Canavan Task 2 - Sequence alignments

Sequence Search

Sorry, guys, we're a bit behind schedule! Hope to have everything finished before 11pm tonight (Monday) and hope that's early enough for you to read. Sorry again! Susi and Fanny

Meh, take your time, not like we aren't busy with our introductory talk ;) - jonas

Sequence

The native ASPA sequence that we used for the current task is shown below:

UniProt: P45381

>hsa:443 ASPA, ACY2, ASP; aspartoacylase; K01437 aspartoacylase [EC:3.5.1.15] (A)
MTSCHIAEEHIQKVAIFGGTHGNELTGVFLVKHWLENGAEIQRTGLEVKPFITNPRAVKK
CTRYIDCDLNRIFDLENLGKKMSEDLPYEVRRAQEINHLFGPKDSEDSYDIIFDLHNTTS
NMGCTLILEDSRNNFLIQMFHYIKTSLAPLPCYVYLIEHPSLKYATTRSIAKYPVGIEVG
PQPQGVLRADILDQMRKMIKHALDFIHHFNEGKEFPPCAIEVYKIIEKVDYPRDENGEIA
AIIHPNLQDQDWKPLHPGDPMFLTLDGKTIPLGGDCTVYPVFVNEAAYYEKKEAFAKTTK
LTLNAKSIRCCLH

---

Search

BLASTP

We ran BlastP on student machines with the big_80 as a reference database.

Command: blastall -p blastp -d /mnt/project/pracstrucfunc12/data/big/big_80 -i P45381_wt.fasta -o blastp_p45381_wt_big80.out

Parameters	default E-Value = 10	E-Value 10e-10
results	196	94
best E-Value	1e-155	1e-155
worst E-Value	9.6	e-15
comment	Most of the resulting proteins are Aspartoacylases of other species. Most of the results with EValue > e-15 are Succinylglutamate Desuccinylases, which catalyze a reaction similar to Aspartoacylase.	The results are the same as for the first run, just with an earlier cutoff

PSIBLAST

PSIBlast was used in the same fashion as BLAST, with the big_80 as the background database. Commands:

• Running 2 iterations and default E-Value 0.002
  • blastpgp -d /mnt/project/pracstrucfunc12/data/big/big_80 -i P45381_wt.fasta -o psiblast_it2_p45381_wt_big80.out -j 2

• 2 iterations, more strict E-value cutoff of 10E-10
  • blastpgp -d /mnt/project/pracstrucfunc12/data/big/big_80 -i P45381_wt.fasta -o psiblast_it2_h10e10_p45381_wt_big80.out -j 2 -h 10e-10

• 10 iterations, default Evalue 0.002
  • blastpgp -d /mnt/project/pracstrucfunc12/data/big/big_80 -i P45381_wt.fasta -o psiblast_it10_p45381_wt_big80.out -j 10

• 10 iterations, E-value cutoff 10E-10
  • blastpgp -d /mnt/project/pracstrucfunc12/data/big/big_80 -i P45381_wt.fasta -o psiblast_it10_h10e10_p45381_wt_big80.out -j 10 -h 10e-10

Parameters	it2, def E-Value (0.002)	it2 E-Value 10e-10	it10 def E-Value (0.002)	it10 E-Value 10e-10
time	~2m30	~2m30	~10m	time: ~10m
results	500	93	500	500
best E-Value	1e-142	1e-145	5e-70	7e-70
worst E-Value	3e-4	2e-29	8e-38	1e-38
comments	Results with best EValues are mostly Aspartoacylases, Sequences previously not found are mostly Succinylglutamate Desuccinylases	results mainly Aspartoacylases	- converged after 8 rounds - most significant results include more Succinylglutamate Desuccinylases than Aspartoacylases	- all 10 iterations were done (no early convergence) - aspartoacylases slightly more frequent in lower E-Values (< E-58), but no significant difference in E-Values for aspas and succis

HHBLITS

Run HHBlits on student machines with Uniprot20 database.

Commands:

• 2 iterations:
  • hhblits -i P45381_wt.fasta -d /mnt/project/pracstrucfunc12/data/hhblits/uniprot20_current -o hhblits_p45381_def.out

• 8 iterations:
  • hhblits -i P45381_wt.fasta -d /mnt/project/pracstrucfunc12/data/hhblits/uniprot20_current -n 8 -o hhblits_p45381_n10.out

-n number of iterations (def 2)

Parameters	it 2	it 8
time	2m50	~6m
results	274	500
best E-Value	2e-110	2.9e-68
worst E-Value	0.0011	9.5e-09
comment	mixed results with Aspartoacylases and Succi	very varying results: Aspartoacylasen, Succinylasen, Zinc Proteins

Summary and Comparison

Along with the expactations one can find more hits with Psi-Blast than with a simple Blast search.

In general, one can distinguish between two kinds of proteins, that frequently are identified by the sequence searches:

• Aspartoacylases
• Succinylglutamate Desuccinylases

BlastP

A simple blast search yields only about 90 significant hits if one considers a threshold of 10e-10 as a significance cutoff. As one can see in Figure ??, the restriction of the E-Value results in less hits with a low sequence similarity.

Comparison of distribution of Sequence Identiy between the two BlastP runs

Psi Blast

Increasing the amount of iterations performed in a PSI-Blast search, obviously increases the running time. One can see, that the best ranked hits of the runs with 10 iterations have lower E-Values than the best hits of the runs with less iterations. Yet, the result includes a larger amount of significant hits with higher E-Values. This means, increasing the iterations finds further distantly related sequences, which is the expected outcome. This outcome is also represented in the distribution of sequence identities. As one can see in figure ??, running PSI-Blast with 10 iterations results in hits with a lower sequence identity to our query sequence than the hits from the run with 2 iterations.

When restricting the E-Value Cutoff for the profile built-up, we found that more hits are classified as Aspartoacylases than as Succinylglutamate Desuccinylases. The running time, as well as the E-Values of the resulting hits did not change significantly. The majority of the results from the runs with only two iterations, has moderate sequence identities with a broad distribution between 10% and 50%. In contrast, the results from the run with 10 iterations split up into two groups of hits which form cluster at about 15% and 35% sequence identity. This difference is also represented in the E_Value distribution. The runs with 10 iterations result in Hits with moderate E_Values between -200 and -40 log(E_Values). The runs with 10 iterations in contrast result in many low significant hits (log(E_Value > -20)) and a variety of high significant hits.

Figure ?? Distribution of Sequence Id between Psi-Blast runs with 2 iterations vs 10 iterations (using E-Value 10e-10)

Figure ?? Distribution of Sequence Id for Psi-Blast runs with 2 iterations with different E-Values (def E-Value vs E-Value of10e-10)

Figure ?? Distribution of Sequence Id for Psi-Blast runs with 10 iterations with different E-Values (def E-Value vs E-Value of10e-10)

Figure ?? Distribution of logarithmic E_Values for the four different PSIBlast runs

HHBlits

Running HHBlits with 2 iterations yields a small amount of hits (270) with very low (2e-110) and very high (0.0011) E-Values. To increase the amount of hits, we repeated the HHBlits search with the maximum amount of 8 iterations which resulted in a broader output with more Hits with lower averaged E-Values (compare figure ??). Regarding the Sequence Identity distribution, running HHBlits with 8 iterations results in more distant related Hits (see Figure ??).

Figure ?? Sequence identity distributions of HHBlits run with 2 and with 8 iterations.

Figure ?? logarithmic E_Value distributions of HHBlits run with 2 and with 8 iterations.

Overlap

As one can see in Figure ??, roughly 40 percent of the resulting hits are unique to each method. From our considerations, about 25 percent of the hits are significant hits, that could be further investigated (overlap of 50 percent).

Figure ??
Distribution of overlapping Hits for the eight different used Sequence Searches.

Default E-Values: as could be expected, the normal BLAST search is mostly contained in the PsiBLAST search with two iterations. HHBlits found a large number of different hits, with only 48 out of 274 common hits in common with the BLAST searches.

Taking PsiBLAST with 10 iterations into account brings in a large number of common sequences among the three searches (110), which could be interesting since there seems to be high conversation among them.

Strict E-Values for PsiBLAST and default E-Value for HHBlits with 2 iterations: The number of common hits among all three is now substantially lower, while PsiBLAST with two and ten iterations share a great number of their hits.

Increasing the number of HHBlits-iteration yields more hits for HHBlits, but does not increase the number of common hits with PSI-Blast in 2 or 10 iterations. However, 10 sequences are common and could be interesting for further investigation.

Further Evaluation

We tried to further validate the sequence search hits via structural similarity. Unfortunately none of the resulting Hits was a PDB Hit. Furthermore we tried to map the sequence identifiers against the UniProtKB/Swiss-Prot PDB cross-references (http://www.uniprot.org/docs/pdbtosp.txt). Again, this mapping yielded no results, which is why we cannot include any structural information for our ongoing research. When inspecting the annotation for the sequence hits, we already found, that the majority of the hits codes for Aspartoacylases or respectively the highly related protein Succinylglutamate Desuccinylases. Since there already exists a crystal structure of the human Aspartoacylase, it is only reasonable that one will not find other structures for this class of proteins. Additionally, a huge amount of hits codes for not yet characterized proteins, which also will hardly be an interesting target for crystallization.

Multiple Sequence Alignments

For generating our dataset for the MSA we clustered all Hits into Sequence Identity groups:

• >90%: 1
• 60-89%: 59
• 40-59%: 197
• 20-39%: 1141

Since we only got one hit with an sequence Identity >90% we decided to group out hits as follows: three groups of sequences with eight members each:

• 60-99%
• 40-59%
• 20-39%

We chose those hits from the respective groups, that have been found by at least 4 methods (overlap of 50%).

id                     eVal  identity coverage alignment_length

id                     eVal  identity coverage alignment_length
# 60-99% sequence identity

Q8BZC2 1.7e-25 90 E1BVP5 e-140 72 H2RVG4 e-141 63 G3VM93 e-105 72 F6ZFQ0 e-139 78 F8WFU8 e-145 86 Q28C61 e-132 68 H2M5L4 e-133 64

# 40-59% sequence identity

G5BTW1 e-133 43 G6FRX8 e-103 39 F7NV91 e-112 39 G1Q6P7 e-120 42 H0WH68 e-135 44 F2PFG6 e-119 40 H2MX25 5e-81 40 Q1Z2X2 e-115 38

1. 20-39% sequence identity

Q2F9Q7 e-109 31 Q8YQC1 e-117 41 E1SMZ8 e-108 39 D7E1T3 e-110 36 A5GQV1 7e-92 33 E8LP14 e-107 31 F9TUZ3 e-106 30 A6VUE4 e-101 35

General Results

When comparing the three MSAs one can identify some conserved regions. Especially the two groups with sequence identities <60% show very similar MSAs.

There are three strongly conserved motivs:

• GGTHGNE
• DLNR
• DLHNT

ClustalW

command: clustalw -align -infile=./db_over60.fa -outfile=./clustalw_msa_60.aln

In the alignment of the >60% group the first two motifs are not colored in the alignment. This is due to two very short sequences which produce gaps in the alignment and thus lower the consensus.

Jalview Representation of the Muscle Alignment with the dataset with 20-39% sequence identity. Colored are conserved residues (>65%).

Jalview Representation of the Muscle Alignment with the dataset with 40-59% sequence identity. Colored are conserved residues (>65%).

Jalview Representation of the Muscle Alignment with the dataset with 60-100% sequence identity.Colored are conserved residues (>65%).

Muscle

When comparing the three MSAs one can identify some conserved regions. Especially the two groups with sequence identities <60% show very similar MSAs.

There are three strongly conserved motivs:

• GGTHGNE
• DLNR
• DLHNT

In the alignment of the >60% group the first two motifs are not colored in the alignment. This is due to two very short sequences which produce gaps in the alignment and thus lower the consensus.

Jalview Representation of the Muscle Alignment with the dataset with 20-39% sequence identity. Colored are conserved residues (>65%).

Jalview Representation of the Muscle Alignment with the dataset with 40-59% sequence identity. Colored are conserved residues (>65%).

Jalview Representation of the Muscle Alignment with the dataset with 60-100% sequence identity.Colored are conserved residues (>65%).