Dynamics and flexibility analysis reveal hinge-shift residues that help WW domain variants fold correctly and recover peptide binding affinity.
Computational modeling maps protein folding intermediates and scores their stability and druggability to find ligand-targetable states.
Static single-cell snapshots can obscure temporal transitions; this method builds discretized time series and directed graphs to separate concurrent and sequential regulation.
Variable anchor points on target molecules resolve localization errors in binding free energy calculations by sampling multiple stable structures.
Machine learning models predict protein structural features and biophysical properties from amino acid sequences.
A simulation system applies dynamics-based constraints to manage non-consumed compound availability across biological network reactions.
Single-stranded short activating RNA molecules target non-coding transcripts to up-regulate target genes without viral vectors.
Computer program algorithm generates network maps from segmented molecular interaction datasets for inflammation analysis.
Measuring metabolite concentrations constrains genome scale metabolic models for accurate flux prediction.
A state dataset coordinates resource distribution across biological cell sub-models, eliminating master allocation artifacts.
A 3D simulation apparatus models intracellular responses using distributed modules to quantify protein production and degradation.
A compiler converts biological data into technique-specific configurations to generate simulation models automatically.
Topological analysis of gene networks identifies disease subtypes, resolving the trade-off between biomarker accuracy and statistical complexity.
Determines nucleotide counts in adjacent transcriptional regions and mRNA folding energies to predict polycistronic operon performance.
A constraint-based modeling approach calculates intracellular flux distributions using extracellular data from reference strains.