Topic
Construct (python library)
About: Construct (python library) is a research topic. Over the lifetime, 1440 publications have been published within this topic receiving 25281 citations. The topic is also known as: construct.
Papers published on a yearly basis
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Papers
TL;DR: The ESM Metage-nomic Atlas as discussed by the authors is the first large-scale structural characterization of metagenomic proteins, with more than 617 million structures, including more than 225 million high confidence predictions.
Abstract: Artificial intelligence has the potential to open insight into the structure of proteins at the scale of evolution. It has only recently been possible to extend protein structure prediction to two hundred million cataloged proteins. Characterizing the structures of the exponentially growing billions of protein sequences revealed by large scale gene sequencing experiments would necessitate a break-through in the speed of folding. Here we show that direct inference of structure from primary sequence using a large language model enables an order of magnitude speed-up in high resolution structure prediction. Leveraging the insight that language models learn evolutionary patterns across millions of sequences, we train models up to 15B parameters, the largest language model of proteins to date. As the language models are scaled they learn information that enables prediction of the three-dimensional structure of a protein at the resolution of individual atoms. This results in prediction that is up to 60x faster than state-of-the-art while maintaining resolution and accuracy. Building on this, we present the ESM Metage-nomic Atlas. This is the first large-scale structural characterization of metagenomic proteins, with more than 617 million structures. The atlas reveals more than 225 million high confidence predictions, including millions whose structures are novel in comparison with experimentally determined structures, giving an unprecedented view into the vast breadth and diversity of the structures of some of the least understood proteins on earth.
2,260 citations
TL;DR: A hierarchy of objects is derived such that no object at one level has a wait-free implementation in terms of objects at lower levels, and it is shown that atomic read/write registers, which have been the focus of much recent attention, are at the bottom of the hierarchy.
Abstract: A wait-free implementation of a concurrent data object is one that guarantees that any process can complete any operation in a finite number of steps, regardless of the execution speeds of the other processes. The problem of constructing a wait-free implementation of one data object from another lies at the heart of much recent work in concurrent algorithms, concurrent data structures, and multiprocessor architectures. First, we introduce a simple and general technique, based on reduction to a concensus protocol, for proving statements of the form, “there is no wait-free implementation of X by Y.” We derive a hierarchy of objects such that no object at one level has a wait-free implementation in terms of objects at lower levels. In particular, we show that atomic read/write registers, which have been the focus of much recent attention, are at the bottom of the hierarchy: thay cannot be used to construct wait-free implementations of many simple and familiar data types. Moreover, classical synchronization primitives such astest&set and fetch&add, while more powerful than read and write, are also computationally weak, as are the standard message-passing primitives. Second, nevertheless, we show that there do exist simple universal objects from which one can construct a wait-free implementation of any sequential object.
2,154 citations
TL;DR: This chapter presents an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure, and shows the potential for this technique to bridge the sequence-structure gap in protein structure modeling.
Abstract: Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. This chapter presents an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of similar protocols (correction of protcols) has resulted in models of useful accuracy for domains in more than half of all known protein sequences.
2,055 citations
TL;DR: Atomsk is a unified program that allows to generate, convert and transform atomic systems for the purposes of ab initio calculations, classical atomistic simulations, or visualization, in the areas of computational physics and chemistry.
1,531 citations
TL;DR: In 2010, the American Heart Association defined a novel construct of cardiovascular health to promote a paradigm shift from a focus solely on disease treatment to one inclusive of positive health promotion and preservation across the life course in populations and individuals as discussed by the authors .
Abstract: In 2010, the American Heart Association defined a novel construct of cardiovascular health to promote a paradigm shift from a focus solely on disease treatment to one inclusive of positive health promotion and preservation across the life course in populations and individuals. Extensive subsequent evidence has provided insights into strengths and limitations of the original approach to defining and quantifying cardiovascular health. In response, the American Heart Association convened a writing group to recommend enhancements and updates. The definition and quantification of each of the original metrics (Life's Simple 7) were evaluated for responsiveness to interindividual variation and intraindividual change. New metrics were considered, and the age spectrum was expanded to include the entire life course. The foundational contexts of social determinants of health and psychological health were addressed as crucial factors in optimizing and preserving cardiovascular health. This presidential advisory introduces an enhanced approach to assessing cardiovascular health: Life's Essential 8. The components of Life's Essential 8 include diet (updated), physical activity, nicotine exposure (updated), sleep health (new), body mass index, blood lipids (updated), blood glucose (updated), and blood pressure. Each metric has a new scoring algorithm ranging from 0 to 100 points, allowing generation of a new composite cardiovascular health score (the unweighted average of all components) that also varies from 0 to 100 points. Methods for implementing cardiovascular health assessment and longitudinal monitoring are discussed, as are potential data sources and tools to promote widespread adoption in policy, public health, clinical, institutional, and community settings.
1,180 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2026 | 74 |
| 2025 | 2,334 |
| 2024 | 2,977 |
| 2023 | 7,755 |
| 2022 | 12,816 |
| 2021 | 651 |