per se. Molecular docking-based methods use empirical scoring functions to find
the best docking conformations, and these methods are computationally less
expensive. Therefore, they can be applied to assess many protein–ligand complexes. The ligand can be docked to various mutant proteins to predict their binding
strength before and after mutations, and this will allow one to understand the effect
of the mutation on the binding strength. The accuracy of docking-based methods
relies on the accuracy of the scoring function, and they are best suited for rank
ordering of compounds rather than computing the absolute free energy of binding.
The major issue with docking-based methods is that most docking programs treat
proteins as rigid entities, and therefore, mutations in highly flexible protein–ligand
systems are poorly understood [19]. However, in recent times there have been
several attempts to incorporate protein flexibility in molecular docking [20]. This
has largely improved the enrichment scores. Due to the limited scope of this
chapter, such docking methods will not be discussed here and have been treated
elsewhere [21–25]. Molecular dynamics-based methods can incorporate flexibility
in the protein–ligand complexes, and in most cases, are the methods of choice as a
conformational sampling tool to explore the phase space accessible to the system
under study. The conformations sampled are used to compute the free energy
change. However, the drawback of MD-based methods is the computational cost,
which is several magnitudes higher compared to docking-based methods.
Another critical issue that must be addressed about the structure-based methods
is, how fast predictions can be made, in addition to how reliable are the predictions.
These methods find application in drug discovery programs, wherein additional
filters can be placed to weed out molecules likely to encounter a high level of
resistance or assist in suitably modifying leads to inhibit the mutant proteins. Drug
discovery itself is an extremely lengthy and expensive process, and an additional
filter like resistance should be economical in terms of time as well as money.
Moreover, such methods should also assist medicinal chemists during lead optimization stages to identify potential groups that will help evade drug resistance and
avoid late-stage failures that lead to huge financial losses.
2 Molecular Dynamics Simulations and Free Energy
Calculations
2.1 Overview of MD and Conformational Sampling
Methods
Computer simulations are very useful in predicting changes in molecular properties
brought about by alterations in an atom or a group of atoms, particularly, amino
acid residues. Therefore, they find good application in predicting the effect of
mutations on drug binding at the active site or elsewhere. Protein design experiments clarify the effect of a mutation on drug or substrate binding, thereby facilitating prediction of drug-resistant mutations. This way the program can be used to
4
E. A. F. Martis and E. C. Coutinho
the best docking conformations, and these methods are computationally less
expensive. Therefore, they can be applied to assess many protein–ligand complexes. The ligand can be docked to various mutant proteins to predict their binding
strength before and after mutations, and this will allow one to understand the effect
of the mutation on the binding strength. The accuracy of docking-based methods
relies on the accuracy of the scoring function, and they are best suited for rank
ordering of compounds rather than computing the absolute free energy of binding.
The major issue with docking-based methods is that most docking programs treat
proteins as rigid entities, and therefore, mutations in highly flexible protein–ligand
systems are poorly understood [19]. However, in recent times there have been
several attempts to incorporate protein flexibility in molecular docking [20]. This
has largely improved the enrichment scores. Due to the limited scope of this
chapter, such docking methods will not be discussed here and have been treated
elsewhere [21–25]. Molecular dynamics-based methods can incorporate flexibility
in the protein–ligand complexes, and in most cases, are the methods of choice as a
conformational sampling tool to explore the phase space accessible to the system
under study. The conformations sampled are used to compute the free energy
change. However, the drawback of MD-based methods is the computational cost,
which is several magnitudes higher compared to docking-based methods.
Another critical issue that must be addressed about the structure-based methods
is, how fast predictions can be made, in addition to how reliable are the predictions.
These methods find application in drug discovery programs, wherein additional
filters can be placed to weed out molecules likely to encounter a high level of
resistance or assist in suitably modifying leads to inhibit the mutant proteins. Drug
discovery itself is an extremely lengthy and expensive process, and an additional
filter like resistance should be economical in terms of time as well as money.
Moreover, such methods should also assist medicinal chemists during lead optimization stages to identify potential groups that will help evade drug resistance and
avoid late-stage failures that lead to huge financial losses.
2 Molecular Dynamics Simulations and Free Energy
Calculations
2.1 Overview of MD and Conformational Sampling
Methods
Computer simulations are very useful in predicting changes in molecular properties
brought about by alterations in an atom or a group of atoms, particularly, amino
acid residues. Therefore, they find good application in predicting the effect of
mutations on drug binding at the active site or elsewhere. Protein design experiments clarify the effect of a mutation on drug or substrate binding, thereby facilitating prediction of drug-resistant mutations. This way the program can be used to
4
E. A. F. Martis and E. C. Coutinho