However, a major drawback in alanine scanning is that when mutating a large
amino acid residue to alanine one can only study the effect of decreasing the side
chain or loss of charged groups in the binding site. It is difficult to understand the
resistant mutation, wherein there is a change in charged amino acid residue, for
example, arginine replacing aspartate or a large amino acid replaces a small amino
acid residue. Nevertheless, computational alanine scanning has been successfully
used to predict mutational hotspots.
Hao et al. [100] reported a modification of computational alanine scanning
(CAS), named computational mutation scanning (CMS) to study drug resistance in
six HIV-1 protease inhibitors. This protocol is an improvised version of the classical CAS that enables a geometry optimization step and incorporates entropy
calculations by means of normal model analysis. Using a single trajectory approach
and modifying the standard MM-PBSA protocol, to allow for mutations with other
amino acid residues, they computed the change in the binding affinities (DDG) of 77
drug-mutant combinations (includes single and double mutants). They obtained
promising results with *83% consistency with the experimental observations,
demonstrating that the prowess of the method lies in identifying the binding hotspots. However, Hao et al., do not report the change in the binding affinity for
various substrates, from which they could have investigated the substrate-envelope
hypothesis for the HIV-1 protease. This could have led to interesting findings
facilitating our understanding about those mutations that would lead to a decrease in
the enzyme function, either leading to the death of the organism or compelling a
compensatory mutation to counter the lethal effects of any mutation. This information can be used to unravel the role and need for double, triple or even multiple
mutations.
Tse and Verkhivker [101] used CAS along with residue interaction network to
elucidate the effects of inhibitor binding on the network of residues in ABL kinase.
They showed the utility of this combination in deducing the critical networks of
amino acid residues and the changes that follow upon inhibitor binding, using a
selective kinase inhibitor (nilotinib) and two promiscuous (bosutinib and dasatinib)
kinase inhibitors. The changes in the interaction networks in the enzyme holds key
hints to unravel the mystery of how drug-resistant mutations are seen for ABL
kinase inhibitors. Moreover, the mutations that occur far from the binding site can
also be explained, since a mutation far off from the site can affect drug binding
through a cascade of events that eventually percolate into the binding site through
the changes in the residue interaction network. CAS followed by MM-PBSA added
the energetic component to locate the hotspots that could lead to drug resistance in
the kinase inhibitors
3.2 MM-PB(GB)-SA
MM-GBSA or MM-PBSA are two widely used free energy methods employed to
understand the effects of mutations on the drug binding affinity, moreover, these
16
E. A. F. Martis and E. C. Coutinho
amino acid residue to alanine one can only study the effect of decreasing the side
chain or loss of charged groups in the binding site. It is difficult to understand the
resistant mutation, wherein there is a change in charged amino acid residue, for
example, arginine replacing aspartate or a large amino acid replaces a small amino
acid residue. Nevertheless, computational alanine scanning has been successfully
used to predict mutational hotspots.
Hao et al. [100] reported a modification of computational alanine scanning
(CAS), named computational mutation scanning (CMS) to study drug resistance in
six HIV-1 protease inhibitors. This protocol is an improvised version of the classical CAS that enables a geometry optimization step and incorporates entropy
calculations by means of normal model analysis. Using a single trajectory approach
and modifying the standard MM-PBSA protocol, to allow for mutations with other
amino acid residues, they computed the change in the binding affinities (DDG) of 77
drug-mutant combinations (includes single and double mutants). They obtained
promising results with *83% consistency with the experimental observations,
demonstrating that the prowess of the method lies in identifying the binding hotspots. However, Hao et al., do not report the change in the binding affinity for
various substrates, from which they could have investigated the substrate-envelope
hypothesis for the HIV-1 protease. This could have led to interesting findings
facilitating our understanding about those mutations that would lead to a decrease in
the enzyme function, either leading to the death of the organism or compelling a
compensatory mutation to counter the lethal effects of any mutation. This information can be used to unravel the role and need for double, triple or even multiple
mutations.
Tse and Verkhivker [101] used CAS along with residue interaction network to
elucidate the effects of inhibitor binding on the network of residues in ABL kinase.
They showed the utility of this combination in deducing the critical networks of
amino acid residues and the changes that follow upon inhibitor binding, using a
selective kinase inhibitor (nilotinib) and two promiscuous (bosutinib and dasatinib)
kinase inhibitors. The changes in the interaction networks in the enzyme holds key
hints to unravel the mystery of how drug-resistant mutations are seen for ABL
kinase inhibitors. Moreover, the mutations that occur far from the binding site can
also be explained, since a mutation far off from the site can affect drug binding
through a cascade of events that eventually percolate into the binding site through
the changes in the residue interaction network. CAS followed by MM-PBSA added
the energetic component to locate the hotspots that could lead to drug resistance in
the kinase inhibitors
3.2 MM-PB(GB)-SA
MM-GBSA or MM-PBSA are two widely used free energy methods employed to
understand the effects of mutations on the drug binding affinity, moreover, these
16
E. A. F. Martis and E. C. Coutinho