Working with these two programs greatly reduces the amount of work necessary to generate models, as it is possible to thread a protein primary sequence onto a 3D template and get an immediate feedback of how well the threaded protein will be accepted by the reference structure before submitting a request to build missing loops and refine sidechain packing.

Swiss-PdbViewer can also read electron density maps, and provides various tools to build into the density. In addition, various modeling tools are integrated and command files for popular energy minimization packages can be generated.

Currently Swiss-PdbViewer is released for Microsoft Windows and Macintosh OS. In order to run Swiss-PdbViewer in Ubuntu OS, a windows application loader WINE must be installed.

What is Wine?

Wine is an implementation of Windows APIs on top of X and Unix. Think of Wine as a Windows compatibility layer. Wine provides a program loader, allowing many unmodified Windows binaries to run under Intel Unixes. Wine works on most popular Intel Unixes, including Linux, FreeBSD, and Solaris.

In Linux Ubuntu OS, Wine can be easily installed through Ubuntu Software Center.

Steps to install Wine

To install Wine, simply follow these steps.

1. Go to Applications -> Ubuntu Software Center.

2. Click the tab Get Free Software.
3. In the search bar type keyword “wine“.

4. Select the hit Wine Microsoft Windows Compatibility Layer and press forward button.
5. Click the Install button.

Steps to run SPDBV

1. Download the windows compatible Swiss-PdbViewer application from SPDBV download site and decompress it.
2. Open the decompressed folder. Right click the application spdbv.exe and open with Wine Windows Program Loader.

Other bioinformatics tools such as RasMol, Clustal X, TreeView, SeaView, PyMOL, Cn3D, EMBOSS, fastDNAml, etc can be installed instantly through Ubuntu Software Center.

Note: You must be on-line while installing the application.

The protocols under Simulation serve the following types of users:

1. Novice protein modelers and computational chemists who are interested primarily in running pre-defined simulation protocols to provide simple minimization or dynamics of the molecular structure under study. Novice protein modelers and computational chemists will find that the protocols are an easy way to run predetermined workflows with a minimal amount of configuration.
2. Expert protein modelers and computational chemists who are interested in running several simulation steps independently and trying out different parameterizations.

Analysis Goals

You can use Simulation methods to:

• Analyze molecular trajectories
• Calculate energies using molecular mechanics or QM-MM based methods
• Calculate interaction energies
• Equilibrate a molecule with molecular dynamics
• Perform a dynamics simulation to adjust the temperature of a molecule
• Perform molecular dynamics on a pre-equilibrated molecule
• Minimize a molecule using molecular mechanics or QM-MM
• Calculate the radial distribution function for two sets of atoms in a trajectory
• Solvate a system by adding water molecules
• Perform a set of minimization and equilibration steps followed by molecular dynamics
• Calculate fundamental motions (Principal Components) of a trajectory
• Prepare structures for simulation using the forcefield tools
• Apply constraints on atoms in structures before performing simulation analysis
• Analyze molecular trajectories

Simulation Tasks

The following table describes the basic tasks that are possible with the Simulation methods. Read more..

The protein modeling methods provided in Discovery Studio provide an extensive set of tools for the construction of molecular structures and analysis of their behavior.

Protein Modeling in Accelrys Discovery Studio 2.1

Analysis Goals

Protein Modeling provides tools and protocols that allow you to:

• Build and edit proteins or peptides
• Modify protein structures and generate reports of protein structures
• Identify and report abnormalities in a protein structure
• Align sequences according to sequence similarity or structure similarity
• Build homology models for a protein sequence
• Refine side-chain conformation of a protein structure
• Refine conformation of one or more loop regions of a protein structure
• Mutate residues and optimize structure
• Predict protein docking conformations
• Model antibody structure and variable loop conformations
• Analyze the sequence conservation pattern of a sequence family
• Identify conserved functional patches in a protein structure

Protein Modeling Tasks

The following table describes the basic tasks that are possible with the Protein Modeling methods in Discovery Studio. Click here for tutorial….

Bioinformatics derives knowledge from computer analysis of biological data. These can consist of the information stored in the genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology and medicine.

Roughly, bioinformatics describes any use of computers to handle biological information. In practice the definition used by most people is narrower; bioinformatics to them is a synonym for “computational molecular biology” – the use of computers to characterize the molecular components of living things.

Simple definition for bioinformatics

“The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information“.

Three main aspects:

There are three main aspects need to be addressed in the above definition. The first is that it is a very systematic way of dealing with biological data. Therefore, constructing an infrastructure such as large scale database and server systems for genomes and proteomes is an important part of it.

The second is that it views the processes and mechanisms of life as information processing. For example, it puts a weight on how the regulation can be modeled and generalized as well as how a specific four gene transcription systems works in a bacterium.

The third aspect is that it is multi-disciplinary employing experimental biology, theoretical science and computers. Every science field uses experiments, theories and computers. Bioinformatics is multidisciplinary: However, bioinformatics requires a very tight integration of these as a one single subject. In other words, an ideal bioinformatist should be able to understand what the Hidden Markov Model, Monte Carlo method, relational database system; object oriented programming language and a cluster of Linux operating systems as well as TCA cycle, PCR (polymerase chain reaction) and transcription elongation factors. Obviously no one can master all the interdisciplinary skills but the bioinformatics field as a whole can encompass them. Six fields of Bioinformatics: Perhaps the best way of feeling bioinformatics as an integrated discipline is to look at all the major parts of it. There are different schemes to divide bioinformatics.

Omics approach:

The various -omics fields in biology are under the broad term of bioinformatics. They all aim to understand molecules as networks. The essence of such omics study lines in networks and the interactions of nodes within the networks. Therefore, genomics is not just collecting all the information of genes but studying their relationships, controls, and emergent properties.

(1) Genomics (DNA oriented)

(2) Transcriptomics (RNA oriented)

(3) Proteomics (protein oriented)

(4) Metabolomics (biological pathways oriented)

(5) Physiomics (disease and physiological level of study)

(6) Systeomics (systematic study)

(7) Glycomics (study of glycomes)

(8) Interactomics (study of interactions)

(9) Medical informatics.

Five domains of Bioinformatics:

Another scheme is on how we represent the data. Large-scale biological data can be represented in different forms for different computation and analysis. The common ones are:

(1) Sequence

(2) Structure

(3) Interaction

(4) Expression

(5) Function

Bioinformatics Applications:

Bioinformatics has various applications in research in medicine, biotechnology, agriculture etc. Following research fields has integral component of Bioinformatics

1. Computational Biology: The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems.

2. Genomics: Genomics is any attempt to analyze or compare the entire genetic complement of a species or species (plural). It is, of course possible to compare genomes by comparing more-or-less representative subsets of genes within genomes.

3. Proteomics: Proteomics is the study of proteins – their location, structure and function. It is the identification, characterization and quantification of all proteins involved in a particular pathway, organelle, cell, tissue, organ or organism that can be studied in concert to provide accurate and comprehensive data about that system. Proteomics is the study of the function of all expressed proteins. The study of the proteome, called proteomics, now evokes not only all the proteins in any given cell, but also the set of all protein isoforms and modifications, the interactions between them, the structural description of proteins and their higher-order complexes, and for that matter almost everything ‘post-genomic’.

4. Pharmacogenomics: Pharmacogenomics is the application of genomic approaches and technologies to the identification of drug targets. In Short, pharmacogenomics is using genetic information to predict whether a drug will help make a patient well or sick. It Studies how genes influence the response of humans to drugs, from the population to the molecular level.

5. Pharmacogenetics: Pharmacogenetics is the study of how the actions of and reactions to drugs vary with the patient’s genes. All individuals respond differently to drug treatments; some positively, others with little obvious change in their conditions and yet others with side effects or allergic reactions. Much of this variation is known to have a genetic basis. Pharmacogenetics is a subset of pharmacogenomics which uses genomic/bioinformatic methods to identify genomic correlates, for example SNPs (Single Nucleotide Polymorphisms), characteristic of particular patient response profiles and use those markers to inform the administration and development of therapies. Strikingly such approaches have been used to “resurrect” drugs thought previously to be ineffective, but subsequently found to work with in subset of patients or in optimizing the doses of chemotherapy for particular patients.

6. Cheminformatics: The mixing of those information resources [information technology and information management] to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the arena of drug lead identification and optimization. Related terms of cheminformatics are chemi-informatics, chemometrics, computational chemistry, chemical informatics, chemical information management/science, and cheminformatics. But we can distinguish chemoinformatics and chemical informatics as follows:

   1. Chemical informatics: Computer-assisted storage, retrieval and analysis of chemical information, from data to chemical knowledge. This definition is distinct from ‘Chemoinformatics’ (and the synonymous cheminformatics and chemiinformatics) which focus on drug design.

   2. Chemometrics: The application of statistics to the analysis of chemical data (from organic, analytical or medicinal chemistry) and design of chemical experiments and simulations.

7. Computational Chemistry: A discipline using mathematical methods for the calculation of molecular properties or for the simulation of molecular behavior. It also includes, e.g., synthesis planning, database searching, combinatorial library manipulation.

8. Structural Genomics or Structural Bioinformatics: Refers to the analysis of macromolecular structure particularly proteins, using computational tools and theoretical frameworks. One of the goals of structural genomics is the extension of idea of genomics, to obtain accurate three-dimensional structural models for all known protein families, protein domains or protein folds. Structural alignment is a tool of structural genomics.

9. Comparative Genomics: The study of human genetics by comparisons with model organisms such as mice, the fruit fly, and the bacterium E. coli.

10. Biophysics: The British Biophysical Society defines biophysics as: “an interdisciplinary field which applies techniques from the physical sciences to understanding biological structure and function”.

11. Biomedical Informatics / Medical Informatics: Biomedical Informatics is an emerging discipline that has been defined as the study, invention, and implementation of structures and algorithms to improve communication, understanding and management of medical information.

12. Mathematical Biology: Mathematical biology also tackles biological problems, but the methods it uses to tackle them need not be numerical and need not be implemented in software or hardware. It includes things of theoretical interest which are not necessarily algorithmic, not necessarily molecular in nature, and are not necessarily useful in analyzing collected data.

13. Computational Chemistry: Computational chemistry is the branch of theoretical chemistry whose major goals are to create efficient computer programs that calculate the properties of molecules (such as total energy, dipole moment, vibrational frequencies) and to apply these programs to concrete chemical objects. It is also sometimes used to cover the areas of overlap between computer science and chemistry.

14. Functional Genomics: Functional genomics is a field of molecular biology that is attempting to make use of the vast wealth of data produced by genome sequencing projects to describe genome function. Functional genomics uses high-throuput techniques like DNA microarrays, proteomics, metabolomics and mutation analysis to describe the function and interactions of genes.

15. Pharmacoinformatics: Pharmacoinformatics concentrates on the aspects of bioinformatics dealing with drug discovery.

16. In silico ADME-Tox Prediction: (Brief description) – Drug discovery is a complex and risky treasure hunt to find the most efficacious molecule which do not have toxic effects but at the same time have desired pharmacokinetic profile. The hunt starts when the researchers look for the binding affinity of the molecule to its target. Huge amount of research requires to be done to come out with a molecule which has the reliable binding profile. Once the molecules have been identified, as per the traditional methodologies, the molecule is further subjected to optimization with the aim of improving efficacy. The molecules which show better binding is then evaluated for its toxicity and pharmacokinetic profiles. It is at this stage that most of the candidates fail in the race to become a successful drug.

17. Agroinformatics / Agricultural Informatics: Agroinformatics concentrates on the aspects of bioinformatics dealing with plant genomes.

18. Systems Biology: Systems biology is the coordinated study of biological systems by investigating the components of cellular networks and their interactions, by applying experimental high-throughput and whole-genome techniques, and integrating computational methods with experimental efforts.

19. Bioprogramming: Bioprogramming identifies and assembles the main technical and biological criteria which are used to define the physical design of the facility. The output of this phase is the Bioprogramme, a document which contains statements of the concept and objectives, the design criteria, a biological and operational plan, and relevant schedules. It can also contain illustrations which enhance or clarify statements. Currently programmers are interested in developing BioPerl, BioPython, BioJava, and BioLinux.

20. Drug Design & Development: Drug Design (often represented as molecular modeling, in protein structure prediction) is the approach of finding drugs by design, based on their biological targets. Typically a drug target is a key molecule involved in a particular metabolic or signalling pathway that is specific to a disease condition or pathology, or to the infectivity or survival of a microbial pathogen. This field is related with Chemoinformatics.

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