Transcript Comparación de secuencias

Comparación de secuencias
(Sequence comparison)
Objetivo
• Aprovechar información funcional y/o
estructural identificando homología entre
secuencias
• Diferencia entre Homología e identidad
• Dos secuencias se consideran homólogas
cuando:
• Tienen el mismo origen evolutivo
– Tienen función y estructura similares
• Homologous sequences - sequences that share a common
evolutionary ancestry
• Similar sequences - sequences that have a high percentage of
aligned residues with similar physicochemical properties
(e.g., size, hydrophobicity, charge)
IMPORTANT:
• Sequence homology:
• An inference about a common ancestral relationship, drawn when
two sequences share a high enough degree of sequence similarity
• Homology is qualitative
• Sequence similarity:
• The direct result of observation from a sequence alignment
• Similarity is quantitative; can be described using percentages
• Ejercicio
Nuestras proteínas son una
minoría
Proteínas posibles de 50
Aminoácidos ?
•
•
•
•
•
MALRTGGPAL VVLLAFWVAL GPCHLQGTDP GASADAEGPQ CPVACTCSHD
MRCAPTAGAA LVLCAATAGL LSAQGRPAQP EPPRFASWDE MNLLAHGLLQ
5020: 100000000000000000000000000000000 proteínas posibles
Proteínas distintas que existen en la naturaleza: unas 200.000
Porcentaje de reales sobre posibles:
0.0000000000000000000000002% (o sea nada, prácticamente)
Más definiciones
• Orthologs: secuencias que corresponden
exactamente a la misma
función/estructura en organismos distintos
• Paralogs: secuencias producto de
duplicaciones en un mismo organismo.
Normalmente implican cambios de
función.
ORTHOLOGS AND PARALOGS INTO LOCUS ß FROM GLOBINS
orthologs
paralogs
Homology and prediction
• Very divergent protein sequences may
suport similar structures
• Similar protein structures will probably
have related or similar functions
3D STRUCTURE VERSUS SEQUENCE
Sequence alignment between human myoglobin,  and
 globins from hemoglobin
Comparison of 3D structures of human myoglobin,  and
 globins from hemoglobin
myoglobin
-globin
-globin
Comparison of 3D structures of human myoglobin,  and
 globins from hemoglobin
myoglobin
-globin
-globin
Homology and prediction
• La comparación de secuencias es el método más simple
para identificar la existencia de homología.
• Identidad > 30% en proteína implica homología
• Identidad > 80-90% es normal en ortólogos de especies
cercanas
• Identidad 10-30%. Si existe homología, es indetectable
(“twilight zone”)
¿DNA o proteína?
• Ambas proporcionan información sobre
homología
• DNA: Solamente la identidad entre bases
es relevante
• Proteína: Existen equivalencia funcional
entre aminoácidos
Apareamientos canónicos
(Watson-Crick)
Unicamente la identidad es relevante
Mismatch costs are not usually used in aligning
DNA or RNA sequences, because no substitution is
"better" than any other (in general)
• Código genético
Pos 1
• Trp, Met (1)
• Leu, Ser, Arg (6)
• resto (2)
• Iniciación AUG
• Stop (3)
Degeneración en
la tercera
posición
XYC = XYU
XYA ~ XYG
Posición 2
Pos 3
U
C
A
G
U
Phe
Phe
Leu
Leu
Ser
Ser
Ser
Ser
Tyr
Tyr
Stop
Stop
Cys
Cys
Stop
Trp
U
C
A
G
C
Leu
Leu
Leu
Leu
Pro
Pro
Pro
Pro
His
His
Gln
Gln
Arg
Arg
Arg
Arg
U
C
A
G
A
Ile
Ile
Ile
Met
Thr
Thr
Thr
Thr
Asn
Asn
Lys
Lys
Ser
Ser
Arg
Arg
U
C
A
G
G
Val
Val
Val
Val
Ala
Ala
Ala
Ala
Asp
Asp
Glu
Glu
Gly
Gly
Gly
Gly
U
C
A
G
Aminoácidos “equivalentes”
• Hidrofóbicos
– Ala (A), Val (V), Met (M), Leu (L), Ile (I), Phe (F), Trp (W), Tyr (Y)
• Pequeños
– Gly (G), Ala (A), Ser (S)
• Polares
– Ser (S), Thr (T), Asn (N), Gln (Q), Tyr (Y)
– En la superficie de la proteína polares y cargados son
equivalentes
• Cargados
– Asp (D), Glu (E) / Lys (K), Arg (R)
• Dificilmente sustituibles
– Gly (G), Pro (P), Cys (C), His (H)
3D visualization of some conserved residues in globin family
(Myoglobin structure)
Prolin in a turn
Histidin
For the hemo
coordination bonds
2 conserved glycines in 2
separate helix crossing each
other
• La secuencia de DNA diverge más rápidamente
– mutación o recombinación altera el DNA pero debe
mantener la función/estructura
• La comparación de proteínas permite localizar
homologías más lejanas
Alineamiento de secuencias
• Medir la homología entre secuencias
requiere un “alineamiento”
Homología alta:
AWTRRATVHDGLMEDEFAA
AWTRRATVHDGLCEDEFAA
Homología baja:
AWTKLATAVVVFEGLCEDEWGG
AWTRRAT---VHDGLMEDEFAA
Tipos alineamiento
• “pairwise”
– Dos secuencias
• Multiple
– Más de dos secuencias
• Global
– Toda la secuencia se considera
• Local
– Unicamente se alinean regiones parecidas
Estrategias
Depende del objetivo
• Comparación de secuencias
– Objetivo: medir homología, identificar
aminoácidos equivalentes
• global, ”pairwise”/múltiple
• Búsqueda en bases de datos
– Objetivo: Identificar homólogos en un
conjunto grande de secuencias
• Local, “pairwise”
Alineamiento manual proteína
• Requiere “oficio”
– Conocer propiedades de aminoácidos
– Conocer la proteína
• Permite incorporar información adicional
– Aminoácidos funcionales
– Aminoácidos necesarios para mantener la estructura
–…
• Es lento y poco reproducible
Alineamiento automático (problema
de optimización)
• Requiere
– un método objetivo de comparar aminoácidos o
bases para “puntuar” el alineamiento (matrices de
comparación)
– algoritmo para encontrar el alineamiento con la
máxima puntuación
• Es reproducible y rápido
• No permite, en general, introducir información
adicional
Tipos de matrices
• Identidad
• Propiedades físico-químicas
• Genéticas (sustitución de codones)
• Evolutivas
La aplicación sucesiva de la matriz PAM permite simular varias generaciones
PAM 40, PAM 100, PAM 250
•Evolutionary distance considered is constant
•Bigger number bigger divergence. Less stringent
Evolutionary distances considered are variable
More modern than PAM but similar results.
Smaller is n bigger divergence. Less stringency
Blosum 62
High Penalty for very
different aminoacids
Small positive score for
changes in similar
aminoacids
Small positive score for
commonaminoacids
Infrequente aminoacids
have high score
¿Which matrix to use??
• No clear answer
• All matrix evaluate functional equivalence
between aminoacids in the light of
evolution and conservation: la
equivalencia funcional entre aminoácidos
Choice of a Matrix!
BLOSUM90
PAM30
Rat versus
mouse protein
BLOSUM80
PAM120
BLOSUM62
PAM180
BLOSUM45
PAM240
Rat versus
bacterial
protein
Query Length
Substitution Matrix
Gap Costs
<35
PAM-30
(9,1)
35-50
PAM-70
(10,1)
50-85
BLOSUM-80
(10,1)
(10,1)
85
BLOSUM-62
PAM Point Accepted Mutatiton
Gaps (inserciones/delecciones)
• Normalmente localizados en loops
AWTKLATAVVVFEGLCEDEWGG
AWTRRAT---VHDGLMEDEFAA
Gaps (inserciones/delecciones)
• Esquemas de puntuación:
– Dependiendo de estructura 2ª
– Valor constante
– Función lineal
go + n.gl
Global versus local alignment
• Global alignment
– Finds best possible alignment across entire length of 2
sequences
– Aligned sequences assumed to be generally similar over entire
length
• Local alignment
– Finds local regions with highest similarity between 2 sequences
– Aligns these without regard for rest of sequence
– Sequences are not assumed to be similar over entire length
Global or Local ?
• 1. Searching for conserved motifs in DNA or protein
sequences?
• 2. Aligning two closely related sequences with
similar lengths?
• 3. Aligning highly divergent sequences?
• 4. Generating an extended alignment of closely
related sequences?
• 5. Generating an extended alignment of closely
related sequences with very different lengths?
Local vs. Global Alignment (cont’d)
• Global Alignment
--T—-CC-C-AGT—-TATGT-CAGGGGACACG—A-GCATGCAGA-GAC
| || | || | | | |||
|| | | | | ||||
|
AATTGCCGCC-GTCGT-T-TTCAG----CA-GTTATG—T-CAGAT--C
• Local Alignment—better alignment to find
conserved segment
tccCAGTTATGTCAGgggacacgagcatgcagagac
||||||||||||
aattgccgccgtcgttttcagCAGTTATGTCAGatc
Comparación de secuencias contra
bases de datos
Secuencia incógnita
ATTVG...LMN
Base de datos
De secuencias
AGLM...WTKR
TCGGLMN..HICG
WRKCPGL
...
Requiere algoritmos de comparación muy rápidos
Diasdvantages from global
alignment
• Slow
• Scores whole sequence
Global alignment server
– Do not recognize multidomain proteins
A
B
A
C
C’
B
D
Alineamiento local
• 10 – 100x más rápidos
• Reconocen dominios individuales
• No proporcionan necesariamente el mejor
alineamiento!
• BLAST, FASTA
Basic Local Alignment Search
Tool
Blast NCBI
Basic Local Alignment Search Tool
Blast NCBI
The Basic Local Alignment Search Tool (BLAST) finds regions of local similarity between
sequences. The program compares nucleotide or protein sequences to sequence
databases and calculates the statistical significance of matches. BLAST can be used
to infer functional and evolutionary relationships between sequences as well as help
identify members of gene families.
Formatos entrada
E parameter (Expected threshold)
•
Expect
The Expect value (E) is a parameter that describes the number of hits one
can "expect" to see just by chance when searching a database of a
particular size. It decreases exponentially with the Score (S) that is
assigned to a match between two sequences. Essentially, the E value
describes the random background noise that exists for matches between
sequences. For example, an E value of 1 assigned to a hit can be
interpreted as meaning that in a database of the current size one might
expect to see 1 match with a similar score simply by chance. This means
that the lower the E-value, or the closer it is to "0" the more "significant" the
match is. However, keep in mind that searches with short sequences, can
be virtually indentical and have relatively high EValue. This is because the
calculation of the E-value also takes into account the length of the Query
sequence. This is because shorter sequences have a high probability of
occuring in the database purely by chance.
E value (Expect)
• E value:
•
Expect: This setting specifies the statistical significance threshold for reporting
matches against database sequences. The default value (10) means that 10
such matches are expected to be found merely by chance, according to the
stochastic model of Karlin and Altschul (1990). If the statistical significance
ascribed to a match is greater than the EXPECT threshold, the match will not be
reported. Lower EXPECT thresholds are more stringent, leading to fewer
chance matches being reported.
Number of
letters in query
Number of letters
in data baseScore
E = K.m.n.e-l.S
•
•
Warning:
 E →  Falsos negativos
Normalization factors
Score
Estadística
• Indice de referencia:
E: número de falsos positivos esperado
• Búsquedas esporádicas: 0.01 – 0.001
• Búsquedas masivas (anotación genoma):
10-6
Programas Blast
•
blastp
– amino acid query sequence vs. protein sequence database
•
blastn
– nucleotide query sequence vs. nucleotide sequence database
•
blastx
– nucleotide query sequence translated in all reading frames vs. protein sequence
database
•
tblastn
– protein query sequence vs. a nucleotide sequence database translated in all
reading frames
•
tblastx
– six-frame translations of a nucleotide query vs. the six-frame translations of a
nucleotide sequence database.
¿Qué programa usar?
• La comparación en proteína permite
ampliar el espectro de búsqueda (aunque
comparemos DNA!)
• Blastn → blastx, tblastx
• Blastp → tblastn
– Degeneración del código genético
– Equivalencia funcional entre aminoácidos
BLAST substitution matrices
•
A key element in evaluating the quality of a pairwise sequence alignment is the
"substitution matrix", which assigns a score for aligning any possible pair of
residues. The theory of amino acid substitution matrices is described in [1], and
applied to DNA sequence comparison in [2]. In general, different substitution
matrices are tailored to detecting similarities among sequences that are
diverged by differing degrees [1-3]. A single matrix may nevertheless be
reasonably efficient over a relatively broad range of evolutionary change [1-3].
Experimentation has shown that the BLOSUM-62 matrix [4] is among the best
for detecting most weak protein similarities. For particularly long and weak
alignments, the BLOSUM-45 matrix may prove superior. A detailed statistical
theory for gapped alignments has not been developed, and the best gap costs
to use with a given substitution matrix are determined empirically. Short
alignments need to be relatively strong (i.e. have a higher percentage of
matching residues) to rise above background noise. Such short but strong
alignments are more easily detected using a matrix with a higher "relative
entropy" [1] than that of BLOSUM-62. In particular, short query sequences can
only produce short alignments, and therefore database searches with short
queries should use an appropriately tailored matrix. The BLOSUM series does
not include any matrices with relative entropies suitable for the shortest queries,
so the older PAM matrices [5,6] may be used instead. For proteins, a provisional
table of recommended substitution matrices and gap costs for various query
lengths is: