Protein secondary structure prediction methods TDVEAAVNSLVNLYLQASYLS “From sequence to structure”

Slides:



Advertisements
Similar presentations
Protein Structure.
Advertisements

Secondary structure prediction from amino acid sequence.
Protein Structure Prediction
PDB-Protein Data Bank SCOP –Protein structure classification CATH –Protein structure classification genTHREADER–3D structure prediction Swiss-Model–3D.
Prediction to Protein Structure Fall 2005 CSC 487/687 Computing for Bioinformatics.
Intro to Bioinformatics Summary. What did we learn Pairwise alignment – Local and Global Alignments When? How ? Tools : for local blast2seq, for global.
Protein secondary structure prediction methods TDVEAAVNSLVNLYLQASYLS “From sequence to structure”
An Introduction to Bioinformatics Protein Structure Prediction.
Structure Prediction. Tertiary protein structure: protein folding Three main approaches: [1] experimental determination (X-ray crystallography, NMR) [2]
Today’s menu: -UniProt - SwissProt/TrEMBL -PROSITE -Pfam -Gene Onltology Protein and Function Databases Tutorial 7.
Tutorial 5 Motif discovery.
Computational Biology, Part 10 Protein Structure Prediction and Display Robert F. Murphy Copyright  1996, 1999, All rights reserved.
Protein secondary structure prediction methods TDVEAAVNSLVNLYLQASYLS “From sequence to structure”
The Protein Data Bank (PDB)
. Protein Structure Prediction [Based on Structural Bioinformatics, section VII]
Today’s menu: -UniProt - SwissProt/TrEMBL -PROSITE -Pfam -Gene Onltology Protein and Function Databases Tutorial 7.
PDB-Protein Data Bank SCOP –Protein structure classification CATH –Protein structure classification genTHREADER–3D structure prediction Swiss-Model–3D.
Introduction to Bioinformatics - Tutorial no. 8 Predicting protein structure PSI-BLAST.
Protein Structure July 2, 2006 Learning objectives-Understand the basis of the secondary structure prediction program- Psi-PRED. Introduce the concept.
Protein Structure Prediction II
Protein and Function Databases
Protein structure prediction May 24, 2005 Return of Quiz#3 Writing assignments-please hand in. Learning objectives-Understand the basis of secondary structure.
Introduction to Bioinformatics - Tutorial no. 8 Protein Prediction: - PROSITE - Pfam - SCOP - TOPITS - genThreader.
Protein structure prediction 29/01/2015 Mail: Prof. Neri Niccolai Simone Gardini
Pattern databasesPattern databasesPattern databasesPattern databases Gopalan Vivek.
Bioinformatics for biomedicine Protein domains and 3D structure Lecture 4, Per Kraulis
Protein Tertiary Structure Prediction
Practical session 2b Introduction to 3D Modelling and threading 9:30am-10:00am 3D modeling and threading 10:00am-10:30am Analysis of mutations in MYH6.
Secondary Structure Prediction Protein Analysis Workshop 2008 Bioinformatics group Institute of Biotechnology University of helsinki Hung Ta
Proteins Secondary Structure Predictions Structural Bioinformatics.
Secondary Structure Prediction
EMBL-EBI Adel Golovin MSDsite The project is funded by the European Commission as the TEMBLOR, contract-no. QLRI-CT under the RTD programme.
Secondary Structure Prediction and Signal Peptides Protein Analysis Workshop 2012 Bioinformatics group Institute of Biotechnology University of helsinki.
Protein Secondary Structure Prediction. Input: protein sequence Output: for each residue its associated Secondary structure (SS): alpha-helix, beta-strand,
Multiple Alignment and Phylogenetic Trees Csc 487/687 Computing for Bioinformatics.
Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices Yan Liu Sep 29, 2003.
© Wiley Publishing All Rights Reserved. Protein 3D Structures.
Neural Networks for Protein Structure Prediction Brown, JMB 1999 CS 466 Saurabh Sinha.
Module 3 Sequence and Protein Analysis (Using web-based tools) Working with Pathogen Genomes - Uruguay 2008.
Computational prediction of protein-protein interactions Rong Liu
Biological Networks. Can a biologist fix a radio? Lazebnik, Cancer Cell, 2002.
1 Enter the following Micro-RNA sequence into the box Run MFold and look at the results MFold Using MFold to predict RNA secondary structure
MolIDE2: Homology Modeling Of Protein Oligomers And Complexes Qiang Wang, Qifang Xu, Guoli Wang, and Roland L. Dunbrack, Jr. Fox Chase Cancer Center Philadelphia,
Protein structure prediction May 26, 2011 HW #8 due today Quiz #3 on Tuesday, May 31 Learning objectives-Understand the biochemical basis of secondary.
Protein Structure & Modeling Biology 224 Instructor: Tom Peavy Nov 18 & 23, 2009
Protein Tertiary Structure. Protein Data Bank (PDB) Contains all known 3D structural data of large biological molecules, mostly proteins and nucleic acids:
Study of Protein Prediction Related Problems Ph.D. candidate Le-Yi WEI 1.
Motif discovery and Protein Databases Tutorial 5.
Protein Modeling Protein Structure Prediction. 3D Protein Structure ALA CαCα LEU CαCαCαCαCαCαCαCα PRO VALVAL ARG …… ??? backbone sidechain.
Protein Structure Prediction ● Why ? ● Type of protein structure predictions – Sec Str. Pred – Homology Modelling – Fold Recognition – Ab Initio ● Secondary.
Introduction to Protein Structure Prediction BMI/CS 576 Colin Dewey Fall 2008.
Sequence Based Analysis Tutorial March 26, 2004 NIH Proteomics Workshop Lai-Su L. Yeh, Ph.D. Protein Science Team Lead Protein Information Resource at.
HMMs and SVMs for Secondary Structure Prediction
Query sequence MTYKLILNGKTKGETTTEAVDAATAEKVFQYANDN GVDGEWTYTE Structure-Sequence alignment “Structure is better preserved than sequence” Me! Non-redundant.
Comparative methods Basic logics: The 3D structure of the protein is deduced from: 1.Similarities between the protein and other proteins 2.Statistical.
Structural classification of Proteins SCOP Classification: consists of a database Family Evolutionarily related with a significant sequence identity Superfamily.
Lecture 10 CS566 Fall Structural Bioinformatics Motivation Concepts Structure Solving Structure Comparison Structure Prediction Modeling Structural.
Marlou Snelleman 2012 Protein structure. Overview Sequence to structure Hydrogen bonds Helices Sheets Turns Hydrophobicity Helices Sheets Structure and.
Protein Tertiary Structure Prediction Structural Bioinformatics.
Proteins Structure Predictions Structural Bioinformatics.
3.3b1 Protein Structure Threading (Fold recognition) Boris Steipe University of Toronto (Slides evolved from original material.
Predicting Structural Features Chapter 12. Structural Features Phosphorylation sites Transmembrane helices Protein flexibility.
Sequence: PFAM Used example: Database of protein domain families. It is based on manually curated alignments.
Secondary Structure Prediction
Secondary structure prediction
Homology Modeling of the Human Olfactory Receptor O2D2
חיזוי ואפיון אתרי קישור של חלבון לדנ"א מתוך הרצף
Aligning Sequences You have learned about: Data & databases Tools
Protein structure prediction.
Neural Networks for Protein Structure Prediction Dr. B Bhunia.
Presentation transcript:

Protein secondary structure prediction methods TDVEAAVNSLVNLYLQASYLS “From sequence to structure”

What are they? –Sequence-based tools for protein structure prediction. What do they do? 1.they Search for similar protein sequences in a database. 2.Based on the similarity to these sequences they predicts aspects of protein structure and function

What kind of prediction can we perform? –S–Secondary Structure: Helix, Strands, Loops (PHDsec,PSIPRED). –P–Predicts transmembrane helices (PHDhtm,MEMSAT,TMHMM). –F–Fold structure (genTHREADER). –S–Solvent accessibility: important for the prediction of ligand binding sites (PHDacc). –O–Other features: Coiled Coils, Globular regions, Disulfide Bonds and more…

Secondary Structure prediction: Query SwissProt BLASTp Query Subject psiBLAST, MaxHom MSA Machine Learning Approach HHHLLLHHHEEE Known structures

PROF sec and PSIpred Two secondary structure prediction tools: PROFsec –Based on sequence family alignments (MAXHOM) PSIpred –Based on PSI-BLAST profiles

PROFsec:Profile-based neural network prediction of secondary structures. ASP:Predicts conformational switches (e.g from α-helix to β-sheet. ProSite:Scan Prosite for functional motifs. PredictNLS:Predicts nuclear localization based on known data.

DISULFIND:Find disulfide bonds. ProDom:Search database of putative domains. PROFacc:Predict Solvent Accessibility COLIS:Predict coiled-coil structures SEG:Sequence Complexity PHDhtm:Transmembrane domains.

OUTPUT

number of aligned sequences in HSSP file (Homology derived Secondary Structure)

OBS_sec: Observed secondary structures (if any). PROF_sec:Predicted secondary structures. Rel_sec:Reliability of the prediction. SUB_sec:Subset of predictions with high reliability. O_3_sec:Observed surface accessibility. P_3_sec:Predicted surface accessibility. b = 0-9%, i = 9-36%, e = %. Rel_acc:Reliability of prediction. SUB_acc:Subset of predictions with high reliability. H= Helix, E=Sheet, L=loop

PSIpred Input sequence Type of Analysis (PSIPRED,MEMSAT, genTHREAD)

PSIpred Filtering Options address GO!

PSIPRED secondary structure prediction

PROFsecPSIpred ? ?

TMHMM – transmembrane domain prediction

Predict StartEnd

ferritin

Java based visualization tools PDB file Accession number General Info

PDB provides the atomic coordinates of the structure : Which can be viewed by different visualization tools

Experimental Description. i.e. crystallography info.

Structure classification

Sequence and Secondary Structure information

Sequence Details TurnHelix