mutation will enable us to understand the fitness cost. Pioneering work in this line
was done by Gulnik et al. [1]. In this work, they have determined the catalytic
efficiency (Eq. 18a) of HIV-1 protease following few active and non-active site
mutations. This principle was incorporated in terms of free energy change by
Warshel et al., and they employed this method (Eq. 18b) to computationally predict
the likely mutations that could potentially abolish drug binding leading to drug
resistance. This method is aptly named as “Vitality approach” wherein higher
vitality values indicate that the resistance is more likely as there is little chance of
increase in the catalytic efficiency of the enzyme. The basic workflow adopted by
Warshel et al. [106, 107] is to estimate the change in the drug binding before and
after mutation, depicted in the first part of Eq. 18b and then estimate the catalytic
efficiency by determining the binding of the substrate by modelling the transition
state (TS) conformation of the enzyme, depicted in the second part of Eq. 18b.
However, the challenge of employing this method to predict likely mutations is that
a thorough knowledge of the catalytic mechanism of the enzyme is essential.
Nonetheless, this method is far more accurate and truly predictive in nature. This is
exemplified by the fact that Warshel et al. successfully used this method on six
clinical agents active against HIV-1 protease.
Vitality value ¼
K i k cat
K m
mutant
K i k cat
K m
WT
ð18aÞ
ln
c M
c N
ffi
1
RT
DDG
N!M
bind drug
ð
ÞÀDDG
N!M
bind TS
ð Þ
À
Á
ð18bÞ
where K i = inhibition constant; k cat = constant that defines the turnover rate of an
enzyme-substrate complex to the product; K m = Michaelis constant.
4 Concluding Remarks
This chapter describes important computational methods that have been proven
extremely helpful in gaining insights into mutations leading to drug resistance. We
have attempted to introduce methods used to compute the free energy of binding
along with their mathematical formulations, practical implementation and pros and
cons of such methods. Finally, we have discussed a few applications of such
methods to study drug resistance.
Acknowledgements E. A. F. Martis and E. C. Coutinho are grateful to Ian R. Craig, Ph.D.
(BASF, Ludwigshafen) for his critical comments and feedback on this chapter. The authors are
grateful to Department of Science and Technology (DST), Department of Biotechnology
(DBT) and Council of Scientific and Industrial Research (CSIR) for their financial support to build
the High-Performance Computing system at the Department of Pharmaceutical Chemistry,
18
E. A. F. Martis and E. C. Coutinho
was done by Gulnik et al. [1]. In this work, they have determined the catalytic
efficiency (Eq. 18a) of HIV-1 protease following few active and non-active site
mutations. This principle was incorporated in terms of free energy change by
Warshel et al., and they employed this method (Eq. 18b) to computationally predict
the likely mutations that could potentially abolish drug binding leading to drug
resistance. This method is aptly named as “Vitality approach” wherein higher
vitality values indicate that the resistance is more likely as there is little chance of
increase in the catalytic efficiency of the enzyme. The basic workflow adopted by
Warshel et al. [106, 107] is to estimate the change in the drug binding before and
after mutation, depicted in the first part of Eq. 18b and then estimate the catalytic
efficiency by determining the binding of the substrate by modelling the transition
state (TS) conformation of the enzyme, depicted in the second part of Eq. 18b.
However, the challenge of employing this method to predict likely mutations is that
a thorough knowledge of the catalytic mechanism of the enzyme is essential.
Nonetheless, this method is far more accurate and truly predictive in nature. This is
exemplified by the fact that Warshel et al. successfully used this method on six
clinical agents active against HIV-1 protease.
Vitality value ¼
K i k cat
K m
mutant
K i k cat
K m
WT
ð18aÞ
ln
c M
c N
ffi
1
RT
DDG
N!M
bind drug
ð
ÞÀDDG
N!M
bind TS
ð Þ
À
Á
ð18bÞ
where K i = inhibition constant; k cat = constant that defines the turnover rate of an
enzyme-substrate complex to the product; K m = Michaelis constant.
4 Concluding Remarks
This chapter describes important computational methods that have been proven
extremely helpful in gaining insights into mutations leading to drug resistance. We
have attempted to introduce methods used to compute the free energy of binding
along with their mathematical formulations, practical implementation and pros and
cons of such methods. Finally, we have discussed a few applications of such
methods to study drug resistance.
Acknowledgements E. A. F. Martis and E. C. Coutinho are grateful to Ian R. Craig, Ph.D.
(BASF, Ludwigshafen) for his critical comments and feedback on this chapter. The authors are
grateful to Department of Science and Technology (DST), Department of Biotechnology
(DBT) and Council of Scientific and Industrial Research (CSIR) for their financial support to build
the High-Performance Computing system at the Department of Pharmaceutical Chemistry,
18
E. A. F. Martis and E. C. Coutinho