SEMBA

SEMBA predicts binding affinities between amyloid proteins and designed analogs to analyze oligomer-driven β-cell toxicity and inform interventions for amylin-related diabetes.

Key Features:

• Energy Function Analysis: Employs an energy function that computes Lennard-Jones, Coulomb, and solvation energies to assess how mutations affect protein stability and binding affinity.
• Amyloid Oligomer Interaction: Probes interactions between amyloid oligomers and designed analogs and evaluates extension of oligomers into longer fibrils via strategic binding to reduce oligomer toxicity.
• Mutation Design and Analysis: Facilitates design and analysis of amylin analogs containing specific mutations (T9K, L12K, S28H, T30K) chosen by a parsimonious approach to minimize interaction with existing therapeutics such as pramlintide.
• Mathematical Modeling: Integrates an extended mathematical model of the insulin–glucose relationship to evaluate how changes in oligomer concentration influence insulin release and β-cell fitness.
• Scoring Matrices and R-scores: Generates binding affinity scoring matrices and R-scores to provide quantitative measures of interaction strength between amyloid proteins and designed analogs.

Scientific Applications:

• Diabetes Research: Supports analysis of amyloid oligomer toxicity mechanisms and strategies to prolong β-cell survival in diabetes contexts.
• Drug Design: Informs design of amylin analogs intended for co-administration with existing therapies by evaluating potential interactions and binding affinities.
• Protein Stability Studies: Enables investigation of how specific mutations affect amylin stability, oligomerization propensity, and functional behavior in amyloid-related disease research.

Methodology:

SEMBA computes energy terms (Lennard-Jones, Coulomb, solvation), generates binding affinity scoring matrices and R-scores, and incorporates an extended insulin–glucose mathematical model to relate oligomer concentration to β-cell function.