Gate-based quantum computing for protein design.

Protein design is a technique to engineer proteins by permuting amino acids in the sequence to obtain novel functionalities. However, exploring all possible combinations of amino acids is generally impossible due to the exponential growth of possibilities with the number of designable sites. The present work introduces circuits implementing a pure quantum approach, Grover's algorithm, to solve protein design problems. Our algorithms can adjust to implement any custom pair-wise energy tables and protein structure models. Moreover, the algorithm's oracle is designed to consist of only adder functions. Quantum computer simulators validate the practicality of our circuits, containing up to 234 qubits. However, a smaller circuit is implemented on real quantum devices. Our results show that using [Formula: see text] iterations, the circuits find the correct results among all N possibilities, providing the expected quadratic speed up of Grover's algorithm over classical methods (i.e., [Formula: see text]).

Disrupting the α-synuclein-ESCRT interaction with a peptide inhibitor mitigates neurodegeneration in preclinical models of Parkinson's disease.

Accumulation of α-synuclein into toxic oligomers or fibrils is implicated in dopaminergic neurodegeneration in Parkinson's disease. Here we performed a high-throughput, proteome-wide peptide screen to identify protein-protein interaction inhibitors that reduce α-synuclein oligomer levels and their associated cytotoxicity. We find that the most potent peptide inhibitor disrupts the direct interaction between the C-terminal region of α-synuclein and CHarged Multivesicular body Protein 2B (CHMP2B), a component of the Endosomal Sorting Complex Required for Transport-III (ESCRT-III). We show that α-synuclein impedes endolysosomal activity via this interaction, thereby inhibiting its own degradation. Conversely, the peptide inhibitor restores endolysosomal function and thereby decreases α-synuclein levels in multiple models, including female and male human cells harboring disease-causing α-synuclein mutations. Furthermore, the peptide inhibitor protects dopaminergic neurons from α-synuclein-mediated degeneration in hermaphroditic C. elegans and preclinical Parkinson's disease models using female rats. Thus, the α-synuclein-CHMP2B interaction is a potential therapeutic target for neurodegenerative disorders.

Wafer-Scale Epitaxial Growth of an Atomically Thin Single-Crystal Insulator as a Substrate of Two-Dimensional Material Field-Effect Transistors.

As the electron mobility of two-dimensional (2D) materials is dependent on an insulating substrate, the nonuniform surface charge and morphology of silicon dioxide (SiO2) layers degrade the electron mobility of 2D materials. Here, we demonstrate that an atomically thin single-crystal insulating layer of silicon oxynitride (SiON) can be grown epitaxially on a SiC wafer at a wafer scale and find that the electron mobility of graphene field-effect transistors on the SiON layer is 1.5 times higher than that of graphene field-effect transistors on typical SiO2 films. Microscale and nanoscale void defects caused by heterostructure growth were eliminated for the wafer-scale growth of the single-crystal SiON layer. The single-crystal SiON layer can be grown on a SiC wafer with a single thermal process. This simple fabrication process, compatible with commercial semiconductor fabrication processes, makes the layer an excellent replacement for the SiO2/Si wafer.

A few-layer covalent network of fullerenes.

The two natural allotropes of carbon, diamond and graphite, are extended networks of sp3-hybridized and sp2-hybridized atoms, respectively1. By mixing different hybridizations and geometries of carbon, one could conceptually construct countless synthetic allotropes. Here we introduce graphullerene, a two-dimensional crystalline polymer of C60 that bridges the gulf between molecular and extended carbon materials. Its constituent fullerene subunits arrange hexagonally in a covalently interconnected molecular sheet. We report charge-neutral, purely carbon-based macroscopic crystals that are large enough to be mechanically exfoliated to produce molecularly thin flakes with clean interfaces-a critical requirement for the creation of heterostructures and optoelectronic devices2. The synthesis entails growing single crystals of layered polymeric (Mg4C60)∞ by chemical vapour transport and subsequently removing the magnesium with dilute acid. We explore the thermal conductivity of this material and find it to be much higher than that of molecular C60, which is a consequence of the in-plane covalent bonding. Furthermore, imaging few-layer graphullerene flakes using transmission electron microscopy and near-field nano-photoluminescence spectroscopy reveals the existence of moiré-like superlattices3. More broadly, the synthesis of extended carbon structures by polymerization of molecular precursors charts a clear path to the systematic design of materials for the construction of two-dimensional heterostructures with tunable optoelectronic properties.

HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.

MOTIVATION: Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioactive peptides. Precise design of helices for binding or biological activity is still a challenging problem. RESULTS: Here, we present HelixGAN, the first generative adversarial network method to generate de novo left-handed and right-handed alpha-helix structures from scratch at an atomic level. We developed a gradient-based search approach in latent space to optimize the generation of novel α-helical structures by matching the exact conformations of selected hotspot residues. The designed α-helical structures can bind specific targets or activate cellular receptors. There is a significant agreement between the helix structures generated with HelixGAN and PEP-FOLD, a well-known de novo approach for predicting peptide structures from amino acid sequences. HelixGAN outperformed RosettaDesign, and our previously developed structural similarity method to generate D-peptides matching a set of given hotspots in a known L-peptide. As proof of concept, we designed a novel D-GLP1_1 analog that matches the conformations of critical hotspots for the GLP1 function. MD simulations revealed a stable binding mode of the D-GLP1_1 analog coupled to the GLP1 receptor. This novel D-peptide analog is more stable than our previous D-GLP1 design along the MD simulations. We envision HelixGAN as a critical tool for designing novel bioactive peptides with specific properties in the early stages of drug discovery. AVAILABILITY AND IMPLEMENTATION: https://github.com/xxiexuezhi/helix_gan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

Cys2His2 zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate engagement of ZF domains with DNA has made their design challenging. Here we describe the screening of 49 billion protein-DNA interactions and the development of a deep-learning model, ZFDesign, that solves ZF design for any genomic target. ZFDesign is a modern machine learning method that models global and target-specific differences induced by a range of library environments and specifically takes into account compatibility of neighboring fingers using a novel hierarchical transformer architecture. We demonstrate the versatility of designed ZFs as nucleases as well as activators and repressors by seamless reprogramming of human transcription factors. These factors could be used to upregulate an allele of haploinsufficiency, downregulate a gain-of-function mutation or test the consequence of regulation of a single gene as opposed to the many genes that a transcription factor would normally influence.

Electrically switchable anisotropic polariton propagation in a ferroelectric van der Waals semiconductor.

Tailoring of the propagation dynamics of exciton-polaritons in two-dimensional quantum materials has shown extraordinary promise to enable nanoscale control of electromagnetic fields. Varying permittivities along crystal directions within layers of material systems, can lead to an in-plane anisotropic dispersion of polaritons. Exploiting this physics as a control strategy for manipulating the directional propagation of the polaritons is desired and remains elusive. Here we explore the in-plane anisotropic exciton-polariton propagation in SnSe, a group-IV monochalcogenide semiconductor that forms ferroelectric domains and shows room-temperature excitonic behaviour. Exciton-polaritons are launched in SnSe multilayer plates, and their propagation dynamics and dispersion are studied. This propagation of exciton-polaritons allows for nanoscale imaging of the in-plane ferroelectric domains. Finally, we demonstrate the electric switching of the exciton-polaritons in the ferroelectric domains of this complex van der Waals system. The study suggests that systems such as group-IV monochalcogenides could serve as excellent ferroic platforms for actively reconfigurable polaritonic optical devices.

Computational and artificial intelligence-based methods for antibody development.

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.

Burden of Severe Illness Associated With Laboratory-Confirmed Influenza in Adults Aged 50-64 Years, 2010-2011 to 2016-2017.

Background: Understanding the burden of influenza is necessary to optimize recommendations for influenza vaccination. We describe the epidemiology of severe influenza in 50- to 64-year-old residents of metropolitan Toronto and Peel region, Canada, over 7 influenza seasons. Methods: Prospective population-based surveillance for hospitalization associated with laboratory-confirmed influenza was conducted from September 2010 to August 2017. Conditions increasing risk of influenza complications were as defined by Canada's National Advisory Committee on Immunization. Age-specific prevalence of medical conditions was estimated using Ontario health administrative data. Population rates were estimated using Statistics Canada data. Results: Over 7 seasons, 1228 hospitalizations occurred in patients aged 50-64 years: 40% due to A(H3N2), 30% A(H1N1), and 22% influenza B. The average annual hospitalization rate was 15.6, 20.9, and 33.2 per 100 000 in patients aged 50-54, 55-59, and 60-64 years, respectively; average annual mortality was 0.9/100 000. Overall, 33% of patients had received current season influenza vaccine; 963 (86%) had ≥1 underlying condition increasing influenza complication risk. The most common underlying medical conditions were chronic lung disease (38%) and diabetes mellitus (31%); 25% of patients were immunocompromised. The average annual hospitalization rate was 6.1/100 000 in those without and 41/100 000 in those with any underlying condition, and highest in those with renal disease or immunocompromise (138 and 281 per 100 000, respectively). The case fatality rate in hospitalized patients was 4.4%; median length of stay was 4 days (interquartile range, 2-8 days). Conclusions: The burden of severe influenza in 50- to 64-year-olds remains significant despite our universal publicly funded vaccination program. These data may assist in improving estimates of the cost-effectiveness of new strategies to reduce this burden.

Torsional periodic lattice distortions and diffraction of twisted 2D materials.

Twisted 2D materials form complex moiré structures that spontaneously reduce symmetry through picoscale deformation within a mesoscale lattice. We show twisted 2D materials contain a torsional displacement field comprised of three transverse periodic lattice distortions (PLD). The torsional PLD amplitude provides a single order parameter that concisely describes the structural complexity of twisted bilayer moirés. Moreover, the structure and amplitude of a torsional periodic lattice distortion is quantifiable using rudimentary electron diffraction methods sensitive to reciprocal space. In twisted bilayer graphene, the torsional PLD begins to form at angles below 3.89° and the amplitude reaches 8 pm around the magic angle of 1. 1°. At extremely low twist angles (e.g. below 0.25°) the amplitude increases and additional PLD harmonics arise to expand Bernal stacked domains separated by well defined solitonic boundaries. The torsional distortion field in twisted bilayer graphene is analytically described and has an upper bound of 22.6 pm. Similar torsional distortions are observed in twisted WS2, CrI3, and WSe2/MoSe2.

Antibodies as drugs-a Keystone Symposia report.

Therapeutic antibodies have broad indications across diverse disease states, such as oncology, autoimmune diseases, and infectious diseases. New research continues to identify antibodies with therapeutic potential as well as methods to improve upon endogenous antibodies and to design antibodies de novo. On April 27-30, 2022, experts in antibody research across academia and industry met for the Keystone symposium "Antibodies as Drugs" to present the state-of-the-art in antibody therapeutics, repertoires and deep learning, bispecific antibodies, and engineering.

Modulation Doping of Single-Layer Semiconductors for Improved Contact at Metal Interfaces.

Single layers of two-dimensional (2D) materials hold the promise for further miniaturization of semiconductor electronic devices. However, the metal-semiconductor contact resistance limits device performance. To mitigate this problem, we propose modulation doping, specifically a doping layer placed on the opposite side of a metal-semiconductor interface. Using first-principles calculations to obtain the band alignment, we show that the Schottky barrier height and, consequently, the contact resistance at the metal-semiconductor interface can be reduced by modulation doping. We demonstrate the feasibility of this approach for a single-layer tungsten diselenide (WSe2) channel and 2D MXene modulation doping layers, interfaced with several different metal contacts. Our results indicate that the Fermi level of the metal can be shifted across the entire band gap. This approach can be straight-forwardly generalized for other 2D semiconductors and a wide variety of doping layers.

Targeting the Receptor-Binding Motif of SARS-CoV-2 with D-Peptides Mimicking the ACE2 Binding Helix: Lessons for Inhibiting Omicron and Future Variants of Concern.

The COVID-19 pandemic continues to spread around the world, with several new variants emerging, particularly those of concern (VOCs). Omicron (B.1.1.529), a recent VOC with many mutations in the spike protein's receptor-binding domain (RBD), has attracted a great deal of scientific and public interest. We previously developed two D-peptide inhibitors for the infection of the original SARS-CoV-2 and its VOCs, alpha and beta, in vitro. Here, we demonstrated that Covid3 and Covid_extended_1 maintained their high-affinity binding (29.4-31.3 nM) to the omicron RBD. Both D-peptides blocked the omicron variant in vitro infection with IC50s of 3.13 and 5.56 µM, respectively. We predicted that Covid3 shares a larger overlapping binding region with the ACE2 binding motif than different classes of neutralizing monoclonal antibodies. We envisioned the design of D-peptide inhibitors targeting the receptor-binding motif as the most promising approach for inhibiting current and future VOCs of SARS-CoV-2, given that the ACE2 binding interface is more limited to tolerate mutations than most of the RBD's surface.

Beam steering at the nanosecond time scale with an atomically thin reflector.

Techniques to mold the flow of light on subwavelength scales enable fundamentally new optical systems and device applications. The realization of programmable, active optical systems with fast, tunable components is among the outstanding challenges in the field. Here, we experimentally demonstrate a few-pixel beam steering device based on electrostatic gate control of excitons in an atomically thin semiconductor with strong light-matter interactions. By combining the high reflectivity of a MoSe2 monolayer with a graphene split-gate geometry, we shape the wavefront phase profile to achieve continuously tunable beam deflection with a range of 10°, two-dimensional beam steering, and switching times down to 1.6 nanoseconds. Our approach opens the door for a new class of atomically thin optical systems, such as rapidly switchable beam arrays and quantum metasurfaces operating at their fundamental thickness limit.

PepNN: a deep attention model for the identification of peptide binding sites.

Protein-peptide interactions play a fundamental role in many cellular processes, but remain underexplored experimentally and difficult to model computationally. Here, we present PepNN-Struct and PepNN-Seq, structure and sequence-based approaches for the prediction of peptide binding sites on a protein. A main difficulty for the prediction of peptide-protein interactions is the flexibility of peptides and their tendency to undergo conformational changes upon binding. Motivated by this, we developed reciprocal attention to simultaneously update the encodings of peptide and protein residues while enforcing symmetry, allowing for information flow between the two inputs. PepNN integrates this module with modern graph neural network layers and a series of transfer learning steps are used during training to compensate for the scarcity of peptide-protein complex information. We show that PepNN-Struct achieves consistently high performance across different benchmark datasets. We also show that PepNN makes reasonable peptide-agnostic predictions, allowing for the identification of novel peptide binding proteins.

Evidence for 4e charge of Cooper quartets in a biased multi-terminal graphene-based Josephson junction.

In a Josephson junction (JJ) at zero bias, Cooper pairs are transported between two superconducting contacts via the Andreev bound states (ABSs) formed in the Josephson channel. Extending JJs to multiple superconducting contacts, the ABSs in the Josephson channel can coherently hybridize Cooper pairs among different superconducting electrodes. Biasing three-terminal JJs with antisymmetric voltages, for example, results in a direct current (DC) of Cooper quartet (CQ), which involves a four-fermion entanglement. Here, we report half a flux periodicity in the interference of CQ formed in graphene based multi-terminal (MT) JJs with a magnetic flux loop. We observe that the quartet differential conductance associated with supercurrent exhibits magneto-oscillations associated with a charge of 4e, thereby presenting evidence for interference between different CQ processes. The CQ critical current shows non-monotonic bias dependent behavior, which can be modeled by transitions between Floquet-ABSs. Our experimental observation for voltage-tunable non-equilibrium CQ-ABS in flux-loop-JJs significantly extends our understanding of MT-JJs, enabling future design of topologically unique ABS spectrum.

Diagnosis, Treatment, and Management of Painful Scar: A Narrative Review.

Painful scars can develop after surgery or trauma, with symptoms ranging from a minor itch to intractable allodynia. The problem of the painful scar may involve both intraneural and extraneural structures, requiring a systematic approach to diagnosis and treatment of this neuropathic pain condition that can impact quality of life and function profoundly. In this review, we outline the algorithm for the diagnosis, management, medical and surgical treatment of painful scars.

Phage display identification of nanomolar ligands for human NEDD4-WW3: Energetic and dynamic implications for the development of broad-spectrum antivirals.

The recognition of PPxY viral Late domains by the third WW domain of the human HECT-E3 ubiquitin ligase NEDD4 (NEDD4-WW3) is essential for the budding of many viruses. Blocking these interactions is a promising strategy to develop broad-spectrum antivirals. As all WW domains, NEDD4-WW3 is a challenging therapeutic target due to the low binding affinity of its natural interactions, its high conformational plasticity, and its complex thermodynamic behavior. In this work, we set out to investigate whether high affinity can be achieved for monovalent ligands binding to the isolated NEDD4-WW3 domain. We show that a competitive phage-display set-up allows for the identification of high-affinity peptides showing inhibitory activity of viral budding. A detailed biophysical study combining calorimetry, nuclear magnetic resonance, and molecular dynamic simulations reveals that the improvement in binding affinity does not arise from the establishment of new interactions with the domain, but is associated to conformational restrictions imposed by a novel C-terminal -LFP motif in the ligand, unprecedented in the PPxY interactome. These results, which highlight the complexity of WW domain interactions, provide valuable insight into the key elements for high binding affinity, of interest to guide virtual screening campaigns for the identification of novel therapeutics targeting NEDD4-WW3 interactions.

Chloroaluminate Anion Intercalation in Graphene and Graphite: From Two-Dimensional Devices to Aluminum-Ion Batteries.

A rechargeable aluminum-ion battery based on chloroaluminate electrolytes has received intense attention due to the high abundance and chemical stability of aluminum. However, the fundamental intercalation processes and dynamics in these battery systems remain unresolved. Here, the energetics and dynamics of chloroaluminate ion intercalation in atomically thin single crystal graphite are investigated by fabricating mesoscopic devices for charge transport and operando optical microscopy. These mesoscopic measurements are compared to the high-performance rechargeable Al-based battery consisting of a few-layer graphene-multiwall carbon nanotube composite cathode. These composites exhibit a 60% capacity enhancement over pyrolytic graphite, while an ∼3-fold improvement in overall ion diffusivity is also obtained exhibiting ∼1% of those in atomically thin single crystals. Our results thus establish the distinction between intrinsic and ensemble electrochemical behavior in Al-based batteries and show that engineering ion transport in these devices can yet lead to vast improvements in battery performance.

Crossover between strongly coupled and weakly coupled exciton superfluids.

In fermionic systems, superconductivity and superfluidity occur through the condensation of fermion pairs. The nature of this condensate can be tuned by varying the pairing strength, which is challenging in electronic systems. We studied graphene double layers separated by an atomically thin insulator. Under applied magnetic field, electrons and holes couple across the barrier to form bound magneto-excitons whose pairing strength can be continuously tuned by varying the effective layer separation. Using temperature-dependent Coulomb drag and counterflow current measurements, we were able to tune the magneto-exciton condensate through the entire phase diagram from weak to strong coupling. Our results establish magneto-exciton condensates in graphene as a model platform to study the crossover between two bosonic quantum condensate phases in a solid-state system.