Results for “protein-modeling”

Applies physical principles to model biological systems, including protein folding, membrane transport, molecular forces, and neural signaling.

Predict 3D binding poses of small molecule ligands to protein targets using diffusion-based molecular docking, supporting single complexes, batch processing, and virtual screening.

Analyze and engineer protein glycosylation by scanning sequences for N-glycosylation sequons, predicting O-glycosylation hotspots, and accessing curated glycoengineering tools for therapeutic antibody optimization and vaccine design.

Performs differential expression analysis on label-free quantitative (LFQ) proteomics data from MaxQuant and DIA-NN outputs, including preprocessing, imputation, statistical testing, and visualization.

Run cloud-native molecular modeling and drug-design workflows including pKa prediction, docking, molecular dynamics, and protein-ligand cofolding via a Python API.