Protein bioinforma-cs. Åsa Björklund CMB/LICR

Transcription

1 Protein bioinforma-cs Åsa Björklund CMB/LICR

2 In this lecture Protein structures and 3D structure predic-on Protein domains HMMs Protein networks Protein func-on annota-on / predic-on

3 PTM Localiza-on Degrada-on Muta-ons Selec-on Gene mrna Polypep-de Folding 3D protein Protein complex ATGATCATGGTTACAGGT AUGAUCAUGGUUACAGGU MAHRKYLI Structure is more conserved than sequence!

4 This lecture Protein structures and structure predic-on Protein domains Mo-fs, Profiles and HMMs Some servers Protein networks Func-on predic-on

5 Protein sequence databases UniProtKB Swiss- Prot Quality PIR- PSD TrEMBL Quan-ty UniMES (metagenomics samples) Entrez Protein (NCBI) Coding regions from GenBank and Swissprot, PIR, PDB, etc. RefSeq Non redundant databases Uniref100, Uniref90 etc. NCBI nr

6 Protein structure databases (Gutmanas et al. 2013) 1. So_ X- ray tomogram of a fission yeast cell 2. Electron tomogram of ribosomes in the cytosol 3. Cryo- EM reconstruc-on of the 80S ribosome from yeast 4. Crystal structure of the 50S ribosomal subunit (PDB entry 3uzk) 5. Crystal structure revealing how tmrna and the small protein SmpB enable the kirromycin- stalled 70S ribosome to proceed with transla-on (PDB entries 4abr and 4abs)

7 PDB Protein Data Bank Main database of three-dimensional protein structures Also contains structures of other macromolecules (DNA, RNA, carbohydrates) PDB entry format resembles EMBL Currently (May 2013) 90,424 entries

8 Viewing protein structures Pymol Jmol Rasmol Download PDB file Open in viewer Select, color, rotate

9 Structure predic-on De novo folding Molecular Dynamics Based on energy minimiza-on, depends on star-ng structure Works well for small pepe-des, not feasible for very large proteins Homology modelling Template from homologous structure(s) Refinement based on energy minimiza-on Swiss- modeller, 3D- Jigsaw Consensus methods Combines predic-ons from several servers Pcons.net

10 Docking Predict interac-ons between proteins or protein and ligand Most proteins change conforma-on upon binding Ligand docking commonly used in drug design HADDOCK, PatchDock, ClusPro

11 Protein domains

12 Protein domains Defined as independent folding unit or independent evolving unit Each domain has a characteris-c structure and/or func-on conserved in evolu-on Domains are o_en combined to create mul-- domain proteins O_en grouped into families and superfamilies Known domains in ~80% of all proteins, covering ~58% of the residues

13 Protein domains

14 Protein Structure Classifica-on Databases QUALITY SCOP : All manual CATH : Semi-automatic FSSP : All automatic ENTREZ: All automatic QUANTITY

15 Structural Classifica-on Of Proteins (SCOP) Attempt to classify all proteins in PDB according to structural and evolutionary relationships Hierarchical classification system Classification based on human expertise Superfamily contains HMMs for all SCOP families (

16 SCOP Main secondary structure elements Class " Fold Superfamily Family All- alpha All- beta Alpha/beta

17 SCOP Arrangements of secondary structure elements Class " Fold Superfamily Family Globin- like Prion- like Alpha- beta knot

18 SCOP Low sequence similarity but conserved structure and/ or func-on Class " Fold Superfamily Family

19 SCOP Significant sequence similarity and similar structure and func-on Class " Fold Superfamily Family

20 Domains based on sequence conserva-on Pfam - pfam.sanger.ac.uk PfamA manually curated PfamB autmated clustering PfamClans groups families into superfamilies Smart - smart.embl- heidelberg.de Manually curated, specializes in signalling, extracellular and chroma-n- associated proteins. ProDom - prodom.prabi.fr Automated clustering

21 Other mo-fs DNA/RNA/Protein binding mo-fs Transmembrane helices (TMH) Signal pep-des Pospransla-onal modifica-on (PTM) signals Secondary structure Disordered regions

22 Hidden Markov Models (HMMs) Different states, with different probabili-es of each symbol (aa or nt) at each state Transi-on probabili-es between states Insert states Silent states

23 HMM models: key concepts - No magic involved: just an extension of the profile - Enables modelling of deletions and insertions - Very useful for protein domains, HMMs for many different domain databases such as SCOP, Pfam etc. are available for download or web-based searches - Common programs to build HMMs from MSAs and scan sequence databases for matches are HMMER and SAM.

24 HMMER Program package by Sean Eddy (hmmer.janelia.org) Create HMMs from Mul-ple Sequence Alignment (MSA) Run searches with HMMs, ex. Pfam Requires a few unix commands Has webserver for homology searches

25 Membrane protein topology predic-on Predict transmembrane helices (TMH) based on hydrophobicity profile A TMH is normally about 20aa Reentrant regions can create mispredic-ons Posi-ve inside rule guides the direc-on O_en includes homology informa-on Most methods use HMMs for predic-on

26 Membrane protein topology predic-on hpp://topcons.cbr.su.se/

27 Predic-on of signal pep-des A signal pep-de is a short (3-60 amino acids long) pep-de chain that directs the post- transla-onal transport of a protein. Signal pep-des may also be called targe-ng signals, signal sequences, transit pep-des, or localiza-on signals.

28 Predic-on of cleavage site and localiza-on SignalP - predicts the presence and loca-on of signal pep-de cleavage sites in amino acid sequences from different organisms: Gram- posi-ve prokaryotes Gram- nega-ve prokaryotes Eukaryotes TargetP - predicts the subcellular loca-on of eukaryo-c proteins. The loca-on assignment is based on the predicted presence of any of the N- terminal presequences: chloroplast transit pep-de (ctp), mitochondrial targe-ng pep-de (mtp) or secretory pathway signal pep-de (SP). The methods combines predic-on from several ar-ficial neural networks and HMMs.

29 InterPro Integrates domain/mo-f predic-ons from several databases ProDom: sequence- clusters built from UniProtKB using PSI- BLAST. PROSITE paperns: simple regular expressions. PROSITE and HAMAP profiles: sequence matrices. PRINTS fingerprints, un- weighted Posi-on Specific Sequence Matrices (PSSMs). PANTHER, PIRSF, Pfam, SMART, TIGRFAMs, Gene3D and SUPERFAMILY: hidden Markov models (HMMs). TMHMM, SignalP

30 Protein interac-on networks Yeast PPI (hpp:// Connec-vity/degree = number of interac-on partners Hubs = highly connected proteins Scale- free topology most genes have low connec-vity, few have high. Mainly yeast2hybrid and tandem affinity purifica-ons

31 Protein networks Protein protein interac-ons (PPI) IntAct DIP - dip.doe- mbi.ucla.edu/ Pathways KEGG - Biocarta - Reactome - Regulatory networks

32 Predic-ng the func-on of a gene Expression papern From RNAseq or Microarrays Same expression papern - > involved in the same pathway or has similar func-on Homology interac-ons are o_en conserved between species

33 Predic-ng the func-on of a gene Phylogene-c profiles Same conserva-on papern - > involved in same pathways

34 Predic-ng the func-on of a gene Gene fusions (Rosepa stone theory) Fusion of two proteins that interact can be convenient since their expression can be co- regulated.

35 Predic-ng the func-on of a gene Genomic context Adjacent genes may be regulated together (operons in bacteria)

36 Predic-ng the func-on of a gene Automated liperature mining Genes that o_en are found in the same abstracts are more likely to interact or have related func-ons Gene-c interac-on Genes with similar knock- down phenotypes or rescuing phenotypes are likely to have similar func-ons

37 STRING hpp://string.embl.de A database that combines different predic-ons of func-onal links Includes experimental databases such as DIP, BIND, KEGG, Biocharta etc. Bayesian sta-s-cs with weigh-ng of the different data sources and valida-on against known interac-ons.

38 STRING hpp://string.embl.de

39 Gene ontology (GO) Func-onal classifica-on of genes/proteins All different databases and annotators use different defini-ons of the same func-on => creates a problem in bioinforma-cs. The GO Consor-um gives standardized annota-ons to genes and proteins with rela-onships between terms.

40 Gene ontology (GO) Divided into three categories: (for cytochrome C) Molecular func-on (electron transporter ac-vity) Cellular component (mitochondrial matrix) Biological process (oxida-ve phosphoryla-on) Uses Directed Acyclic Graphs (DAGs) Evidence codes, ex. TAS (Traceable author statement) or IEP (Inferred from expression papern)

41 Never trust a server blindly! Always do control experiments: PosiCve controls: submit sequences for which you know the right answer. NegaCve controls: random or shuffled sequences. Try several different methods and use the consensus