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Lamiable, A.et al. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex.Nucleic Acids Res.44, W449–54 (2016). ArticleCASGoogle Scholar Singh, S.et al. PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residue...
Peptide Structure Prediction Server Pep-Fold http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD/ Tool to predict peptide structures from amino acid sequences PEPstrMOD http://osddlinux.osdd.net/raghava/pepstrmod Server to predict the tertiary structure of small peptides Protein Structure...
Hyperparameter optimization is a challenging task. Optuna51is a Python tool for hyperparameter optimization that leverages the Tree-structured Parzen Estimator (TPE)52technique. Its aim is to determine the most promising hyperparameter configurations through Bayesian probability inference and iteratively opti...
Prediction of Multi-Class Peptides by T-cell Receptor Sequences with Deep Learning-Li Zhang-ICBS20242024-07-23, 视频播放量 52、弹幕量 0、点赞数 1、投硬币枚数 0、收藏人数 2、转发人数 0, 视频作者 BIMSA, 作者简介 ,相关视频:国际基础科学大会-On the existenc
Currently, there are various bioinformatics tools focused on the prediction of peptides with different activities. However, none of the current tools can predict ligand peptides of potassium channels. In this work, we developed a tool called PPLK+C; this is the first tool that can predict ...
Molecular modeling can provide valuable insights into amyloid oligomer formation and structure by allowing the visualization, interpretation, and prediction of the conformations, motions, and interactions of the peptides and proteins involved [4]. These simulations allow observation of that which cannot be...
DNA Construct Design ToolNew GenSmart™ Design CRISPR GenCRISPR gRNA + HDR Design ToolNew Peptide Chart of Amino Acid Structure Amino Acid Code Peptide Property Calculator Peptide Analyzing Tool Peptide Library Design Tools Antigen Prediction
Anticancer peptides (ACPs) perform a promising role in discovering anti-cancer drugs. The growing research on ACPs as therapeutic agent is increasing due to its minimal side effects. However, identifying novel ACPs using wet-lab experiments are generally
Furthermore, the experimental data could be fed back to a machine learning algorithm, and to develop a prediction tool of peptide sequences for efficient cell penetration. In summary, the NNJA platform can be very powerful to generate CPPs for specific delivery purposes. First of all, the ...