Protein Functional Site Prediction

Protein Functional Site Prediction

• The identification of protein regions responsible for stability and function is an especially important post-genomic problem

• With the explosion of genomic data from recent sequencing efforts, protein functional site prediction from only sequence is an increasingly important bioinformatic endeavor.

What is a “Functional Site”?

• Defining what constitutes a “functional site” is not trivial

• Residues that include and cluster around known functionality are clear candidates for functional sites

• We define a functional site as catalytic residues, binding sites, and regions that clustering around them.

Protein

Protein + Ligand

Functional Sites (FS)

Regions that Cluster Around FS

Phylogenetic motifs

• PMs are short sequence fragments that conserve the overall familial phylogeny

• Are they functional?

• How do we detect them?

Phylogenetic motifs

• PMs are short sequence fragments that conserve the overall familial phylogeny

• Are they functional?• How do we detect them? • First we design a simple heuristic to find

them• Then we see if the detected sites are

functional

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Whole Tree



Windowed Tree Whole Tree



Partition Metric Score: 6


Phylogenetic Motif Identification

• Compare all windowed trees with whole tree and keep track of the partition metric scores

• Normalize all partition metric scores by calculating z-scores

• Call these normalized scores Phylogenetic Similarity Z-scores (PSZ)

• Set a PSZ threshold for identifying windows that represent phylogenetic motifs

Set PSZ Threshold

Regions of PMs

Map PMs to the Structure




Set PSZ Threshold


Map


Set PSZ Threshold

PMs in Various Structures

PMs and Traditional Motifs

TIM

Phylogenetic Similarity False Positive Expectation

Cytochrome P450


Enolase


Glycerol Kinase


Myoglobin


Evaluating alignments

• For a given alignment compute the PMs

• Determine the number of functional PMs

• Those identifying more functional PMs will be classified as better alignments

Protein datasets

Running time

Functional PMsPAl=blueMUSCLE=redBoth=green

(a)=enolase, (b)ammonia channel,(c)=tri-isomerase, (d)=permease,(e)=cytochrome

Protein Functional Site Prediction

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