a glance tics

8/6/2019 A Glance tics

1/25

IMMUNOINFORMATICS


2/25

CONTENTS

` Introduction to immunoinformatics

` Databases

` Receptor mapping

` HLA Supertypes

` HLAPeptide Binding Prediction


3/25

Immunoinformatics

Immunoinformatics is an emerging specialization of bioinformatics that focuses upon the structure, functionand interactions of the molecules involved in immunity.

The appropriate use of informatics technique greatly improve the efficiency of immunology research.

Immunoinformatic is very complex and can be seen as acombinatorial science


4/25

Le arningAlgorithms,Patt e rn

Re cognition,Adaptiv e

Me mori e s,Int e llig e nt

Age nts

D e sign of Exp e rim e nts,

DataInt e rpr e tation

COMPUTERSCIENCE IMMUNOLOGY

COMPUTERSCIENCE IMMUNO

L OGY

DATABASES COMPUTATIONAL MODELS

DATABASES COMPUTATIONAL

MODE L S

COMPUTATIONAL

IMMUNO L OGY

COMPUTATIONAL EXPERIMENTS

COMPUTATIONA L EXPERIMENTS

IMMUNOINFORMATICS


5/25

MHC class IIgets peptidesfrom outsidethe cellthroughphagocytosis.

These areexogenous,usually. Blockedfrom peptidesin the ER.Exposed topeptides fromlysosomes,in a vesicle.recognized byCD4 T-cells

MHC class Igets peptidesfrom thecytoplasm.These areendogenous,

usually.Exposed topeptides fromtheproteosome, in the ER.Recognized byCD8 T-cells


6/25

Antibody div e rsity


7/25

Data manag e m e nt

` Manag e m e nt and analysis of immunologicaldata plays a major rol e in immunoinformatics

General Dtatbases :

G e nBank , Prosit e EMB L , DDBJ , PIR , PDB , SWISS-PROT ,G e nP e pt

Advantages

Significant infrastructur e

Int e rfac e s for data e xtraction andanalysis

Curation and quality assuranc e of data

C e ntrally acc e ssibl e

Standardis e d formats facilitatingautomation

Ind e p e nd e ntly maintain e d and fund e d

Disadvantages

Q uality control of cont e nt

Error propagation

Typically poor annotation of f e atur e s

Obsol e t e , incompl e t e , or r e dundante ntri e sL ack of synchronisation

Application of standards (nom e nclatur ee tc.)


8/25

Sp e cialist databas e s

KABAT IMGT IMGT FIMM MHCPEPS L AD MHCDBSYFPEITHI

Advantag e sMor e d e tail e d information

Cr e at e d and maintain e d by th e domaine xp e rts

High l e ve l of quality assuranc e of data

B e tt e r complianc e to standards

Hav e sp e cialist tools

Disadvantag e sIrr e gular updat e sL ow l e ve l of automationLe ss r e liabl e for acc e ss and curr e ncy

Funding unc e rtainty


9/25

IMGT ONTO L OGY


10/25

Scientific chart rules and data conceptIMGT-ONTOLOGY

main conceptsIMGT Scientific chart rules Examples of IMGT expertised data concepts

IDENTIFICATION Standardiz e d k e ywords Sp e ci e s, mol e cul e typ e , r e c e ptor typ e , chain typ e , g e n e

typ e , structur e , functionality, sp e cificity

DESCRIPTION Standardiz e d lab e ls and

annotations

Cor e (V-, D-, J-, C-REGION)

Prototyp e s

L ab e ls for s e qu e nc e s

L ab e ls for 2D and 3D structur e s

C L ASSIFICATION Re f e r e nc e s e qu e nc e s

Standardiz e d IG and TR g e n e nom e nclatur e

(group, subgroup,

ge n e , and all e le )

Nom e nclatur e of th e human IG and TR g e n e s [ e ntry in 1999

in GDB, HGNC, and L ocus L ink and Entr e z G e n e at NCBI]

Alignm e nt of all e le s

Nom e nclatur e of th e IG and TR g e n e s of all v e rt e brat e

Sp e ci e s

NUMEROTATION IMGT uniqu e numb e ring for: V- and V-

L IKE-DOMAINs C- and C- L IKE-DOMAINsG- and G- L IKE-DOMAINs

Prot e in displays

Colli e rs d e Pe rl e s (13)FR-IMGT and CDR-IMGT d e limitations

Structural loops and b e ta strands d e limitations

ORIENTATION Ori e ntation of g e nomic

instanc e s r e lativ e to e ach oth e r

Chromosom e ori e ntation

L ocus ori e ntation

G e n e ori e ntation

DNA strand ori e ntation

OBTENTION Standardiz e d origin and

m e thodology


11/25

IMGT


12/25

Mapping of T-Cell Epitopes, MHC Binders, and TAP Binders


13/25

S earches S ignificanceKeyword search searches based on user defined keyword

Peptide search This allow user to search a peptide in MHCbinding or non-binding peptide in database

TAP search peptides interacting with the TransporterAssiociated with Antigen Processing (TAP)

Peptide mapping This allows the search of known MHCbinders/non-binders,TAP binders/non-bindersand T cell epitopes/non epitopes available inMHCBN database for query antigen orprotein sequence

MHC Blast Searches similar sequence in database of MHC proteins in MHCBN,


14/25

MHC class I pathway

Peptides

ER

CTL(TCD8+)

Intracellular pathogen (virus, mycobacteria)

Proteasome

Cytosolic protein

MHC I

ERTAP

TCR

CD8

Xenoreactive Complex AHIII 12.2 TCR bound toP1049 ( ALWGFFPVLS) /HLA-A2.1

MHCclass I

T-CellReceptor

VE

V F

F-2-Microglobulin

1lp9


15/25

Grouping of Class I HLA Alleles Using Electrostatic DistributionMaps of the Peptide Binding Grooves

` HLA Modeling1. Id e ntification of suitabl e t e mplat e s from prot e in databank (PDB),2. S e le ction of structural t e mplat e s,3. Targ e tto- t e mplat e alignm e nt,4. Mod e l building, and5. G e n e ration of 3D mod e ls.


16/25

Grouping H L A all e le s bas e d on th e ir e le ctrostaticdistribution of th e p e ptid e binding groov e s

Grouping of models Th e mod e ls w e r e th e n group e d by visual insp e ction bas e d on th e natur e of color inth e binding groov e .

Re d color r e f e rs to e le ctron e gativ e groov e ,Blu e to e le ctropositiv e groov e ,

White

to ne

utral groove

Mix e d for a mixtur e of r e d, blu e , and whit e groov e

Tabl e 1 summariz e s th e grouping of 1,000 class I H L A mod e ls (310 H L A-A, 570 H L A-B, and 120 H L A-C) usingth e typ e of e le ctrostatic pot e ntial in th e p e ptid e binding groov e


17/25

Re sult

Grouping of human l e ukocyt e antig e n (H L A) all e le s into n e gativ e (r e d), positiv e (blu e ), n e utral(whit e ), and mix e d (r e d, blu e , and whit e ) mod e ls


18/25


19/25


20/25


21/25

1. Finding th e b e st fit conformation (docking) of p e ptid e s within th e MHC binding groov e

2. Scr ee ning pot e ntial bind e rs from th e background.

Two-st e p approach to pr e dict MHC-bindingp e ptid e s


22/25

Docking is a computationally e xhaustiv e proc e dur eL arg e numb e r of possibl e p e ptid e conformations

3 global translational d e gr ee s of fr ee dom3 global rotational d e gr ee s of fr ee dom1 conformational d e gr ee of fr ee dom for e ach

rotatable

bond

y

x

z

R

N C C E

C

O

J ]

>10 10 possibl e conformations for a 10-r e sidu e p e ptid e


23/25

Rapid docking of p e ptid e to MHC

Anchoring root fragmentsto reduce search space

(Pseudo-Brownian rigid bod y docking )

Loop modeling (Loop closureof central backbone b y satisfaction of spatial

restraints)Ligand backbone and side-chain refinement (entire

backbone and interacting side-chains

2

3

1


24/25

Q u e stions


25/25

a glance tics

Documents