HyperProtein Description
HyperProtein is the latest offering from Hypercube, Inc., concentrating on the computational analysis of protein sequences. This innovative product not only examines one-dimensional sequences but also delves into the resulting three-dimensional structures of proteins. A key aspect of HyperProtein is its exploration of the intricate relationship between a protein's sequence and its structural form. In contrast to standalone software that targets specific functions like sequence alignment, HyperProtein combines a wide array of Bioinformatics and Molecular Modeling tools, providing a comprehensive approach to the science that begins with a protein sequence. By integrating these diverse tools, HyperProtein aims to enhance the understanding of protein functions and interactions at a molecular level, making it a valuable resource for researchers in the field.
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Evo 2
Evo 2 represents a cutting-edge genomic foundation model that excels in making predictions and designing tasks related to DNA, RNA, and proteins. It employs an advanced deep learning architecture that allows for the modeling of biological sequences with single-nucleotide accuracy, achieving impressive scaling of both compute and memory resources as the context length increases. With a robust training of 40 billion parameters and a context length of 1 megabase, Evo 2 has analyzed over 9 trillion nucleotides sourced from a variety of eukaryotic and prokaryotic genomes. This extensive dataset facilitates Evo 2's ability to conduct zero-shot function predictions across various biological types, including DNA, RNA, and proteins, while also being capable of generating innovative sequences that maintain a plausible genomic structure. The model's versatility has been showcased through its effectiveness in designing operational CRISPR systems and in the identification of mutations that could lead to diseases in human genes. Furthermore, Evo 2 is available to the public on Arc's GitHub repository, and it is also incorporated into the NVIDIA BioNeMo framework, enhancing its accessibility for researchers and developers alike. Its integration into existing platforms signifies a major step forward for genomic modeling and analysis.
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ESMC
ESMC represents the newest advancement in the ESM series of protein language models, pushing the boundaries of representation learning within the field of protein biology. With training on billions of evolutionary sequences, it adeptly captures representations that encapsulate a mechanistic understanding of protein structure and function. The model utilizes a transformer architecture, focusing on sequences as its primary modality, and is trained on a vast dataset comprising up to 6 billion proteins. ESMC is tailored for various protein science applications, such as predicting structures, annotating functions, designing proteins, and exploring evolutionary connections among proteins. Additionally, it possesses the capability to create novel proteins based on partial sequences, structures, or functional constraints, thereby enabling researchers to investigate innovative avenues in protein design and biological discovery. Accessible through the Biohub Platform, ESMC can be utilized via an API and the ESM Python package, which includes quickstart resources for installation, API key generation, and platform connectivity, ensuring a seamless experience for users. This comprehensive accessibility encourages a broader engagement with protein research and enhances collaborative efforts in the scientific community.
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Integrations
No Integrations at this time
Company Details
Company:
Hypercube
Website:
www.hypercubeusa.com
Media
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Product Details
Platforms
Web-Based
HyperProtein Features and Options
Chemical Software
Certificates of Analysis
Chemical Process Simulation
Computer-Assisted Structure Elucidation (CASE)
ISO Management
Inventory Management
Particle Tracking
Reporting & Statistics
Samples Tracking
Traceability
Uncertainty Analysis
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