Molecular Operating Environment

Last updated
MOE
Developer(s) Chemical Computing Group
Written in Scientific Vector Language
Operating system Cross-platform
Type Molecular modelling
Website www.chemcomp.com/MOE-Molecular_Operating_Environment.htm/

Molecular Operating Environment (MOE) is a drug discovery software platform that integrates visualization, modeling and simulations, as well as methodology development, in one package. MOE scientific applications are used by biologists, medicinal chemists and computational chemists in pharmaceutical, biotechnology and academic research. MOE runs on Windows, Linux, Unix, and macOS. Main application areas in MOE include structure-based design, [1] fragment-based design, [2] ligand-based design, pharmacophore discovery, medicinal chemistry applications, biologics applications, structural biology and bioinformatics, protein and antibody modeling, molecular modeling and simulations, virtual screening, cheminformatics & QSAR. The Scientific Vector Language (SVL) is the built-in command, scripting and application development language of MOE.

Contents

History

The Molecular Operating Environment was developed by the Chemical Computing Group under the supervision of President/CEO Paul Labute. [3] Founded in 1994 [4] and based in Montreal, Quebec, Canada, this private company is dedicated to developing computation software that will challenge, revolutionize, and aid in the scientific methodology. The Chemical Computing Group contains a team of mathematicians, scientists, and software engineers constantly altering and updating MOE in order to improve the fields of theoretical/computational chemistry and biology, molecular modeling, and computer-driven molecular design. [5] Researchers specializing in pharmaceutics (drug-discovery); computational chemistry; biotechnology; bioinformatics; cheminformatics; molecular dynamics, simulations, and modeling are the main clients of the Chemical Computing Group.

Software

As discussed before, MOE is a versatile software with main applications in 3D molecular visualization; structure-based protein-ligand design; antibody and biologics design, structure-based protein engineering; SAR and SPR visualization; ligand-based design; protein, DNA/RNA modeling; virtual screening; 3D pharmacophore screening; fragment-based discovery; structural bioinformatics; molecular mechanics and dynamics; peptide modeling; structural biology; cheminformatics and QSAR. [5]

Molecular Modeling and Simulations

Example of a molecule imported and visualized in a software used for molecule modeling and simulations. Molecular Modeling.png
Example of a molecule imported and visualized in a software used for molecule modeling and simulations.

Molecular modeling and simulations is a process often used in computational chemistry, but there is wide application for researchers in a variety of fields. This theoretical approach allows scientists to extensively study the properties of molecules, and using the data can provide insight into how these molecules may behave in biological and/or chemical systems. [6] This information is vital to the design of new materials and chemicals.

Molecular Docking

Molecular docking is a computation study used to primarily analyze the binding affinity of a ligand and a receptor. Often times, proteins are studied using this technique, because data from molecular docking allows scientists to predict if a ligand will bind to a specific molecule and if so, how strongly. [7] Molecular docking can be used to predict the binding mode of already known ligands and/or novel ligands, and as a binding affinity predictive instrument. [8] Binding affinity is measured by the change in energy and the more negative the energy, the more stable the complex and the tighter the ligand binds to the receptor. [9] Data from molecular docking can be used to construct new compounds that are more or less efficient at binding to a specific molecule. Molecular docking is extensively used throughout drug discovery for these reasons. [10]

Visual representation of molecular docking taking place. A ligand and a receptor or docked in order to determine if the new complex formed is energetically favorable. Docking representation 2.png
Visual representation of molecular docking taking place. A ligand and a receptor or docked in order to determine if the new complex formed is energetically favorable.

Preparing for molecular docking studies can involve many steps. When docking proteins, proteins are obtained from the Protein Data Bank (PDB), which is an online, open access resources containing the classification, structure/folding, organism, sequence length, mutations, genome, sequence, and other data relating to proteins. [11] The structure of a protein can precisely be determined through a process known as X-ray crystallography. This process involves a concentrated beam of X-rays that is directed at a crystal. [12] When X-rays are projected to a crystal structure, the crystal diffracts the X-rays in specific directions. [13] These directions allow scientists to map and determine the detailed structure of proteins, which is then recorded and uploaded to the PDB. [14]

Methods

The protein structure file is downloaded from the PDB and opened in a molecular docking software. There are many programs that can facilitate molecular docking such as AutoDock, DOCK, FlexX, HYDRO, LIGPLOT, SPROUT, STALK, [15] and Molegro Virtual Docker. [16] Alternatively, some protein structures have not been experimentally determined through the use of X-ray crystallography and therefore, are not found on the PDB. In order to produce a protein molecule that can be used for docking, scientists can use the amino acid sequence of a protein and a program named UniProt to find protein structures in the PDB that have similar amino acid sequences. [17] The amino acid sequence of the protein that is being constructed is then used in combination with the protein structure found in the PDB with the highest percent similarity (template protein) in order to create the target protein used in docking. Although this method does not produce an exact model of the target protein, it allows scientists to produce the closest possible structure in order to conduct computational methods and gain some insight into the behavior of a protein. After constructing the necessary molecules for docking, they are imported into a computational docking software such as MOE. In this program, proteins can be visualized and certain parts of the molecule can be isolated in order to obtain more precise data for a region of interest. A cavity, or region where the molecular docking will take place, is set around the binding site, which is the region in the receptor protein where the ligand attaches to. After specifying the cavity, molecular docking settings are configured and the program is run in order to determine the binding energy of the complex.

Molecular Dynamics (MD)

Molecular dynamics simulation of a single atom into a crystal lattice. Cudeposition.gif
Molecular dynamics simulation of a single atom into a crystal lattice.

Molecular dynamic simulations is a computational study that predicts the movement of every atom in a molecule over time. [18] Molecular dynamics can evaluate the movement of water, ions, small and macromolecules, or even complex systems which is extremally useful for reproducing the behavior of chemical and biological environments. [19] This theoretical approach allows scientists to gain further insight into how molecules may behave with respect to each other, specifically if a molecule will leave or remain in a binding pocket. If a molecule remains in a binding pocket, this often indicates that the molecule creates a stable complex with the receptor and is energetically favorable. [20] On the other hand, if the molecule leaves the binding pocket, this indicates that the complex is not stable. This information is then utilized to design new compounds with characteristics that may have a greater or lesser affinity for a receptor.

Applications and Usage

Drug Discovery

Graphic depicting the process of drug discovery. "Lead compounds and SAR" and "Structural Characterization of Protein-Ligand Complex" is data obtained as a result of computational chemistry methods. Drug discovery cycle 2.png
Graphic depicting the process of drug discovery. "Lead compounds and SAR" and "Structural Characterization of Protein-Ligand Complex" is data obtained as a result of computational chemistry methods.

Drug discovery is a process that involves the use of computational, experimental, and clinical studies in order to design new therapeutics. [21] This process is lengthy and costly, yet it is the most popular process to date in developing successful treatments and medicines for a variety of diseases. The increasing use of drug discovery can be attributed to new technology that allow for computation/theoretical studies. Data from computation/theoretical studies is often the foundation and reasoning for the development of new drugs. [22] Without promising theoretical data, these compounds may not be synthesized and tested during experimental studies. Molecular modeling, molecular docking, and MD simulations are some of many computation studies that takes places during drug discovery, allowing scientists to thoroughly study the structure and properties of organic and inorganic molecules. By studying these properties, scientists can gain insight to predict the affinity of molecules in biological and chemical systems in order to determine how a therapeutic may react with different types of chemicals, receptors, and other conditions found in humans or other animals. For example, molecular dynamics is often used throughout drug discovery in order to identify structural cavities that are important for determining binding affinity. [19] This data is then compiled and analyzed to determine if certain therapeutics should be synthesized and tested clinically, or if further optimization is required for the design of new medicines that are more effective. [23]

Pesticides and Herbicides

Computational chemistry can also be applied to the development of safer pesticides and herbicides. Recently, the increasing use of pesticides and herbicides has raised much controversy due to environmental and public health concerns. It was found that although these chemicals are designed to kill target pests, its effects can often harm other organisms, humans included. [24] Some types of pesticides and herbicides such as organophosphates and carbamates can affect the nervous system in humans, while others were found to be carcinogenic, irritate the skin or eyes, and even affect the hormone or endocrine system. [25] Furthermore, neonicotinoids is another type of pesticide that recently gained popularity due to its effectiveness at targeting aphids and other pests that hinder agriculture production. [26] Although there are not many human health concerns associated with neonicotinoids (which is another reason for its popularity), the increasing use of this pesticide has been linked to Colony Collapse Disorder (CCD), or the rapid disappearance of adult bees. [27] Due to this pattern, the European Union has banned the outdoor use of three neonicotinoid pesticides in an attempt to mitigate CCD. [28] Clearly, there are multiple issues regarding the use of these pesticides and herbicides. A call for safer and more efficient pesticides and herbicides is being accomplished with the help of computational/theoretical methods.

Future Implications

Process of protein folding beginning from the amino acid sequence (polypeptide chain). Protein folding figure.png
Process of protein folding beginning from the amino acid sequence (polypeptide chain).

Computational/theoretical chemistry and biology methods are continuously pushing the horizon. Recently, DeepMind, which is a company specializing in the development of artificial intelligence (AI), created an AI system named AlphaFold. [29] AlphaFold is the most advanced system to date that can accurately predict a protein's 3D structure from its amino acid sequence. [30] The protein folding problem first began to emerge around the 1960s and ever since, scientists have struggled in determining methods to precisely predict the way a protein will fold solely based on the amino acid sequence. [31] However, with recent advances in technology, AlphaFold has made a breakthrough in this long lasting issue. By utilizing a database with over 350,000 structures, AlphaFold can determine the shape of a protein in a few minutes with atomic accuracy. [32] The ability to predict the structure of millions of unknown proteins can help to combat disease, find more effective medicines, and unlock other unknowns that govern life. This technological breakthrough will revolutionize future research and will have profound effects for the scientific community.

Related Research Articles

<span class="mw-page-title-main">Molecular dynamics</span> Computer simulations to discover and understand chemical properties

Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. The atoms and molecules are allowed to interact for a fixed period of time, giving a view of the dynamic "evolution" of the system. In the most common version, the trajectories of atoms and molecules are determined by numerically solving Newton's equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanical force fields. The method is applied mostly in chemical physics, materials science, and biophysics.

<span class="mw-page-title-main">Structural bioinformatics</span> Bioinformatics subfield

Structural bioinformatics is the branch of bioinformatics that is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins, RNA, and DNA. It deals with generalizations about macromolecular 3D structures such as comparisons of overall folds and local motifs, principles of molecular folding, evolution, binding interactions, and structure/function relationships, working both from experimentally solved structures and from computational models. The term structural has the same meaning as in structural biology, and structural bioinformatics can be seen as a part of computational structural biology. The main objective of structural bioinformatics is the creation of new methods of analysing and manipulating biological macromolecular data in order to solve problems in biology and generate new knowledge.

<span class="mw-page-title-main">Binding site</span> Molecule-specific coordinate bonding area in biological systems

In biochemistry and molecular biology, a binding site is a region on a macromolecule such as a protein that binds to another molecule with specificity. The binding partner of the macromolecule is often referred to as a ligand. Ligands may include other proteins, enzyme substrates, second messengers, hormones, or allosteric modulators. The binding event is often, but not always, accompanied by a conformational change that alters the protein's function. Binding to protein binding sites is most often reversible, but can also be covalent reversible or irreversible.

<span class="mw-page-title-main">Molecular mechanics</span> Use of classical mechanics to model molecular systems

Molecular mechanics uses classical mechanics to model molecular systems. The Born–Oppenheimer approximation is assumed valid and the potential energy of all systems is calculated as a function of the nuclear coordinates using force fields. Molecular mechanics can be used to study molecule systems ranging in size and complexity from small to large biological systems or material assemblies with many thousands to millions of atoms.

<span class="mw-page-title-main">Drug design</span> Inventive process of finding new medications based on the knowledge of a biological target

Drug design, often referred to as rational drug design or simply rational design, is the inventive process of finding new medications based on the knowledge of a biological target. The drug is most commonly an organic small molecule that activates or inhibits the function of a biomolecule such as a protein, which in turn results in a therapeutic benefit to the patient. In the most basic sense, drug design involves the design of molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modeling techniques. This type of modeling is sometimes referred to as computer-aided drug design. Finally, drug design that relies on the knowledge of the three-dimensional structure of the biomolecular target is known as structure-based drug design. In addition to small molecules, biopharmaceuticals including peptides and especially therapeutic antibodies are an increasingly important class of drugs and computational methods for improving the affinity, selectivity, and stability of these protein-based therapeutics have also been developed.

<i>In silico</i> Latin phrase referring to computer simulations

In biology and other experimental sciences, an in silico experiment is one performed on computer or via computer simulation. The phrase is pseudo-Latin for 'in silicon', referring to silicon in computer chips. It was coined in 1987 as an allusion to the Latin phrases in vivo, in vitro, and in situ, which are commonly used in biology. The latter phrases refer, respectively, to experiments done in living organisms, outside living organisms, and where they are found in nature.

<span class="mw-page-title-main">Ligand (biochemistry)</span> Substance that forms a complex with a biomolecule

In biochemistry and pharmacology, a ligand is a substance that forms a complex with a biomolecule to serve a biological purpose. The etymology stems from Latin ligare, which means 'to bind'. In protein-ligand binding, the ligand is usually a molecule which produces a signal by binding to a site on a target protein. The binding typically results in a change of conformational isomerism (conformation) of the target protein. In DNA-ligand binding studies, the ligand can be a small molecule, ion, or protein which binds to the DNA double helix. The relationship between ligand and binding partner is a function of charge, hydrophobicity, and molecular structure.

<span class="mw-page-title-main">Docking (molecular)</span> Prediction method in molecular modeling

In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when a ligand and a target are bound to each other to form a stable complex. Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules using, for example, scoring functions.

<span class="mw-page-title-main">Force field (chemistry)</span> Concept on molecular modeling

In the context of chemistry and molecular modelling, a force field is a computational method that is used to estimate the forces between atoms within molecules and also between molecules. More precisely, the force field refers to the functional form and parameter sets used to calculate the potential energy of a system of atoms or coarse-grained particles in molecular mechanics, molecular dynamics, or Monte Carlo simulations. The parameters for a chosen energy function may be derived from experiments in physics and chemistry, calculations in quantum mechanics, or both. Force fields are interatomic potentials and utilize the same concept as force fields in classical physics, with the difference that the force field parameters in chemistry describe the energy landscape, from which the acting forces on every particle are derived as a gradient of the potential energy with respect to the particle coordinates.

Protein–ligand docking is a molecular modelling technique. The goal of protein–ligand docking is to predict the position and orientation of a ligand when it is bound to a protein receptor or enzyme. Pharmaceutical research employs docking techniques for a variety of purposes, most notably in the virtual screening of large databases of available chemicals in order to select likely drug candidates. There has been rapid development in computational ability to determine protein structure with programs such as AlphaFold, and the demand for the corresponding protein-ligand docking predictions is driving implementation of software that can find accurate models. Once the protein folding can be predicted accurately along with how the ligands of various structures will bind to the protein, the ability for drug development to progress at a much faster rate becomes possible.

<span class="mw-page-title-main">Virtual screening</span>

Virtual screening (VS) is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme.

In the fields of computational chemistry and molecular modelling, scoring functions are mathematical functions used to approximately predict the binding affinity between two molecules after they have been docked. Most commonly one of the molecules is a small organic compound such as a drug and the second is the drug's biological target such as a protein receptor. Scoring functions have also been developed to predict the strength of intermolecular interactions between two proteins or between protein and DNA.

Molecular binding is an attractive interaction between two molecules that results in a stable association in which the molecules are in close proximity to each other. It is formed when atoms or molecules bind together by sharing of electrons. It often, but not always, involves some chemical bonding.

<span class="mw-page-title-main">Chemical Computing Group</span> Software company in Canada

Chemical Computing Group is a software company specializing in research software for computational chemistry, bioinformatics, cheminformatics, docking, pharmacophore searching and molecular simulation. The company's main customer base consists of pharmaceutical and biotechnology companies, as well as academic research groups. It is a private company that was founded in 1994; it is based in Montreal, Quebec, Canada. Its main product, Molecular Operating Environment (MOE), is written in a self-contained programming system, the Scientific Vector Language (SVL).

Computational Resources for Drug Discovery (CRDD) is one of the important silico modules of Open Source for Drug Discovery (OSDD). The CRDD web portal provides computer resources related to drug discovery on a single platform. It provides computational resources for researchers in computer-aided drug design, a discussion forum, and resources to maintain a Wikipedia related to drug discovery, predict inhibitors, and predict the ADME-Tox property of molecules. One of the major objectives of CRDD is to promote open source software in the field of chemoinformatics and pharmacoinformatics.

<span class="mw-page-title-main">BindingDB</span>

BindingDB is a public, web-accessible database of measured binding affinities, focusing chiefly on the interactions of proteins considered to be candidate drug-targets with ligands that are small, drug-like molecules. As of March, 2011, BindingDB contains about 650,000 binding data, for 5,700 protein targets and 280,000 small molecules. BindingDB also includes a small collection of host–guest binding data of interest to chemists studying supramolecular systems.

Computer Atlas of Surface Topography of Proteins (CASTp) aims to provide comprehensive and detailed quantitative characterization of topographic features of protein, is now updated to version 3.0. Since its release in 2006, the CASTp server has ≈45000 visits and fulfills ≈33000 calculation requests annually. CASTp has been proven as a confident tool for a wide range of researches, including investigations of signaling receptors, discoveries of cancer therapeutics, understanding of mechanism of drug actions, studies of immune disorder diseases, analysis of protein–nanoparticle interactions, inference of protein functions and development of high-throughput computational tools. This server is maintained by Jie Liang's lab in University of Illinois at Chicago.

Chemoproteomics entails a broad array of techniques used to identify and interrogate protein-small molecule interactions. Chemoproteomics complements phenotypic drug discovery, a paradigm that aims to discover lead compounds on the basis of alleviating a disease phenotype, as opposed to target-based drug discovery, in which lead compounds are designed to interact with predetermined disease-driving biological targets. As phenotypic drug discovery assays do not provide confirmation of a compound's mechanism of action, chemoproteomics provides valuable follow-up strategies to narrow down potential targets and eventually validate a molecule's mechanism of action. Chemoproteomics also attempts to address the inherent challenge of drug promiscuity in small molecule drug discovery by analyzing protein-small molecule interactions on a proteome-wide scale. A major goal of chemoproteomics is to characterize the interactome of drug candidates to gain insight into mechanisms of off-target toxicity and polypharmacology.

LeDock is a proprietary, flexible molecular docking software designed for the purpose of docking ligands with target proteins. It is available for Linux, macOS, and Windows.

FlexAID is a molecular docking software that can use small molecules and peptides as ligands and proteins and nucleic acids as docking targets. As the name suggests, FlexAID supports full ligand flexibility as well side-chain flexibility of the target. It does using a soft scoring function based on the complementarity of the two surfaces.

References

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