Structural Alignments (Phenylketonuria)

Summary

Structural alignments are used to determine the functional and evolutionary relationships between protein structures. <ref name="struc_align"> Walter Pirovano, K Anton Feenstra and Jaap Heringa (2008): "The meaning of alignment: lessons from structural diversity". BMC Bioinformatics Vol.9:556. doi:10.1186/1471-2105-9-556 </ref> In this task, we first generated a dataset of different related and unrelated structures to our protein sequence (PAH). Subsequently, we used different methods and measurements to quantify structural similarity between the given structures. Then, we generated structural alignments for the evaluation of some sequence-based alignments of Task 2. The results and appendant discussions are shown below.

Explore structural alignments

Lab journal

Dataset generation

Our protein (PAH) has the CATH Code 1.10.800.10 (Phenylalanine Hydroxylase). We used, for the generation of the dataset, similar and dissimilar structures to this protein. Thus, we added the following structures into it:

• reference structure of PAH: 2PAH (96,41% identity)
• identical sequence with filled binding site: 1LRM (100% identity --> pdb entry: looked at 3D structure and saw two filled binding site with the ligands: FE and HBI)
• identical sequence with unfilled binding site: not found anyone
• low sequence identity: 3LUY (32,2% - no pdb ID under 30%)
• high sequence identity: pdb ID: 2PHM (89,7%)
• CAT: 1J8U (CATH Code: 1.10.800.10) - there is no other category than this for CAT
• CA: 2B5U (CATH Code: 1.10.287.620)
• C: 3BQO (CATH Code: 1.25.40.210)
• other CATH category: 1V8H (CATH Code: 2.60.40.10)

Now we want to apply different structural alignment methods with this dataset. In this case, each structure has only to be superimposed on the reference structure and not on the other structures too.

Pymol

Pymol is a python-enhanced and open source molecular visualization tool. It is particularly suitable for 3D visualization of proteins and small molecules as well as their density, surfaces and trajectories. It also includes molecular editing like aligning or superimposition of two molecules. <ref> http://sourceforge.net/projects/pymol/ short Pymol summary, retrieved June 02, 2013 </ref>

// TODO: pictures of one or two structures with defined binding site:

1. with all atoms
2. only C-alpha
3. only binding site

What changes and why?

LGA

The LGA (Local-Global Alignment) method affords the possibility to compare fragments or whole protein structures in sequence dependent and independent modes. The generated data can successfully be used in a scoring function to rank two structures related to the level of similarity between them. It allows structure classification when many proteins are analyzed, as well as clustering of similar protein structure fragments. <ref name="lga"> Adam Zemla (2003): "LGA: a method for finding 3D similarities in protein structures". Nucleic Acids Research Vol.31(13):3370-3374. doi:10.1093/nar/gkg571 </ref>

SSAP / CATHEDRAL (used by CATH)

SSAP ...
uses Cβ

TopMatch

TopMatch ...
uses Cα

SAP or CE

SAP? ->Error!
->
CE
CE-PDB ...

Modelling scores

To compare the different models, the RMSDs (root-mean-square deviation) are compared. In TopMatch the same formular is taken but called Er (root-mean-square error). The RMSD gives the squared distance between corresponding positions of two superimposed proteins in Ångström. The results are shown in <xr id="rmsd"/>. <figtable id="rmsd">

RMSD results
Method	1lrm	3luy	2phm	1j8u	2b5u	3bqo	1vh8
LGA-RMSD	0.81	3.30	0.88	0.73	3.07	3.59	3.42
SSAP-RMSD	0.99	18.77	1.24	1.02	39.16	22.39	7.27
TopMatch-Er	0.60	1.98	0.81	0.63	1.21	1.12	3.25
CE-RMSD	0.65	5.13	0.95	0.68	4.06	4.68	5.92

Root-mean-square deviation/error in Ångström for the four protein structure alignment predictors LGA, SSAP, TopMatch and CE.

</figtable>

• lowest RMSDs: TopMatch
• LGA and CE sometimes the one sometimes the other is better. For very similar structures CE better, otherwise LGA???
• worst/highest RMSDs: SSAP, maybe the Cβ are more distant???

• careful about the sidechains: here always 2pah.A as query is taken and xx.A as target

...

Evaluate sequence alignments

Lab journal
<figtable id="model_rmsd">

LGA and hhsearch results
	LGA				hhsearch
pdb	RMSD	LGA_S	LGA_Q	seq_id	probability	e-value	identities(%)
1phz	0.83	90.65	32.44	99.34	100.00	6.9e-165	92
1j8u	0.73	90.29	35.83	99.67	100.00	3.1e-135	100
2v27	1.70	62.77	12.55	96.02	100.00	3.6e-74	32
2qmx	3.18	7.46	1.25	4.88	98.20	1.1e-09	36
3luy	2.82	7.17	1.24	13.89	98.07	3.3e-09	22
1qey	0.64	3.65	1.63	0.00	54.00	3.4	67
1wyp	2.67	8.43	1.37	0.00	29.42	15	19
1a6s	3.15	6.93	1.08	11.43	20.59	29	36

...

</figtable>

• last two have a very low probability...

References

<references/>