ELIHKSIR

ELIHKSIR predicts interaction specificity within two-component systems (TCS) by inferring evolutionary interfacial couplings and local fields from histidine kinase (HK) and response regulator (RR) sequence data using a global probabilistic model to compute Hamiltonian specificity scores.

Key Features:

• Global probabilistic modeling: Uses a global probabilistic model to infer statistical dependencies across HK and RR sequences.
• Interfacial couplings and local fields: Identifies interfacial couplings and local fields from sequence data.
• Hamiltonian specificity scores: Constructs Hamiltonian scores to quantify encoded specificity at individual sequence positions.
• Proteome-scale scoring: Generates specificity scores for 6676 organisms.
• Mutation simulation: Simulates mutations in proteins and quantifies resultant changes in TCS network specificity.
• Network-level predictions: Predicts specificity across entire TCS networks for a broad range of organisms.
• Analyses and visualizations: Produces analyses and visualizations of interaction specificity and network changes.

Scientific Applications:

• HK–RR specificity prediction: Predicts cognate and non-cognate HK–RR interaction specificity within TCS.
• Crosstalk analysis: Characterizes potential crosstalk between non-cognate HK–RR pairs.
• Evolutionary inference: Infers evolutionary constraints on interfacial residues and the evolution of TCS specificity.
• Mutational impact assessment: Assesses effects of point mutations on interaction specificity and network rewiring.
• Signaling network analysis: Analyzes signaling pathway dynamics and network architecture in bacteria and plants.

Methodology:

Infer interfacial couplings and local fields from sequence data using a global probabilistic model, construct Hamiltonian scores to quantify specificity, generate specificity scores across organisms, and perform in silico mutation simulations.