AIMC Topic: HEK293 Cells

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Prime editor with rational design and AI-driven optimization for reverse editing window and enhanced fidelity.

Nature communications
Prime editing (PE) is a precise tool for introducing genetic mutations in eukaryotes. Extending the efficient editing scope and mitigating undesired byproducts are possible. We introduce reverse PE (rPE), a SpCas9-directed variant that enabled DNA ed...

Toxicological evaluation of Artocarpus lacucha ethyl acetate extract: in vitro and in vivo assessment.

Journal of ethnopharmacology
ETHNOPHARMACOLOGICAL RELEVANCE: Artocarpus lacucha Buch. -Ham. (syn. Artocarpus lakoocha) (A. lacucha), is a tropical fruit tree and a member of the Moraceae family. Fruit, bark, foliage, and roots of A. lacucha are broadly utilized in folklore medic...

An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor.

Acta pharmacologica Sinica
The GluN1/GluN3A receptor, a unique excitatory glycine receptor recently identified in the central nervous system, challenges traditional perspectives of N-methyl-D-aspartate (NMDA) receptor diversity and glycinergic signaling. Its role in emotional ...

PeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification.

Journal of proteome research
The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, d...

A synthetic protein-level neural network in mammalian cells.

Science (New York, N.Y.)
Artificial neural networks provide a powerful paradigm for nonbiological information processing. To understand whether similar principles could enable computation within living cells, we combined de novo-designed protein heterodimers and engineered v...

Cyto-Safe: A Machine Learning Tool for Early Identification of Cytotoxic Compounds in Drug Discovery.

Journal of chemical information and modeling
Cytotoxicity is essential in drug discovery, enabling early evaluation of toxic compounds during screenings to minimize toxicological risks. assays support high-throughput screening, allowing for efficient detection of toxic substances while conside...

Effective genome editing with an enhanced ISDra2 TnpB system and deep learning-predicted ωRNAs.

Nature methods
Transposon (IS200/IS605)-encoded TnpB proteins are predecessors of class 2 type V CRISPR effectors and have emerged as one of the most compact genome editors identified thus far. Here, we optimized the design of Deinococcus radiodurans (ISDra2) TnpB ...

Machine Learning-Driven Discovery and Evaluation of Antimicrobial Peptides from Mucus Proteome.

Marine drugs
Marine antimicrobial peptides (AMPs) represent a promising source for combating infections, especially against antibiotic-resistant pathogens and traditionally challenging infections. However, traditional drug discovery methods face challenges such a...

Deep learning large-scale drug discovery and repurposing.

Nature computational science
Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochon...

Synergistic Polymer Blending Informs Efficient Terpolymer Design and Machine Learning Discerns Performance Trends for pDNA Delivery.

Bioconjugate chemistry
Cationic polymers offer an alternative to viral vectors in nucleic acid delivery. However, the development of polymer vehicles capable of high transfection efficiency and minimal toxicity has remained elusive, and continued exploration of the vast de...