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The reranking of the peptides docked to siRNA, together with explicit water molecular dynamics simulations, appears to be well suited to describe and predict the interaction of CPPs with siRNA.
A system was developed to evaluate and predict the interaction between protein pairs by using the widely used shape complementarity search method as the algorithm for docking simulations between the proteins.
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To date, several tools have been developed to investigate and predict the interactions of transcription factors, mRNAs and miRNAs.
Mei et al. (2012) further extended this BLM approach to incorporate the capacity of learning from neighbors and predict the interactions for new drug or target candidates.
Understanding and predicting the interaction between the shallow aquifer and surface water is a key component for fine-tuning the process.
Mechanistic model-based analysis has been used to aid in drug discovery and development, specifically in understanding and predicting the interaction of small molecule inhibitors with pathways.
The emergence of multi resistant bacterial strains, on the one hand and the question of the relevance of riboswitches and ribozymes and non coding RNA sequences in general, has renewed interest in understanding and predicting the interaction between RNA and small molecules.
Our method has no use of the negative samples and predicts the interaction of each drug-protein pair.
This tool parses the structure coordinate files, removes the hetero atoms and water molecules, and predicts the interaction based on coordinate distance between atoms of amino acid residues of small peptide and the protein.
Understanding and predicting the interactions between small molecules and such proteins could therefore help in the discovery of new lead compounds.
It is important to correctly and efficiently predict the interaction of substrate-enzyme and to predict their product in metabolic pathway.
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CEO of Professional Science Editing for Scientists @ prosciediting.com