Protein Structure

1. Protein Structure • July 1, 2007 • Learning objectives-Understand the basis of the secondary structure prediction program-Psi-PRED. Introduce the concept of a neural network and how a neural network can help to predict secondary structure. Become familiar with motif finding programs. Become familiar with the Protein Data Bank. • Workshop-Analysis of p53 with Psi-PRED and analysis of PTEN protein with BLIMPs.

2. Some Prediction Methods • ab initio methods • Based on physical properties of aa’s and bonding patterns • Statistics of amino acid distributions in known structures • Chou-Fasman • Neural networks

3. Psi-BLAST Predict Secondary Structure (PSIPRED) • Three stages: • 1) Generation of sequence profile • 2) Prediction of initial secondary structure • 3) Filtering of predicted structure

4. PSIPRED • Uses multiple aligned sequences for prediction. • Software program was create using sequence weights derived from a training set of folds with known structures. • Used a two-stage neural network to predict structure based on position specific scoring matrices generated by Psi-BLAST (Jones, 1999) • First network converts a window of 15 aa’s into a raw score of h,e (sheet), c (coil) or terminus • Second network filtered the first output. For example, an output of hhhhehhhh might be converted to hhhhhhhhh. • Obtained a Q3 value of 70-78% (may be the highest achievable)

5. What is Q3? SPredicted Output x 100 SActual Output Q3 =

6. Neural networks • Computer neural networks are based on simulation of adaptive • learning in networks of real neurons. • Neurons connect to each other via synaptic junctions which are either • stimulatory or inhibitory. • Adaptive learning involves the formation or suppression of the right • combinations of stimulatory and inhibitory synapses so that a set • of inputs produce an appropriate output.

7. Neural Networks (cont. 1) • The computer version of the neural network involves • identification of a set of inputs (in this case, • amino acid sequence) which transmit through a network of • connections. • At each layer, inputs are numerically • weighted and the combined result passed to the next • layer. • Ultimately a final output of helix, sheet or • coil, is produced.

8. PSIPRED 90% of training set was used (known structures) 10% was used to evaluate the performance of the neural network during the training session.

9. PSIPRED • During the training phase, selected sets of proteins of known structure were scanned, and if the decisions were incorrect, the input weightings were adjusted by the software to produce the desired result. • Training runs were repeated until the success rate is maximized. • Careful selection of the training set is an important aspect of this technique. The set must contain a wide a range of different fold types without duplications of structural types that may bias the decisions.

10. PSIPRED • An additional component of the PSIPRED procedures involves sequence alignment with similar proteins. • The rationale is that some amino acids positions in a sequence contribute more to the final structure than others. (This has been demonstrated by systematic mutation experiments in which each consecutive position in a sequence is substituted by a spectrum of amino acids. Some positions are remarkably tolerant of substitution, while others have unique requirements.) • To predict secondary structure accurately, one should place little weight on the tolerant positions, which clearly contribute little to the structure, and strongly emphasize the intolerant positions.

11. Provides info on tolerant or intolerant positions Row specifies aa position 15 groups of 21 units (1 unit for each aa plus one specifying the end) Filtering network 4 4 three outputs are helix, strand or coil

12. Example of Output from PSIPRED PSIPRED PREDICTION RESULTS Key Conf: Confidence (0=low, 9=high) Pred: Predicted secondary structure (H=helix, E=strand, C=coil) AA: Target sequence Conf: 923788850068899998538983213555268822788714786424388875156215 Pred: CCEEEEEEEHHHHHHHHHHCCCCCCHHHHHHCCCCCEEEEECCCCCCHHHHHHHCCCCCC AA: KDIQLLNVSYDPTRELYEQYNKAFSAHWKQETGDNVVIDQSHGSQGKQATSSVINGIEAD 10 20 30 40 50 60

13. Recognizing motifs in proteins. • PROSITE is a database of protein families and domains. • Most proteins can be grouped, on the basis of similarities in their sequences, into a limited number of families. • Proteins or protein domains belonging to a particular family generally share functional attributes and are derived from a common ancestor.

14. PROSITE Database • Contains 1087 different proteins and more than 1400 different patterns/motifs or signatures. • A “signature” of a protein allows one to match a protein to a specific function based on structure and/or function. • An example of an entry in PROSITE is: http://ca.expasy.org/cgi-bin/nicedoc.pl?PDOC50020

15. A-T-H-[DE]-X-V-X(4)-{ED} This pattern is translated as: Ala, Thr, His, [Asp or Glu], any, Val, any, any, any, any, any but not Glu or Asp How are the profiles constructed in the first place? Sequences are aligned manually by expert in field. Then a profile is created. ALRDFATHDDVCGK.. SMTAEATHDSVACY.. ECDQAATHEAVTHR..

16. Example of a PROSITE record ID ZINC_FINGER_C3HC4; PATTERN. PA C-X-H-X-[LIVMFY]-C-X(2)-C-[LIVMYA]

17. PROSITE Database • FindProfile is a program that searches the Prosite database. It uses dynamic programming to determine optimal alignments. If the alignment produces a high score, then the match is given. • If a “hit” is obtained the program gives an output that shows the region of the query that contains the pattern and a reference to the 3-D structure database if available.

18. Example of output from FindProfile

19. Other algorithms that search for protein patterns. • BLIMPs-A program that uses a query sequence to search the BLOCKs database. (written by Bill Alford) • BLOCKs- database of multiply aligned ungapped segments corresponding to the most highly conserved regions of proteins. • The blocks that comprise the BLOCKs Database are made automatically by searching for the most highly conserved regions in groups of proteins documented in the Prosite Database and other databases.

20. Example of entry in BLOCKS database Median of standardized scores for true positives Min and max dist to next block Family description ID p99.1.2414; BLOCK AC BP02414A; distance from previous block=(29,215) DE PROTEIN ZINC-FINGER NUCLEAR FIN BL LCC; width=27; seqs=8; 99.5%=1080; strength=1292 RPT1_MOUSE|P15533 ( 101) EKLRLFCRKDMMVICWLCERSQEHRGH 62 Y129_HUMAN|Q14142 ( 30) RVAELFCRRCRRCVCALCPVLGAHRGH 100 RFP_HUMAN|P14373 ( 101) EPLKLYCEEDQMPICVVCDRSREHRGH 49 RFP_MOUSE|Q62158 ( 110) EPLKLYCEQDQMPICVVCDRSREHRDH 51 RO52_HUMAN|P19474 ( 97) ERLHLFCEKDGKALCWVCAQSRKHRDH 54 RO52_MOUSE|Q62191 ( 101) EKLHLFCEEDGQALCWVCAQSGKHRDH 52 TF1B_HUMAN|Q13263 ( 215) EPLVLFCESCDTLTCRDCQLNAHKDHQ 65 TF1B_MOUSE|Q62318 ( 216) EPLVLFCESCDTLTCRDCQLNAHKDHQ 65 Sequence weight (higher number is more distant) Start position of the sequence segment

21. How does BLIMPS search the BLOCKS database? • It transforms each BLOCK into a position specific scoring matrix (PSSM). • Each PSSM column corresponds to a BLOCK position and contains values based on frequency of occurrence at that position. • A comparison is made between the query sequence and the BLOCK by sliding the PSSM over the query. • For every alignment each sequence position receives a score. • This sliding window procedure is repeated until all PSSMs are used.

22. Example of a pattern search using BLIMPS Note that any score less than 1000 may be due to chance. The score above 1000 is a score that is better than 95.5% of the true negatives.

23. 3D structure data • The largest 3D structure database is the Protein Database • It contains over 15,000 records • Each record contains 3D coordinates for macromolecules • 80% of the records were obtained from X-ray diffraction studies, 16% from NMR and the rest from other methods and theoretical calculations

24. Part of a record from the PDB ATOM 1 N ARG A 14 22.451 98.825 31.990 ATOM 2 CA ARG A 14 21.713 100.102 31.828 ATOM 3 C ARG A 14 22.583 101.018 30.979 ATOM 4 O ARG A 14 22.105 101.989 30.391 ATOM 5 CB ARG A 14 21.424 100.704 33.208 ATOM 6 CG ARG A 14 20.465 101.880 33.215 ATOM 7 CD ARG A 14 20.008 102.147 34.637 ATOM 8 NE ARG A 14 18.999 103.196 34.718 ATOM 9 CZ ARG A 14 18.344 103.507 35.833 ATOM 10 NH1 ARG A 14 18.580 102.835 36.952 ATOM 11 NH2 ARG A 14 17.441 104.479 35.827