determination.

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 ., This Perspective reviews tools developed over the past five years in the macromolecular modeling, docking and design software Rosetta.

An accurate energy function is an essential component of biomolecular structural modeling and design. The comparison of differently derived energy functions enables analysis of the strengths and weaknesses of each energy function and provides independent benchmarks for evaluating improvements within a given energy function. We compared the molecular mechanics Amber empirical energy function to two versions of the Rosetta energy function (talaris2014 and REF2015) in decoy discrimination and loop modeling tests. In decoy discrimination tests, both Rosetta and Amber (ff14SBonlySC) energy functions performed well in scoring the native state as the lowest energy conformation in many cases, but several false minima were found in with both talaris2014 and Amber ff14SBonlySC scoring functions. The current default version of the Rosetta energy function, REF2015, which is parametrized on both small molecule and macromolecular benchmark sets to improve decoy discrimination, performs significantly better than talaris2014, highlighting the improvements made to the Rosetta scoring approach. There are no cases in Rosetta REF2015, and 8/140 cases in Amber, where a false minimum is found that is absent in the alternative landscape. In loop modeling tests, Amber ff14SBonlySC and REF2015 perform equivalently, although false minima are detected in several cases for both. The balance between dihedral, electrostatic, solvation and hydrogen bonding scores contribute to the existence of false minima. To take advantage of the semi-orthogonal nature of the Rosetta and Amber energy functions, we developed a technique that combines Amber and Rosetta conformational rankings to predict the most near-native model for a given protein. This algorithm improves upon predictions from either energy function in isolation and should aid in model selection for structure evaluation and loop modeling tasks.

An accurate energy function is an essential component of biomolecular structural modeling and design. The comparison of differently derived energy functions enables analysis of the strengths and weaknesses of each energy function, and provides independent benchmarks for evaluating improvements within a given energy function. We compared the molecular mechanics Amber empirical energy function to two versions of the Rosetta energy function (talaris2014 and REF2015) in decoy discrimination and loop modeling tests. Both Rosetta's talaris2014 and Amber's ff14SBonlySC energy functions performed well in scoring the native state as the lowest energy conformation in many cases. In 24/150 cases with Rosetta, and in 2/150 cases using Amber, a false minimum is found that is absent in the alternative landscape. In 21/150 cases, both energy function-generated landscapes featured false minima. The newest version of the Rosetta energy function, REF2015, which has more physically-derived terms than talaris2014, performs significantly better, highlighting the improvements made to the Rosetta scoring approach. To take advantage of the semi-orthogonal nature of these energy functions, we developed a Pareto optimization approach that combines Amber and Rosetta energy landscapes to predict the most near-native model for a given protein. This algorithm improves upon predictions from either energy function in isolation, and should aid in model selection for structure prediction and loop modeling tasks.

Biophysical interactions between proteins and peptides are key determinants of genotype-fitness landscapes, but an understanding of how molecular structure and residue-level energetics at protein-peptide interfaces shape functional landscapes remains elusive. Combining information from yeast-based library screening, next-generation sequencing and structure-based modeling, we report comprehensive sequence-energetics-function mapping of the specificity landscape of the Hepatitis C Virus (HCV) NS3/4A protease, whose function — site-specific cleavages of the viral polyprotein — is a key determinant of viral fitness. We elucidate the cleavability of 3.2 million substrate variants by the HCV protease and find extensive clustering of cleavable and uncleavable motifs in sequence space indicating mutational robustness, and thereby providing a plausible molecular mechanism to buffer the effects of low replicative fidelity of this RNA virus. Specificity landscapes of known drug-resistant variants are similarly clustered. Our results highlight the key and constraining role of molecular-level energetics in shaping plateau-like fitness landscapes from quasispecies theory.

Multispecificity–the ability of a single receptor protein molecule to interact with multiple substrates–is a hallmark of molecular recognition at protein-protein and protein-peptide interfaces, including enzyme-substrate complexes. The ability to perform structure-based prediction of multispecificity would aid in the identification of novel enzyme substrates, protein interaction partners, and enable design of novel enzymes targeted towards alternative substrates. The relatively slow speed of current biophysical, structure-based methods limits their use for prediction and, especially, design of multispecificity. Here, we develop a rapid, flexible-backbone self-consistent mean field theory-based technique, MFPred, for multispecificity modeling at protein-peptide interfaces. We benchmark our method by predicting experimentally determined peptide specificity profiles for a range of receptors: protease and kinase enzymes, and protein recognition modules including SH2, SH3, MHC Class I and PDZ domains. We observe robust recapitulation of known specificities for all receptor-peptide complexes, and comparison with other methods shows that MFPred results in equivalent or better prediction accuracy with a ~10-1000-fold decrease in computational expense. We find that modeling bound peptide backbone flexibility is key to the observed accuracy of the method. We used MFPred for predicting with high accuracy the impact of receptor-side mutations on experimentally determined multispecificity of a protease enzyme. Our approach should enable the design of a wide range of altered receptor proteins with programmed multispecificities., Author summary Across biology, many proteins that recognize peptides are multispecific; they interact with multiple binding partners of disparate sequence. Computational prediction of these multiple peptide partners would enable greater understanding of individual protein-recognition domains. Additionally, the ability to customize protein-recognition domains by designing them to recognize and act upon a new set of peptides and not bind their original binding partners would be useful in drug design and biotechnology. Current methods for predicting multispecificity operate on a timescale that is too slow to be used for design. Here, we present a method, MFPred, for predicting multispecificity. MFPred robustly recapitulates protein-recognition domain specificity for a range of proteins, at comparable accuracy and with considerable speed-up relative to current methods. We apply MFPred to predicting altered multispecificity in a mutant protease to demonstrate its relevance to design. The rapidity and accuracy of MFPred should enable its use in investigating and modulating biological processes.

The substrate specificity of protease enzymes constitutes the molecular basis of their diverse and complex biological roles. Rapid and accurate prediction of extended substrate specificity would also enable the design of custom enzymes capable of selectively cleaving biotechnologically or therapeutically relevant proteins. However, current in silico approaches for protease specificity prediction rely on, and are therefore limited by, machine learning of sequence patterns in known experimental data. Here, we describe a general structure-based approach for predicting peptidase substrates. We develop a structure-guided sequence sampling technique using self-consistent mean field theory for rapid specificity profile prediction and implement it in the Rosetta software. We show that computationally calculated specificity profiles closely match experimentally determined ones. Further, we construct atomic resolution models of thousands of substrate-enzyme complexes for each of six model proteases belonging to the four major protease mechanistic classes, and develop a discriminatory scoring function using enzyme design modules from Rosetta and Amber-MMPBSA. We rank putative peptide substrates in order of their interaction energies with a modeled near attack conformation of the enzyme active site. We show that the energetic patterns obtained from these simulations can be used to robustly classify known cleaved and uncleaved peptides, and these patterns have greater discriminatory power compared to purely sequence-based statistical inference. Combining sequence and energetic patterns for a given protease using machine-learning algorithms further improves classification performance, and analysis of atomic resolution models affords physical insight into the structural basis for the observed specificities. We tested the predictive capability of the approach by designing and experimentally characterizing the cleavage of four previously unidentified, but less rapidly cleaved, novel substrates for the Hepatitis C virus NS3/4 protease using an in vivo assay.