Rapid, Accurate, Precise and Reproducible Binding Affinity Calculations using Ensembles of

187 Pages·2017·6.8 MB·English

Rapid, Accurate, Precise and
Reproducible Binding Affinity
Calculations using Ensembles of
Molecular Dynamics
Simulations
Agastya Prakash Bhati
A dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
of
University College London.
Department of Chemistry
University College London
April 30, 2018

I, Agastya Prakash Bhati, confirm that the work presented in this thesis
is my own. Where information has been derived from other sources, I confirm
that this has been indicated in the thesis.
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Bhagwad Gita - Chapter 2, verse 47
(You have the right to work only but never to its fruits. Let not the fruits of
action be your motive, nor let your attachment be to inaction.)
Dedication
I dedicate this thesis to my parents Dr. Om Prakash and Dr. Praveena
Bhati. This is another milestone in the journey. With your blessings,
I hope to continue making progress in life.
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Abstract
The accurate prediction of the binding affinities of ligands to proteins is a
major goal in drug discovery and personalised medicine. The use of in silico
methods to predict binding affinities has been largely confined to academic
research until recently, primarily due to the lack of their reproducibility, as
well as unaffordably longer time to solution. In this thesis, I mainly describe
the ensemble based molecular dynamics approaches, ESMACS and TIES, that
provide a route to reliable predictions of free energies meeting the require-
ments of speed, accuracy, precision and reliability. The performance of both
these methods when applied to a diverse set of protein targets and ligands
is reported. The results are in very good agreement with experimental data
while the methods are repeatable by construction. Statistical uncertainties of
the order of 0.5 kcal/mol or less are achieved. These methods have been fur-
ther extended to incorporate enhanced sampling techniques based on replica
exchange (also known as parallel tempering) to handle situations where con-
formational sampling is difficult using standard molecular dynamics. A critical
assessment of free energy estimators like MBAR has been made for their appli-
cation in binding affinity prediction. The methodologies described are shown
to have a positive impact in the drug design process in the pharmaceutical
domain as well as in personalised medicine, with concomitant potential major
industrial and societal impact. Finally, our automated workflow, comprising
theBindingAffinityCalculator(BAC)togetherwiththeFabSimaredescribed.
These tools and services help us complete the entire execution in 8 hours or
less, depending on the high performance architecture and hardware available.

Impact Statement
Classical molecular dynamics (MD) simulations have been in use to simulate
proteins for about 4 decades now. Over the last couple of decades, there has
been a lot of advancement in the development of techniques based on classical
MD simulations to calculate free energy, which is a thermodynamic property,
of biomolecular systems. In spite of such tremendous progress, such techniques
suffer from the issue of non-reproducible results, due to the practice of per-
forming only a single insufficiently long simulation, making them unreliable.
It has been well acknowledged in the literature that the predicted free energies
yielded by such techniques vary by non-negligible values on simply repeating
the calculation. In this work, I have recognised the underlying cause of this
problemanddevelopedprotocolstoovercomeit. Ihaveintroducedtheconcept
of “ensemble simulation” and developed two new methods namely, “thermody-
namic integration with enhanced sampling (TIES)” and “enhanced sampling
of molecular dynamics with approximation of continuum solvent (ESMACS)”,
basedonit. Inthisthesis,IhaveprovidedsufficientevidencetoshowthatTIES
and ESMACS yield accurate, precise and reproducible free energies rapidly for
a variety of biomolecular systems. In this way, I have successfully addressed
a fundamental issue in the field of MD-based free energy calculations. This
has attracted great attention from my peers (as of now, my research article
introducing TIES has been cited 16 times in the short time of 16 months).
The aforementioned methods have direct applications in important areas like
drug design and precision medicine as highlighted below.
Impact Statement
I have shown in my work that even the improved MD-based free energy meth-
ods, thatacceleratetheconvergenceofresultsusingbettersamplingtechniques
and statistically optimal free energy estimators, exhibit a similar variation in
their results on repeating the calculations. This observation will be of great
interest to all the researchers working in the field of free energy calculations.
TIES and ESMACS constitute complex computational workflows and imple-
menting them manually is quite tedious and error-prone. Therefore, in order
to make them easily usable, I developed an automation toolkit comprising of
the “Binding Affinity Calculator (BAC)” and “FabSim”. It provides an error-
proof way of implementing these workflows and helps perform the calculations
rapidly, which further enhances their applicability as explained in the following
paragraph.
There is a huge scope for the pharmaceutical companies to employ MD-based
free energy methods in the initial stages of drug discovery, which would help
them cut down both the cost and time required to develop new drugs. In
the case of precision medicine, these methods can be used as a tool to predict
the effect of clinically observed mutations on the affinity of drugs (a common
cause of the development of drug resistance). However, the lack of reliability
and the long time-to-solution of free energy methods had been limiting their
application in both these areas. TIES and ESMACS are not constrained by
suchlimitationsandhavebeensuccessfullyemployedinmycollaborationswith
two pharmaceutical companies, GlaxoSmithKline and Pfizer.
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