Predikce struktury proteinů Struktura proteinů ADSQJSSNRAGEFSIPPNTDFRAIFFANAAE QQHIKLFIG DSQEPAAYH KLTTRDG PREATL NSGNGKIRFEVSVNGKPSATDARLAPINGK KSDGSPFTVNFGIVVSEDGHDSDYNDGIVV LQWPIG prímami (sekvence) Pred: _^_i-»> , Pred i CCCCCEECCCCCCCCCCCCEEEECCCCCEEEEEEECCCCC M; DSQEPMVHKLTTPDCPPEATIJISCIICKIRFEVSVNCKPS -QD=>- CCHHEEEECCCCCCCCCCCEEEEEEEECCCCCCCCCCCEE ATDARLAPIMCKKSDGSPFTVNFGIWS EDGHD S DYN DG I sekundární kvartérní terciární Primární struktura ^ Sekvence aminokyselin zapsaná od N' konce k C konci Peptide bonds N'konec C'konec Amino Amino Amino acid 1 acid 2 acid 3 Sekundární struktura ^ Definována pomocí torzních úhlů peptidové páteře > Pro každou aminokyselinu lze definovat tři úhly: > - úhel kolem vazby N-Cct > Lp - úhel kolem vazby Ca-C(karb.) > co - úhel kolem peptidové vazby (180°, výjimečně 0°) > Stabilizována pomocí vodíkových můstků mezi atomy peptidové kostry * Lp 10 Ramachandranův diagram Ramachandranův diagram > Každé aminokyselině odpovídá jeden bod v diagramu Sekundární struktura > Stabilní konformace polypeptidového řetězce > Důležité pro udržení 3D struktury > a-šroubovice (helix), |3-skládaný list (sheet), otáčky smyčky > Cca 50 % aminokyselin je součástí ct a P struktur Šroubovice (helix) > a-helix - nejčastější > 310-helix - obvykle na začátku nebo na konci a-helixu > n-helix - málo stabilní, málo častý Šroubovice (helix) 2D alpha-helix 3/10-helix Pi-helix Vodíkové můstky Residua na otáčku Vinutí (Ä na 1 AK) a-helix 3m-helix n-helix Oj... Ni+4 Oj... Ni+3 Oj... Ni+5 3,6 1,5 310-hehx a-helix ^.Q*^ 0 Ti-helix Reverse turns. Type I Type II \é A é A i+1 i i+1 Ostatní • Úseky které nespadají do kategorií helix nebo list • Kombinace povolených torzních úhlů • Nestabilní konformace • Nestandardní konformace (glycin, prolin) • Otáčky (turns), „náhodné klubko" (random coil) Znázornění 2D struktury 2D > Písmeny - H (helix), E (extended sheet), C (coil) > Barevně - např. červená (helix), žlutá (skládaný list) > Grafickými elementy - spirála/válec (helix), plochá šipka (skládaný list), linka (ostatní) MQVWPIEGIKKFETLSYLPPLTVEDLLKQIEYLLRSKWVPCLEFSKVG ----------EEEE--------HHHHHHHHHHHHH---EEEEEE ) 51 101 151 201 251 G T Y R Q LFHPEQLI SGKEDAANNYARG H Y G L Q G F LVFHSFGGGTGSGFTSLLMER L S V V E P Y nsHltthttlehsdcafmvdn E A M s Q ■■ SSI TASLRFDGALNVDL T M T N A Y H E Q LSVAEI TNACFEPANQMVKCD P R ■ ■■■■ K T K R T I QFVDWCPTGFKVGI N Y Q T T A V A EAWARLDHKFDLMYAKRAFVH W Y D Y E E V GADSYEDEDEGEEY I = TL— = /V=| -vAA ps l sgf r km a f psgkvegchvqvtcgtttlhglwlddtvycprhv i c t a e dmlmpnyedl l irk5nhsflvqagnvqlrvighsmqncllrlkvdtsnpktpkykfvriqpgqtfsvlacy '1ng5psgvyqcamrphht i kgs f l ng s cg svg f n i d ydc v 5 f c yhhhhe l ptg vh agtd l e vAAAAAA^ DSSP Le9end imnhi ne e ™g kf ygpfvdrqtaqaagtdttitlhvlawlyaaving ''""m ' 3i ' ni íu "1 -v/W-v/W*-v/WWW- pdbye pltqdhvd i lgplsaqtg i avldmcaalkel l q h g V G /\/\ H empty: no secondary structure assigned beta bridge bend turn beta strand 3/10-helix alpha helix Dělení proteinů dle 2D struktury 2D Zejména pro účely klasifikace, hledání společných rysů Každý protein obsahuje mj. smyčky a ohyby > Jen a struktury > Jen P struktury >a/P > Motivy kombinující a i P struktury > a + P > Oddělené domény tvořené jen a nebo jen P strukturami > Malé proteiny > Speciální případy např. obsahující ionty kovů, stabilizované disulfidickými můstky Terciární struktura > Konkrétní umístění jednotlivých atomů polypeptidového řetězce v prostoru > Stabilizována pomocí: • Vodíková vazba (H-můstek) mezi polárními AK, mezi hlavním řetězcem • Iontová interakce - nabité AK • Hydrofobní interakce - nepolární AK • „Stacking" [n-n, CH-n interakce) - aromatické AK • Kovalentní vazba síra-síra - cystein / cystin • Vazba iontů kovů El 14 Od 2D ke 3D > Motivy > 2-3 prvky sekundární struktury > Foldy > Kombinace jednoduchých motivů > Domény > Tvořeny motivy/foldy > Část struktury s vlastní funkcí (nejmenší funkční jednotka) > Nezávislá jednotka (alespoň částečně nezávislá) Jednoduché motivy Helix-turn-helix P-vlásenka Složené a-motivy/foldy 7-helix barel 4-helix bundle Složené P-motivy/foldy Řecký klíč P-meandr •••• m p-barel Složené a/P-motivy/foldy Databases of Protein Folds SCOP (http://scop.berkelev.edu/) - known domain structure • Structural Classification of Proteins • Class-Fold-Superfamily-Family • Manual assembly by inspection Superfamily (http://supfam.org/SUPERFAMILY/) - predicted domain structures • HMM models for each SCOP fold • Fold assignments to all genome ORFs • Assessment of specificity/sensitivity of structure prediction • Search by sequence, genome and keywords CATH + Gene3D (http://www.biochem.ucl.ac.uk/bsm/cath/) - both • Class - Architecture - Topology - Homologous Superfamily • Manual classification at Architecture level • Automated topology classification using SSAP (Orengo & Taylor) PDB eFold (http://www.ebi.ac.uk/msd-srv/ssm/) • Fully automated using the DALI algorithm (Holm & Sander) Pfam (http://pfam.xfam.org)- domain sequences (MSA, HMM) AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk) Structural classification of proteins (SCOP) 2D h ttps://scop.mrc-lm b. cam .ac. uk/ Aboul Conlact Download The legacy SCOP websites can be accessed at SCOP 1.75 and SCOP2 prototype SCOP 2 SCOP: Structural Classification of Proteins Nearly all proteins have structural similarities with other proteins and, in some of these cases, share a common evolutionary origin. The SCOP database, created by manual inspection and abetted by a battery of automated methods, aims to provide a detailed and comprehensive description of the structural and evolutionary relationships between all proteins whose structure is known. As such, It provides a broad survey of all known protein folds, detailed information about the close relatives of any particular protein, and a framework for future research and classification. Latest update on 2020-03-31 Includes 44,218 non-redundant domains representing 532,428 protein structures. Folds, superfamilles and families statistics here. Keyword and ID search Sequence search Enter free text, SCOP ID, PDB ID or UniProl ID Go ] Browse by structural class Browse by protein type • All alpha proteins • All beta proteins • Alpha and beta proteins(a/b) • Alpha and beta proteins(a+b] • Small proteins • Globular proteins • Membrane proteins • Fibrous proteins • Non-globular/lntrinsically unstructured proteins CATH - Protein structure classification database 2D >Domény jsou klasifikovány podle CATH hierarchie > Třída (Class) • Podle sekundární struktury • Jen a, jen (3, a i (3, minimum sekundární struktury > Architektura • 3D uspořádání sekundární struktury > Topologie/fold • Jak jsou prvky sekundární struktury uspořádané za sebou > Homologní nadrodina • V případě, že jsou domény evolučně příbuzné (homologní proteiny) https://www.cathdb.info/ 24 Kvartem í struktura > Vzájemná kombinace více řetězců (monomerů) > Podle typu podjednotek: • Homooligomery (identické jednotky) • Heterooligomery (alespoň dva různé typy jednotek) > Komplexy proteinů s dalšími makromolekulami • Ribosom, proteasom, replikační komplex,... > Nadmolekulární komplexy • Virové částice, buněčná membrána, organely,... Predikce struktury > Predikce struktury znamená přiřazení strukturních atributů jednotlivým aminokyselinám (2D struktura, koordináty-tvorba 3D modelu) >Struktura 2D a 3D je konzervovaná více než samotná sekvence > Vstupní informace: • Sekvence • Fyzikálně-chemické parametry • Informace v databázích >Výstup: • Model struktury (2D, 3D, 4D) Proč predikovat strukturu? > Klasifikace proteinů > Vytvoření modelu struktury pro další studium > Před poveď funkce proteinu • Homologní struktury • Vazebná místa > Analýza povrchu • Přístupnost solventu, tunely, kavity Predikce sekundární struktury 2D > Predikce 3 základních typů: H (helix), E (p-list), C/- (smyčka/vše ostatní) > 1. GENERACE • ab-initio • Vychází z fyzikálně-chemických vlastností a ze statistik pro jednotlivé aminokyseliny 35 1. Generace - ob inicio Relative Amino acid Propensity Values for Secondary Structure Elements Used in the Chou Fasman Methods Amino Acid (a-Helix) P (/3-Strand) P(Turn) Alanine 1.42 0.83 0.66 Arginine 0.98 0.93 0.95 Asparagine 0.67 0.89 1.56 Aspartic acid 1.01 0.54 1.46 Cysteine 0.70 1.19 1.19 Glutamic acid 1.51 0.37 0.74 Glutamine 1.11 1.11 0.98 Glycine 0.57 0.75 1.56 Histidine 1.00 0.87 0.95 Isoleucine 1.08 1.60 0.47 Leucine 1.21 1.30 0.59 Lysine 1.14 0.74 1.01 Methionine 1.45 1.05 0.60 Phenylalanine 1.13 1.38 0.60 Proline 0.57 0.55 1.52 Serine 0.77 0.75 1.43 Threonine 0.83 1.19 0.96 Tryptophan 0.83 1.19 0.96 Tyrosine 0.69 1.47 1.14 Valine 1.06 1.70 0.50 Typické znaky a-šroubovice > Často je částečně exponovaná • Jedna strana je otočená dovnitř proteinu (hydrofobní) a druhá ven (hydrofilní) • Residuum (aminokyselina) n, n+3, n+4, n+7 míří na stejnou stranu >Transmembránový helix • Všechny aminokyseliny hydrofobní icke znaky p -list (musí být stabilizován jinou částí polypeptidového řetězce!) U (3 -listu se střídají boční řetězce po 180° pro částečně zanořený (3 -list platí, že každé liché reziduum je polární, každé sudé nepolární, u plně zanořeného jsou všechna nepolární... tj. residua směřující na stejnou stranu by měla mít stejný charakter Second strand in CD8 Polar face i lil tilt if if Parallel MIX Antiparallel 2D a-šroubovice nebo P-list? 1— ELKAHIRVDLTLQ « ELKAHIRVDLTLQ ELKAHIRVDLTLQ R Polární Nepolární 39 2D cx-šroubovice nebo |3-list? 1— ELKAHIRVDLTLQ « ELKAHIRVDLTLQ ELKAHIRVDLTLQ p ^ Polární Nepolární Analýza hydrofobních klastrů (HCA) > Sekvence „se namotá" na válec (a-helix) > HCA graf je zobrazení válce v rovině > Hydrofobní aminokyseliny jsou ohraničeny a tvoří specifické tvary pro a-helixy a (3-listy 2D Hydrophobic Cluster Analysis 2D suppor human a 1 antitrypsin 227 ...GNATA PDEGK QH E NE THDUTKFLENEDRR. . . 263 ...♦NAĎA &DE»K QH ENE'.DHDIIDKFLENEDRR. . . ...00000 00000 00 000 OOOllOOliOOOOOO... D (10001) * V (11), M (101) RPBS Web Portal - H CA 2D https://mobyle.rpbs.univ-paris-diderotir/cgi-bin/portal.py?form=HCA#forms::HCA RPBS Web Portal Programs □ Peptides a Sequence a structure □ Test Tutorials a Data formats & Howtocite & Overview « PDBInput 5 Policy lnterEvDock2 y MTi Auto Dock/Mil OpenScn ft Patch Search_ [more] Welcome Forms Data Bookmarks Jobs Tutorials HCA X HCA 1.0.2 Hydrophobic Cluster Analysis. query. Jata.icn c í- mm Predikce sekundární struktury 1— > Predikce 3 základních typů: H (helix), E (P-list), C/- (smyčka/vše ostatní) > 1. GENERACE • ab-initio • Vycházela z fyzikálně-chemických vlastností a ze statistik pro jednotlivé aminokyseliny > 2. GENERACE • Zahrnuje i vliv okolních aminokyselin > 3. GENERACE • Homology-based models • Metody strojového učení • Využívá multiple sequence alignmentu a toho, že 2D struktura je více konzervovaná než sekvence Metody založené na homologii (Homology- based) > Vychází z předpokladu, že 2D struktura je více konzervovaná než sekvence 1. Multiple sequence alignment 2. Predikce sekundárních struktur pro každou sekvenci zvlášť 3. Porovnání p re d i kovaných sekundárních struktur s alignmentem 4. Konsenzus sekundární struktury 2D ECTTA C T I G T T T A T T l JG T C T A T T i G T T T A T T i O T C T A C T J O A T T A T T J G A T T G (T T J Q A T T A T T J •TCTATTi H C T T A T T > ICT T A T T > « C T T A T T i '.O T r T T T T I ATTCľGAGCTGAA ATTCGAGCAGAA ATTCGAGC ATTCGAGC ATTCGAGC ATTCGAGCC ATrCGGGľT TGA/ T G A í TGA/ ITTCGTGCTC taggtc ttggt: ttggcc taggtc ttggtc taggtc t agg t r TAGGar TCGGac : T C GGa C T C G G G C ■ t a a fi a r ECCHHCEEEECCCEE ^* HHHHHCCCCEEECCH —* HHHHHCCEEEECCHH \ HHHHCCCEEEECHHC HHHCHCCEEEECCHH HHHCHCCEEEECCHH HHHHHCCEEEECCHH ECCHHCEEEECCCEE HHHHHCCCCEEECCH HHHHCCCEEEECHHC HHHHHCCEEEECCHH 45 Metody strojového učení (Machine learning) > Model, který je natrénovaný na známé sadě dat > Neuronové sítě > Skryté Markovovy modely vstupní vrstva skryté vrstvy výstupní vrstva 46 PSIPRED 2D >Predikce sekundární struktury pomocí 2 neuronových sítí > Časově náročnější > Ve srovnání s většinou programů na predikci sekundární struktury má lepší výsledky http://bioinf.cs.ucl.ac.uk/psipred/ Choose prediction methods Popular Analyses * PSIPRED 4.0 (Predict Secondary Structure) MEMSAT-SVM (Membrane Helix Prediction) Contact Analysis DeepMetaPSICOV 1.0 (Structural Contact Prediction) Fold Recognition Gen THREADER (Rapid Fold Recognition) Structure Modelling Bioserf 2.0 (Automated Homology Modelling) DMPfold 1.0 Fast Mode (Protein Structure Prediction) Domain Prediction DomPred (Protein Domain Prediction) Function Prediction FFPred 3 (Eurkaryotic Function Prediction) DISOPRED3 (Disopred Prediction) pGenTHREADER (Profile Based Fold Recognition) MEMPACK (TM Topology and Helix Packing) pDomTHREADER {Protein Domain Fold Recognition) Domserf 2.1 (Automated Domain Homology Modelling) PSIPRED 2D 10 30 4: 5: 1 M P R S V P P N 1 S P L P Q K D D S S L S A S E YPN I A S R T A P P S P S A V R R T H S L L S E T 51 H T G Y Q S L E S Q M E A GET T s L L G K T R E N H R G T P R R S Y T S I S A I p T P DNY L R H 101 S L T S G S L R R S R H H S R A N s Q S L R F S R R S S I D D E Q D E D L P P S A K D G M T A S F L 151 D E R N W Y D Q F T S T D W V H D s I A D G A R L R E L R K R K D ■ HI llll F D G A Q G w 1 L 201 V A L I G C I T A A 1 A Y F V D V T E D F V F D L K E G F C T T R W F H N R E S C C A D T L D c s L 251 W R SWS Q I L SPS G S D N G W V D H S M F V L W V V I L S V I S C Y L llll K T V VPS s V S 301 L T T L D E N L GAG T S R G T N H D A S E D N SPA s LI N P K A H Y P T I S T R P A M T Y Y S A 351 A G S G V A E V K V 1 N S G F V L H G Y L G F K T L V I K T I A L V F S V S S G L S L G KEG P Y V 401 H , G A C V G N 1 A C R L F S K Y N D N D G K R REV L S A S A A llll V A F G A P I G G V L F S 451 L E V S Y Y F P P K F R T F F C C I A A A III K F L N P Y G T S K I V L F Q V R Y V T [1 W E 501 L L G V L G G A A G mil A SSL V'.' A K S F R K L S I I K R W P M L E V I L V A 551 IV N R Y A K L P V S E L L F E L ASP c D P E S V T S T G L C P T E D G III 1 1 S 601 L III F V I III L T V V T F G I K V P A G I Y V p S M V V G llll llll H V V III V V K 651 F P N F F L F S TCP V Y S G M E S C V V P G V YAM V A A G A T llll T R L S V T L III L F E 701 L T G S L D H V L P F S L A V L C A K W T A D A I E P R S I Y D L llll N S Y P F L D N K I Q L L 751 S D DEL G D I V R P V R K S R V I D I S E S P F V P A T E L R S llll L L M A G E L D S G L P 1 801 L R N D I L C G 1 1 P A P DLE Y A L D T I E D E E H T M C L M S M D T A S A V V D S E DSN G N S 851 W V D F R R Y I DPA P I S L D I H S P I D L V Y Q C F A K L G L R Y L C V L R D G a Y A G L V H K 901 A F V K Y V K E N E 10 2Z 4: 50 □ Strand Disordered, protein binding Extracellular Helix Putative Domain Boundary I Re-entrant Helix Coil Membrane Interaction Cytoplasmic I J Disordered Transmembrane Helix Signal Peptide Rozšíření predikce 2D struktury 2D > Predikce více typů 2D struktury (dle DSSP - Database of Secondary Structure Assignments) >a-helix(H) > p-bridge (B) > 310-helix (G) > turn (T) > Tt-helix (I) >bend(S) > P-řetězec, extended strand (E) > ostatní, coil (C) > Predikce přístupnosti solventu > Predikce transmembránových helixů 49 Predikce terciární struktury Klasifikace proteinů Předpověď funkce Vytvoření modelu pro další studium Ab initio Homologní modelování Threading („navlékání") ňfcUge terciární struktury ki4 /^Atoů Předpovt. Vytvoření modeiu C?/£n / ^'um * Ab initio * Homologní modelování * Threading („navlékání") Metody pro predikci funkce „klasické" metody: vícenásobné aminokyselinové přiložení pozitivní alignment pouze mezi sekvencemi stejné rodiny G#«,i,4-^ff jfj|ý2aL20 stiukti cdkvlíldidvlvedsltplwdtdlgdnwlgacid GICa1,3-Glf»B |.;r|tóI-£. CO^ij. APKV: rLDADIICQGTIEPLINFSFPDDKV MWT Gala1,3-Glca-R RFalJ,F. typí QIKV: ľLDADIACKGSIQELIDLNFAENEI AWA Glcg1.2-Glcg-R RfíuIE.coli LDRLLYLDADWCKGDIS QLLHLGLN - GAVAAWK ■■llgi^H «TRossmanri' Gala1,6-Mana-BH« LPcA R- ' Glca1,3-Mana-R SpsA ß3-GlcAT GnTI IERLLYLDADVLAVSFVDELFTRNFQGKALAAVDD t VRKIIFVDADAIVRTDIKELYDMDLGGAPY YTPF DDD YFNAGVLLINLKKWR YFNSGFLLINTAQWA YFNAGFILIXIPLWT YFNSGWYLDLKKWA YFNAGVLLFDWSACR YHISALYWDLKRFR 15— EDD p4-GalT DVD Dvě pozorované topologie 3D struktur glykosyltransferas SpsA-fold C-term Stem Golgi lumen TMD Cytoplasm (Procaryotes/Phage) ß-GIcT (BGT, phage T4) n.c. inv p4-GlcNAcT (MurG, E.coli) GT28 inv ß-GIcT (GtfB, M. orientalis) GT1 inv (Procaryotes) SpsA (B. subtilis) GT2 inv a4-GalT (LgtC, N.meningitis) GT8 (Eucaryotes) P4-GalT1 (bovine) GT7 inv p2-GlcNAcT (GnT I, rabbit) GT13 P3-GIcAT I (human) GT43 inv GT6 ret Glycogenin (rabbit) GT8 ret ot3-GalNacT (GTA, human) GT6 a3-GalT (GTB, human) GT6 ret inv ret ret N-term Nadrodina s BGT foldem MurG (p-GlcNAcT) GT28 E. COl'l Ha eta/., 2000 GtfB (p-GlcT) GT1 A. orientalis Mulichak eta/, 2001 BGT (p-GlcT) n.c. Phage T4 Vrielink eta/, 1994 Nadrodina s SpsA foldem Společná NBD SpsA [GT2] Charnok etal, 1999, 2001 Hum P3-GIcAT [GT43] Pedersen er al, 2000 Rabbit GnTI [GT13] Unligil etal, 2000 Bovine p4-GalT [GT7] Gastinel etal, 1999 Ramakrishnan ef al, 2001, 200 LgtC (a4-GalT) [GT8] Neisseria meningitidis Persson ef al, 2001 Bovine oc3-GalT [GT6] Gastinel er al, 2001 Boix er al, 2001,2002 ^/\ Predikce terciární struktury Klasifikace proteinů Předpověď funkce Vytvoření modelu pro další studium Ab initio Homologní modelování Threading („navlékání") El Threading El Porovnává možnost přiložení sekvence na proteiny známých foldů „navlékání" = rozpoznání a přiřazení proteinového foldu aminokyselinové sekvenci S využitím strukturních databází (PDB, SCOP, CATH) je vytvořena databáze existujících foldů - sekvence je porovnávána s touto databází (3D profilů) a na jejich základě jsou konstruovány 3D-modely 3D profil - každému reziduu v 3D struktuře je přiřazena environmentálni proměnná (obsah polárních atomů v postranním řetězci, skrytá plocha, sekundární elementy, apod.) vycházející z předpokladu, že okolí rezidua je více konzervováno než aminokyselina samotná. Reziduum může být také popsáno pomocí svých interakcí Výsledná kvalita modelu shoda je popsána pomocí Z-skóre nebo energie U multidoménových struktur je potřeba aminokyselinovou sekvenci rozdělit na jednotlivé domény a analyzovat je separátně PLLSASIVSAPWTSETYVDIPGLYLDVAKAGIRDGKLQVILNVPTPYATGNNFPGIYFAIATNQGWADGCFTYSSKV PESTGRMPFTLVATIDVGSGVTFVKGQWKSVRGSAMHIDSYASLSAIWGTAAPSSQGSGNQGAETGGTGAGNIG GGGERDGTFNLPPHIKFGVTALTHAANDQTIDIYIDDDPKPAATFKGAGAQDQNLGTKVLDSGNGRVRVIVMANGR PSRLGSRQVDIFKKSYFGIIGSEDGADDDYNDGIVFLNWPLG ERDGTFNLPPHIKFGVTALTHAANDQTIDIYIDDDPKPAATFKGAGAQDQNLGTKVLDSGNGRVRVIVMANGRPSR LGSRQVDIFKKSYFGIIGSEDGADDDYNDGIVFLNWPLGPLLSASIVSAPWTSQTYVDIPGLYLDVAKAGIRDGKLQ VILNVPTPYATGNNFPGIYFAIATNQGWADGCFTYSSKVPESTGRMPFTLVATIDVGSGVTFVKGQWKSVRGSAM HIDSYASLSAIWGTAAPSSQGSGNQGAETGGTGAGNIGGGGKLAAALEIKRASQPELAPEDPEDVEHHHHHH EMBO3S_001 e:-e: ;i : :■: r.-^z3i ::. EMBO3S_001 EMBO3S_001 EMBO33_001 EMBO33_001 EKBO33_001 EMBO33_001 EKBO33_001 EMBO3S_001 EMBO33_001 EHBO3S_Q01 EMB03S 001 if: ;7rriF?K:;:r;T>.Lr:-i^.::: :t:::v::::;t ff_--rr . HLGTKVL33GHGRVRVTVMAHGRP2R:. Fi FQVE-Fit" T YFGIIG3EDGAD _---------------P LL 3 A3 IV3 APWT 3 ETYVDIPGL YLEVAKAGIRD I I I I I 11 1111111 U 11IIIIII III IIIIIIII L DDYNDGrJTLNKP LG P LL 3ASI "3 APWT3QTYVDIPGLYLEVAKAGIRD ■ GKLCVILNVPT P Y AT GKKF P GIY FA IATNQGWADGC FT Y3 3 KVE E S T GR L GKLCVILNVPT P YAT GKKF PGIY FA IATWQGWADGC FTYS 3 KVPE ST GR ■ MP FT LVATIDVG5 GVT FVKGQKK3VR G3 AJfH ID3YASLSAIWGT AAP S 3 C. L MP FT LVATIDVGJGVTFVKGQWK5VRG3AKKID3YA3L3AIKGTAAP3 SQ ■ G 3 GNQGAET GGT GAGNIGGGGERDST FKL? PKIKFGYT ALTHAANECTID . G3 GNQGAETGGTGAGNIGGGG----------------------------- ■ IV:rr: = :-:JAATFKGAGACDCKLGTr.^D33K3RVR"i™AiIGBE3RLG3 ,-------QUI----------LEIK-----------------RA3---- : RCVSIFKK3YFGIIG3EDGADD□YKD5IVFLKXE1G 271 I -CPE---------LAPEDPEDVXHHH-------HHH 3D* Threading PHYRE2 (3D-PSSM) http://www.sbq.bio.ic.ac.uk/phyre2 Threading at 2D level and scoring at 3D level : matching of secondary structure elements, and propensities of the residues in the query sequence to occupy varying levels of solvent accessibility The PSIPRED Protein Sequence Analysis Workbench http://bioinf.cs.ucl.ac.uk/psipred/ GenTHREADER Rapid fold recognition, matching your sequence against a library of whole PDB chains. pGenTHREADER Highly sensitive fold recognition using profile-profile comparison (whole chain library). pDomTHREADER Highly sensitive homologous domain recognition using profile-profile comparison (domain library). I-TASSER https://zhanqlab.ccmb.med.umich.edu/l-TASSER/ a hierarchical approach to protein structure and function prediction. It first identifies structural templates from the PDB by multiple threading approach LOMETS, with full-length atomic models constructed by iterative template fragment assembly simulations. Function insights of the target are then derived by threading the 3D models through protein function database BioLiP. Phyre2 Server pro 3D predikci struktur pomocí threadingu Vysoce výkonný - poměrně spolehlivá detekce foldu i při nízké homologii (i pod 15%) El http://\www.sbg.bio.ic.ac.uk/phyre2/ Template Alignment Coverage 3D Model Confidence ^ ifJ Template Information i m/r PDB header:sugar binding protein Chain: B: PDB Molecule:ergic 53 protein; PDBTitle: the crystal structure of the carbohydrate recognition? domain of the glycoprotein sorting receptor p58/ergic-533 reveals a novel metal binding site and conformational changes associated with calcium ion binding PDB headensugar binding protein Chain: A: PDB Molecule:emp47p (forml); PDBTitle: crystal structure of emp^7p carbohydrate recognition domain? (ad), tetragonal crystal form Fold:Concanavalln Alike lectlns/gk Superfamih/:Concanavalin A-1 Ike lectlns/gli Family: Lectin leg-like I PDB header :protmi transport I Chain: H PUB Molecule: vesicular Integra -nembrar-p protPln vipjrj; I PDBIitle: crystal strjeturo of vlp36 exoplasnnc/lumenal domain, metal free Phyre2 ARDLVIPMIYCGHGl User sequence Homologous sequences Search the 10 million known sequences for homologues using PSI-Blast. Phyre2 ARDLVIPMIYCGHGl User sequence PSI-Blast Hidden Markov model Capture the mutational propensities at each position in the protein An evolutionary fingerprint Phyre2 ~ 65,000 known 3D structures Phyre2 Phyre2 Phyre2 Phyre2 Hidden Markov model for sequence of KNOWN structure Phyre2 Phyre2 ARDLVIPMIYCGHG Alignments of user sequence to known structures ARDL—VIPMIYCGHGY JTDLCDLIPV--CGMAY Sequence of known structure ranked by confidence. AFDLCDLIPV--CGMAY Phyre2 Phyre2 ARDLVIPMIYCGHG PSI-Blast Very powerful - able to reliably detect extremely remote homology HMM-HMM matching Routinely creates accurate models even when sequence identity is <15% ARDL—VIPMIYCGHGY 3D-Model AFDLCDLIPV--CGMAY Sequence of known structure Fold library last updated: 20 Apr 2019 | UNIREF50 protein sequence database updated: 7 Feb 2017 | SCOP version 1.75 SDVDIEAGQTLVQVVNISNGETWVAIQLPAQYRSFDLVFENVSPSTSGSVLVAQMAPQSGGVYGSNYS GSGWGNDLGGGGFYGYSEAKWMCLWPANRSGPNSKTGIYGTCKLMNLNQSNAVPSVTSNLFAPTAY KNEPGYANVGGCCQKIRGLASSIQFAFALHGGNVPQNTDTFSGGTIKVYGWN 3D-fold calculation based on known structures Model quality evaluation surface residue-residue interactions ^--- residue-solvent interactions residue-residue and residue-solvent interaction __S "Quality" scores Glykogensynthasa - rodina GT3 (v rodině v době analýzy nebyla vyřešena 3D-struktura) http://www.sbg.bio.ic.ac.uk/phvre/qphvre output/95cbaa7600a9bfff/su mmary.html w Quickphyre results for job synt_- Mozilla Fírefox Soubor Opravy Zobrazit Historie Záložky Nástroje Nápověda C ^ ( 'f http://www.sbg.bioJC.ac.uk''phyre./qphyre_outpuV95cbaa7600a9bfff/ P N ej navštevovanejší ^ Jak začít Přehled zpráv <ž http://www.ncbi.nlm..., http://www,glycoscie.,, " CHMI Radar Departme... {• Quickphyre results f or job cand_ x f Quickphyre results for job synt_ X {■ Quickphyre results for job synt_ X a -1183-1 Gocgte Fold Recognition View Alignments SCOP Code d2bisa1 18% i.d. dlrzua To predict functional residues and GO classification, try ConFunc E-value Estimated Precision BioText Fold/PDB descriptor Superfamily w % í*uí n > fy SB 3.9e-36 6.1e-36 6.1e-31 n/a n/a n/a UDP-Glycosyltransferase/glycogen UDP-Glycosyltransferase/glycogen C Phosphorylase Phosphorylase ti UDP-Glycosyltransferase/glycogen UDP-Glycosyltransferase/glycogen C Phosphorylase Phosphorylase ti PDB headentransferase Chain: A: PDB Molecule:predicted glycosyltransferases; A co protein, který nemá v sekvenčních databázích žádný homolog t) Quickphyre results for job rs20 - Mozilla Firefox Soubor Úpravy Zobrazit Historie Záložky Nástroje Nápověda » Cí X ^_t ( f I http://www.sbg.biojc.ac.uk/phyre/qphyre_output/964f07M319f5953/sum P N ej navštevovanej š í ^ Jak začít Přehled zpráv ^ http://www.ncbi.nlm..., http://www,glycoscie... " CHMI Radar Departme,., Fold Recognition Fold/PDB View Alignments SCOP Code d1eh9a2 (length:67) 24% i.d. c2fsdA (length: 142) 19% i.d. 1Ü-4T i |iILt_:t I. c2ct4A (length:70) 11% i.d. If E-value 50 Estimated BioText 50 56 rVa n/a n/a descriptor Superfamily Glycosyl hydrolase domain domain Glycosyl hydrolase PDB header:virus/viral protein Chain: A: PDB Molecule:putative baseplate protein; PDB headers gnahng protein j _______ Chain: A: PDB Molecule:cdc42- interacting protein 4; Family (beta-test) alpha- Amylases, C-terminal beta-sheet domain n a PDBTitle: a common fold for the receptor binding domains of2 lactococcal phages? the crystal structure of the head3 domain of phage bill 70 PDBTitle: solution strutcure of the sh3 domain of the cdc42-2 interacting protein 4 n a n a AB2L structure overview Structure: 4 helical bundle Top model Model (left) based on template d2ja9al Top template information Fold:OB-fold Superfamily:Nucleic acid-binding proteins Family:Cold shock DNA-binding domain-like Confidence and coverage Confidence: Coverage: 38 residues ( 20% of your sequence) have been modelled with 24.1% confidence by the single highest scoring template. Image coloured by rainbow N -» C terminus Model dimensions (A): X:24.236 Y:23.853 Z:38.403 You may wish to submit your sequence to Phy real arm. This will automatically scan your sequence every week for new potential templates as they appear in the Phyre2 library. Please note: You must be registered and logged in to use Phyrealarm. 3D viewing l-TASSER Několikrát vyhodnocen jako nejlepší predikční server https://zhanglabxcmb.med.umicri.edu/l-TASSER/ El Predicted Secondary structure ieq-.ienc-: "■"; zzz::=::"■"z::zz:-.z::zz rv:f;tl: zz::::_r zzzzv.::zz-.zz::zz zzz zzz::zz::z-:.::nr:izzzzzz-zz:z:z:zz:-zzzz -z-.zz::z^zzzzzzz^z.zz,zzzzzrZ~zzz::zy:zz:z:-z::zzzzzzzz'BZZ-:rz'zzz::zzz-rr:^zz Prediction CCCCCCCCCCCCCCCCCCSSCCCKr-IHHHHCCCCCCCCCCCCSSCCSSSSSSSSSCCCSSSSSCCCCCCCCCCSSCCCCCCCCCSSSSCCCCCSSSSSSCCCCCSSSSSSCCCCCCCCSCCCCCCCCCCSSSSSCCCCSSSSSSCC Conf.Score a 55 577 e 5 57 -_ e e e 555e.5" 523 5 555126: j Z'i 5 e ? e 2 D3 5 jl Z Zi 7 5 a a e 25~2 5 e " 515 a e 5 e £ e 7 7 11 £ ?a77 e ae ~ -." =■ e j e £ ^ ^5 ^ e ? ^ ^ e 2 5 t £ffjf e f; £ e £^ ^; £ ? ^; £ ? ^ e ? Predicted Solvent Accessibility Prediction 4010300122332334330101110541223474173143331314332310323312232132332312312303132212000002372100000003411010122332303213312232222000001372200000103 Values range from 0 (buried residue) to 9 (highly exposed residue] Top 5 Models predicted by l-TASSER Estimated accurac?ofModell: 0.47=0 15 (TM-score) 11.3=4.5.4 (RMSD) (Head more about C-score of generated models) Prozkoumání možností a principů fungování l-TASSERu bude domácím úkolem Homologní modelování • Je založeno na existenci blízkého strukturního homologu (typicky 50 % sekvenční podobnosti a více, minimálně 30%) • Využívá skutečnosti, že dva proteiny ze stejné rodiny a s podobnou sekvencí mají i podobnou 3D strukturu • Kromě sekvence našeho proteinu potřebujeme znát strukturu homologního proteinu = templát • Pro vysoce homologní sekvence je spolehlivost velmi vysoká MODELLER Mostly used program in academie environment for serious homology modeling SWISS-MODEL An automated knowledge-based protein modelling server Homologní modelování 1. Alignment zadané sekvence a sekvence templátu 2. Extrakce proteinové páteře ze struktury templátu a umístění postranních řetězců 3. Modelování otoček a smyček 4. Minimalizace energie 5. Validace namodelované struktury Swiss-Model • Výběr modelu (manuální, automatický) • Podle vybraného modelu pak predikuje strukturu zadané sekvence • Součástí výstupu je sada parametrů hodnotících kvalitu modelu. Při využití více templátu je tak možno porovnat jednotlivé modely SWISS-MODEL An automated knowledge-based protein modelling server - Start BLAST for highly similar template structure identification - No suitable templates found! - Run HESearch to detect remotely related template structures - Unfortunately, we could not identify useful template structures - For troubleshooting, please see our article in Nature Protocols: - Bordoli, L., Kiefer, F., Arnold, K., Benkert, P., Battey, J. and Schwede, T. (2009). Protein structure homology modelling using SWISS-MODEL Workspace. Nature Protocols, 4, 1. Computation of this workunit has stopped. Please see the following log report for details: Started: Wed May 13 06:59:31 2009 (sms_automode) Reading user input sequence No Templates found. Simple automated template selection could not identify suitable templates. Please use advanced Template Selection under [Tools] to select a template and prepare a workunit using the project mode. Ab initio • Nejuniverzálnější- vychází pouze ze sekvence • Výpočetně nejnáročnější • Zahrnuje řadu kroků: • Predikce 2D struktury • Modelování jednotlivých fragmentů • Kombinace fragmentů navzájem • Doplnění smyček a flexibilních úseků • Nízká spolehlivost zejm. pro větší proteiny Ab initio >lci4A (87 residues) TTSQKHRDFVAEPGE KPVGS LAGIG EVLGKKLEERGFDKAYWLGQF LVLKKDEDL FREW LKDTCGANAKQS RDC FGC L R EWCDAF L El Quark RaptorX Rossetta Top 5 Final Structure Model By dragging your mouse on the images, you rotate and Predicted Secondary Structure 20 40 60 80 I I I I Sequence TTSQKHRDFVAEPGE KPVGS LAGTGEVLGKK LEERG FDKAYWLGQF LVLKKDEDL FREWLKDTCGANAKQSRDC FGC LREWCDAF L Conf .Score 988899999879999987447898899999999979659999999999958889999999999968899999999999999999859 H:Helix; S;Strand; C:Coil Predicted Solvent Accessibility 20 40 I I I I Sequence TTSQKHRDFVAEPGE KPVGS LAGIGEVLGKK LEERG FDKAYWLGQF LVLKKDEDL FREWLKDTCGANAKQSRDC FGC LREWCDAF L Prediction 5533 39221123223321120110032002102421132002000200113232310220022102031310310010022803324 Values range from 0 (buried residue) to 9 (highly exposed residue) Download Model Download Model 2 Download f.iodei ~ Download Model 5 De novo modelling with Rossetta (David Baker lab, Univ. of Washington) •• In contrast to threading, Rosetta does de novo prediction - doesn't use templates/homologous structures •• instead performs Monte Carlo search through space of conformations to find minimal energy conformation De novo modelling with Rossetta fragments are selected from known structures the window-fragment matches are calculated using - PSI-BLAST to build a profile model of the sequence - the predicted secondary structure of the sequence 1-9 10-18 19-27 28-36 37^)5 46-54 Native......> Structures of similar local sequences--> Kjr >v«- jwtr Vw' Aft A^i *V ft* w w w *3 »5 •3 8 fx De novo Modeling with Rosetta Stage I. Fragment Assembly -Aft, "Vs/ 'WW -fW '"-^ u>s-s^' Jtf, -y># wvW -Art SnJ ir^-r* "^A^ <*A V VW* 1}, Vt/* rWi WVi -A«< -VW ~v*y^r*^V^ l!U^ A, -Art, H«f W j"^^. ,w -v v*Jt« -W ^fW ^ <,^> «-? WvV De novo Modeling with Rosetta Stage II. All-atom refinement •A** -nrJ ****** >*^vy r***' Tt/ "*V* -W^rS- **^>* Mt/- >-V4 ^ v»W -«t!V "WW ^ «rf WW -^TSJT -?v* v^Vv- Ingredients of a high resolution potential 1. Van der waals packing 2. Hydrogen bonds 0 2 4 6 8 _180 -120 -60 0 60 120 180 Atom-atom distance Scoring Function Takes Into Account • residue environment (solvation) • residue pair interactions (electrostatics, disulfides) • strand pairing (hydrogen bonding) • strand arrangement into sheets • helix-strand packing • steric repulsion • etc. • scoring function search progressively adds terms during search • initially on the steric overlap term is used • then all but "compactness" terms are used • etc. • search is initiated from different random seeds WEB server - Robetta http://robetta.bakerlab.org Response Times To prevent unnecessary usage we require two manual steps for full structure predictions. The first step is to submit your sequence for domain and template detection. The second step is to continue for 3-D models. You may only select one domain at a time for structure predictions. The second step is computationally expensive so please continue with this step only if necessary. You may help increase computing resources for this service by joining our distributed computing project Rosetta@.HOME and spreading the word out to friends and colleagues. • -10 minutes - hours for domain and template detection. • -1 day - weeks for high accuracy homology models (templates detected with high confidence > 0.8 and sequence identity > 40%). • ~1 week - months for difficult targets. Zhang Lab - QUARK O © QUARK ONLINE (/lb Initio Protein Structure Prediction QUARK is a computer algorithm for ab initio protein structure prediction and protein peptide folding, which aims to construct the correct protein 3D model from amino acid sequence only. QUARK models are built from small fragments (1 -20 residues long) by replica-exchange Monte Carlo simulation under the guide of an atomic-level knowledge-based force field. QUARK was ranked as the No 1 server in Free-modeling (FM) in CASP9 and CASP10 experiments. Since no global template information is used in QUARK simulation, the server is suitable for proteins that do not have homologous templates in the PDB library. Go to example to view an example of QUARK output. The server is only for non-commercial use. Questions about the QUARK server can be posted at the Service System Discussion Board. Cut and paste your sequence (in FASTA format, less than 200 AA. Example input Driving innovation in protein structure prediction: "CASP" Critical Assessment of Structure Prediction Five blind predictions per target CASPl (1994) CASP1 TARGET (1rsy) "successful" fold recognition 2tbv RMSD: 16.0 A CASP 11 (2014) CASP11 in numbers Number of groups registered 208 including: expert groups 123 prediction servers 85 Number of regular targets released 100 including all-group (human) targets 55 Targets canceled for all/manual prediction 7/10 Number of refinement targets released 37 Number of assisted prediction targets released 71 Number of targets received from Joint Center for Structural Genomics (JCSG): 32 Structural Genomics Consortium (SGC): 4 Midwest Center for Structural Genomics (MCSG): 8 Northeast Structural Genomics Consortium (NESG): 5 New York Structural Genomics Research Center (NYSGRC): 6 Non-SGI research Centers and others (Others): 40 Seattle Structural Genomics Center for Infectious Disease (SSGCID): 4 NatPro PSI:Biology (NatPro): 1 http://predictioncenter.org/caspll/results.cgi 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction A Cfl CASP12 in numbers Number of groups registered 192 including: expert groups 112 prediction servers 80 Number of regular targets released 82 including ail-group (human) targets 56 Targets canceled and not re-released for all/manual prediction 11 / 11 Number of refinement targets released 42 Number of assisted prediction targets released 14 Prediction category Number of groups/servers contributing Number of models designated a s 1 Total number of Tertiary structure predictions 128/43 8362 37672 Data assisted predictions 16/1 109 528 Residue-residue contacts 38 / 30 3077 3077 Accuracy estimation 47 / 32 3700 7400 Interface accuracy 3/0 65 66 Refinement 39/5 1457 6227 All (unique): 188 / 80 16770 54970 http://predictioncenter.org/caspl2/results.cgi 13th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction CASP13 in numbers Number of groups registered 210 including: expert groups 123 prediction servers 87 Number of tertiary structure prediction targets released 90 (including all-group targets) (82) Number of hetero-multimer targets released 13 Number of refinement targets released 31 Number of assisted prediction targets released 60 Targets canceled (all / human) (10 / 12) Targets available/expired for manual non-QA prediction 0/72 Targets available/expired for server non-QA prediction 0/80 Targets available/expired for QA prediction 0/80 Targets available/expired for assisted prediction 0/59 Targets available/expired for multimer prediction 0/12 Prediction category Number of groups/servers contributing Number of models designated as 1 Total number of models Tertiary structure predictions 107/ 39 7542 35982 Oligomeric predictions 40/ 9 662 2861 Data assisted predictions 24/ 5 456 2017 Residue-residue contacts 46 / 25 3914 3914 Accuracy estimation 52/ 41 4332 8687 Refinement 33/ 6 847 3788 All (unique): 185/ 87 17753 57249 http://predictioncenter.org/caspl3/results.cgi De novo successes: all-ß CASP7 target T0316 (domain 3) Native Model 2.0 A over 61 residues De novo successes: all-a Native Model 1.4 A over 90 residues Is protein folding solved? CASP 14(2020) Ranking of participants in CASP14, as per the sum of the Z-scores of their predictions (provided that these are greater than zero). One group, 427, named AlphaFold 2, shows an incredible improvement with respect to the second best group, 473 (BAKER). This figure was obtained from the official CASP14 webpage on Tuesday 1st December, 2020. mm I Groups Models based on templates identified by sequence similarity remain the most accurate. Over the course of the CASP experiments there have been enormous improvements in this area. However, the overall accuracy improvements that we have seen in the first 10 years of CASP remained unmatched until CASP12 (2016), when a new burst of progress happened rKryshtafovvch et al, 20181. In two years from 2014 to 2016, the backbone accuracy of the submitted models improved more than in the preceeding 10 years. The next CASP continued the trend rcroll et al, 20191, and the 2014-2018 model accuracy improvement doubled that of 2004-2014 (see left plot). Several factors contributed to this, including more accurate alignment of the target sequence to that of available templates, combining multiple templates, improved accuracy of regions not covered by templates, successful refinement of models, and better selection of models from decoy sets due to improved methods for estimation of model accuracy. CASP14 marked an extraordinary increase in the accuracy of the computed three-dimensional protein structures with the emergence of the advanced deep learning method AlphaFold2. Models built with this method proved to be competitive with the experimental accuracy (GDT_TS>90) for ~2/3 of the targets and of high accuracy (GDT_TS>80) for almost 90% of the targets (middle plot). The accuracy of CASP14 models for TBM targets significally superseeded accuracy of models that can be built by simple transcription of information from templates, and reached the level of GDT_TS=92 on average, which is significantly higher than the corresponding averages in previous two CASPs (right plot). Template-based modeling targets >,o 80 s „■ 51 • 8 60 1| 20 R 1 s)__ K.(< —yfl Target Difficulty combined rank by seq.id. and coverage of the best template 100 90 80 1 70 60 50 40 AlphaFold2 results on CASP14 targets 20% 40% 60% 80% 100% Cumulative percentage of targets Best model vs best structural template TBM and FM/TBM targets 63.2.91.9• J l^'ntjM ■ CASP14 □ CASP12_avg -CASP14 ---- CASP12 O CASP13_avg O CASP14_avg .......... CASP13 40 60 LGA_S, best template 999999999999999999^ CASP15 (2022) showed enormous progress in modeling multimolecular protein complexes. The assembly modeling (a.k.a. quaternary structure modeling, oligomeric modeling, multimeric modeling) has been assessed in CASP since 2016 (CASP12). Typically, models were of good accuracy when templates were available for the structure of the whole target complex. After the success of AlphaFold2 in CASP14 (2020), it was expected that deep learning methodology that brought monomeric modeling to qualitatively new level will be extended to multimeric modeling. Indeed, CASP15 showed that newly developed methods are capable of accurate reproducing structures of oligomeric complexes and outperform CASP14 methods by a large margin. In particular, the accuracy of models almost doubled in terms of the Interface Contact Score (ICS a.k.a. Fl) and increased by 1/3 in terms of the overall fold similarity score LDDTo (left panel). An impressive example of multimeric modeling is shown in the right panel below. CASP15: T11130 model 239_2: Fl=92.2; LDDTo=0.913 Jakou metodu zvolit? 1. Mám homologní protein se známou strukturou -> homologní modelování 2. Využiji experimentální data > Threading > Kombinace více templátů pro jednotlivé části struktury > Různé predikční nástroje 3. Ab initio modelování smyček a částí sekvence bez vhodného templátu 4. Mám unikátní sekvenci - ab initio Predikce kvartérní struktury Zahrnuje různé úrovně, např.: • Predikce vazebných míst • Predikce aminokyselin podílejících se na interakci • Odhad oligomerního stavu • Protein-protein docking (protein-nukleová kyselina docking) > SW dosud často nedokonalý nízká spolehlivost predikce > Složitější postupy většinou nejsou automatizované Predikce kvartérní struktury 4D ľ Programy většinou vycházejí z podobnosti sekvence a/nebo 3D struktury se známými proteiny Příklady SW: • Quatldent • QuaBingo • M-TASSER • Quad-PRE t > B»pnr* J > v MO>. f< Biophysical Journal PI.ICID:M.IC21S626I) '0 las I M-TASSER: An Algorithm for Protein Quaternary Structure Pr <^^^^tu^i Huiling Chen and Jeffrey Skolnick* 0> Abstract In a cell, it has been estimated that each protein on average interacts with rougk in tens of thousands of proteins known or suspected to have interaction partners fraction have solved protein structures. To partially address this problem, we ha\ TASSER. a hierarchical method to predict protein quaternary structure from seq template identification by multimeric threading, followed by multimer model as: The final models are selected by structure clustering. M-TASSER has been tested comprising 341 dimers having templates with weak sequence similarity and 246 Research Article Quad-PRE: A Hybrid Method to Predict I Quaternary Structure Attributes Hodnocení kvality predikčních nástrojů - CASP > Critical Assessment of Techniques for Protein Structure Prediction > 2020-CASP14 > Predikce vyřešených, ale zatím nepublikovaných struktur > Rozsáhlá analýza predikčních programů > Predikce terciárních struktur > Identifikace neuspořádaných oblastí > Funkční predikce (predikce vazebných míst) > Interakce mezi doménami, podjednotkami a proteiny > Hodnocení spolehlivosti 3D struct 1 re evaluatio - Targets and Domains count: A 1 ■s 1 - ES T0674 - T0703 T070 - T0733 TO 734 - T 0763 Refine T0644-D1 ... T0645-D1 Panip; 4f>-.1-17 .....1 T0648-D1 .....1 m T0649-D1 1 □ a' í T0650-D1 T0651 .....1 T0651-D1 Pf T0651-P2 R □ 1 - 0 1 ť 1 El I T0652 . T0652-D1 .....1 St) T0652-D2 .....i sog 1 T0653-D1 3 Ale! ! pozor na domény ! NCBI - Blast (Basic Local Alignment Search Tool) (National Centre for Biotechnology Information) Prohledávání databází známých aminokyselinových sekvencí > celý protein Putative conserved domains have been detected, click on the image below tor detailed results. 1 SI 1*1 tSt 3*9 HI 2V Query ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^J superfmillet [ PA-IIL superfaw.il y ] Mult.i-ilnn-tin-: P i X A Distribution of 100 Blast Hits on the Query Sequence 0 Mouse over to see the defline click to show alignments Color key for alignment scores <40 40-50 80-200 >=200 I I 1 I SO 100 150 200 I 250 NCBI-Blast Prohledávání databází známých aminokyselinových sekvencí > celý protein Conserved domains on [lciimio] Lo:al query sequence I Graphical summary show options » 1 50 1)* 150 20* 2Í0 288 Hon-specif ic hits PA-IIL Superfanilies PA-IIL superfanlly < ► Search for similar doma n architectures | ® Refine search | ® List of domain hits ' Ii Description Pstmld Mu ti-(loin E-value MPA-IIL[cfan07472], Fucose-binding lectin II (PA-IIL); In Pseudomcnas aeruginosa the fucose-binding lectin II (PA-IIL) contributes to the ... 203639 no 3.60e45 MPixA[pfam 12306], Inclusion body protein; This family of proteins is found in bacteria Proteins in this family ate typically... 204875 yes 4.88e43 NCBI-Blast Prohledávání databází známých aminokyselinových sekvencí > celý protein Fucoti-binding lectin II (PA-IIL) Pub Med References Z) 31 sľeseľje - KHnMRnMHHgwsnoi tn m—am. pjjat. u; .aass svnt m 31 T94S9312 »t rgmgiciLaranairxtai qlt an.zsasnBv usr. :i; .tssst anc a»; at ü st e es*: n mmmmmmmmqjwmmp «»■ [JJ -umELTnas». [i]-Da ras* sk» n Jl 12 S5 TU 13 1ST °m-.TTi -r^rm«».Tj-iT «- T—m JLPX ;: ; iTTVaKIDiV _-: UHX5 31 1335BS1S* it< ■WBBMBMMBHBBB m :;; atttc^a^pj :■: -u stnt rau x r mmmmmmemmmpmkmi n»K-.ximiaQpQ. r.: .usz. :i; -lasoi borh aax~> e mjuurx.-rr,:~aukxzir.ztt m MMHE id .tS7. [33 .aoss amt «3 31 iľTiaiu 1 roaawBWiMWWpwwWM ia LWBBWí :i;.rsi.'33-Mnc».i3.].gnt] e: narv_» 14 "-i- »■^■"«■■»jf;■»; t™ umnaaa. ::;.dj una acot ti NCBI-Blast Prohledávání databází známých aminokyselinových sekvencí > celý protein I rt-:^*: PtXA ] Inclusion body protein veteiíij m frj*f«e-.ly tec Cy«r* «n n-duse* My pfVtML. e*lly between 173 «nd 1*1 MM ■« aaC* tm len- I«X. * * a tS ? t ; : :: .: »r:: * PubMed References I :: Er.::.rt p fa ml2306 s : ajs *a: as a ~i>zt :*iat -a- —j'z tiai o-v; OD-a -pTaml2306 s a;; :: 3- r:-a - s_;-»-a- [ ^ŕa-T4t J CaFTMk: owet I ImOM,: ^ to u CabrBS: 10« t^at timer: mimeahmmiwi •t 2 - : j 1- .3. 12] ■ Z- :*: SBC. [11 .vtxvwcoj siiuĽnruciz m «1 1234 M 6» 13 .[3] Q* 'Q11mW dmi aat. 11?] ■ ptsi [13 STÍ MSB3M33. ;s; TT r- 1231B2ttt 10 -[33 3D 73 7 7AV77J7T** ■nat. n?] r ľ í: (d a- onirTs*. :_: ľ s17 v TS •t 33T17B12 s . \i: as. hoj »•75; z OrLLCZSI. [B] 3M33flBM3tBB TS 23424B32B 27 • [3] BQB 3JLVT3TXT arx. [Ml 1 • A :-: 7. PJC P3S3X33BB 9: •* 1727S4SS2 2 - [3] 1«Z2UkIATT3ATI LLS.112] A rm [*J 7. rrrtsosa. z rxi 5 CS ■* B374S3B2 :z: 7 XXJL.I12] a ptjli :i: x iocvsj33. :s: P3m3B3m3JPBB 03 «1 .:<::>:• 30 .13] gr.*sll*.~»T3 S3T t«. 112] T ptpt dl z. Lrrtaes. :e: S * X « z S3 «t IT7722231 10 -t3J J2232MMMB». 11] 2*3. [12] ■K [X] • m i. r_rv j: TS si 236424271 30 . 0 r."3r.tcn."."DTJĽr na- íii] r y n] ■B xnocsrr. 17] 7 'TT P B4 InterPro protein sequence analysis & classification InterPro is an integrated database of predictive protein signatures used for the classification and automatic annotation of proteins and genomes. InterPro classifies sequences at superfamily, family and subfamily levels, predicting the occurrence of functional domains, repeats and important sites. InterPro adds in-depth annotation, including GO terms, to the protein signatures. European Bioinformatics Institute - http://www.ebi.ac.uk/ a • s - j #»ii- e- o-« E 61 > Toe* > Prolem Functonal AnaN/M InterProScan Results Summary Table Tool Output ] inierProScan Visual Output Download in SVG format Submission Details Submit Another Job InterProScan (version: 4A) Sequence Sequence.! length 2U crc m 1FAC4C40C249M64 Intel Pro Hitch Query Sequence ■ IPR010W7 Cakium-meduted lectin CiDSA 2.60.120.400» -PF074 72«.-SSFS2026» - ■ no d#icr*>tion ■ 'A III ■ Cj i o u m- midumů bean IF>ftu2l087 U nchara denied protem famih/ PixA/AidA □ntovrrt ■stetarAMív ■schal» htuxmm ■fantm* bum: O Europe«i ftoinfcw matici Institute 2006-2012. EB i\ anOutilJtton o/ the European MoIkuIji SioIocjv Labwjlory. Proč potřebujeme predikci domén Prohledávání sekvenčních databází bez predikce domén může být neúspěšné Automatická predikce struktury se zaměří jen na nejlépe „definovanou" část - whole protein http://www.sbg.bio.ic.ac.uk/phyre2/privre2 output/al32b051273537c4/su mmary.htm i * Template Alignment Coverage 3D Model Confidence ■ °/o i.d. Template Information i i Ägnn„ i n 100.0 60 'DB headensugar-binding protein Chain: C: PDB Molecule:bcla; ^DBTitle: crystal structure of bcla lectin from burkholderia2 cenocepacia in complex with alpha-methyl-mannoside at 1.73 angstrom resolution 2 o***- i *»™. i ü 100.0 43 5DB headensugar binding protein Chain: A: PDB Molecule:lectin; 5DBTitle: c-terminal domain of bc2l-c lectin from burkholderia cenocepacia 3 i*****!- |*-< 1 11 100.0 37 -old:Caldum-mediated lectin Superfamily:Calcium-mediated lectin -amily:Calcium-mediated lectin NCBI-Blast Prohledávání databází známých aminokyselinových sekvencí > celý protein Conserved domains on [íciimio] Lo:al query sequence I Graphical summary show options » 1 50 1)* 150 20* 2Í0 288 Hon-specif ic hits PA-IIL Superfanilies PA-IIL super-family < Search for similar doma n architectures | ® Refine search | ® • List of domain hits ' Ii Description Pssmld Multi-dom E-value MPA-IIL[cfan07472]. Fucose-binding lectin II (PA-IIL); In Pseudomcnas aeruginosa the fucose-binding lectin II (PA-IIL) contributes to the ... 203639 no MPixA[pfam 12306], Inclusion body protein; This family of proteins is found in bacteria Proteins in this family are typically... 204875 yes 3 60e45 4.88*43 Phyre - C-term http://www.sbg.bioJc.ac.uk/phyre2/phyre2_output/e332blecabb8dOa6/summa Template Information ' Alignment | Alignment Mm PDB headensugar binding protein |chain:A: PDB Molecule: ... : PDBTitle: c-terminal domain of bc2l-c lectin from burkholderia cenocepacia PDB header:sugar-binding protein Chain: C: PDB Molecule:bcla; PDBTitle: crystal structure of bcla lectin from burkholderia2 cenocepacia in complex with alpha-methyl-mannoside at 1.73 angstrom resolution FoldhCalcium-mediated lectin 30 Superfamily: Calcium-mediated lectin Family: Calcium-mediated lectin Phyre - n-term http://www.sbg.bio.ic.ac.uk/phyre2/phyre2_output/e332blecabb8dOa6/summary.html Template Alignment Coverage 3d Model Confidence % i.d. clsddB O □ Alignmerrt Template Information PDB header:blood dotting Chain: B: PDB Molecule:coagulation factor v; PDBTitle: crystal structure of bovine factor vai PDB headenblood dotting 6 Chain: B: PDB Molecule:coagulation factor viii light chain; PDBTitle: crystal structure of human factor vi Fold : Cup red oxin -like 13 __" !M Family:Multidomain cupredoxins Swissprot - whole protein [ myWorkspace ] [ login SWISS-MODEL Workspace Modelling Tools Repository Documentation Workunit: P000007 - Overview Print/Save this page as f) Model Summary © Model information: Modelled residue range: 169 to 288 Based on template: [2vnvD]* (1-7 A) Sequence Identity [%]: 56.35 Evalue: 0 00e-1 Quality information: QMEAN Z-Score: -0.71 [details]* I Quaternary structure information: [details]* Template (2vnv): DIMER Model built: SINGLE CHAIN Ligand information: [details]* Ligands in the template: CA 3. MMA: 1, S04: 1. Ligands in the model: CA: 2 logs: [Templates]* [Alignment]* [Modelling]* display model: as [pdb]* - as [DeepView project]' - in [AstexViewer]* download model: as [pdb]*-as [Deepview project]*-as [text]* Global Model Quality Estimation © [+/-] — http://swissmodel.expasv.orrj/workspace/index.phpuserid=^ 76edfa75fbal9b2d96e4&func=workspace modellinfi&priid=P000007 Rosetta@home ^ Project - Computing - Community ~ Site • Sign Up Login You don't have to be a scientist to do science. By simply running a free program, you can help advance research in medicine, clean energy, and materials science. Join Rosetta@home tíšň HHMI Äi!).SSsi9n WwashIngton H Rosetta@home needs your help to determine the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases. By running the Rosetta program on your computer while you don't need it you will help us speed up and extend our research in ways we couldn't possibly attempt without your help. You will also be helping our efforts at designing new proteins to fight diseases such as HIV, Malaria, Cancer, and Alzheimer's. Please join us in our efforts! fold Solve Puzzles puzzles » categories groups players recipes contests for Science bl0g * feedback forum wiki faq about credits The Science Behind Foldit Foldit is a revolutionary crowdsourcing computer game enabling you to contribute to important scientific research. This page describes the science behind Foldit and how your playing can help. Page Contents: What is protein folding? Why is this game important? Foldit Scientific Publications News Articles about Foldit News Articles about Rosetta Rosetta@Home Screensaver Community Rules Let's Foldit Podcast Instructions for Educators Terms of Service and Consent Credits GET STARTED: DOWNLOAD Windows (XP/Vlsta/778) Linux (64-bit) http://fold.it/por tal/ Are you new to Foldit? Click here. Are you a student? Click here. Are you an educator? Click here. Only search fold.it What is protein folding? RECOMMEND FOLDIT What is a protein? Proteins are the workhorses in every cell of every living thing. Your body is made up of trillions of cells, of all different kinds: muscle cells, brain cells, blood cells, and more. Inside those cells, proteins are allowing your body to do what it does: break down food to power your muscles, send signals through your brain that control the body, and transport nutrients through your blood. Proteins come in thousands of different varieties, but they all have a lot in ^nmm^n Cnr inc-tcr^^ they're made of the same ittps://fold.it/portal/" :onsists of a long chain of Folded up Streptococcal Protein Puzzle (+) Enlarge This Image Log in Create new account Ramifist naw nasswnrri ust a game? This is an example of a puzzle that a human can see the obvious answer to - fix the sheet that is sticking out! (+) Enlarge This Image proteins? What other good stuff am I contributing to by playing? Proteins are found in all living things, including plants. Certain types of plants are grown and converted to biofuel, but the conversion process is not as fast and efficient as it could be. A critical step in turning plants into fuel is breaking down the plant material, which is currently done by microbial enzymes (proteins) called "cellulases". Perhaps we can find new proteins to do it better. Can humans really help computers fold We're collecting data to find out if humans' pattern-recognition and puzzle-solving abilities make them more efficient than existing computer programs at pattern-folding tasks. If this turns out to be true, we can then teach human strategies to computers and fold proteins faster than ever! Structure Superposition The key is finding corresponding points between the two structures Structure Superposition A The key is finding corresponding points between the two structures Algorithms for Structure Superposition Distance based methods: DALI (Holm & Sander): Aligning scalar distance plots SSAP (Orengo & Taylor): Dynamic programming using intramolecular vector distances MINAREA (Falicov and Cohen): Minimizing soap-bubble surface area CE (Shindyalov & Bourne) Vector based methods: VAST (Bryant): Graph theory based secondary structure alignment 3D Search (Singh and Brutlag) & 3D Lookup (Holm and Sander): Fast secondary structure index lookup Both LOCK (Singh & Brutlag) LOCK2 (Ebert & Brutlag): Hierarchically uses "Adaptive" FATCAT(Flexible structure AlignmenT by Chaining Aligned fragment pairs allowing Twists, Ye & Godzik) - not further maintained? http://fatcat.godziklab.org/fatcat/ DALI Based on aligning 2-D intra-molecular distance matrices Computes the best subset of corresponding residues from the two proteins such that the similarity between the 2-D distance matrices is maximized Searches through all possible alignments of residues using Monte-Carlo and Branch-and-Bound algorithms VAST-Vector Alignment Search Tool Identifying similar structures by purely geometric criteria (and to identify distant homologs that cannot be recognized by sequence comparison). Find similarly shaped individual protein molecules or 3D domains (VAST+: similarly shaped macromolecular complexes) • Aligns only secondary structure elements (SSE) • Represents each SSE as a vector • Finds all possible pairs of vectors from the two structures that are similar • Uses a graph theory algorithm to find maximal subset of similar vector pairs • Overall alignment score is based on the number of similar pairs of vectors between the two structures FoldMiner: Structure Similarity Search Based on LOCK2 Alignment FoldMiner aligns query structure with all database structures using LOCK2 FoldMiner up weights secondary structure elements in query that are aligned more often FoldMiner outperforms CE and VAST is searches for structure similarity The best to test as first: Distance based methods DALI http://ekhidna2.biocenter.helsinki.fi/dali/ Vector and distance based method FoldMiner (L0CK2) - local installation needed "Adaptive" FATCAT http://fatcat.godziklab.org/fatcat/ Závěrem > Struktura je klíčová pro správnou funkci proteinu > Predikovat na základě sekvence (ID) lze 2D, 3D i 4D strukturu > Vždy je nutné kriticky kontrolovat výstupy programů > Ideální je využít více predikčních programů s různou metodologií a porovnat výsledky