Protein-Ligand Binding | Implicit Solvation | HIV-related
We use of statistical thermodynamics concepts to develop models and computational algorithms to study biophysical processes. Given the complexity of biological systems, it is important to strike a balance between theoretical rigor and physiochemical intuition to capture essential features of the systems and deliver accurate yet computationally tractable models. Because computer hardware and algorithms continue to advance, the rigor/computational complexity "sweet spot" is a continuously moving target. Many models that were computationally intractable only few years ago are not routine. Therefore in this area it is important to be on top of both theoretical concepts as well as the latest technological advances.
## Thermodynamics of Protein-Ligand BindingBackground. Molecular recognition is an essential
component for virtually all biological processes. Many
pharmaceutical drugs act by binding to enzymes and signaling
proteins, thereby altering their activity. There is great
interest in the development of computer models capable of
predicting accurately the strength of protein-ligand
association. Modelling protein-ligand equilibria, however, is
a very challenging and still largely unsolved problem. Ideally
such a model should incorporate enough detail to address
important medicinal questions such as drug specificity,
resistance, and toxicity. Often these properties are very
sensitive to subtle changes (sometimes involving only few
atoms) in ligand composition and protein sequence. Models that
describe key parts of the system at the atomic level have the
best chance of resolving these differences.
The Binding Energy Distribution Analysis Method
(BEDAM) We have recently developed a novel approach to
absolute binding free energy estimation and analysis we called
the Binding Energy Distribution Analysis Method
(BEDAM)(Gallicchio et al. 2010) based
on a sound statistical mechanics theory of molecular
association and efficient computational strategies built upon
parallel Hamiltonian replica exchange sampling and histogram
reweighting. The method takes its name from the technique it
employs to extract standard binding free energies from the
statistical analysis of the probability distributions of the
energies of association over a series of conformational
ensembles connecting the bound and unbound states. The
ability to carry out extensive conformational sampling is one
of the main advantages of BEDAM over existing FEP and absolute
binding free energies protocols in explicit solvent which
suffer from limited exploration of conformational
space. Benchmarking calculations illustrate the power
and accuracy of the methodology.(Lapelosa et al. 2012). The BEDAM method has been employed in the international SAMPL blind challenges where computational methods are tested on their ability to predict undisclosed experimental data. The method has earned top marks in all SAMPL challenges so far.(Gallicchio & Levy 2012)(Gallicchio et al. 2014)(Gallicchio et al. 2015) Ongoing development is aimed at optimizing and automating BEDAM calculations so that they can be used for binding free energy-based screening of large ligand libraries, to complement and build upon conventional virtual screening docking protocols. Importance of Solvation Effects. It is hard to
overstate the importance of water-solute
interactions. Virtually all biological processes occur in
water solution and often water molecules play a fundamental
role in enzymatic reactions, and in modulating binding and
conformational equilibria. Atomistic models of biomolecules need to include some
description of water-solute interactions to achieve at least
qualitative level of fidelity. A variety of approaches
have been tried to model solvation effects.
The AGBNP model. The Analytical Generalized Born plus
Non-Polar (AGBNP)
model (Gallicchio & Levy 2004)
(Gallicchio et al. 2009) originated from
the need of an implicit solvent model that would incorporate
as much realism as possible and at the same time would be
usable with Molecular Dynamics, which requires analytical
(that is differentiable) and computationally efficient energy
functions. AGBNP is based on an efficient implementation of
the pairwise descreening form of the Generalized Born (GB)
model, an approximate description of the continuum dielectric
electrostatic model. GB basically augments conventional fixed
charge force field interactions (Coulomb interactions and
Lennard-Jones interactions) with "GB interaction" terms that
depend on parameters, called Born radii, that depend on the
solute geometry. The calculations of the Born radii is by far
the most complex part of the computation of the GB
energy.
AGBNP also includes non-electrostatic terms incorporating lessons learned in over a decade of research. We found that models based on the decomposition of non-polar hydration into a cavitation free energy (described by the solute surface area) and van der Waals dispersion forces (modelled using a function based on the atomic Born radii) give a better description of of fundamental processes such as protein folding and association. The research community is recognizing the benefits of this decomposition and is progressively abandoning the traditional surface area (SA) models of non-polar hydration in favor of these new models. One of the design principles of AGBNP is to reduce as much as possible the use of empirically adjusted parameters. For example many commonly used GB/SA implicit solvent models employ empirically adjusted functional forms not only for energetic terms but also for essentially geometrical quantities, such as surface areas and Born radii. I believe that the this practice leads to inaccuracies and poor transferability. We were able to show that advanced computational geometry algorithms make the parametrization of geometrical quantities unnecessary and, by doing so, leads to models that are accurate not only on average but also in the details. Being able to accurately model both large and small conformational changes is important in many applications and in particular in protein-ligand binding. We are continuously updating the AGBNP model to expand its range of applicability. We introduced (Gallicchio et al. 2009) new terms to better describe solvation effects beyond the standard linear continuum dielectric approximation. We have also been working on improving the quality of geometrical solute descriptors such as atomic surface areas. The AGBNP model is currently yielding promising results for protein-ligand binding free energies (see above) and we intend to continue to optimize it to extend its accuracy.
## HIV modelling
Vaccine Design. The discovery of an effective vaccine against AIDS, although so
far elusive, remains the best option to eradicate the disease
especially in poor countries. In collaboration with the Arnold's
group we applied for the first time molecular simulations and
binding free energy concepts to aid the formulation of HIV
vaccine constructs. The idea is to display HIV epitopes on the
coat of a rhinovirus (the virus the common cold) to create a
chimeric virus that would confer protection against the HIV
virus. The initial goal was to optimize antigenicity by
presenting the epitope in such a way that it would bind strongly
to a known human neutralizing neutralizing antibody (2F5). Here
the composition of the binding interface was biologically
constrained and therefore preorganization of the epitope to the
bound conformation was the only viable route for optimizing the
binding affinity. We hypothesized that those presentation
constructs with the highest fraction of epitope conformations
compatible with antibody complexation would minimize the
reorganization free energy and present the highest binding
affinity for the antibody. We thereby conducted molecular
simulations to guide the optimization of the presentation of the
epitope which resulted in a series of detailed predictions
regarding the length and positioning of the epitope that would
result in good
binding.(Lapelosa et al. 2009)
Subsequent biochemical work in Arnold's laboratory confirmed the
computational prediction and, remarkably, yielded some of the
most antigenic vaccine constructs of this kind to
date.(Lapelosa et al. 2010) To this day
this study is only one of a few examples of modeling
applications to vaccine design, and the only computational study
to our knowledge that successfully applied reorganization free
energy concepts to macromolecular binding. It also represents a
fine example of the potential benefits of tight collaboration
between modelers and structural biologists. |

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