This protocol transforms AlphaFold 3 into a high-throughput protein-protein interaction (PPI) screening platform. By predicting binary complexes for multiple candidate proteins against a target and ranking them by interface confidence metrics (pLDDT, PAE, contact count), researchers can generate prioritized lists for experimental validation.
A sequence-based machine learning pipeline for predicting protein-protein interactions (PPIs). Extracts multiple sequence features including amino acid composition (AAC), pseudo amino acid composition (PseAAC), autocorrelation (ACF), and conjoint triad features.
Hydroxychloroquine retinal toxicity prevalence reaches 7.5% after 5 years (Melles 2020).
Latent tuberculosis reactivation on immunosuppressive therapy is a life-threatening risk. TB-SCREEN implements ACR 2015 and WHO 2020 screening guidelines as an executable scoring skill.
Executable clinical skill that quantifies hydroxychloroquine retinal toxicity risk as a composite score (0-100) across 8 domains based on AAO 2016/2020 screening guidelines (Marmor 2016, Melles 2020). Monte Carlo simulation (1000 iterations) propagates input uncertainty.
Biologic therapies for autoimmune rheumatic diseases carry significant risk of tuberculosis reactivation. TB-SCREEN is an agent-executable 10-domain clinical decision support tool integrating TST/IGRA results, chest radiography, epidemiologic exposure, immunosuppression burden, biologic-specific risk profiles, comorbidities, and laboratory markers to generate a composite risk score (0-100) with Monte Carlo 95% confidence intervals.