Emerging In Silico Tools For Investigational New Drug ... · an low cost and highly efficacious drug for cardiovascular disease (CVD) is a big challenge in pharmaceutical industries

1

Emerging In Silico Tools For Investigational New Drug Discovery

For Cardiovascular Diseases.

P.B.RameshBabu1 , K.Ramalingam

2

1Professor and Head,

2 UG Student

Dept. of Genetic Engineering

BIHER, BIST, Bharath University

Chennai- 600073.

[email protected]

Abstract

Cardiovascular disease is the leading cause of death in emerging life style diseases. Finding

an low cost and highly efficacious drug for cardiovascular disease (CVD) is a big challenge in

pharmaceutical industries. In this paper we report recent advancements in in silico tools used in

finding an effective new drug for CVD. The drug discovery process pursued by major

pharmaceutical companies begins with target identification and validation, assay development

and high-throughput screening, the aim being to identify new leads. Bioinformatics has

significant advantage over traditionally expensive and time consuming „wet lab‟ research

methods because computational tools give the most predictive and accurate information in new

drug discovery program. Molecular docking may be defined as an optimization problem, which

would describe the “best-fit” orientation of a ligand that binds to a particular protein of interest.

However since both the ligand and the protein are flexible, a “hand-in-glove” analogy is more

appropriate than “lock-and-key”. During the course of the process, the ligand and the protein

adjust their conformation to achieve an overall “best-fit” and this kind of conformational

adjustments resulting in the overall binding is referred to as “induced-fit”. The focus of

molecular docking is to computationally stimulate the molecular recognition process. The aim

of molecular docking is to achieve an optimized conformation for both the protein and ligand

and relative orientation between protein and ligand such that the free energy of the overall

system is minimized.

Keywords: protein ligand docking, pdb viewr, sequence alignment, docking score

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International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 2753-2767ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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Introduction

The definition of bioinformatics is not universally agreed upon. Generally speaking, we

define it as the creation and development of advanced information and computational

technologies for problems in biology, most commonly molecular biology (but increasingly in

other areas of biology). Two approaches are particularly popular within the molecular docking

community. One approach uses a matching technique that describes the protein and the ligand

as complementary surfaces. The second approach simulates the actual docking process in

which the ligand-protein pairwise interaction energies are calculated. Both approaches have

significant advantages as well as some limitations. Geometric matching/ shape

complementarity methods describe the protein and ligand as a set of features that make them

dockable. These features may include molecular surface/ complementary surface descriptors.

In this case, the receptor‟s molecular surface is described in terms of its solvent accessible

surface area and the ligand‟s molecular surface is described in terms of its matching surface

description. The complementarity between the two surfaces amounts to the shape matching

description that may help finding the complementary pose of docking the target and the ligand

molecules. Another approach is to describe the hydrophobic features of the protein using turns

in the main-chain atoms. Yet another approach is to use a Fourier shape descriptor technique.

Whereas the shape complementarity based approaches are typically fast and robust, they

cannot usually model the movements or dynamic changes in the ligand/ protein conformations

accurately, although recent developments allow these methods to investigate ligand flexibility.

Shape complementarity methods can quickly scan through several thousand ligands in a matter

of seconds and actually figure out whether they can bind at the protein‟s active site, and are

usually scalable to even protein-protein interactions. They are also much more amenable to

pharmacophore based approaches, since they use geometric descriptions of the ligands to find

optimal binding.

Simulation

The simulation of the docking process as such is a much more complicated process. In this

approach, the protein and the ligand are separated by some physical distance, and the ligand

finds its position into the protein‟s active site after a certain number of “moves” in its

International Journal of Pure and Applied Mathematics Special Issue

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conformational space. The moves incorporate rigid body transformations such as translations

and rotations, as well as internal changes to the ligand‟s structure including torsion angle

rotations. Each of these moves in the conformation space of the ligand induces a total energetic

cost of the system, and hence after every move the total energy of the system is calculated. The

obvious advantage of the method is that it is more amenable to incorporate ligand flexibility

into its modeling whereas shape complementarity techniques have to use some ingenious

methods to incorporate flexibility in ligands. Another advantage is that the process is

physically closer to what happens in reality, when the protein and ligand approach each other

after molecular recognition. A clear disadvantage of this technique is that it takes longer time

to evaluate the optimal pose of binding since they have to explore a rather large energy

landscape. However grid-based techniques as well as fast optimization methods have

significantly ameliorated these problems.

Mechanics of docking

To perform a docking screen, the first requirement is a structure of the protein of interest.

Usually the structure has been determined using a biophysical technique such as x ray

crystallography, or less often, NMR spectroscopy. This protein structure and a database of

potential ligands serve as inputs to a docking program. The success of a docking program

depends on two components: the search algorithm and the scoring function.

Search algorithm

The search space consists of all possible orientations and conformations of the protein paired

with the ligand. With present computing resources, it is impossible to exhaustively explore the

search space—this would involve enumerating all possible distortions of each molecule

(molecules are dynamic and exist in an ensemble of conformational states) and all possible

rotational and translational orientations of the ligand relative to the protein at a given level of

granularity. Most docking programs in use account for a flexible ligand, and several are

attempting to model a flexible protein receptor. Each "snapshot" of the pair is referred to as a

pose. There are many strategies for sampling the search space. Here are some examples:

Scoring function


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The scoring function takes a pose as input and returns a number indicating the likelihood

that the pose represents a favorable binding interaction. Most scoring functions are physics-

based molecular mechanics force fields that estimate the energy of the pose; a low (negative)

energy indicates a stable system and thus a likely binding interaction. An alternative approach

is to derive a statistical potential for interactions from a large database of protein-ligand

complexes, such as the Protein Data Bank, and evaluate the fit of the pose according to this

inferred potential.

There are a large number of structures from x ray crystallography for complexes between

proteins and high affinity ligands, but comparatively fewer for low affinity ligands as the later

complexes tend to be less stable and therefore more difficult to crystallize. Scoring functions

trained with this data can dock high affinity ligands correctly, but they will also give plausible

docked conformations for ligands that do not bind. This gives a large number of false positive

hits, i.e., ligands predicted to bind to the protein that actually don't when placed together in a

test tube. One way to reduce the number of false positives is to recalculate the energy of the

top scoring poses using (potentially) more accurate but computationally more intensive

techniques such as Generalized Born or Poisson-Boltzmann methods.

Macromolecular docking

Macromolecular docking is the computational modelling of the molecular structure of

complexes formed by two or more interacting biological macromolecules. The sequences were

obtained from NCBI home Page (Fig 1 & 2) Protein-protein complexes are the most commonly

attempted targets of such modelling, followed by protein-nucleic acid complexes. The term

"docking" originated in the late 1970s, with a more restricted meaning; then, "docking" meant

refining a model of a complex structure by optimizing the separation between the interactors

but keeping their relative orientations fixed.

Successful docking requires two criteria:

Generating a set configurations which reliably includes at least one nearly correct one.

Reliably distinguishing nearly correct configurations from the others .For many interactions,

the binding site is known on one or more of the proteins to be docked. This is the case for


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antibodies and for competitive inhibitors. In other cases, a binding site may be strongly

suggested by mutagenic or phylogenetic evidence (Figure 3 & 4). Configurations where the

proteins interpenetrate severely may also be ruled out a priori. After making exclusions based

on prior knowledge or stereochemical clash, the remaining space of possible complexed

structures must be sampled exhaustively, evenly and with a sufficient coverage to guarantee a

near hit. Each configuration must be scored with a measure that is capable of ranking a nearly

correct structure above at least 100,000 alternatives. This is a computationally intensive task,

and a variety of strategies have been developed.

Reciprocal space methods

Each of the proteins may be represented as a simple cubic lattice. Then, for the class of

scores which are discrete convolutions, configurations related to each other by translation of

one protein by an exact lattice vector can all be scored almost simultaneously by applying the

convolution theorem. It is possible to construct reasonable, if approximate, convolution-like

scoring functions representing both stereochemical and electrostatic fitness.

Reciprocal space methods have been used extensively for their ability to evaluate enormous

numbers of configurations. They lose their speed advantage if torsional changes are introduced.

Another drawback is that it is impossible to make efficient use of prior knowledge. The

question also remains whether convolutions are too limited a class of scoring function to

identify the best complex reliably.

Monte Carlo methods

In Monte Carlo, an initial configuration is refined by taking random steps which are

accepted or rejected based on their induced improvement in score until a certain number of

steps have been tried. The assumption is that convergence to the best structure should occur

from a large class of initial configurations, only one of which needs to be considered. Initial

configurations may be sampled coarsely, and much computation time can be saved. Because of

the difficulty of finding a scoring function which is both highly discriminating for the correct

configuration and also converges to the correct configuration from a distance, the use of two


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levels of refinement, with different scoring functions, has been proposed. Torsion can be

introduced naturally to Monte Carlo as an additional property of each random move.

Monte Carlo methods are not guaranteed to search exhaustively, so that the best configuration

may be missed even using a scoring function which would in theory identify it. How severe a

problem this is for docking has not been firmly established.

GOLD - Protein-Ligand Docking

GOLD is a program for calculating the docking modes of small molecules in protein binding

sites and is provided as part of the GOLD Suite, a package of programs for structure

visualisation and manipulation (Hermes), for protein-ligand docking (GOLD) and for post-

processing (GoldMine) and visualisation of docking results. Hermes acts as a hub for many of

CCDC's products, for more information please refer to the Hermes product page. The product

of a collaboration between the University of Sheffield, GlaxoSmithKline plc and CCDC,

GOLD isvery highly regarded within the molecular modelling community for its accuracy and

reliability.

GOLD features include:

A genetic algorithm (GA) for protein-ligand docking

An easy to use interface with interactive docking set-up via Hermes

A comprehensive docking set-up wizard

Full ligand flexibility

Partial protein flexibility, including protein side chain and backbone flexibility for up to

ten user-defined residues

Energy functions partly based on conformational and non-bonded contact information

from the CSD

A variety of constraint options

Improved flexible ring handling

Automatic consideration of cavity bound water molecules

Improved handling and control of metal coordination geometries

Improved parameterisation for kinases and heme-containing proteins


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Automatic derivation of GA settings for particular ligands

A choice of GoldScore, ChemScore, Astex Statistical Potential (ASP) or Piecewise

Linear Potential (PLP) scoring functions

Extensive options for customising or implementing new scoring functions through a

Scoring Function Application Programming Interface, allowing users to modify the

GOLD scoring-function mechanism in order to either: implement their own scoring

function or enhance existing scoring functions; customise docking output

A ChemScore Receptor Depth Scaling (RDS) rescore option so that the score attributed

to hydrogen bonds is scaled depending on the depth in the binding pocket

GOLD has been fully validated against 305 diverse and extensively checked protein-ligand

complexes from the PDB (CCDC/Astex Test Set). 72% of GOLD's top-ranked solutions were

found to be accurate using stringent success criteria. A further 85 diverse, high quality drug-

like complexes have been validated; GOLD reproduces the observed binding mode within 2.0

Angstroms for 81% of the structures (Astex Diverse Set). More recently the Astex Diverse Set

has been used to analyse GOLD's cross-docking performance (the Astex Non Native Set).

GOLD's genetic algorithm parameters are optimised for virtual screening applications. GOLD

is optimised for parallel execution on processor networks; a distributed version of GOLD is

available for use on commercial PC GRID systems.


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Figure 1 : NCBI Homepage for protein sequence search. Go to the drop down menu and select

the protein option. Type the required search in the search box. The window displaying the

search results for the required protein

Figure 2 : Result of protein sequence search in NCBI site for gp120. Copy the resulting

sequence. Paste the sequence on a notepad and save it. Open the swiss pdb viewer homepage

Load the raw sequence from the notepad. Select the sequence file from the list


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Figure 3 : Image of the raw sequence Select all of the atoms in the structure. Save the current

selectionSelect the swissmodel option in tools menu. Enter your e-mail id and name. Load the

PDB file of the TEMPLATE sequence Select the pdb file from the list. Image of the pdb file

inserted. Go to the WIND menu and select the Alingment

Figure 4 : Type in your password in the pop up menu. Go to the WIND menu and select the

LAYERS INFO option. Image displaying the layers info window. Go to the FIT menu and

select the Magic fit. Image displaying the stucture after magic fit. Go to swissmodel menu and

select the submit template search option.


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Figure 5 : Pop up window which shows the project title and then we select ok.. Window

displaying job completion. Graphical representaion of the required template. Go to controls

menu and select docking option. Activate the docking from the sub menu appearing. Image

displayin the progress of docking for the given molecules. Image displaying the final structure

of the complex consisting of GP120 and CD4+. found 1419 clusters from 2000 docking

solutions in 2.47 seconds.

Discussion:-

Prediction of three dimensional structure of a target protein from the amino acid sequence

(primary structure) of a homologous (template) protein for which an X-ray or NMR structure

is available. A Model is desirable when either X-ray crystallography or NMR spectroscopy

cannot determine the structure of a protein in time or at all. The built model provides a wealth

of information of how the protein functions with information at residue property level. This

information can than be used for mutational studies or for drug design.

As per the protein structure prediction methods like Homology Modeling, Threading and Ab

initio methods, we are supposed to find the template for our sequence of interest. While finding

the template we have looked for the % identity or similarity between the sequence of interest

and template (Figure 5). As per the modeling scenario, if the % identity is more than 60%, we

should go for Homology modeling, if is in the range of 25-60%; should go for threading

method and if it is below 20-25%; should go for Ab Initio method. As per the % identity we


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have got from template after sending template selection request either through Swiss PDB

viewer or directly through the online Swiss model server, we have chosen the homology

modeling method for structure prediction. Modeling for the Sequences of interest has done by

Swiss PDB Viewer offline tool or by directly the automated mode for structure prediction

available online on Swiss-Model Server. It has given us with the final predicted structure based

on the template structure so as to predict the function of the sequence of interest. Here we have

got the structures of HIV1 gp120 on the basis of template 2B4C. And Human CD4+ structure

on the basis of the template.

In docking, we are supposed to manipulate the receptor and ligand molecules before we will

be going for docking. Manipulations are to be done according to the Tool which we are going

to use for docking purpose. Here we have used Hex docking platform which has manipulating

criteria in terms of enabling solvent, enabling hetero and enabling Arg/Lysine. This has to be

done by the enabling all this options so as to create the live environment for docking as that of

in vivo process of ligand and receptor binding. When we have started with the docking, first

thing we considered is Estart and then simultaneously Emin and Emax. These values are to be

considered energy should be minimized so as to make the molecule stable as, more the

rotatable bonds in ligand, the more difficult it will be to find good binding modes in repeated

docking experiments. Thus final result that is the Etotal should lie in between Emin and Emax.

ETotal should be always less so as to get the maximum stability to docking complex for perfect

merge and also less than Estart.

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Emerging In Silico Tools For Investigational New Drug ... · an low cost and highly efficacious drug for cardiovascular disease (CVD) is a big challenge in pharmaceutical industries