Wednesday, 5 October 2011

The Nobel Prize in Physiology or Medicine 2011 Bruce A. Beutler, Jules A. Hoffmann, Ralph M. Steinman


                                         
Bruce Beutler, of the US, Jules Hoffmann from France and Ralph Steinman from Canada


The Nobel Prize in Physiology or Medicine 2011
shall be divided, with one half jointly to
Bruce A. Beutler and Jules A. Hoffmann
for their discoveries concerning the activation of innate immunity
and the other half to
Ralph M. Steinman
for his discovery of the dendritic cell and its role in adaptive immunity

 

Summary

This year's Nobel Laureates have revolutionized our understanding of the immune system by discovering key principles for its activation.
Scientists have long been searching for the gatekeepers of the immune response by which man and other animals defend themselves against attack by bacteria and other microorganisms. Bruce Beutler and Jules Hoffmann discovered receptor proteins that can recognize such microorganisms and activate innate immunity, the first step in the body's immune response. Ralph Steinman discovered the dendritic cells of the immune system and their unique capacity to activate and regulate adaptive immunity, the later stage of the immune response during which microorganisms are cleared from the body.
The discoveries of the three Nobel Laureates have revealed how the innate and adaptive phases of the immune response are activated and thereby provided novel insights into disease mechanisms. Their work has opened up new avenues for the development of prevention and therapy against infections, cancer, and inflammatory diseases.

Two lines of defense in the immune system

We live in a dangerous world. Pathogenic microorganisms (bacteria, virus, fungi, and parasites) threaten us continuously but we are equipped with powerful defense mechanisms (please see image below). The first line of defense, innate immunity, can destroy invading microorganisms and trigger inflammation that contributes to blocking their assault. If microorganisms break through this defense line, adaptive immunity is called into action. With its T and B cells, it produces antibodies and killer cells that destroy infected cells. After successfully combating the infectious assault, our adaptive immune system maintains an immunologic memory that allows a more rapid and powerful mobilization of defense forces next time the same microorganism attacks. These two defense lines of the immune system provide good protection against infections but they also pose a risk. If the activation threshold is too low, or if endogenous molecules can activate the system, inflammatory disease may follow.
The components of the immune system have been identified step by step during the 20th century. Thanks to a series of discoveries awarded the Nobel Prize, we know, for instance, how antibodies are constructed and how T cells recognize foreign substances. However, until the work of Beutler, Hoffmann and Steinman, the mechanisms triggering the activation of innate immunity and mediating the communication between innate and adaptive immunity remained enigmatic.

Discovering the sensors of innate immunity

Jules Hoffmann made his pioneering discovery in 1996, when he and his co-workers investigated how fruit flies combat infections. They had access to flies with mutations in several different genes including Toll, a gene previously found to be involved in embryonal development by Christiane Nüsslein-Volhard (Nobel Prize 1995). When Hoffmann infected his fruit flies with bacteria or fungi, he discovered that Toll mutants died because they could not mount an effective defense. He was also able to conclude that the product of the Toll gene was involved in sensing pathogenic microorganisms and Toll activation was needed for successful defense against them.
Bruce Beutler was searching for a receptor that could bind the bacterial product, lipopolysaccharide (LPS), which can cause septic shock, a life threatening condition that involves overstimulation of the immune system. In 1998, Beutler and his colleagues discovered that mice resistant to LPS had a mutation in a gene that was quite similar to the Toll gene of the fruit fly. This Toll-like receptor (TLR) turned out to be the elusive LPS receptor. When it binds LPS, signals are activated that cause inflammation and, when LPS doses are excessive, septic shock. These findings showed that mammals and fruit flies use similar molecules to activate innate immunity when encountering pathogenic microorganisms. The sensors of innate immunity had finally been discovered.
The discoveries of Hoffmann and Beutler triggered an explosion of research in innate immunity. Around a dozen different TLRs have now been identified in humans and mice. Each one of them recognizes certain types of molecules common in microorganisms. Individuals with certain mutations in these receptors carry an increased risk of infections while other genetic variants of TLR are associated with an increased risk for chronic inflammatory diseases.

A new cell type that controls adaptive immunity

Ralph Steinman discovered, in 1973, a new cell type that he called the dendritic cell. He speculated that it could be important in the immune system and went on to test whether dendritic cells could activate T cells, a cell type that has a key role in adaptive immunity and develops an immunologic memory against many different substances. In cell culture experiments, he showed that the presence of dendritic cells resulted in vivid responses of T cells to such substances. These findings were initially met with skepticism but subsequent work by Steinman demonstrated that dendritic cells have a unique capacity to activate T cells.
Further studies by Steinman and other scientists went on to address the question of how the adaptive immune system decides whether or not it should be activated when encountering various substances. Signals arising from the innate immune response and sensed by dendritic cells were shown to control T cell activation. This makes it possible for the immune system to react towards pathogenic microorganisms while avoiding an attack on the body's own endogenous molecules.

From fundamental research to medical use

The discoveries that are awarded the 2011 Nobel Prize have provided novel insights into the activation and regulation of our immune system. They have made possible the development of new methods for preventing and treating disease, for instance with improved vaccines against infections and in attempts to stimulate the immune system to attack tumors. These discoveries also help us understand why the immune system can attack our own tissues, thus providing clues for novel treatment of inflammatory diseases.

 

Bruce A. Beutler was born in 1957 in Chicago, USA. He received his MD from the University of Chicago in 1981 and has worked as a scientist at Rockefeller University in New York, at UT Southwestern Medical Center in Dallas, where he discovered the LPS receptor, and the Scripps Research Institute in La Jolla, CA. Very recently, he rejoined the University of Texas Southwestern Medical Center in Dallas as professor in its Center for the Genetics of Host Defense.
Jules A. Hoffmann was born in Echternach, Luxembourg in 1941. He studied at the University of Strasbourg in France, where he obtained his PhD in 1969. After postdoctoral training at the University of Marburg, Germany, he returned to Strasbourg, where he headed a research laboratory from 1974 to 2009. He has also served as director of the Institute for Molecular Cell Biology in Strasbourg and during 2007-2008 as President of the French National Academy of Sciences.
Ralph M. Steinman was born in 1943 in Montreal, Canada, where he studied biology and chemistry at McGill University. After studying medicine at Harvard Medical School in Boston, MA, USA, he received his MD in 1968. He was affiliated with Rockefeller University in New York since 1970, where he was professor of immunology from 1988. Sadly, Ralph Steinman passed away before the news of his Nobel Prize reached him.

Tuesday, 4 October 2011

Rosetta Web Servers

The Rosetta macromolecular modeling suite has a very wide range of applications, and although installing and running Rosetta, becomes increasingly easier over the years, some non-experts would still prefer to run it over a comfortable to use web-server. We have assembled here the list of Rosetta applications for which such a web-server exists. Bookmark for future modeling tasks.


Robetta (Baker Lab)
This is the original and oldest Rosetta server, created to do what Rosetta was created to do: structure prediction. Nowadays the server offers more than just structure prediction and includes the following functions. Robetta is known for its good performance in CASP competitions, though the human Rosetta operators usually get better scores. There are 4 main categories you can use Robetta for:
  • Structure prediction: Given a simple fasta sequence of your protein, performs ab-initio (or homology based) structure prediction. Beware – this takes a lot of time. It’s worth to note that one can also use “Rosetta NMR” through this server by including NMR derived constraints into the folding simulations.
  • Fragment libraries creation: Given a fasta file – creates PDB based fragment libraries for all overlapping fragments of sizes 3 and 9. (This is required for several flavors of local Rosetta runs).
  • Interface Alanine scanning: Given the structure of a protein-protein complex, performs computational alanine scan over all interface residues (or on residues specified by user). Provides estimated ddG values for these mutations and allows the detection of hotspot residues. (By Tanja Kortemme)
  • DNA-interface scan: Given the structure of a protein-DNA complex, this server estimates the changes to DNA binding affinity and specificity that result from exhaustive point mutations at individual amino acid positions in the interface.

RosettaDesign (Kuhlman Lab)
Rosetta design can be used to identify sequences compatible with a given protein backbone, or to detect mutations at protein-protein interfaces that would increase binding affinity. Some of Rosetta design’s successes include the design of a novel protein fold, redesign of an existing protein for greater stability, increased binding affinity between two proteins, and the design of novel enzymes.

RosettaDock (Gray Lab)
The RosettaDock protein-protein docking server predicts the structure of protein complexes given the structures of the individual components and an approximate binding orientation. The server uses the Rosetta 2.1 protein structure modeling suite. It’s important to note that this is a “Local docking” server, i.e. the input complex should be in the approximate binding site. RosettaDock has a module that can handle “Global docking” (i.e. with no information on the binding site) but this is too computational demanding for a web-server. (You can detect initial potential binding sites using protein interface predictors).

RosettaAntibody (Gray Lab)
Antibody modeling server for prediction of antibody Fv structures. The antibody homology modeling server is built on the Rosetta structure prediction suite comprising of knowledge-based techniques for template selection, grafting for non-H3 CDR loops, de novo loop modeling for creating CDR-H3, and docking to optimize the relative orientation of the light and heavy chains.

RosettaBackrub (Kortemme Lab)
Flexible backbone protein structure modeling and design server based on the “backrub” method (first described by Davis et al). The server input is a protein structure (a single protein or a protein-protein complex) uploaded by the user and a choice of parameters and modeling method: prediction of point mutant structures, creation of conformational ensembles given the input protein structure and flexible backbone design.
  • Point mutations: This function utilizes the backrub protocol and applies it to the neighborhood of a mutated amino acid residue to model conformational changes in this region.
  • Backrub ensembles:
    • Backrub Conformational Ensemble: Backrub is applied to the entire input structure to generate a flexible backbone ensemble of modeled protein conformations.
    • Backrub Ensemble Design: This method first creates an ensemble of structures to model protein flexibility. In a second step, the generated protein structures are used to predict an ensemble of low-energy sequences consistent with the input structures, using computational design implemented in Rosetta. The output is a sequence profile of this family of structures.
  • Interface sequence plasticity prediction: First, the backrub algorithm is applied to the uploaded protein-protein complex to generate a flexible backbone conformational ensemble of the entire complex. Then, for each of the resulting structure, residue positions given by the user (up to 10) are subjected to protein design. All generated sequences are ranked by their Rosetta force field score. Sequences with favorable scores both for the total protein complex and the interaction interface are used to build a sequence profile.

RosettaFlexPepDock (Furman Lab)
Rosetta FlexPepDock is a high-resolution peptide docking (refinement) protocol, implemented within the Rosetta framework. The input for this server is a PDB file of a complex between a protein receptor (first chain) and an estimated conformation for a peptide (second chain). FlexPepDock was shown to be able to accurately refine the peptide structure starting from up to 5.5A RMSD of the native conformation, allowing full flexibility to the peptide and side-chain flexibility to the receptor.

FunHunt Server (Furman Lab)
FunHunt is a classifier of correct protein-protein complex orientations. The input to FunHunt are two possible orientations of a complex. A local docking run is performed on the two complexes using RosettaDock. FunHunt then uses features gathered from these docking runs – representing the local energy landscapes of the orientations, and chooses the near-native orientation among both (assuming that one of the orientations is the near native one).

Wednesday, 28 September 2011

Tools for Analysis of Genetic Diversity and Mapping



Tools for Analysis of Genetic Diversity and Mapping
1- 5th November 2011
Organized by
Bioinformatics Centre
Central Plantation Crops Research Institute
Kudlu P.O., Kasaragod. 671124
Kerala, India.

TRAINING TOPICS:
1. Plant genetic resources and core collections.
2. Spatial analysis of plant genetic resources data.
3. Genetic diversity analysis/clustering.
4. Population structure/dynamics.
5. Phylogenetic analysis.
6. Construction of linkage maps.
7. Single-point linkage analysis.
8. QTL analysis.
9. Association mapping

bioinformatics topics as well as hands on session.
LEVEL OF PARTICIPANTS:
Researchers /PG students in the subject area Biotechnology/Bioinformatics/Biochemistry/Agriculture.
NUMBER OF PARTICIPANTS: Limited to 10-15 on first come first serve basis
HOW TO REGISTER: Filled
nomination form must accompany the application fee for Rs.500/-
(Rs.250/- for Students) vide Demand Draft in favor of ICAR unit, CPCRI,
Kasaragod. Nomination of the candidates along with payment should be
before 20th October 2011
received on or

ONLINE REGISTRATION:
Register online at http://www.bioinfcpcri.org and send the filled nomination form with registration fee by speed post.
Selected participants will be informed bye-mail.

Wednesday, 21 September 2011

AUTODOCK -protein ligand docking (Easy Steps) Running in WINDOWS


AUTODOCK MANUAL FOR WINDOWS

(Autodock version -1.4 v, using MGL Tool)

Preparing Protein File

Go to File -> Read molecule -> open pdb file

Go to Edit -> Hydrogen -> Add -> choose polar only

Edit -> Charges -> Add -> Kollman Charges (Eg.-8.91)

Go to File -> Save -> Save as protein.pdbq


Preparing Ligand File

Go to ligand -> Input -> open ligand or drug file in pdb format (do not open mol file or any other format - For molecular file conversion use DUNDEE PRODRG Server)

Go to ligand -> Torsion Tree -> Detect Root

Go to ligand -> Output -> Save as ligand.pdbqt


Preparing Grid Parameter File

Go to Grid -> Macromolecule -> select molecule -> choose protein file -> click O.K-> save as protein.pdbqt

Go to Grid -> Set Map type -> Click Directly - Click Accept

Go to Grid -> Grid Box -> Enter X,Z,Y value of your preferred active sites (For predicting active sites, use Q-Site Finder server and submit your protein, there choose minimum x,z,y coordinate values of active sites as a Grid Box value)

Go to File -> Click Saving Current

Go to Grid -> output -> save as protein.gpf


Preparing Docking Parameter Files

Go to Docking -> Macromolecule -> Set Rigid File Name -> open protein.pdbqt

Go to Docking -> Ligand -> Choose -> select a ligand file -> Accept

Go to Docking -> Search Parameters -> Genetic Algorithm (GA)--> Accept

Go to Docking -> other options -> choose -> Autodock4 parameters

Go to Docking -> output -> Lamarkian GA -> save as protein.dpf


Running AutoGrid

Click - Run Autogrid

Change autogrid3 to autogrid4


Running AutoDock

Click - Run Autodock

Change autodock3 to autodock4


ANALYZING AUTODOCK RESULTS


Open dlg file in notepad and check the rank (Control+F three times), and find the RUN of rank 1. choose the pdb coordinates of rank 1. copy and paste the coordinates and paste it in protein pdb file. Open the saved pdb file in Pymol or any visualization software.

Tuesday, 20 September 2011

DeepView - Swiss-PdbViewer


The SIB Swiss Institute of Bioinformatics presents:

Swiss-PdbViewer

DeepView

v3.7

Description

Swiss-PdbViewer (aka DeepView) is an application that provides a user friendly interface allowing to analyze several proteins at the same time. The proteins can be superimposed in order to deduce structural alignments and compare their active sites or any other relevant parts. Amino acid mutations, H-bonds, angles and distances between atoms are easy to obtain thanks to the intuitive graphic and menu interface.
Swiss-PdbViewer (aka DeepView) has been developped since 1994 by Nicolas Guex. Swiss-PdbViewer is tightly linked to SWISS-MODEL, an automated homology modeling server developed within the Swiss Institute of Bioinformatics (SIB) at the Structural Bioinformatics Group at the Biozentrum in Basel.
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 residues can be mutated.
Finally, as a special bonus, POV-Ray scenes can be generated from the current view in order to make stunning ray-traced quality images. An example can be found here.

Monday, 19 September 2011

Application of DNA microarrays in structural and functional genomics.


DNA microarrays started their career in the last decade of XX century. From that time a wide spectrum of microarray-based methods have been developed, contributing to the fast progress of structural and functional genomics. In a typical microarray experiment, DNA probes (short and long oligonucleotides, cDNA clones), complementary to the target sequences, are deposited on a solid surface . Target samples (DNA or RNA extracted from cells or tissues) are amplified (if necessary), fluorescently labeled, and hybridized with a microarray. In a two-color assay control and investigated samples are labeled with two different dyes, mixed and hybridized with the same array . Post hybridization scanning of a microarray with one/two lasers excite the fluorescent dyes that emit light. A digital microarray image generated by the scanner is then processed to convert signal intensities into quantitative data organized in tab-delimited files . These raw data files are submitted to normalization and analysis, including statistical testing and clustering , to retrieve biologically relevant information.
There are two basic applications of DNA microarrays: studying genome structure and gene expression analysis. In genome structure studies the following microarray-based methods are applied:
-          CGH (comparative genomic hybridization) arrays, at the beginning consisted of long chromosomal fragments (e.g. BAC clones, cDNA clones), sufficient for detection of large changes – deletions, insertions and copy number variation, today much more sensitive due to oligonucleotide probes application 
-          SNP arrays, useful in genotyping, designed to identify not only SNP (single nucleotide polymorphism), but also LOH (loss-of-heterozygosity), CNV (copy number variation), AI (allelic imbalance), UPD (uniparental disomy); at present DNA chips for identification millions of SNP and copy number variants in human genome are available from Affymetrix  or Illumina 
-          tiling arrays with oligonucleotide probes covering the whole genome sequence (except for repetitive sequences), applied for CGH, chromatin immunoprecipitation (ChIP-on-chip, based on analysis of DNA-proteins interactions ), methyl-DNA immunoprecipitation (MeDIP-chip, epigentetic studies), chromatin hypersensitive sites localization (DNase-chip), gene annotation and mapping 
-          multiple-tiling arrays, e.g. with double-tiled probes spanning the entire yeast genome 
-          TIP-chips (Transposon Insertion site Profiling chips), used for detection of transposons in yeast .
Although sequencing by hybridization did not gain popularity, array-like structures are common in DNA sequencing technologies. DNA microarrays are also used for studying relationships between organisms and microbial strain identification .
Isoenergetic microarrays, built of short oligonucleotide probes, help to predict secondary structure of RNA molecules. More often, RNA molecules in microarray experiment serve as targets for functional studies - differential expression of mRNAs and microRNAs . Such analysis determines which genes are up- and down-regulated comparing to control samples. Gene expression profiles (signatures) are specific for particular samples and conditions, e.g. developmental stage, disease type, stress . Here, the following arrays are applied:
-          Short oligonucleotide microarrays with probes synthesized in situ (DNA chips, multiple probes per gene) 
-          Microarrays consisted of long oligonucleotide probes (usually one per gene), synthesized in situ or pre-synthesized and deposited on the solid surface
-          cDNA (complementary DNA) arrays, where the probes, cDNA clones, are spotted onto a glass slide using printing robots
-          Tiling arrays, covering the whole genome sequence, applied for discovering new transcriptionally active regions and splice variants 
-          Exon arrays with separate probes designed to detect each exon, useful in detection of splicing isoforms .
In case of organisms with not sequenced genomes, cross-species hybridization (CSH, with microarray designed for a relative species), is a solution to follow gene expression . 
There is no doubt that microarrays are powerful tools in biology and medicine, applicable for genotyping, biomarker selection, medical diagnosis and prognosis, screening of potential therapeutics (pharmacogenomics), etc.. However, at the large-scale research field, sequencing technologies have more advantages, being more precise, accurate and less dependent on technical and statistical errors . The contemporary science started also to benefit from the integrated approaches, including sequencing as well as gene expression profiling with microarrays, as it is applied in cancer research area . 

The following materials and learning resources are available in this lecture:
[1] DNA microarray definition and description


[2] Film: “DNA microarrays” 


[3] Film: “Microarrays” 


[4] Film: “Microarray Gene Expression” 


[5] DNA microarrays and chips – description from “Genomes 2” 


[6] Article “The incredible shrinking world of DNA microarrays” 


[7] Article “DNA Microarrays: a Powerful Genomic Tool for Biomedical and Clinical Research” 


[8] Lecture: Introduction to array CGH analysis 


[9] Article “Identification of disease genes by whole genome CGH arrays” 


[10] SNP array


[11] Film: “Microarray Method for Genetic Testing” 


[12] Affymetrix SNP arrays 


[13] Illumina Whole Genome Genotyping arrays 


[14] ChIP-on-chip technology 


[15] Tiling array 


[16] Sequencing by hybridization 


[17] Article “Application of DNA microarray technology for detection, identification, and characterization of food-borne pathogens” 


[18] Article abstract “Binding of short oligonucleotides to RNA…” 


[19] Cross-species hybridization (CSH)


[20] Film: “Diagnostic Micoarrays” 


[21] Article “Progress in the Application of DNA Microarrays” 


[22] DNA Microarrays in Medicine 


[23] Article "The next generation of microarray research: applications in evolutionary and ecological genomics