coupling in the active SH3-SH2-Abl complex, suggesting that the SH2-KD interactions can allosterically stabilize the catalytically competent position of the αC-helix
and thus exert control over the kinase activity [61]. The same system was also
studied by using microsecond all-atom simulations and differential scanning calorimetry. The results from the dynamics of the SH3-SH2 tandem indicate a two-state
switch, alternating between conformations observed in the autoinhibited and active
complexes [62]. As a conclusion, computational studies of Abl and Src kinases
regulation have indicated a complex interplay between the SH3 and SH2 domains,
the SH2 linker, and the catalytic domain. These studies are in line with experimental
results.
5 Discussion
Many important topics have not been discussed above, like phosphorylation effects
and kinase dimerization. The main issue in modeling has hopefully been discussed
sufficiently to draw some conclusions. The following conclusions are based on case
studies in kinase modeling but, at the same time, are, after some modifications,
generally applicable to all drug discovery type modeling efforts.
Successful modeling starts with the appropriate question or proper research
hypothesis. This research question will lay the foundation for the selection of protein
structures to be used. In the case of kinase modeling, one must know if potentially
available protein structures (X-ray, Cryo-EM, or homology modeling) are in a
biologically relevant state. The next critical point is the correct ligand/library
preparation. Far too often, ligand tautomers/protomers are not based on detailed
studies, but modelers trust too much in automatic procedures. The third critical point
is too high confidence in docking and MD simulation results. Docking can, after all,
create some kind of binding pose to almost all of the compounds in virtual screening
libraries, although only a very small number of molecules are actually binding the
target protein. The same is true for MD simulations. Many papers show single 100 ns
simulations time stating that this is enough to identify binding/association/affinity.
Since biological assays are usually done as triplicates, we should ask why this is not
done with MD simulations [63, 64]. Instead of believing in one individual binding
pose proposed by docking or short MD simulation, one should run several computational experiments with different setups, repeat MD simulations, and study how
robust the proposed binding mode is to small changes in the system. At the same
time, one should not think that empirical data are always superior over computational results. As discussed above, X-ray structures often do have issues affecting
ligand structure, protein conformation, and structural determination, and sometimes
the whole protein structure is wrong [65, 66]. This means that like in modeling and
biological assays, one must look at all the structural biology data and combine
information from different sources.
Most of us like good food and wine/beer/water. We also know that good food and
drink cannot be created if we are using bad and rotten raw materials or dirty water.
36
A. Poso
and thus exert control over the kinase activity [61]. The same system was also
studied by using microsecond all-atom simulations and differential scanning calorimetry. The results from the dynamics of the SH3-SH2 tandem indicate a two-state
switch, alternating between conformations observed in the autoinhibited and active
complexes [62]. As a conclusion, computational studies of Abl and Src kinases
regulation have indicated a complex interplay between the SH3 and SH2 domains,
the SH2 linker, and the catalytic domain. These studies are in line with experimental
results.
5 Discussion
Many important topics have not been discussed above, like phosphorylation effects
and kinase dimerization. The main issue in modeling has hopefully been discussed
sufficiently to draw some conclusions. The following conclusions are based on case
studies in kinase modeling but, at the same time, are, after some modifications,
generally applicable to all drug discovery type modeling efforts.
Successful modeling starts with the appropriate question or proper research
hypothesis. This research question will lay the foundation for the selection of protein
structures to be used. In the case of kinase modeling, one must know if potentially
available protein structures (X-ray, Cryo-EM, or homology modeling) are in a
biologically relevant state. The next critical point is the correct ligand/library
preparation. Far too often, ligand tautomers/protomers are not based on detailed
studies, but modelers trust too much in automatic procedures. The third critical point
is too high confidence in docking and MD simulation results. Docking can, after all,
create some kind of binding pose to almost all of the compounds in virtual screening
libraries, although only a very small number of molecules are actually binding the
target protein. The same is true for MD simulations. Many papers show single 100 ns
simulations time stating that this is enough to identify binding/association/affinity.
Since biological assays are usually done as triplicates, we should ask why this is not
done with MD simulations [63, 64]. Instead of believing in one individual binding
pose proposed by docking or short MD simulation, one should run several computational experiments with different setups, repeat MD simulations, and study how
robust the proposed binding mode is to small changes in the system. At the same
time, one should not think that empirical data are always superior over computational results. As discussed above, X-ray structures often do have issues affecting
ligand structure, protein conformation, and structural determination, and sometimes
the whole protein structure is wrong [65, 66]. This means that like in modeling and
biological assays, one must look at all the structural biology data and combine
information from different sources.
Most of us like good food and wine/beer/water. We also know that good food and
drink cannot be created if we are using bad and rotten raw materials or dirty water.
36
A. Poso