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There has been considerable progress in building models to predict the effect of missense substitutions in protein-coding genes, fueled in large part by progress in applying deep learning methods to sequence data. These models have the potential to enable clinical variant annotation on a large scale and hence increase the impact of patient sequencing in guiding diagnosis and treatment. To realize this potential, it is essential to provide reliable assessments of model performance, scope of applicability, and robustness. As a response to this need, the ClinGen Sequence Variant Interpretation Working Group, Pejaver et al., recently proposed a strategy for validation and calibration of in-silico predictions in the context of guidelines for variant annotation. While this work marks an important step forward, the strategy presented still has important limitations. We propose core principles and recommendations to overcome these limitations that can enable both more reliable and more impactful use of variant effect prediction models in the future., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024. Published by Elsevier Inc.)

When nature evolves a gene over eons at scale, it produces a diversity of homologous sequences with patterns of conservation and change that contain rich structural, functional, and historical information about the gene. However, natural gene diversity accumulates slowly and likely excludes large regions of functional sequence space, limiting the information that is encoded and extractable. We introduce upgraded orthogonal DNA replication (OrthoRep) systems that radically accelerate the evolution of chosen genes under selection in yeast. When applied to a maladapted biosynthetic enzyme, we obtained collections of extensively diverged sequences with patterns that revealed structural and environmental constraints shaping the enzyme's activity. Our upgraded OrthoRep systems should support the discovery of factors influencing gene evolution, uncover previously unknown regions of fitness landscapes, and find broad applications in biomolecular engineering.

Purpose: Children undergoing either posterior spinal fusion (PSF) or index insertion of growing rods for neuromuscular or genetic/syndromic scoliosis may require post-operative care on the paediatric intensive care unit (PICU). Demands on this limited resource result in frequent bed shortage related cancellations. In response, an ad-hoc or 'pop-up' ward-based high-dependency unit (POPUP-HDU) was developed. This converts a ward bed to POP-HDU bed for the required time. This study assesses the safety and efficacy of postoperative management that utilises POPUP-HDU as an alternative to a PICU bed., Methods: Retrospective review of 111 consecutive children undergoing posterior surgery for scoliosis between June 2016 and April 2023. The inclusion criteria included a diagnosis of genetic/syndromic or neuromuscular scoliosis; PSF or primary insertion of distraction-based growth rods and requirement for postoperative care in a PICU. We excluded those children that were mandated to go to PICU post-operatively for any reason by the anaesthetic team., Results: 49 patients (mean age 13.0 years) were managed on PICU, and 62 (mean age 11.4 years) on POPUP-HDU. The groups were matched with respect to body weight, curve magnitude, operative duration, type of fusion procedure performed, the presence of cardiac malformations, the use of home breathing support, the number of operated levels, pelvic instrumentation and intraoperative blood loss. 8 patients in the PICU, and 16 in the POP-HDU groups were readmitted back to PICU following step-down to the hospital ward (p = 0.27). The median PICU length of stay was 1 day in the PICU group and less than a day in POPUP-HDU (for those that needed to be subsequently admitted to PICU). The median total length of hospital stay was 10 days in the PICU group, and 8 days in POPUP-HDU (p < 0.05). 14 patients developed medical complications in the PICU group, compared to 19 in POPUP-HDU. There were no bedshortage cancellations in POPUP-HDU, compared to 23 in PICU., Conclusions: For children with neuromuscular, genetic or syndromic scoliosis undergoing PSF or growth rods that are not deemed suitable for immediate ward-level post-operative care, POPUP-HDU provided a safe alternative to PICU for appropriate patients and was associated with shorter hospital stay and fewer cancellations for lack of PICU beds., Level of Evidence: Therapeutic Level III., (© 2024. The Author(s), under exclusive licence to Scoliosis Research Society.)

Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function, evolution, and design. Here, we expand an emerging microfluidic platform to measure catalytic constants- k cat and K M -for hundreds of diverse naturally occurring sequences and mutants of the model enzyme Adenylate Kinase (ADK). This enables us to dissect the sequence-catalysis landscape's topology, navigability, and mechanistic underpinnings, revealing distinct catalytic peaks organized by structural motifs. These results challenge long-standing hypotheses in enzyme adaptation, demonstrating that thermophilic enzymes are not slower than their mesophilic counterparts. Combining the rich representations of protein sequences provided by deep-learning models with our custom high-throughput kinetic data yields semi-supervised models that significantly outperform existing models at predicting catalytic parameters of naturally occurring ADK sequences. Our work demonstrates a promising strategy for dissecting sequence-catalysis landscapes across enzymatic evolution and building family-specific models capable of accurately predicting catalytic constants, opening new avenues for enzyme engineering and functional prediction.

Introduction: Variants of uncertain significance (VUS) surged with affordable genetic testing, posing challenges for determining pathogenicity. We examine the pathogenicity of a novel VUS P93S in Annexin A11 (ANXA11) - an amyotrophic lateral sclerosis/frontotemporal dementia-associated gene - in a corticobasal syndrome kindred. Established ANXA11 mutations cause ANXA11 aggregation, altered lysosomal-RNA granule co-trafficking, and transactive response DNA binding protein of 43 kDa (TDP-43) mis-localization., Methods: We described the clinical presentation and explored the phenotypic diversity of ANXA11 variants. P93S's effect on ANXA11 function and TDP-43 biology was characterized in induced pluripotent stem cell-derived neurons alongside multiomic neuronal and microglial profiling., Results: ANXA11 mutations were linked to corticobasal syndrome cases. P93S led to decreased lysosome colocalization, neuritic RNA, and nuclear TDP-43 with cryptic exon expression. Multiomic microglial signatures implicated immune dysregulation and interferon signaling pathways., Discussion: This study establishes ANXA11 P93S pathogenicity, broadens the phenotypic spectrum of ANXA11 mutations, underscores neuronal and microglial dysfunction in ANXA11 pathophysiology, and demonstrates the potential of cellular models to determine variant pathogenicity., Highlights: ANXA11 P93S is a pathogenic variant. Corticobasal syndrome is part of the ANXA11 phenotypic spectrum. Hybridization chain reaction fluorescence in situ hybridization (HCR FISH) is a new tool for the detection of cryptic exons due to TDP-43-related loss of splicing regulation. Microglial ANXA11 and related immune pathways are important drivers of disease. Cellular models are powerful tools for adjudicating variants of uncertain significance., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β-lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications., (© 2024. The Author(s).)

Hepatitis B virus (HBV) is a small double-stranded DNA virus that chronically infects 296 million people. Over half of its compact genome encodes proteins in two overlapping reading frames, and during evolution, multiple selective pressures can act on shared nucleotides. This study combines an RNA-based HBV cell culture system with deep mutational scanning (DMS) to uncouple cis- and trans-acting sequence requirements in the HBV genome. The results support a leaky ribosome scanning model for polymerase translation, provide a fitness map of the HBV polymerase at single-nucleotide resolution, and identify conserved prolines adjacent to the HBV polymerase termination codon that stall ribosomes. Further experiments indicated that stalled ribosomes tether the nascent polymerase to its template RNA, ensuring cis-preferential RNA packaging and reverse transcription of the HBV genome., Competing Interests: Declaration of interests Y.Y., W.M.S., and C.M.R. filed a patent application, US 62/741,032, with Rockefeller University on September 19, 2019, entitled “RNA-Based Methods to Launch Hepatitis B Virus Infection.” Patent pending. C.M.R. is a shareholder and member of the scientific advisory board at VIR Biotechnology., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The orphan G protein-coupled receptor (GPCR) GPR161 plays a central role in development by suppressing Hedgehog signaling. The fundamental basis of how GPR161 is activated remains unclear. Here, we determined a cryogenic-electron microscopy structure of active human GPR161 bound to heterotrimeric G s . This structure revealed an extracellular loop 2 that occupies the canonical GPCR orthosteric ligand pocket. Furthermore, a sterol that binds adjacent to transmembrane helices 6 and 7 stabilizes a GPR161 conformation required for G s coupling. Mutations that prevent sterol binding to GPR161 suppress G s -mediated signaling. These mutants retain the ability to suppress GLI2 transcription factor accumulation in primary cilia, a key function of ciliary GPR161. By contrast, a protein kinase A-binding site in the GPR161 C terminus is critical in suppressing GLI2 ciliary accumulation. Our work highlights how structural features of GPR161 interface with the Hedgehog pathway and sets a foundation to understand the role of GPR161 function in other signaling pathways., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Recent developments in protein design rely on large neural networks with up to 100s of millions of parameters, yet it is unclear which residue dependencies are critical for determining protein function. Here, we show that amino acid preferences at individual residues-without accounting for mutation interactions-explain much and sometimes virtually all of the combinatorial mutation effects across 8 datasets (R 2 ~ 78-98%). Hence, few observations (~100 times the number of mutated residues) enable accurate prediction of held-out variant effects (Pearson r > 0.80). We hypothesized that the local structural contexts around a residue could be sufficient to predict mutation preferences, and develop an unsupervised approach termed CoVES (Combinatorial Variant Effects from Structure). Our results suggest that CoVES outperforms not just model-free methods but also similarly to complex models for creating functional and diverse protein variants. CoVES offers an effective alternative to complicated models for identifying functional protein mutations., (© 2024. The Author(s).)

Effective pandemic preparedness relies on anticipating viral mutations that are able to evade host immune responses to facilitate vaccine and therapeutic design. However, current strategies for viral evolution prediction are not available early in a pandemic-experimental approaches require host polyclonal antibodies to test against 1-16 , and existing computational methods draw heavily from current strain prevalence to make reliable predictions of variants of concern 17-19 . To address this, we developed EVEscape, a generalizable modular framework that combines fitness predictions from a deep learning model of historical sequences with biophysical and structural information. EVEscape quantifies the viral escape potential of mutations at scale and has the advantage of being applicable before surveillance sequencing, experimental scans or three-dimensional structures of antibody complexes are available. We demonstrate that EVEscape, trained on sequences available before 2020, is as accurate as high-throughput experimental scans at anticipating pandemic variation for SARS-CoV-2 and is generalizable to other viruses including influenza, HIV and understudied viruses with pandemic potential such as Lassa and Nipah. We provide continually revised escape scores for all current strains of SARS-CoV-2 and predict probable further mutations to forecast emerging strains as a tool for continuing vaccine development ( evescape.org )., (© 2023. The Author(s).)

Competing Interests: Declaration of interests D.S.M. is a cofounder of Seismic therapeutics, a scientific consultant to Dyno therapeutics, Octant Bio and Tectonic therapeutics. P.H. is listed as a co-inventor in a patent application. P.C. is a co-founder and scientific advisor to UbiquiTx, Inc., and is a listed inventor of multiple patents related to protein design. All other authors declare no competing interests.

The relaxin family peptide receptor 1 (RXFP1) is the receptor for relaxin-2, an important regulator of reproductive and cardiovascular physiology. RXFP1 is a multi-domain G protein-coupled receptor (GPCR) with an ectodomain consisting of a low-density lipoprotein receptor class A (LDLa) module and leucine-rich repeats. The mechanism of RXFP1 signal transduction is clearly distinct from that of other GPCRs, but remains very poorly understood. In the present study, we determine the cryo-electron microscopy structure of active-state human RXFP1, bound to a single-chain version of the endogenous agonist relaxin-2 and the heterotrimeric G s protein. Evolutionary coupling analysis and structure-guided functional experiments reveal that RXFP1 signals through a mechanism of autoinhibition. Our results explain how an unusual GPCR family functions, providing a path to rational drug development targeting the relaxin receptors., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer., (© 2023. The Author(s).)

Bacterial spores resist antibiotics and sterilization and can remain metabolically inactive for decades, but they can rapidly germinate and resume growth in response to nutrients. Broadly conserved receptors embedded in the spore membrane detect nutrients, but how spores transduce these signals remains unclear. Here, we found that these receptors form oligomeric membrane channels. Mutations predicted to widen the channel initiated germination in the absence of nutrients, whereas those that narrow it prevented ion release and germination in response to nutrients. Expressing receptors with widened channels during vegetative growth caused loss of membrane potential and cell death, whereas the addition of germinants to cells expressing wild-type receptors triggered membrane depolarization. Therefore, germinant receptors act as nutrient-gated ion channels such that ion release initiates exit from dormancy.

Gram-negative bacteria surround their cytoplasmic membrane with a peptidoglycan (PG) cell wall and an outer membrane (OM) with an outer leaflet composed of lipopolysaccharide (LPS) 1 . This complex envelope presents a formidable barrier to drug entry and is a major determinant of the intrinsic antibiotic resistance of these organisms 2 . The biogenesis pathways that build the surface are also targets of many of our most effective antibacterial therapies 3 . Understanding the molecular mechanisms underlying the assembly of the Gram-negative envelope therefore promises to aid the development of new treatments effective against the growing problem of drug-resistant infections. Although the individual pathways for PG and OM synthesis and assembly are well characterized, almost nothing is known about how the biogenesis of these essential surface layers is coordinated. Here we report the discovery of a regulatory interaction between the committed enzymes for the PG and LPS synthesis pathways in the Gram-negative pathogen Pseudomonas aeruginosa. We show that the PG synthesis enzyme MurA interacts directly and specifically with the LPS synthesis enzyme LpxC. Moreover, MurA was shown to stimulate LpxC activity in cells and in a purified system. Our results support a model in which the assembly of the PG and OM layers in many proteobacterial species is coordinated by linking the activities of the committed enzymes in their respective synthesis pathways., (© 2023. The Author(s).)

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models' performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence., (© 2022. The Author(s).)

Study Design: To evaluate the mechanical effectiveness of "tuning fork" plate fixation system by comparing with dual iliac screw fixation under different spinal motion through finite element analysis (FEA)., Objective: Lumbosacral deficiencies occur from birth defects or following destruction by tumors. The objective of this study was to evaluate the mechanical effectiveness of the tuning fork plate compared to dual iliac screw system which is the gold standard fixation in treating lumbosacral deficiencies. This is an innovative fixation device for treating lumbosacral deficiencies., Methods: The deficiency model was prepared using a previously developed and validated finite element T10-pelvis model. To create the lumbo-sacral deficiency the segments between L3 and sacrum were removed from the model. The model was then instrumented from T10 to L2 segments and the ilium using either the tuning fork plate or a dual iliac screw construct. With the ilium fixed, the T10 vertebrae was subjected to 10 Nm moment and 400 N follower load to simulate spinal motions. Range of motion (ROM) of spine and stresses on the instrumentation were calculated for 2 fixation devices and compared with each other., Results: The 2 fixation systems demonstrate a comparable motion reduction in all loading modes. Stress values were higher in the dual iliac screw constructs compared with the tuning fork plate fixation system. The factor of safety of the tuning fork plate device was higher than the dual iliac screw fixation by 50%., Conclusions: Both fixation devices had similar performance in motion reduction at spine levels. However, based on predicted implant stresses there were less chances of implant failure in the fork plate fixation, compared to the dual iliac screw system.

The transmembrane (TM) channel-like 1 (TMC1) and TMC2 proteins play a central role in auditory transduction, forming ion channels that convert sound into electrical signals. However, the molecular mechanism of their gating remains unknown. Here, using predicted structural models as a guide, we probed the effects of 12 mutations on the mechanical gating of the transduction currents in native hair cells of Tmc1/2 -null mice expressing virally introduced TMC1 variants. Whole-cell electrophysiological recordings revealed that mutations within the pore-lining TM4 and TM6 helices modified gating, reducing the force sensitivity or shifting the open probability of the channels, or both. For some of the mutants, these changes were accompanied by a change in single-channel conductance. Our observations are in line with a model wherein conformational changes in the TM4 and TM6 helices are involved in the mechanical gating of the transduction channel.

In response to starvation, endospore-forming bacteria differentiate into stress-resistant spores that can remain dormant for years yet rapidly germinate and resume growth in response to nutrients. The small molecule dipicolinic acid (DPA) plays a central role in both the stress resistance of the dormant spore and its exit from dormancy during germination. The spoVA locus is required for DPA import during sporulation and has been implicated in its export during germination, but the molecular bases are unclear. Here, we define the minimal set of proteins encoded in the Bacillus subtilis spoVA operon required for DPA import and demonstrate that these proteins form a membrane complex. Structural modeling of these components combined with mutagenesis and in vivo analysis reveal that the C and Eb subunits form a membrane channel, while the D subunit functions as a cytoplasmic plug. We show that point mutations that impair the interactions between D and the C-Eb membrane complex reduce the efficiency of DPA import during sporulation and reciprocally accelerate DPA release during germination. Our data support a model in which DPA transport into spores involves cycles of unplugging and then replugging the C-Eb membrane channel, while nutrient detection during germination triggers DPA release by unplugging it., (© 2022 Gao et al.; Published by Cold Spring Harbor Laboratory Press.)

Purpose: To evaluate the safety of MRI in patients with fragmented retained leads (FRLs) through numerical simulation and phantom experiments., Methods: Electromagnetic and thermal simulations were performed to determine the worst-case RF heating of 10 patient-derived FRL models during MRI at 1.5 T and 3 T and at imaging landmarks corresponding to head, chest, and abdomen. RF heating measurements were performed in phantoms implanted with reconstructed FRL models that produced highest heating in numerical simulations. The potential for unintended tissue stimulation was assessed through a conservative estimation of the electric field induced in the tissue due to gradient-induced voltages developed along the length of FRLs., Results: In simulations under conservative approach, RF exposure at B 1 + ≤ 2 µT generated cumulative equivalent minutes (CEM) 43 < 40 at all imaging landmarks at both 1.5 T and 3 T, indicating no thermal damage for acquisition times (TAs) < 10 min. In experiments, the maximum temperature rise when FRLs were positioned at the location of maximum electric field exposure was measured to be 2.4°C at 3 T and 2.1°C at 1.5 T. Electric fields induced in the tissue due to gradient-induced voltages remained below the threshold for cardiac tissue stimulation in all cases., Conclusions: Simulation and experimental results indicate that patients with FRLs can be scanned safely at both 1.5 T and 3 T with most clinical pulse sequences., (© 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Proteins often accumulate neutral mutations that do not affect current functions but can profoundly influence future mutational possibilities and functions. Understanding such hidden potential has major implications for protein design and evolutionary forecasting but has been limited by a lack of systematic efforts to identify potentiating mutations. Here, through the comprehensive analysis of a bacterial toxin-antitoxin system, we identified all possible single substitutions in the toxin that enable it to tolerate otherwise interface-disrupting mutations in its antitoxin. Strikingly, the majority of enabling mutations in the toxin do not contact and promote tolerance non-specifically to many different antitoxin mutations, despite covariation in homologues occurring primarily between specific pairs of contacting residues across the interface. In addition, the enabling mutations we identified expand future mutational paths that both maintain old toxin-antitoxin interactions and form new ones. These non-specific mutations are missed by widely used covariation and machine learning methods. Identifying such enabling mutations will be critical for ensuring continued binding of therapeutically relevant proteins, such as antibodies, aimed at evolving targets., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Suppression of the host innate immune response is a critical aspect of viral replication. Upon infection, viruses may introduce one or more proteins that inhibit key immune pathways, such as the type I interferon pathway. However, the ability to predict and evaluate viral protein bioactivity on targeted pathways remains challenging and is typically done on a single-virus or -gene basis. Here, we present a medium-throughput high-content cell-based assay to reveal the immunosuppressive effects of viral proteins. To test the predictive power of our approach, we developed a library of 800 genes encoding known, predicted, and uncharacterized human virus genes. We found that previously known immune suppressors from numerous viral families such as Picornaviridae and Flaviviridae recorded positive responses. These include a number of viral proteases for which we further confirmed that innate immune suppression depends on protease activity. A class of predicted inhibitors encoded by Rhabdoviridae viruses was demonstrated to block nuclear transport, and several previously uncharacterized proteins from uncultivated viruses were shown to inhibit nuclear transport of the transcription factors NF-κB and interferon regulatory factor 3 (IRF3). We propose that this pathway-based assay, together with early sequencing, gene synthesis, and viral infection studies, could partly serve as the basis for rapid in vitro characterization of novel viral proteins. IMPORTANCE Infectious diseases caused by viral pathogens exacerbate health care and economic burdens. Numerous viral biomolecules suppress the human innate immune system, enabling viruses to evade an immune response from the host. Despite our current understanding of viral replications and immune evasion, new viral proteins, including those encoded by uncultivated viruses or emerging viruses, are being unearthed at a rapid pace from large-scale sequencing and surveillance projects. The use of medium- and high-throughput functional assays to characterize immunosuppressive functions of viral proteins can advance our understanding of viral replication and possibly treatment of infections. In this study, we assembled a large viral-gene library from diverse viral families and developed a high-content assay to test for inhibition of innate immunity pathways. Our work expands the tools that can rapidly link sequence and protein function, representing a practical step toward early-stage evaluation of emerging and understudied viruses.

Many organisms can survive extreme conditions and successfully recover to normal life. This extremotolerant behavior has been attributed in part to repetitive, amphipathic, and intrinsically disordered proteins that are upregulated in the protected state. Here, we assemble a library of approximately 300 naturally occurring and designed extremotolerance-associated proteins to assess their ability to protect human cells from chemically induced apoptosis. We show that several proteins from tardigrades, nematodes, and the Chinese giant salamander are apoptosis-protective. Notably, we identify a region of the human ApoE protein with similarity to extremotolerance-associated proteins that also protects against apoptosis. This region mirrors the phase separation behavior seen with such proteins, like the tardigrade protein CAHS2. Moreover, we identify a synthetic protein, DHR81, that shares this combination of elevated phase separation propensity and apoptosis protection. Finally, we demonstrate that driving protective proteins into the condensate state increases apoptosis protection, and highlights the ability of DHR81 condensates to sequester caspase-7. Taken together, this work draws a link between extremotolerance-associated proteins, condensate formation, and designing human cellular protection.

Purpose: Intracanal rib head penetration is a well-known entity in dystrophic scoliotic curves in neurofibromatosis type 1. There is potential for spinal cord injury if this is not recognised and managed appropriately. No current CT-based classification system is currently in use to quantify rib head penetration. This article aims to propose and evaluate a novel CT-based classification for rib head penetration primarily for neurofibromatosis but which can also be utilised in other conditions of rib head penetration., Materials and Methods: The grading was developed as four grades: normal rib head (RH) position-Grade 0, subluxed extracanal RH position-Grade 1, RH at pedicle-Grade 2, intracanal RH-Grade 3. Grade 3 was further classified depending on the head position in the canal divided into thirds. Rib head penetration into proximal third (from ipsilateral side)-Grade 3A, into the middle third-Grade 3B and into the distal third-Grade 3C. Seventy-five axial CT images of Neurofibromatosis Type 1 patients in the paediatric age group were reviewed by a radiologist and a spinal surgeon independently to assess interobserver and intraobserver agreement of the novel CT classification. Agreement analysis was performed using the weighted Kappa statistic., Results: There was substantial interobserver correlation with mean Kappa score (k = 0.8, 95% CI 0.7-0.9) and near perfect intraobserver Kappa of 1.0 (95% CI 0.9-1.0) and 0.9 (95% CI 0.9-1.0) for the two readers., Conclusion: The novel CT-based classification quantifies rib head penetration which aids in management planning., (© 2021. Scoliosis Research Society.)

Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences 1-3 . In principle, computational methods could support the large-scale interpretation of genetic variants. However, state-of-the-art methods 4-10 have relied on training machine learning models on known disease labels. As these labels are sparse, biased and of variable quality, the resulting models have been considered insufficiently reliable 11 . Here we propose an approach that leverages deep generative models to predict variant pathogenicity without relying on labels. By modelling the distribution of sequence variation across organisms, we implicitly capture constraints on the protein sequences that maintain fitness. Our model EVE (evolutionary model of variant effect) not only outperforms computational approaches that rely on labelled data but also performs on par with, if not better than, predictions from high-throughput experiments, which are increasingly used as evidence for variant classification 12-16 . We predict the pathogenicity of more than 36 million variants across 3,219 disease genes and provide evidence for the classification of more than 256,000 variants of unknown significance. Our work suggests that models of evolutionary information can provide valuable independent evidence for variant interpretation that will be widely useful in research and clinical settings., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

The predominant approach for antibody generation remains animal immunization, which can yield exceptionally selective and potent antibody clones owing to the powerful evolutionary process of somatic hypermutation. However, animal immunization is inherently slow, not always accessible and poorly compatible with many antigens. Here, we describe 'autonomous hypermutation yeast surface display' (AHEAD), a synthetic recombinant antibody generation technology that imitates somatic hypermutation inside engineered yeast. By encoding antibody fragments on an error-prone orthogonal DNA replication system, surface-displayed antibody repertoires continuously mutate through simple cycles of yeast culturing and enrichment for antigen binding to produce high-affinity clones in as little as two weeks. We applied AHEAD to generate potent nanobodies against the SARS-CoV-2 S glycoprotein, a G-protein-coupled receptor and other targets, offering a template for streamlined antibody generation at large., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

This study computationally assesses the accuracy of an instantaneous wave-free ratio (iFR) threshold range compared to standard modalities such as fractional flow reserve (FFR) and coronary flow reserve (CFR) for multiple intermediate lesions near the left main (LM) coronary bifurcation. iFR is an adenosine-independent index encouraged for assessment of coronary artery disease (CAD), but different thresholds are debated. This becomes particularly challenging in cases of multivessel disease when sensitivity to downstream lesions is unclear. Idealized LM coronary arteries with 34 different intermediate stenoses were created and categorized (Medina) as single and multiple lesion groups. Computational fluid dynamics modeling was performed with physiologic boundary conditions using an open-source software (simvascular1) to solve the time-dependent Navier-Stokes equations. A strong linear relationship between iFR and FFR was observed among studied models, indicating computational iFR values of 0.92 and 0.93 are statistically equivalent to an FFR of 0.80 in single and multiple lesion groups, respectively. At the clinical FFR value (i.e., 0.8), a triple-lesion group had smaller CFR compared to the single and double lesion groups (e.g., triple = 3.077 versus single = 3.133 and double = 3.132). In general, the effect of additional intermediate downstream lesions (minimum lumen area > 3 mm2) was not statistically significant for iFR and CFR. A computational iFR of 0.92 best predicts an FFR of 0.80 and may be recommended as threshold criteria for computational assessment of LM stenosis following additional validation using patient-specific models., (Copyright © 2021 by ASME.)

The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 10 5 -nanobody library that shows better expression than a 1000-fold larger synthetic library. Our results demonstrate the power of the alignment-free autoregressive model in generalizing to regions of sequence space traditionally considered beyond the reach of prediction and design.

Increasing numbers of protein interactions have been identified in high-throughput experiments, but only a small proportion have solved structures. Recently, sequence coevolution-based approaches have led to a breakthrough in predicting monomer protein structures and protein interaction interfaces. Here, we address the challenges of large-scale interaction prediction at residue resolution with a fast alignment concatenation method and a probabilistic score for the interaction of residues. Importantly, this method (EVcomplex2) is able to assess the likelihood of a protein interaction, as we show here applied to large-scale experimental datasets where the pairwise interactions are unknown. We predict 504 interactions de novo in the E. coli membrane proteome, including 243 that are newly discovered. While EVcomplex2 does not require available structures, coevolving residue pairs can be used to produce structural models of protein interactions, as done here for membrane complexes including the Flagellar Hook-Filament Junction and the Tol/Pal complex.

Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides informative data resources for constructing computational models of cell biology. Models that generalize well beyond training data can be used to identify combinatorial perturbations of potential therapeutic interest. Major challenges for machine learning on large biological datasets are to find global optima in a complex multidimensional space and mechanistically interpret the solutions. To address these challenges, we introduce a hybrid approach that combines explicit mathematical models of cell dynamics with a machine-learning framework, implemented in TensorFlow. We tested the modeling framework on a perturbation-response dataset of a melanoma cell line after drug treatments. The models can be efficiently trained to describe cellular behavior accurately. Even though completely data driven and independent of prior knowledge, the resulting de novo network models recapitulate some known interactions. The approach is readily applicable to various kinetic models of cell biology. A record of this paper's Transparent Peer Review process is included in the Supplemental Information., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)