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Peptides, Cyclic

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Training Neural Network Models Using Molecular Dynamics Simulation Results to Efficiently Predict Cyclic Hexapeptide Structural Ensembles.

Journal of chemical theory and computation
Cyclic peptides have emerged as a promising class of therapeutics. However, their design remains challenging, and many cyclic peptide drugs are simply natural products or their derivatives. Most cyclic peptides, including the current cyclic peptide ...

Simplified and enhanced VCD analysis of cyclic peptides guided by artificial intelligence.

Physical chemistry chemical physics : PCCP
Cyclic peptides are privileged structures in medicinal chemistry; however, their solution-state structure characterization is difficult. Vibrational circular dichroism (VCD) spectroscopy is a powerful alternative to NMR, but requires challenging calc...

Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides.

Journal of medicinal chemistry
Cyclic peptides are gaining attention for their strong binding affinity, low toxicity, and ability to target "undruggable" proteins; however, their therapeutic potential against intracellular targets is constrained by their limited membrane permeabil...

CyclicPepedia: a knowledge base of natural and synthetic cyclic peptides.

Briefings in bioinformatics
Cyclic peptides offer a range of notable advantages, including potent antibacterial properties, high binding affinity and specificity to target molecules, and minimal toxicity, making them highly promising candidates for drug development. However, a ...

PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization.

European journal of medicinal chemistry
Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial in...

CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning.

Scientific data
Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometrie...

MuCoCP: a priori chemical knowledge-based multimodal contrastive learning pre-trained neural network for the prediction of cyclic peptide membrane penetration ability.

Bioinformatics (Oxford, England)
MOTIVATION: There has been a burgeoning interest in cyclic peptide therapeutics due to their various outstanding advantages and strong potential for drug formation. However, it is undoubtedly costly and inefficient to use traditional wet lab methods ...

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

Journal of chemical information and modeling
MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in t...

CPconf_score: A Deep Learning Free Energy Function Trained Using Molecular Dynamics Data for Cyclic Peptides.

Journal of chemical theory and computation
Accurate structural feature characterization of cyclic peptides (CPs), especially those with less than 10 residues and -peptide bonds, is challenging but important for the rational design of bioactive peptides. In this study, we performed high-temper...

MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model.

BMC biology
BACKGROUND: Cyclic peptides, known for their high binding affinity and low toxicity, show potential as innovative drugs for targeting "undruggable" proteins. However, their therapeutic efficacy is often hindered by poor membrane permeability. Over th...