Prediction of Local Structure in Proteins Using a Library of Sequence-Structure Motifs

Christopher Bystroff & David Baker

Paper presented by: Tal Blumblum+@cs.cmu.edu

The Approach

• Learn a set of clusters or structure segments that can be identified from short local sequence

• Combine a set of local structural predictions into one whole structure

Methods - Database

• Database of 471 protein sequence families

• By Sander & Schneider 1994

• Each family contains one known sequence structure

• No more than 25% sequence identity between any 2 alignments

• Well determined structures

• Non-membrane proteins

Clustering of Sequence Segments

• Each position in the database is described by a weighted amino acid frequency (Vingron & Argos 1989)

• Similarity between a sequence and a cluster is defined by “Cross-Entropy”:

• Segments of given length (3-15) were clustered via the K-means algorithm

• Unsupervised

Assessing Structure within a clusterand Choice of Paradigm

• Structural similarity between 2 peptide structure segments

• S1i->j is the distance between -carbon atoms i

and j in segments S1

• The paradigm for a cluster was chosen from the top 20 segments as the one with the smallest sum of mda/dme values with the others

True/False Boundaries in Structure Space

• Used for the refinement procedure• Find Natural Boundaries• Compute Histograms of dme & mda vs the

paradigm over all segments in the cluster• The boundary was set to the point where the

histogram first dropped to ½ of its maximum• If reached 130o or 1.3Ao the cluster is rejected• Average boundaries is 81o and 89A• 82 cluster were constructed (I-site library)

DMA-MDA for9 residue serine B-hairpin

Iterative Refinement of Clusters

• For each cluster with good boundaries• Clustering increases P(cluster|sequence)• In order to increase P(structure|cluster)• 2 residues are also observed on each side of each

sequence• All segments that are not within the natural boundaries

of the paradigm are removed• The frequency profile of the cluster is calculated• The database is searched using the new profile and

the highest 400 scored sequences are the new cluster

Cross-Validation and confidence

• A 10 fold cross validation was performed• If the 10 paradigm were not structurally the

same or if the 10 runs did not converge to the same profile then the cluster was rejected

• If the cluster was not rejected a confidence curve was computed as a function of the Dpq sequence to cluster similarity.

• This enables to compare different profile lengths and incorporates P(clust|seq) and P(struct|clust)

Confidence for Similarity

Clustering – What do we want?

• Direction: Sequence -> Structure• We want to as separated as possible cluster of

sequences so that given a test sequence we can assign it to 1 cluster

• Each cluster should have 1 or a few possible structures. Those structures will be used to predict the test protein structure

• P(struct|seq) = cluster P(struct|clust,seq)*P(clust|seq)

= P(struct|clust)* P(clust|seq)

Iterative Peak Removal

• Similar Sequences can map to different structures in some cases

• When this happens, the predominant pattern occludes the second one

• To find those clusters the refinement was performed using subset of the data that excludes the other class members

• This helped identifying two distinct -C-cap extensions which were very similar in sequence

Cluster Weights

• The prediction accuracy is improved by weighting the confidence curves

• Iterative update was used

• Where F+C are the false positive of cluster

C and F-C are the false negative errors

Prediction Protocol

• Given a sequence to predict:

1. Submit the sequence to PHD (Rose 94) to obtain a set of multiple aligned sequences and hence a profile

2. Each segment of the profile is scored against each of the 82 clusters to produce weighted confidences

3. Confidences are sorted

4. The first segment assigns & from its paradigm

5. For all the subsequent segments in the sorted list the prediction is used if it doesn’t conflict with previously assigned &

Results

• Reported on the training set and on 55 independent protein family set

• Local evaluation is measured by agreement over 8 residue window

• 8 residue segment prediction is considered to be correct if non of the & differences is larger than 120o or if the rmsd between the correct and predicted structure was less than 1.4A

• An error is counted per position iff all 8 overlapping segments are incorrect

• Mda is stricter than the commonly used Q3 score

Results

• Training Set– 471 sequences -> 122,510 residues– 95% of 471 had 1 match ¸ 0.8 confidence– 40% of the residues had confidence ¸ 0.6 and

were 71%(mda) correct

Results

Combinations of I-sites and conventional Secondary Structure

Predictions • With the PHD program• Requires translation into Sec Structure or from

SS into torsion angles• Every program performed better in it’s pwn

domain• 64% Q3 because of under predicting loops and

over predicting strands• I-site was much better in loops and specific

angles of turns• Can compliment PHD

Comparison of I-Site & PHD

I-site library

• 82 cluster represents 13 structural motifs

Summary of the I-site library

Conclusions

• Method is fast – requires only profile comparisons

• There is a measure of “confidence” in the prediction

• They do not provide accuracy over the whole protein

• Believe that the strong local sequence-structure relationships (that occur more than 30 times) are present in I-site

Discussion

• NMR studies of isolated peptides of less than 30 residue show that the peptides do not have a well defined structure. The I-site motif are the exceptions

• It might be that the motifs are the areas that adopt structure independence to the rest of the protein

• An extension might be context specific motifs

2 Approaches for global scoring functions

• Derived from the protein Database– Large # of parameters– Complicated

• Potentials– Based on Chemical Intuitions– Simpler– Clearer insights into sequence/structure relations

• They chose the Database approach– Because of the dangers of crafting a measure for a

specific protein family rather than for the whole DB

Scoring Functions

)|()(

)(

)|()()|(

StructureSequencePStructureP

SequenceP

StructureSequencePStructurePSequenceStructureP

• P(Seq|Str) is used when computing sequence profiles for motifs

• P(Structure) is hardest to estimate and contains most of the non-local interactions.

• For ab-initio, P(Structure) captures the features that distinguish folded structures from random chain (local) configurations.

Structure oft independen is )P(Sequence since

Radius of gryation2

Scoring Function• Measures the largest radius from the

center of the fold

Radius of gryation2

Scoring Function• Advantages

– Non-dependent on alpha-beta decomposition - since the generated structures is made from segments of real proteins its alpha-beta decomposition much like of real proteins

• Disadvantages– Structures with beta paired strands are no

more probable than those of unpaired beta strands