De novo designed proteins neutralize lethal snake venom toxins.

Snakebite envenoming remains a devastating and neglected tropical disease, claiming over 100,000 lives annually and causing severe complications and long-lasting disabilities for many more1,2. Three-finger toxins (3FTx) are highly toxic components of elapid snake venoms that can cause diverse pathologies, including severe tissue damage3 and inhibition of nicotinic acetylcholine receptors (nAChRs) resulting in life-threatening neurotoxicity4. Currently, the only available treatments for snakebite consist of polyclonal antibodies derived from the plasma of immunized animals, which have high cost and limited efficacy against 3FTxs5,6,7. Here, we use deep learning methods to de novo design proteins to bind short- and long-chain α-neurotoxins and cytotoxins from the 3FTx family. With limited experimental screening, we obtain protein designs with remarkable thermal stability, high binding affinity, and near-atomic level agreement with the computational models. The designed proteins effectively neutralize all three 3FTx sub-families in vitro and protect mice from a lethal neurotoxin challenge. Such potent, stable, and readily manufacturable toxin-neutralizing proteins could provide the basis for safer, cost-effective, and widely accessible next-generation antivenom therapeutics. Beyond snakebite, our computational design methodology should help democratize therapeutic discovery, particularly in resource-limited settings, by substantially reducing costs and resource requirements for development of therapies to neglected tropical diseases.

Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder.

Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across days. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form. Recent efforts have examined MDD heterogeneity byinvestigating how symptoms influence one another over time across individuals in a system; however, these efforts have assumed that symptom dynamics are static and do not dynamically change over time. Nevertheless, it is possible that individual MDD system dynamics change continuously across time. Participants (N = 105) completed ratings of MDD symptoms three times a day for 90 days, and we conducted time varying vector autoregressive models to investigate the idiographic symptom networks. We then illustrated this finding with a case series of five persons with MDD. Supporting prior research, results indicate there is high heterogeneity across persons as individual network composition is unique from person to person. In addition, for most persons, individual symptom networks change dramatically across the 90 days, as evidenced by 86% of individuals experiencing at least one change in their most influential symptom and the median number of shifts being 3 over the 90 days. Additionally, most individuals had at least one symptom that acted as both the most and least influential symptom at any given point over the 90-day period. Our findings offer further insight into short-term symptom dynamics, suggesting that MDD is heterogeneous both across and within persons over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Ion binding with charge inversion combined with screening modulates DEAD box helicase phase transitions.

Membraneless organelles, or biomolecular condensates, enable cells to compartmentalize material and processes into unique biochemical environments. While specific, attractive molecular interactions are known to stabilize biomolecular condensates, repulsive interactions, and the balance between these opposing forces, are largely unexplored. Here, we demonstrate that repulsive and attractive electrostatic interactions regulate condensate stability, internal mobility, interfaces, and selective partitioning of molecules both in vitro and in cells. We find that signaling ions, such as calcium, alter repulsions between model Ddx3 and Ddx4 condensate proteins by directly binding to negatively charged amino acid sidechains and effectively inverting their charge, in a manner fundamentally dissimilar to electrostatic screening. Using a polymerization model combined with generalized stickers and spacers, we accurately quantify and predict condensate stability over a wide range of pH, salt concentrations, and amino acid sequences. Our model provides a general quantitative treatment for understanding how charge and ions reversibly control condensate stability.

Design of stimulus-responsive two-state hinge proteins.

In nature, proteins that switch between two conformations in response to environmental stimuli structurally transduce biochemical information in a manner analogous to how transistors control information flow in computing devices. Designing proteins with two distinct but fully structured conformations is a challenge for protein design as it requires sculpting an energy landscape with two distinct minima. Here we describe the design of "hinge" proteins that populate one designed state in the absence of ligand and a second designed state in the presence of ligand. X-ray crystallography, electron microscopy, double electron-electron resonance spectroscopy, and binding measurements demonstrate that despite the significant structural differences the two states are designed with atomic level accuracy and that the conformational and binding equilibria are closely coupled.

Defective T-cell response to COVID-19 vaccination in acute myeloid leukaemia and myelodysplastic syndromes.

Limited data exist on COVID-19 vaccination efficacy in patients with acute myeloid leukemia and myelodysplasia with excess blasts (AML/MDS-EB2). We report results from a prospective study, PACE (Patients with AML and COVID-19 Epidemiology). 93 patients provided samples post-vaccine 2 or 3 (PV2, PV3). Antibodies against SARS-COV-2 spike antigen were detectable in all samples. Neutralization of the omicron variant was poorer than ancestral variants but improved PV3. In contrast, adequate T-cell reactivity to SARS-COV-2 spike protein was seen in only 16/47 (34%) patients PV2 and 23/52 (44%) PV3. Using regression models, disease response (not in CR/Cri), and increasing age predicted poor T cell response.

De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles.

A general method for designing proteins to bind and sense any small molecule of interest would be widely useful. Due to the small number of atoms to interact with, binding to small molecules with high affinity requires highly shape complementary pockets, and transducing binding events into signals is challenging. Here we describe an integrated deep learning and energy based approach for designing high shape complementarity binders to small molecules that are poised for downstream sensing applications. We employ deep learning generated psuedocycles with repeating structural units surrounding central pockets; depending on the geometry of the structural unit and repeat number, these pockets span wide ranges of sizes and shapes. For a small molecule target of interest, we extensively sample high shape complementarity pseudocycles to generate large numbers of customized potential binding pockets; the ligand binding poses and the interacting interfaces are then optimized for high affinity binding. We computationally design binders to four diverse molecules, including for the first time polar flexible molecules such as methotrexate and thyroxine, which are expressed at high levels and have nanomolar affinities straight out of the computer. Co-crystal structures are nearly identical to the design models. Taking advantage of the modular repeating structure of pseudocycles and central location of the binding pockets, we constructed low noise nanopore sensors and chemically induced dimerization systems by splitting the binders into domains which assemble into the original pseudocycle pocket upon target molecule addition.

First-Gen Lens: Assessing Mental Health of First-Generation Students across Their First Year at College Using Mobile Sensing.

The transition from high school to college is a taxing time for young adults. New students arriving on campus navigate a myriad of challenges centered around adapting to new living situations, financial needs, academic pressures and social demands. First-year students need to gain new skills and strategies to cope with these new demands in order to make good decisions, ease their transition to independent living and ultimately succeed. In general, first-generation students are less prepared when they enter college in comparison to non-first-generation students. This presents additional challenges for first-generation students to overcome and be successful during their college years. We study first-year students through the lens of mobile phone sensing across their first year at college, including all academic terms and breaks. We collect longitudinal mobile sensing data for N=180 first-year college students, where 27 of the students are first-generation, representing 15% of the study cohort and representative of the number of first-generation students admitted each year at the study institution, Dartmouth College. We discuss risk factors, behavioral patterns and mental health of first-generation and non-first-generation students. We propose a deep learning model that accurately predicts the mental health of first-generation students by taking into account important distinguishing behavioral factors of first-generation students. Our study, which uses the StudentLife app, offers data-informed insights that could be used to identify struggling students and provide new forms of phone-based interventions with the goal of keeping students on track.

Simple mechanisms for the evolution of protein complexity.

Proteins are tiny models of biological complexity: specific interactions among their many amino acids cause proteins to fold into elaborate structures, assemble with other proteins into higher-order complexes, and change their functions and structures upon binding other molecules. These complex features are classically thought to evolve via long and gradual trajectories driven by persistent natural selection. But a growing body of evidence from biochemistry, protein engineering, and molecular evolution shows that naturally occurring proteins often exist at or near the genetic edge of multimerization, allostery, and even new folds, so just one or a few mutations can trigger acquisition of these properties. These sudden transitions can occur because many of the physical properties that underlie these features are present in simpler proteins as fortuitous by-products of their architecture. Moreover, complex features of proteins can be encoded by huge arrays of sequences, so they are accessible from many different starting points via many possible paths. Because the bridges to these features are both short and numerous, random chance can join selection as a key factor in explaining the evolution of molecular complexity.

Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data.

Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and mortality. Despite its popularity in consumer devices, a lack of methodological standards and clinical validation remains a major impediment to step count being accepted as a valid clinical endpoint. Previous works have mainly focused on device-specific step-count algorithms and often employ sensor modalities that may not be widely available. This may limit step-count suitability in clinical scenarios. In this paper, we trained neural network models on publicly available data and tested on an independent cohort using two approaches: generalization and personalization. Specifically, we trained neural networks on accelerometer signals from one device and either directly applied them or adapted them individually to accelerometer data obtained from a separate subject cohort wearing multiple distinct devices. The best models exhibited highly accurate step-count estimates for both the generalization (96-99%) and personalization (98-99%) approaches. The results demonstrate that it is possible to develop device-agnostic, accelerometer-only algorithms that provide highly accurate step counts, positioning step count as a reliable mobility endpoint and a strong candidate for clinical validation.

Author Correction: Origin of complexity in haemoglobin evolution.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Origin of complexity in haemoglobin evolution.

Most proteins associate into multimeric complexes with specific architectures1,2, which often have functional properties such as cooperative ligand binding or allosteric regulation3. No detailed knowledge is available about how any multimer and its functions arose during evolution. Here we use ancestral protein reconstruction and biophysical assays to elucidate the origins of vertebrate haemoglobin, a heterotetramer of paralogous α- and ß-subunits that mediates respiratory oxygen transport and exchange by cooperatively binding oxygen with moderate affinity. We show that modern haemoglobin evolved from an ancient monomer and characterize the historical 'missing link' through which the modern tetramer evolved-a noncooperative homodimer with high oxygen affinity that existed before the gene duplication that generated distinct α- and ß-subunits. Reintroducing just two post-duplication historical substitutions into the ancestral protein is sufficient to cause strong tetramerization by creating favourable contacts with more ancient residues on the opposing subunit. These surface substitutions markedly reduce oxygen affinity and even confer cooperativity, because an ancient linkage between the oxygen binding site and the multimerization interface was already an intrinsic feature of the protein's structure. Our findings establish that evolution can produce new complex molecular structures and functions via simple genetic mechanisms that recruit existing biophysical features into higher-level architectures.

The management of recurrent VTE in cancer patients receiving therapeutic anticoagulation: the use of dual anticoagulant therapy combined with an IVC filter.

Recurrent venous thromboembolism (VTE) occurs frequently in cancer patients, yet there is little published literature to guide clinicians in the management of these patients who are already receiving therapeutic anticoagulation. We report two patients with progressive solid malignancies who presented with recurrent VTE despite therapeutic anticoagulation with once-daily dalteparin. We describe how a novel aggressive strategy using a combination of therapeutic dalteparin twice daily and a vitamin K antagonist (warfarin) prevented further VTE.

Multisystem failure due to three coexisting autoimmune diseases.

Systemic lupus erythematosis (SLE) is a potentially fatal, autoimmune disease, which can affect different organs and can present with protean clinical manifestations. It may be associated with many other autoimmune conditions and two rare such conditions are myelofibrosis and acquired haemophilia. Autoimmune myelofibrosis is a bone marrow disorder characterized by pancytopenia, which can occur in conjunction with the presenting features, or an exacerbation of previously established SLE. Acquired haemophilia is another rare disorder of haemostasis, which can be life threatening without prompt and appropriate treatment. The management of these different conditions in itself poses a difficult problem but when the three conditions present simultaneously in the same individual, the accurate diagnosis and indeed the appropriate management becomes extremely challenging. This report describes a young woman who presented with pancytopenia secondary to myelofibrosis and panserositis with no identifiable precipitating factors. Her condition deteriorated rapidly and she required intensive care support for respiratory failure and renal impairment. A presumed diagnosis of SLE was considered and treatment was initiated which improved and stabilised her condition. However, she developed bleeding complications from acquired haemophilia which required further specialist intervention. Multidisciplinary management of the patient helped in the resolution of the complications and stabilisation of her autoimmune conditions. This report should make physicians aware of the rare presentations of SLE and its complex management.