1. Prediction of Local Structure in Proteins Using a Library of Sequence-Structure Motifs Christopher Bystroff & David Baker Paper presented by: Tal Blum blum+@cs.cmu.edu

2. 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

3. 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

4. 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

5. Assessing Structure within a cluster and Choice of Paradigm Structural similarity between 2 peptide structure segments  S1 i->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

6. 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 130 o or 1.3A o the cluster is rejected Average boundaries is 81 o and 89A 82 cluster were constructed (I-site library)

7. DMA-MDA for 9 residue serine B-hairpin

8. 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

9. 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 D pq sequence to cluster similarity. This enables to compare different profile lengths and incorporates P(clust|seq) and P(struct|clust)

10. Confidence for Similarity

11. 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)

12. 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

13. 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

14. 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  & 

15. 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 120 o 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

16. 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

17. Results

18. 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

19. Comparison of I-Site & PHD

20. I-site library 82 cluster represents 13 structural motifs

21. Summary of the I-site library

23. 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

24. 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

27. 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

28. Scoring Functions 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.

29. Radius of gryation 2 Scoring Function Measures the largest radius from the center of the fold

30. Radius of gryation 2 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