Allopiper
World's First Drug Target Protein Signal Prediction System
Importance of GPCR Signal Transduction Modeling
Accurately predicting GPCR G-protein and β-arrestin signal selectivity is critical for drug design and development.
Beyond simple receptor binding, the therapeutic effect can vary—or adverse effects can intensify—depending on which signaling pathway (G-protein or β-arrestin) is selectively activated following drug binding. Therefore, predicting signal selectivity is essential for Agonist and PAM (positive allosteric modulator) design to maximize desired pharmacological effects while minimizing unwanted side effects.
Innovation in Membrane Protein Signal Transduction Prediction
Allopiper (PPI + ACP) is the world's first platform capable of quantitatively predicting biased signaling in membrane proteins such as GPCRs.
Atomatrix's Allopiper platform structurally interprets allosteric communication pathways (ACP) from ligand binding to intracellular protein-protein interactions (PPI), elucidating the mechanisms of biased agonism—which has been difficult to achieve with conventional CADD technologies—and enabling differentiated pharmacological effect prediction.
To achieve this, Allopiper precisely tracks the entire signal transduction process through allosteric modulation within membrane protein structures and PPI modeling between GPCRs and signaling proteins.
Integrated PPI + ACP Platform
Allopiper provides the world's first multi-layer GPCR signal prediction platform created by independently integrating two distinct analytical systems.
Allosteric Communication Pipeline
Predicts biased signaling pathways and signal network structural changes within GPCR structures through entropy analysis-based conformational change modeling.
Protein-Protein Interaction
Predicts signaling proteins through modeling-based binding structure analysis of GPCR-signaling protein complexes.
Synergy Between Two Platforms
Allopiper's ACP module interprets how signals propagate within GPCRs following ligand (drug)-GPCR binding, while the PPI module interprets which signaling proteins bind to the drug-GPCR complex. By comprehensively evaluating these in a multi-layered approach, it accurately predicts GPCR signal bias.
Allopiper Research Case: AT1R Bias Prediction Using ACP Module Technology
Allopiper accurately predicts from structural dynamics that three different ligands induce completely different signaling pathways for the same AT1R receptor.
AngII(balanced), TRV120026(Gq-biased), TRV120055(βarr2-biased)
Computational Ligand Bias Quantification
Allopiper quantifies computational ligand bias, demonstrating a strong correlation (R² > 0.85) with experimental bias factors.
Allopiper Research Case: Native G-protein Identification
in Class A GPCRs Using PPI Module Technology
Through TM3-TM6 Distance & Receptor:G-protein Energy analysis, it can distinguish with high accuracy which of the major G-protein types—Gs, Gi/o, Gq/11— binds 'natively' to the respective receptor.
Validation Publication:
Lee et al., 2022, Nature Communications
