Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using technique Z then X is better than Y w.r.t. Z Motifs could be functional sites in proteins or functional regions in non- coding DNA

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 Whole Tree

Scan for Similar Trees Whole Tree

Scan for Similar Trees Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 8 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 4 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 8 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 0 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 8 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 0 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

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 PMs to the Structure Map Set PSZ Threshold

Map PMs to the Structure Map Set PSZ Threshold

PMs in Various Structures

PMs and Traditional Motifs

TIM Phylogenetic Similarity False Positive Expectation

TIM Phylogenetic Similarity False Positive Expectation

TIM Phylogenetic Similarity False Positive Expectation

TIM Phylogenetic Similarity False Positive Expectation

Cytochrome P450 Phylogenetic Similarity False Positive Expectation

Cytochrome P450 Phylogenetic Similarity False Positive Expectation

Enolase Phylogenetic Similarity False Positive Expectation

Glycerol Kinase Phylogenetic Similarity False Positive Expectation

Glycerol Kinase Phylogenetic Similarity False Positive Expectation

Myoglobin Phylogenetic Similarity False Positive Expectation

Myoglobin Phylogenetic Similarity False Positive Expectation

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 PMs PAl=blue MUSCLE=red Both=green (a)=enolase, (b)ammonia channel, (c)=tri-isomerase, (d)=permease, (e)=cytochrome