ZIBaffinity

ZIBaffinity was designed to provide high-quality binding affinities ΔG of small chemical compounds to biological target molecules using atomistic molecular dynamics simulations on the basis of the Amber force field and methods of statistical thermodynamics.

Model Formulation

ΔG is calculated as the free energy difference between two distinct states of the ligand molecule, bound to the target and, respectively, free in water. Having uploaded a small drug-like molecule under observation as input, the user needs to select one or more protein target structures from a database of force field-parameterized models resulting in one job per target-ligand combination. Using modern cloud-computing technologies, the job is submitted to and processed on a high performance computer. The main results are then supplied to the user.

 

Ensuing from the uploaded small molecule, numerous MD simulations including different starting positions of the molecular complex (distributed according to the icosahedron’s symmetry) and one unbound simulation are carried out simultaneously. The optimal binding mode (complex conformation) is extracted from that data and provided as a 3D molecular structure serving, along with thermo-statistical data as the basis for absolute or relative binding affinity estimation.

The affinity is estimated as a linear combination of averages of molecular observables according to an extended linear interaction energy model



where the parameter coefficients xi need to be determined empirically in advance. Thus, the target data base only provides protein, for which a training set of ligands with known binding affinities were available for the purpose of training these weights. Currently, only one target structure is provided, the alpha estrogen receptor, for which a highly reliable linear model has been developed.

Further force field parameterized targets that are critical for human health will be added to the data base in the next future.

Simulation Technology
    • Molecules parameterized according to Amber force field (Amber99sb & GAFF)

    • Estimation of ligand charges with the AM1BCC method

    • Explicit solvation through TIP4Pew water model

    • Deterministic molecular dynamics simulations of ligand molecule (bound to target and free in solution) using Gromacs

Features
    • Calculation of absolute binding free energies ΔG for biological host–guest systems

    • Calculation of unweighted thermodynamic contributions to ΔG in case of target molecules lacking training set

    • Determination of a favourable host–guest binding mode

Input/Output
    • Atomic Cartesian coordinates of a small chemical compound in MOL2 or PDB file format uploaded by user

    • One or more target molecules selected by user from a data base of parameterized proteins

    • Atomic coordinates of favourable protein-ligand binding mode output in PDB format

    • Either free energy of binding or, if no weights for linear model exist, the single energy terms

References

V. Durmaz, S. Schmidt, P. Sabri, C. Piechotta, M. Weber: A hands-off linear interaction energy approach to binding mode and affinity estimation of estrogens. Journal of Chemical Information and Modeling, 53:2681-2688, 2013.

V. Spahn, G. Del Vecchio, D. Labuz, A. Rodriguez-Gaztelumendi, N. Massaly, J. Temp, V. Durmaz, P. Sabri, M. Reidelbach, H. Machelska, M. Weber, C. Stein: A non-toxic pain killer designed by modeling of pathological receptor conformations. Science, 355:966-969, 2017.

K. Heye, D. Becker, C. Lütke-Eversloh, V. Durmaz, T. A. Ternes, M. Oetken, J. Oehlmann: Effects of carbamazepine and two of its metabolites on the non-biting midge Chironomus riparius in a sediment full life cycle toxicity test. Water Research, 98:19-72, 2016.