Tools to analyze protein characteristics

Protein sequence

-Family member-Multiple alignments

Identification of conserved regions

Evolutionary relationship (Phylogeny)

3-D fold model

Protein sorting and sub-cellular localization

Anchoring into the membrane

Signal sequence (tags)

Protein modifications

Some nascent proteins contain a specific signal, or targeting sequence that directs them to the correct organelle. (ER, mitochondrial, chloroplast, lysosome, vacuoles, Golgi, or cytosol)

Can we train the computers:To detect signal sequences and predict protein destination?To identify conserved domains (or a pattern) in proteins?To predict the membrane-anchoring type of a protein? (Transmembrane domain, GPI anchor…)To predict the 3D structure of a protein?

Learning algorithms are good for solving problems in pattern recognition because they can be trained on a sample data set.

Classes of learning algorithms:-Artificial neural networks (ANNs)-Hidden Markov Models (HMM)

Questions

Artificial neural networks (ANN)

Machine learning algorithms that mimic the brain. Real brains, however, are orders of magnitude more complex than any ANN.

ANNs, like people, learn by example. ANNs cannot be programmed to perform a specific task.

ANN is composed of a large number of highly interconnected processing elements (neurons) working simultaneously to solvespecific problems.

The first artificial neuron was developed in 1943 by the neurophysiologist Warren McCulloch and the logician Walter Pits.

Hidden Markov Models (HMM)

HMM is a probabilistic process over a set of states, in which the states are “hidden”. It is only the outcome that visible to the observer. Hence, the name Hidden Markov Model.

HMM has many uses in genomics:Gene prediction (GENSCAN)SignalPFinding periodic patterns

Used to answer questions like:What is the probability of obtaining a particular outcome?What is the best model from many combinations?

Expasy server (http://au.expasy.org) is dedicated to the analysis of protein sequences and structures.

The ExPASy (Expert Protein Analysis System)

Sequence analysis tools include: DNA -> Protein [Translate] Pattern and profile searches Post-translational modification and topology prediction Primary structure analysis Structure prediction (2D and 3D) Alignment

PredictProtein: A service for sequence analysis, and structure prediction http://www.predictprotein.org/newwebsite/submit.html

TMpred: http://www.ch.embnet.org/software/TMPRED_form.html

TMHMM: Predicts transmembrane helices in proteins (CBS; Denmark) http://www.cbs.dtu.dk/services/TMHMM-2.0/

big-PI : Predicts GPI-anchor site:http://mendel.imp.univie.ac.at/sat/gpi/gpi_server.html

DGPI: Predicts GPI-anchor site: http://129.194.185.165/dgpi/index_en.html

SignalP: Predicts signal peptide: http://www.cbs.dtu.dk/services/SignalP/

PSORT: Predicts sub-cellular localization: http://www.psort.org/

TargetP: Predicts sub-cellular localization: http://www.cbs.dtu.dk/services/TargetP/

NetNGlyc: Predicts N-glycosylation sites:http://www.cbs.dtu.dk/services/NetNGlyc/

PTS1: Predicts peroxisomal targeting sequences http://mendel.imp.univie.ac.at/mendeljsp/sat/pts1/PTS1predictor.jsp

MITOPROT: Predicts of mitochondrial targeting sequences http://ihg.gsf.de/ihg/mitoprot.html

Hydrophobicity: http://www.vivo.colostate.edu/molkit/hydropathy/index.html

http://www.cbs.dtu.dk/services/: prediction server

NetNGlyc: Predicts N-glycosylation sites: http://www.cbs.dtu.dk/services/NetNGlyc/NetPhos: Predicts phosphorylation of residues: http://www.cbs.dtu.dk/services/NetPhos/NetPhosK: Predicts recognition sites for specific kinases: http://www.cbs.dtu.dk/services/NetPhosK/NetAcet: N-terminal acetylation in eukaryotic proteins: http://www.cbs.dtu.dk/services/NetAcet/NetCGlyc: C-mannosylation sites in mammalian proteins

Multiple alignment

Used to do phylogenetic analysis:Same protein from different speciesEvolutionary relationship: history

Used to find conserved regionsLocal multiple alignment reveals conserved regionsConserved regions usually are key functional regionsThese regions are prime targets for drug developmentsProtein domains are often conserved across many species

Algorithm for search of conserved regions: Block maker: http://blocks.fhcrc.org/blocks/make_blocks.html

Multiple alignment tools

Free programs: Phylip and PAUP: http://evolution.genetics.washington.edu/phylip.html Phyml: http://atgc.lirmm.fr/phyml/

The most used websites : http://align.genome.jp/ http://prodes.toulouse.inra.fr/multalin/multalin.html http://www.ch.embnet.org/index.html (T-COFFEE and ClustalW)

ClustalW: Standard popular software It aligns 2 and keep on adding a new sequence to the alignment Problem: It is simply a heuristics.

Motif discovery: use your own motif to search databases: PatternFind: http://myhits.isb-sib.ch/cgi-bin/pattern_search

http://meme.nbcr.net/meme4_6_0/intro.html

Phylogenetic analysis

Phylogenetic treesDescribe evolutionary relationships between sequences

Major modes that drive the evolution: Point mutations modify existing sequences Duplications (re-use existing sequence) Rearrangement

Two most common methodsMaximum parsimonyMaximum likelihood

http://www.megasoftware.net/mega4/m_con_select.html

The most useful software:

Definitions Homologous:Have a common ancestor. Homology cannot be measured.

Orthologous: The same gene in different species . It is the result of speciation (common ancestral)

Paralogous: Related genes (already diverged) in the same species. It is

the result of genomic rearrangements or duplication

Determining protein Structure-Function

Direct measurement of structureX-ray crystallographyNMR spectroscopy

Site-directed mutagenesis

Computer modelingPrediction of structureComparative protein-structure modeling

Comparative protein-structure modeling

Goal:Construct 3-D model of a protein of unknown structure (target), based on similarity of sequence to

proteins of known structure (templates)

Blue: predicted model by PROSPECT

Red: NMR structure

Procedure:Template selectionTemplate–target alignmentModel buildingModel evaluation

The Protein 3-D Database

The Protein DataBase (PDB) contains 3-D structural data for proteins

Founded in 1971 with a dozen structures

As of June 2004, there were 25,760 structures in the database. All structures are reviewed for accuracy and data uniformity.

Structural data from the PDB can be freely accessed at http://www.rcsb.org/pdb/

80% come from X-ray crystallography16% come from NMR2% come from theoretical modeling

High-throughput methods

Most used websites for 3-D structure prediction

Protein Homology/analogY Recognition Engine (Phyre) at http://www.sbg.bio.ic.ac.uk/phyre/html/index.html

PredictProtein at http://www.predictprotein.org/newwebsite/submit.html

UCLA Fold Recognition at http://www.doe-mbi.ucla.edu/Services/FOLD/

Commercial bioinformatics softwares

CLC Genomics Workbench

Genomics:

454, Illumina Genome Analyzer and SOLiD sequencing data; De novo assembly of genomes of any size; Advanced visualization, scrolling, and zooming tools; SNP detection using advanced quality filtering;

Transcriptomics:

RNA-seq including paired data and transcript-level expression; Small RNA analysis; Expression profiling by tags;

Epigenetics:

Chromatin immunoprecipitation sequencing (ChIP-seq) analysis; Peak finding and peak refinement; Graph and table of background distribution; false discovery rate; Peak table and annotations;

VectorNTI:

Sequence analysis and illustration; restriction mapping; recombinant molecule design and cloning; in silico gel electrophoresis; synthetic biology workflows

AlignX:

BioAnnotator:

ContigExpress:

GenomBench

The bioinformatics not covered in this class

Comparative genomics and Genome browser:http://genome.lbl.gov/vista/index.shtmlhttp://www.sanger.ac.uk/resources/software/artemis/

Genome annotation:http://linux1.softberry.com/berry.phtmlhttp:// rast.nmpdr.org/

Metagenomics:http://metagenomics.anl.gov/

System biology tools.