If you found a new sequence you want to learn everything possible about it. I will give an overview of all kinds of methods and web servers. You can visit them, judge them etc. during the exercise I will use you guys to fill up and update info on servers Also introduce exercise! Prediction methods exist that allow for biological discovery of all kinds of motifs, signals etc. in your sequence. These are based on either the protein sequence itself or its comparison to protein families. Multiple sequence alignments of related sequences can build up consensus sequences of known families, domains, motifs or sites. Combining these predictions with primary biochemical data can provide valuable insights into protein structure and function
Often associated with important properties, such as cellular localization, ligand binding, and so on.
A regular expression is a pattern that can match various text strings; for example, l[0-9]+ matches l followed by one or more digits. Section 14.5.www.nongnu.org/emacsdoc-fr/manuel/glossary.html A template or pattern for a text string. A regular expression indicates, in general terms, what characteristics the text must have to fit its template. XRover® agents use regular expressions to characterize the type of data they wish to extract. Data fitting the pattern of a particular regular expression is matched and then extracted.www.xsb.com/glossary.html A string that can describe several sequences of characters.
A pattern or regular expression is a quantitative descriptor: it either matches or does not. Therefore a good pattern is usually located in a short well-conserved region. The motif has to be long enough (10-20 residues) ex: sequence DAVIS 71 proteins match to this pattern DAVE 1088 proteins match to this pattern (in OWL29.6 ) Does not tolerate similarity If for example Glu -&gt; Asp mutation in the pattern and all the other amino-acids matching the pattern, the expression will be rejected
Post-translational modifications] [Compositional biased regions] [Domains] [DNA or RNA associated proteins] [Enzymes] [Electron transport proteins] [Other transport proteins] [Structural proteins] [Receptors] [Cytokines and growth factors] [Hormones and active peptides] [Toxins] [Inhibitors] [Protein secretion and chaperones] [Others] The annotation document also contains direct information about the motif descriptors: for patterns, amino acid residues involved in the catalytic mechanism, metal ion or substrate binding, or conserved post-translational modifications are indicated. For profiles, it is stated whether they cover the entire domain or protein or only part of it. Finally, the sensitivity and specificity of the motif is also indicated, as well as an expert to contact, if any. Release 19.16, of 06-Dec-2005 (contains 1390 documentation entries that describe 1328 patterns, 4 rules and 577 profiles/matrices).
(i) Such regions are typically enzyme catalytic sites, prosthetic group attachment sites (haem, pyridoxal phosphate, biotin, etc.), metal ion binding amino acids, cysteines involved in disulfide bonds or regions involved in binding a molecule. Even though the scope of a regular expression is limited to these particular biological regions patterns are still very popular because of their intelligibility for users. Use MSA to predict real glycosylation sites. Normally N,S and T mutate easily but if they are conserved =&gt; glyc. Site. Pattern with high probability of occurrence Narekenen!Ca. 1/100 chance on glycosylaton signal. Protein of 450 aa ca. 8% chance that it does not contain a glyc signal. Patterns have been experimentally shown to be associated with some biological property. Many post-translational modification.
AC and ID
Normally, we use a PAM-like matrix to determine the score for each possible match in an alignment. The knowledge about which residue types are good for a certain position in a sequence can be expressed in a profile. This assumes that each match I &lt;-&gt; E is the same. But it isn’t. A profile is a numerical representation of a MSA. In a way a profile is an improved PAM Matrix All methods are supervised. You first select & align – msa -&gt; statisti at each spot Convolution -&gt; moving seq against profile All msa programs use profiles, in one way or another, more or less explicitly
Vertical your seq Horizontal aa + score Various methods can be used to fill a profile table from a multiple alignment. Most frequently, a substitution matrix is used to convert a residue frequency distribution into weights, but alternative methods can be applied including structure-based approaches and methods involving hidden Markov modelling (2–4). These weights (also referred to as scores) are used to calculate a similarity score for any alignment between a profile and a sequence, or part of a profile and a sequence. An alignment with a similarity score higher than or equal to a given threshold value constitutes a motif occurrence. This threshold is estimated by calibrating the profile against a randomized protein database. The normalization procedure used for PROSITE profiles makes the normalized scores independent of the database size, allowing the comparison of scores from different searches (5). The quantitative behaviour of a profile allows the acceptance of a mismatch at a highly conserved position if the rest of the sequence displays a sufficiently high level of similarity and therefore allows the detection of poorly conserved domains such as immunoglobulin, SH2 or SH3. Another advantage of profiles over patterns is that they are not confined to small regions with high sequence similarity. Rather, they attempt to characterize a protein family or domain over its entire length.
A priori E & I are the most different aa 1) Vb at P1 pocket of proteasemet substraat verschil
Sequence search and keyword search There are two possible &quot;states&quot; an insert or match for each position in the signature designated by /I: or /M: the characters which follow these are either a transition score for the various type of inserts which may be found in the profile e.g. &quot;B1=0;&quot; in the case of insert state or a match score for the amino acid scored in that column. This is followed by a column separator &quot;,&quot; More information on the syntax is available in the Prosite documentation From Birney: Sensitive, Low coverage (good for signalling) Start with multiple sequence alignment -uses a symbol comparison table to convert residue frequency distributions into weights Result- table of position-specific amino acid weights and gap costs- calculate a similarity score for any alignment between a profile and a sequence, or parts of a profile and a sequence From: http://ip30.eti.uva.nl/ember-demo/ch3_info_3-1.php A profile is a mathematical description of a sequence alignment; in its simplest sense, it can be viewed as an alternating sequence of &apos;match&apos; and &apos;insert&apos; states. Profile scores vary at different positions in the alignment—so, for example, there is a higher penalty for insertions in conserved regions than in more variable loop regions, where insertions and deletions are common. Profiles are highly complex descriptors and as such are very precise. At present, the number of fully- and partially-annotated profiles is relatively small, so searches of the Profile library may yield no matches. Where matches do occur, a blue question-mark (?) suggests a match is unlikely to be significant, while a red shriek (!) denotes a clear match - E-values quantify the level of significance (further information on Profile statistics is available from the ISREC server). Results are linked to relevant annotation in PROSITE and/or InterPro (ii) A profile is a table of position-specific amino acid weights and gap costs. Various methods can be used to fill a profile table from a multiple alignment. Most frequently, a substitution matrix is used to convert a residue frequency distribution into weights, but alternative methods can be applied including structure-based approaches and methods involving hidden Markov modelling (2–4). These weights (also referred to as scores) are used to calculate a similarity score for any alignment between a profile and a sequence, or part of a profile and a sequence. An alignment with a similarity score higher than or equal to a given threshold value constitutes a motif occurrence. This threshold is estimated by calibrating the profile against a randomized protein database. The normalization procedure used for PROSITE profiles makes the normalized scores independent of the database size, allowing the comparison of scores from different searches (5). The quantitative behaviour of a profile allows the acceptance of a mismatch at a highly conserved position if the rest of the sequence displays a sufficiently high level of similarity and therefore allows the detection of poorly conserved domains such as immunoglobulin, SH2 or SH3. Another advantage of profiles over patterns is that they are not confined to small regions with high sequence similarity. Rather, they attempt to characterize a protein family or domain over its entire length.
Most commonly, such analysis has the goal of predicting membrane-spanning segments (highly hydrophobic) or regions that are likely exposed on the surface of proteins (hydrophilic domains) and therefore potentially antigenic.
Kyte-Doolittle scale: Hydropathic regions achieve a positive value. Setting window size to 5-7 is suggested to be a good value for finding putative surface-exposed regions, whereas a window size of 19-21 yields a plot in which transmembrane domains stand out sharply, with values of at least 1.6 at their centers. Hopp-Woods scale: This scale was developed for predicting potential antigenic sites of globular proteins, which are likely to be rich in charged and polar residues. This scale is essentially a hydrophilic index, with apolar residues assigned negative values. The authors suggest that, using a window size of 6, the region of maximal hydrophilicity is likely to be an antigenic site.
For the first 3 aa there are different approaches: 1) set them to 0 2) average nr 1,2 and 3 3) other
Most of the transmembrane regions in proteins consist of helices although beta-barrel topologies with beta-sheets (porins) have also been found. A sequence and structural study of transmembrane helices Bywater, R.P., Thomas, D., Vriend, G. J. Comp-Aid. Mol. Des. 2001 15:533-552 Not many structures known of transmembrane helix proteins
Avg length of TM helix is 23-25 aa Some euk membranes up to 50 Å Purple bact =&gt; membrane 23 Å A membrane protein can range from single spans to serpentine proteins, which may consist of over 20 helices, each about 20 amino acids long, separated by hydrophilic regions that are looped out alternatively into either the cytoplasm or the extracellular space. Positive aa cannot easily go through membrane. Negative aa can do that better. Daarna vaak aantal positieve inside=cytosolic. Ze gebruiken dit om i/o te voorspellen. NOG: uitleg van 1 van de oefeningen van de signals in seq course opnemen
Parameter: minimum and maximum length of hydrophobic part of TM helix i-o and o-i inside-to-outside outside-to-inside definition : inside is cytosolic check + charge of helix points inside? Pattern i-o o-i has to match Some servers claim 98% correct????!!! This is not even possible if you know the structure... Uit verslag Sander Caerteling: the HMM models (TMHMM 2.0 and HMMTOP 2.0) have overall the best scores, whereas DAS and PRED-TMR2 have the lowest scores.TMHMM 63,9 (80.0, 57,5) #TMS, 59.0 (80.0, 50.6) #TMS&position HMMTOP 68.0 (77,1 , 64.4) #TMS, 62,3 (77.1, 56.3) #TMS&position#TMS is % of correct predicted number of TMS, #TMS&position = % of correct predicted number and position of TMS
Identification of epitopes on proteins would be useful for diagnostic purposes and also in the development of peptide antibodies or vaccines. http://www.epitope-informatics.com/References.htm In case of 3D epitopes in principle the whole native protein must be used for immunization. This is not always possible.
Finally, the peptide must be immunogenic. There is data to suggest that a single antigenic determinant (i.e. the smallest immunogenic peptide) is between 5 and 8 amino acids. Consequently, a peptide length of 15-20 amino acids is preferable as it should contain at least one epitope and adopt a limited amount of conformation.. Sliding Window Approach: Make scale with data about known epitopes. Uit literature: List of succesfull peptides. Count=&gt; AI index for every aa, then moving average Not helical for 2 reasons: Als peptide helical dan kan niet goed gepresenteerd worden als in native protein helical dan steken maar 3 van de 10 naar buiten Geen pro (als je het kunt vermijden): creeert bends dus kan niet linear aan MHC aangeboden worden Geen cys vanwege reactiviteit The helicity is easily determined if you have 3D coordinates. If you have only a sequence, use to predict secondary structure. Helicity in the peptide can be predicted with Agadir
Antigenicity program = sliding window approach N en C liggen solvent accessible en vaak unstructured, dus ook likely to be recognized in mature protein. Deze regels zijn niet gevalideerd, zijn Gerts rules Jameson and Wolf (CABIOS 4:181) Sums secondary structure indices, surface accessibility, backbone flexibility Many epitopes linear, surface loops
One of the fundamental aspects of cellular life is the process through which proteins are routed to their proper final destination within a cell. In many cases, this sorting procedure depends on ‘signals’ that can already be identified by looking at the primary structure of a protein. Thus, targeting to the secretory pathway (via the endoplasmatic reticulum), to mitochondria and to chloroplasts normally depends on an N-terminal presequence or so-called ‘signal peptide’ that can be recognized by receptors on the surface of the appropriate organelle. After targeting, membrane-embedded translocation machineries ensure the delivery of the protein to the interior (or membrane) of the organelle. Owing to such a unique function, protein signals have become a crucial tool in finding new drugs or reprogramming cells for gene therapy; the importance of these signal peptides was emphasized in 1999 when Günther Blobel received the Nobel Prize in physiology or medicine for his discovery that “proteins have intrinsic signals that govern their transport and localization in the cell”. the processes operate in the same way in yeast, plant, and animal cells. These signal sequences are in fact a chain of different amino acids present either as a short &quot;tail&quot; at one end of the protein, or sometimes located within the protein. Proteins translocated across the cytoplasmic membrane of bacteria, the thylakoid membrane in plant chloroplasts and the ER of eukaryotes are all synthesized as precursors with an amino-terminal signal peptide The signal peptides (SPs) and N-terminal transmembrane domains (TMs) are difficult to identify from each other, partially due to both having hydrophobic regions. However, careful analysis of the regions shows that three features in this area may play important roles for the identification. They are the amino acid composition, hydrophobicity and position of hydrophobic region.
H-region: Gly/Pro/Ser often found at end of hydrophobic stretch: helixbreaking N-terminal bacterial signal peptides:Class I - typical signal peptides, cleaved by type I signal peptidases (Spases I) In Class I: N-region: 11 aaH-region: 21 aaC-region: 5 aa 1) See exercise 2 on webpage! 2) Try to find example of non-transmembr signalpeptide
Some common characteristics and differences between signal peptides from different organisms are shown in the following table:
The SignalP World Wide Web server predicts the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms: Gram-positive prokaryotes, Gram-negative prokaryotes, and eukaryotes. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks. SignalP predicts and scores only one signal peptide per protein. In contrast, SPScan, which scans for alternative methionine residues as initiation sites and for alternative signal peptidase I cleavage sites, may yield per protein several alternative signal peptides having the same score. SignalP better than SPScan
Repeats If you want to make a knockout knockout all repeats by NRM repeat can be solved, while complete structure cannot important in several diseases For example 6-8 copies of the WD40 repeat are needed to form a single globular domain. There also many other short repeat motifs that probably do not form a globular fold that might have the repeat datatype. Many large proteins have evolved by internal duplication and many internal sequence repeats correspond to functional and structural units. .
After clustering the repetitive sequence segments into families, we find repeats from eukaryotic proteins have little similarity with prokaryotic repeats, suggesting most repeats arose after the prokaryotic and eukaryotic lineages diverged.
Consequently, protein classes with the highest incidence of repetitive sequences perform functions unique to eukaryotes.
Leucine-rich repeats (LRRs) are 20-29-residue sequence motifs present in tandem arrays a number of proteins with diverse functions, such as hormone – receptor interactions, enzyme inhibition, cell adhesion and cellular trafficking. A number of recent studies revealed the involvement of LRR proteins in early mammalian development, neural development, cell polarization, regulation of gene expression and apoptosis signalling. It was shown that LRRs may be critical to the morphology and dynamics of cytoskeleton. The primary function of these motifs appears to be to provide a versatile structural framework for the formation of protein-protein interactions.
Very long coiled-coils are found in proteins such as tropomyosin, intermediate filaments and spindle-pole-body components. is a program that compares a sequence to a database of known parallel two-stranded coiled-coils and derives a similarity score. By comparing this score to the distribution of scores in globular and coiled-coil proteins, the program then calculates the probability that the sequence will adopt a coiled-coil conformation. COILS: http://www.ch.embnet.org/software/COILS_form.html COILS is a bad name, it suggests unstructured regions!!! If a&d is Leu =&gt; Leu-zipper. Subclass of coiled coils
Seminar one of two
Exploring Protein Sequences – Part 1
Patterns and Motifs
Celia van Gelder
Patterns and Motifs (1)
•In a multiple sequence alignment (MSA) islands of conservation
•These conserved regions (motifs, segments, blocks, features) are
typically around 10-20 aa in length
•They tend to correspond to the core structural or functional
elements of the protein
•Their conserved nature allows them to be used to diagnose family
Patterns and Motifs (2)
•A motif (or pattern or signature) is a regular expression for what
residues can be present at any given position.
•Motifs can contain
- alternative residues
- flexible regions
| | | | |
B or C
Not E,F or G
Patterns and Motifs (3)
•Motifs can not contain
exact match or no match at all
| ?| | | ?|
•PROSITE - A Dictionary of Protein Sites and Patterns
•1328 patterns and 577 profiles/matrices (dec 2005)
•For every pattern or profile there is documentation present (e.g.
- information on taxonomic occurrence
- domain architecture,
- 3D structure,
- main characteristics of the sequence
- some references.
•PROSITE patterns consist of an exact regular expression
•Possible patterns occur frequently in proteins; they may not
actually be present, such as post-translational modification sites
ID ASN_GLYCOSYLATION; PATTERN.
DE N-glycosylation site.
•Notice also in the PROSITE record the number of false positives
and false negatives
•If regular expressions fail to define the motif properly we need a
•Profiles are specific representations that incorporate the entire
information of a multiple sequence alignment.
•A profile is a position-specific scoring scheme and holds for each
position in the sequence 20 scores for the 20 residue types, and
sometimes also two values for gap open and gap elongation.
•Profiles provide a sensitive means of detecting distant sequence
Hydropathy plots are designed to display the distribution of polar and
apolar residues along a protein sequence.
A positive value indicates local hydrophobicity and a negative value
suggests a water-exposed region on the face of a protein.
Hydropathy plots are generally most useful in predicting transmembrane
segments, and N-terminal secretion signal sequences.
Sliding Window Approach
Sum amino acid property (e.g. hydrophobicity values) in a given
Plot the value in the middle of the window
I L I K E I R
4.50+3.80+4.50-3.90-3.50+4.50-4.50 = 5.40 => 5.4/7=0.77
Move to the next position in the sequence
L I K E I R Q
+3.80+4.50-3.90-3.50+4.50-4.50 – 3.50 = => -2.6/7=-0.37
The window size can be changed. A small window produces "noisier" plots that
more accurately reflect highly local hydrophobicity.
A window of about 19 is generally optimal for recognizing the long hydrophobic
stretches that typify transmembrane stretches.
Transmembrane proteins are integral membrane proteins that interact
extensively with the membrane lipids.
Nearly all known integral membrane proteins span the lipid bilayer
Hydropathy analysis can be used to locate possible transmembrane
The main signal is a stretch of hydrophobic and helix-loving amino acids
Transmembrane Helices (2)
In a α-helix the rotation is 100 degrees per amino acid
The rise per amino acid is 1,5 Å
To span a membrane of 30 Å approx. 30/1,5 = 20 amino acids are
Antibodies are a powerful tool for life science research
They find multiple application in a variety of areas including biotechnology,
medicine and diagnosis.
Antibodies can recognize either linear or 3D epitopes
There are rules to predict what peptide fragments from a protein are likely
to be antigenic
1. Antigenic peptides should be located in solvent accessible
regions and contain both hydrophobic and hydrophilic residues
• Determine solvent accessibility in case 3D coordinates are
• If you have only a sequence, predict the accessibilities.
2. The peptide should also adopt a conformation that mimics its
shape when contained within the protein.
• Preferably select peptides lying in long loops connecting
secondary structure motifs.
• Neither the peptide stand-alone, nor the peptide in the full protein
should be helical.
Rules of thumb in antigenic prediction
•N- and C- terminal peptides sometimes work better than peptides
elsewhere in the protein.
•Avoid peptides with internal sequence repeats or near repeats.
•Avoid sequences that look funny (i.e. avoid low complexity sequences).
•Try to avoid prolines and cysteines.
•Last, but not least, use antigenicity prediction programs.
Proteins have intrinsic signals that
govern their transport and
localization in the cell (nucleus, ER,
Specific amino acid sequences
determine whether a protein will
pass through a membrane into a
particular organelle, become
integrated into the membrane, or be
exported out of the cell.
Signal Peptides (2)
The common structure of signal peptides from various proteins is
• a positively charged (N-terminal) n-region
• followed by a hydrophobic h-region (which can adopt an α-helical
conformation in an hydrophobic environment)
• and a neutral but polar c-region (cleavage region; the signal
sequence is cleaved off here after delivering the protein at the
The (-3, -1) rule states that the residues at positions –3 and –1 (relative to
the cleavage site) must be small and neutral for cleavage to occur
Signal Peptides (3)
22.6 aa 25.1 aa 32.0 aa
n-regions only slightly Arg-rich Lys+Arg-rich
slightly longer, less
very long, less
c-regions short, no pattern short, Ser+Ala-rich longer, Pro+Thr-rich
small and neutral
almost exclusively Ala
+1 to +5 region no pattern rich in Ala, Asp/Glu, and Ser/Thr
Prediction of Signal Peptides
Prokaryotes and Eukaryotes:
Specific localization signals:
PredictNLS - Nuclear Localization Signals
ChloroP – Chloroplast transit peptides
NetNes – Nuclear Export Signals
Repeats in proteins
•Although they are usually found in non-coding genomic regions, repeating
sequences are also found within genes.
•Ranging from repeats of a single amino acid, through three residue short
tandem repeats (e.g. in collagen), to the repetition of homologous domains
of 100 or more residues.
•Duplicated sequence segments occur in 14 % of all proteins, but
eukaryotic proteins are three times more likely to have internal repeats
than prokaryotic proteins
Prediction of Repeats
• Repsim (a database of simple repeats)
• Rep (Searches a protein sequence for repeats)
• RADAR (Rapid Automatic Detection and Alignment of Repeats in
• REPRO (De novo repeat detection in protein sequences)
The coiled-coil is a ubiquitous protein motif that is often used to control
It is found in many types of proteins, including transcription factors, viral
fusion peptides, and certain tRNA synthetases.
Most coiled-coil sequences contain heptad repeats - seven residue
patterns denoted abcdefg in which the a and d residues (core positions)
are generally hydrophobic.
A number of programs are available to predict coiled-coil regions in a
protein: COILS, PAIRCOILS, MULTICOILS.