Parametrically guided design of beta barrels and transmembrane nanopores using deep learning.

Francis Crick's global parameterization of coiled coil geometry has been widely useful for guiding design of new protein structures and functions. However, design guided by similar global parameterization of beta barrel structures has been less successful, likely due to the deviations required from ideal beta barrel geometry to maintain extensive inter-strand hydrogen bonding without introducing considerable backbone strain. Instead, beta barrels and other protein folds have been designed guided by 2D structural blueprints; while this approach has successfully generated new fluorescent proteins, transmembrane nanopores, and other structures, it requires considerable expert knowledge and provides only indirect control over the global barrel shape. Here we show that the simplicity and control over shape and structure provided by global parametric representations can be generalized beyond coiled coils by taking advantage of the rich sequence-structure relationships implicit in RoseTTAFold based inpainting and diffusion design methods. Starting from parametrically generated idealized barrel backbones, both RFjoint inpainting and RFdiffusion readily incorporate the backbone irregularities necessary for proper folding with minimal deviation from the idealized barrel geometries. We show that for beta barrels across a broad range of global beta sheet parameterizations, these methods achieve high in silico and experimental success rates, with atomic accuracy confirmed by an X-ray crystal structure of a novel beta barrel topology, and de novo designed 12, 14, and 16 stranded transmembrane nanopores with conductances ranging from 200 to 500 pS. By combining the simplicity and control of parametric generation with the high success rates of deep learning based protein design methods, our approach makes the design of proteins where global shape confers function, such as beta barrel nanopores, more precisely specifiable and accessible.

Twenty-five emerging questions when detecting, understanding, and predicting future fish distributions in a changing climate.

The 2023 Annual Symposium of the Fisheries Society of the British Isles hosted opportunities for researchers, scientists, and policy makers to reflect on the state of art of predicting fish distributions and consider the implications to the marine and aquatic environments of a changing climate. The outcome of one special interest group at the Symposium was a collection of questions, organized under five themes, which begin to capture the state of the field and identify priorities for research and management over the coming years. The five themes were Physiology, Mechanisms, Detect and Measure, Manage, and Wider Ecosystems. The questions, 25 of them, addressed concepts which remain poorly understood, are data deficient, and/or are likely to be impacted in measurable or profound ways by climate change. Moving from the first to the last theme, the questions expanded in the scope of their considerations, from specific processes within the individual to ecosystem-wide impacts, but no one question is bigger than any other: each is important in detecting, understanding, and predicting fish distributions, and each will be impacted by an aspect of climate change. In this way, our questions, particularly those concerning unknown mechanisms and data deficiencies, aimed to offer a guide to other researchers, managers, and policy makers in the prioritization of future work as a changing climate is expected to have complex and disperse impacts on fish populations and distributions that will require a coordinated effort to address.

Diffusing protein binders to intrinsically disordered proteins.

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

Stereoselective Access to Diverse Alkaloid-Like Scaffolds via an Oxidation/Double-Mannich Reaction Sequence.

Sequential oxidative cleavage and double-Mannich reactions enable the stereoselective conversion of simple norbornenes into complex alkaloid-like structures. The products undergo a wide range of derivatization reactions, including regioselective enol triflate formation/cross-coupling sequences and highly efficient conversion to an unusual tricyclic 8,5,5-fused lactam. Overall, the process represents a formal one-atom aza-ring expansion with concomitant bridging annulation, making it of interest for the broader derivatization of alkene feedstocks.

Simultaneous L1-2 Bulged Disc and Mobile Spinal Schwannoma Causing Cauda Equina Syndrome: A Rare Case Report.

BACKGROUND Aside from the rarity of mobile spinal schwannomas, the coexistence of these tumors with herniated intervertebral disc is also scarce. Furthermore, cauda equina syndrome (CES), as a manifestation of intraspinal schwannomas has been reported rarely. Described here is a case of simultaneous lumbar disc bulge and mobile spinal schwannoma presented with intermittent symptoms of CES. CASE REPORT A 62-year-old man presented with severe but intermittent leg pain for 2 weeks, which later progressed to an episode of lower extremity weakness and difficulty in urination. Magnetic resonance imaging revealed an intraspinal tumor that moved in position relative to the L1-2 disc bulge on scans 6 h apart, with associated spontaneous regression in symptoms. The tumor was found to be a mobile spinal schwannoma, originated from a nerve root. A standard microdissection technique was used to remove the tumor through a spinous process-sparing unilateral approach, with complete laminectomy of L1. Use of intraoperative ultrasound facilitated the accurate tumor localization. Postoperatively, the patient no longer had symptoms. CONCLUSIONS This report presents a combination of a common spinal pathology, intervertebral disc herniation, alongside a rare condition, mobile spinal schwannoma, whose uncommon clinical manifestations, such as CES can cause irreversible neurological deficits. Surgeons need to remain vigilant of potential atypical scenarios when treating patients. Surgical treatment challenges regarding the mobility of tumors, such as accurate localization, should be addressed using intraoperative imaging to avoid wrong-level surgery. To mitigate the irreversible neurological complications, patients should receive comprehensive information for alarming signs of CES.

Atomically accurate de novo design of single-domain antibodies.

Despite the central role that antibodies play in modern medicine, there is currently no way to rationally design novel antibodies to bind a specific epitope on a target. Instead, antibody discovery currently involves time-consuming immunization of an animal or library screening approaches. Here we demonstrate that a fine-tuned RFdiffusion network is capable of designing de novo antibody variable heavy chains (VHH's) that bind user-specified epitopes. We experimentally confirm binders to four disease-relevant epitopes, and the cryo-EM structure of a designed VHH bound to influenza hemagglutinin is nearly identical to the design model both in the configuration of the CDR loops and the overall binding pose.

A role for vessel-associated extracellular matrix proteins in multiple sclerosis pathology.

Multiple sclerosis (MS) is unsurpassed for its clinical and pathological hetherogeneity, but the biological determinants of this variability are unknown. HLA-DRB1*15, the main genetic risk factor for MS, influences the severity and distribution of MS pathology. This study set out to unravel the molecular determinants of the heterogeneity of MS pathology in relation to HLA-DRB1*15 status. Shotgun proteomics from a discovery cohort of MS spinal cord samples segregated by HLA-DRB*15 status revealed overexpression of the extracellular matrix (ECM) proteins, biglycan, decorin, and prolargin in HLA-DRB*15-positive cases, adding to established literature on a role of ECM proteins in MS pathology that has heretofore lacked systematic pathological validation. These findings informed a neuropathological characterisation of these proteins in a large autopsy cohort of 41 MS cases (18 HLA-DRB1*15-positive and 23 HLA-DRB1*15-negative), and seven non-neurological controls on motor cortical, cervical and lumbar spinal cord tissue. Biglycan and decorin demonstrate a striking perivascular expression pattern in controls that is reduced in MS (-36.5%, p = 0.036 and - 24.7%, p = 0.039; respectively) in lesional and non-lesional areas. A concomitant increase in diffuse parenchymal accumulation of biglycan and decorin is seen in MS (p = 0.015 and p = 0.001, respectively), particularly in HLA-DRB1*15-positive cases (p = 0.007 and p = 0.046, respectively). Prolargin shows a faint parenchymal pattern in controls that is markedly increased in MS cases where a perivascular deposition pattern is observed (motor cortex +97.5%, p = 0.001; cervical cord +49.1%, p = 0.016). Our findings point to ECM proteins and the vascular interface playing a central role in MS pathology within and outside the plaque area. As ECM proteins are known potent pro-inflammatory molecules, their parenchymal accumulation may contribute to disease severity. This study brings to light novel factors that may contribute to the heterogeneity of the topographical variation of MS pathology.

De novo design of pH-responsive self-assembling helical protein filaments.

Biological evolution has led to precise and dynamic nanostructures that reconfigure in response to pH and other environmental conditions. However, designing micrometre-scale protein nanostructures that are environmentally responsive remains a challenge. Here we describe the de novo design of pH-responsive protein filaments built from subunits containing six or nine buried histidine residues that assemble into micrometre-scale, well-ordered fibres at neutral pH. The cryogenic electron microscopy structure of an optimized design is nearly identical to the computational design model for both the subunit internal geometry and the subunit packing into the fibre. Electron, fluorescent and atomic force microscopy characterization reveal a sharp and reversible transition from assembled to disassembled fibres over 0.3 pH units, and rapid fibre disassembly in less than 1 s following a drop in pH. The midpoint of the transition can be tuned by modulating buried histidine-containing hydrogen bond networks. Computational protein design thus provides a route to creating unbound nanomaterials that rapidly respond to small pH changes.

pH-Responsive, Thermoset Polymer Coatings for Active Protection against Aluminum Corrosion.

This paper describes the synthesis and use of multifunctional methacrylic monomers, which contain basic (amine) functional groups, including an example in which an acid-labile tert-butylcarbamate-protected glycine is used to form a novel methacrylic monomer. The "protected" amino acid-derived functional monomer (BOC-Gly-MA) is copolymerized with an epoxide functional methacrylic monomer (GMA), to deliver novel multifunctional polymers, which are processed into powder coatings and used to study filiform corrosion at the surface of an aluminum substrate. The BOC-Gly-MA-containing copolymers were shown to improve a coating's anticorrosion performance, presenting the lowest average filiform corrosion (FFC) track length, total FFC number, and total corroded surface area (CSA) of the coatings investigated. Further to this, a mode of action for the role of BOC-Gly functional polymers in corrosion protection is proposed, supported by both solution and polymer-aluminum interface studies, delivering new insights into the mode of action of pH-responsive polymer coatings.

Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models.

Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design.

De novo design of high-affinity binders of bioactive helical peptides.

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Macromolecular condensation buffers intracellular water potential.

Optimum protein function and biochemical activity critically depends on water availability because solvent thermodynamics drive protein folding and macromolecular interactions1. Reciprocally, macromolecules restrict the movement of 'structured' water molecules within their hydration layers, reducing the available 'free' bulk solvent and therefore the total thermodynamic potential energy of water, or water potential. Here, within concentrated macromolecular solutions such as the cytosol, we found that modest changes in temperature greatly affect the water potential, and are counteracted by opposing changes in osmotic strength. This duality of temperature and osmotic strength enables simple manipulations of solvent thermodynamics to prevent cell death after extreme cold or heat shock. Physiologically, cells must sustain their activity against fluctuating temperature, pressure and osmotic strength, which impact water availability within seconds. Yet, established mechanisms of water homeostasis act over much slower timescales2,3; we therefore postulated the existence of a rapid compensatory response. We find that this function is performed by water potential-driven changes in macromolecular assembly, particularly biomolecular condensation of intrinsically disordered proteins. The formation and dissolution of biomolecular condensates liberates and captures free water, respectively, quickly counteracting thermal or osmotic perturbations of water potential, which is consequently robustly buffered in the cytoplasm. Our results indicate that biomolecular condensation constitutes an intrinsic biophysical feedback response that rapidly compensates for intracellular osmotic and thermal fluctuations. We suggest that preserving water availability within the concentrated cytosol is an overlooked evolutionary driver of protein (dis)order and function.

HEATR5B associates with dynein-dynactin and promotes motility of AP1-bound endosomal membranes.

The microtubule motor dynein mediates polarised trafficking of a wide variety of organelles, vesicles and macromolecules. These functions are dependent on the dynactin complex, which helps recruit cargoes to dynein's tail and activates motor movement. How the dynein-dynactin complex orchestrates trafficking of diverse cargoes is unclear. Here, we identify HEATR5B, an interactor of the adaptor protein-1 (AP1) clathrin adaptor complex, as a novel player in dynein-dynactin function. HEATR5B was recovered in a biochemical screen for proteins whose association with the dynein tail is augmented by dynactin. We show that HEATR5B binds directly to the dynein tail and dynactin and stimulates motility of AP1-associated endosomal membranes in human cells. We also demonstrate that the Drosophila HEATR5B homologue is an essential gene that selectively promotes dynein-based transport of AP1-bound membranes to the Golgi apparatus. As HEATR5B lacks the coiled-coil architecture typical of dynein adaptors, our data point to a non-canonical process orchestrating motor function on a specific cargo. We additionally show that HEATR5B promotes association of AP1 with endosomal membranes independently of dynein. Thus, HEATR5B co-ordinates multiple events in AP1-based trafficking.

Synthetic Par polarity induces cytoskeleton asymmetry in unpolarized mammalian cells.

Polarized cells rely on a polarized cytoskeleton to function. Yet, how cortical polarity cues induce cytoskeleton polarization remains elusive. Here, we capitalized on recently established designed 2D protein arrays to ectopically engineer cortical polarity of virtually any protein of interest during mitosis in various cell types. This enables direct manipulation of polarity signaling and the identification of the cortical cues sufficient for cytoskeleton polarization. Using this assay, we dissected the logic of the Par complex pathway, a key regulator of cytoskeleton polarity during asymmetric cell division. We show that cortical clustering of any Par complex subunit is sufficient to trigger complex assembly and that the primary kinetic barrier to complex assembly is the relief of Par6 autoinhibition. Further, we found that inducing cortical Par complex polarity induces two hallmarks of asymmetric cell division in unpolarized mammalian cells: spindle orientation, occurring via Par3, and central spindle asymmetry, depending on aPKC activity.

Zero-shot mutation effect prediction on protein stability and function using RoseTTAFold.

Predicting the effects of mutations on protein function and stability is an outstanding challenge. Here, we assess the performance of a variant of RoseTTAFold jointly trained for sequence and structure recovery, RFjoint , for mutation effect prediction. Without any further training, we achieve comparable accuracy in predicting mutation effects for a diverse set of protein families using RFjoint to both another zero-shot model (MSA Transformer) and a model that requires specific training on a particular protein family for mutation effect prediction (DeepSequence). Thus, although the architecture of RFjoint was developed to address the protein design problem of scaffolding functional motifs, RFjoint acquired an understanding of the mutational landscapes of proteins during model training that is equivalent to that of recently developed large protein language models. The ability to simultaneously reason over protein structure and sequence could enable even more precise mutation effect predictions following supervised training on the task. These results suggest that RFjoint has a quite broad understanding of protein sequence-structure landscapes, and can be viewed as a joint model for protein sequence and structure which could be broadly useful for protein modeling.

De novo design of protein structure and function with RFdiffusion.

There has been considerable recent progress in designing new proteins using deep-learning methods1-9. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.

ANXA11 biomolecular condensates facilitate protein-lipid phase coupling on lysosomal membranes.

Phase transitions of cellular proteins and lipids play a key role in governing the organisation and coordination of intracellular biology. The frequent juxtaposition of proteinaceous biomolecular condensates to cellular membranes raises the intriguing prospect that phase transitions in proteins and lipids could be co-regulated. Here we investigate this possibility in the ribonucleoprotein (RNP) granule-ANXA11-lysosome ensemble, where ANXA11 tethers RNP granule condensates to lysosomal membranes to enable their co-trafficking. We show that changes to the protein phase state within this system, driven by the low complexity ANXA11 N-terminus, induce a coupled phase state change in the lipids of the underlying membrane. We identify the ANXA11 interacting proteins ALG2 and CALC as potent regulators of ANXA11-based phase coupling and demonstrate their influence on the nanomechanical properties of the ANXA11-lysosome ensemble and its capacity to engage RNP granules. The phenomenon of protein-lipid phase coupling we observe within this system offers an important template to understand the numerous other examples across the cell whereby biomolecular condensates closely juxtapose cell membranes.

Beyond audit: Embracing QI methodology to drive improvements in lung-protective ventilation.

BACKGROUND: Mechanical ventilation supports patients with respiratory failure during critical illness. Evidence suggests that excessive tidal volumes (regarded as >8 mL/kg predicted body weight [PBW]) cause lung damage through increased lung stretch and alveolar inflammation. Lung-protective ventilation strategies have been shown to decrease morbidity and mortality, and that all patients should receive tidal volumes between 6 and 8 mls/kg PBW. Despite this, studies demonstrate that fewer than half of patients in critical care successfully receive lung-protective ventilation. AIM: The primary aim was to reduce tidal volumes delivered to all patients receiving mandatory ventilation, with a target of >85% of tidal volumes delivered to be compliant with lung-protective ventilation strategies by the end of November 2019. METHODS: A multidisciplinary team of nurses and doctors, based in a UK tertiary hospital, utilized the Institute for Healthcare Improvement's (IHI) quality improvement methodology to improve compliance with lung-protective ventilation. RESULTS: Baseline data demonstrated that only 60.1% of tidal volumes recorded were compliant with lung-protective ventilation. Quality improvement (QI) methodology was utilized to systematically diagnose the aetiology of poor compliance and to produce and implement solutions. Real-time data collection and reporting were utilized to monitor and report improvement. Following 8 months of continuous data collection and repeated PDSA cycles, sustainable compliance with lung-protective ventilation for >85% of tidal volumes was achieved. CONCLUSIONS: The use of QI methodology to implement low tidal volume ventilation has shown a significant improvement in the delivery of lung-protective ventilation. Using QI methodology is central to this sustained improvement and offers a useful tool to systematically approach complex clinical problems. RELEVANCE TO CLINICAL PRACTICE: Lung protective ventilation is critically important in the management of ventilated patients, although compliance in intensive care is variable. Here, we describe how quality improvement methodology can lead to consistent and sustainable improvement in the delivery of lung protective ventilation.