1. Chapter 17 Prediction, Engineering and Design of Protein Structures

2. Protein Engineering vs. Protein Design Protein Engineering: Mutating gene(s) to modify an existing protein. – Capability exists – Many examples can be found Protein Design: Designing an entire protein from scratch to serve a specific purpose. – Unlikely until we can reliably predict folding from sequence – Levinthal’s Paradox: Why we cannot test random combinations – We can predict 2° structure, but prediction of 3° structure will require a shortcut (e.g., energy considerations, kinetics, etc)

3. Prediction of Secondary Structure from Sequence PDBSum (EMBL-EBI) http://www.ebi.ac.uk/pdbsum/ http://www.ebi.ac.uk/pdbsum/ Jpred: http://www.compbio.dundee.ac.uk/www-jpred/http://www.compbio.dundee.ac.uk/www-jpred/ PredictProtein: https://www.predictprotein.org/https://www.predictprotein.org/ Either enter FASTA sequence file or can load new/existing sequence Based on propensity of certain AA’s to form specific structures, or stereochemical considerations (compactness & hydrophobicity related to known tertiary structures), but all are related to extensive analyses of sequences and the applications of scoring matrices

4. FASTA format: versatile, compact with one header line followed by a string of nucleotides or amino acids in the single letter code

5. Pairwise Alignment Potential relationships between proteins or nucleic acids can be explored by comparing 2 or more sequences of amino acids or nucleotides. Difficult to do visually. Computer algorithms help us by: – Accelerating the comparison process – Allowing for “gaps” or indels in sequences (i.e., insertions, deletions) – Identifying substituted amino acids that are structurally or functionally similar (D and E). Pevsner, Bioinformatics and Functional Genomics, 2009 One way to do this is with BLAST (Basic Local Alignment Search Tool) Allows rapid sequence comparison of a query sequence against a database. The BLAST algorithm is fast, accurate, and web-accessible. BLAST lets user select from a variety of scoring matrices to evaluate sequence relatedness.

6. BLAST is… Basic Local Alignment Search Tool NCBI's sequence similarity search tool supports analysis of DNA and protein databases NCBI key features: BLAST 3CLN

7. BLAST  BLAST allows user to search a sequence (the query) against millions of sequences in the NCBI database (the target).  Global alignments (e.g., Needleman-Wunsch) would be time consuming and computationally intensive for this amount of data.  BLAST is designed for local alignment, not global alignment.  Allows for faster searches, can match subsets of proteins (e.g., domains). C-terminal domain of CaM (from 3cln.pdb)

8. BLAST Output from DB Search  Graphic Summary includes conserved domains, when applicable.

9. BLAST Output from DB Search  Graphic Summary includes distribution of blast hits.  Color coded by bit Score.  Higher score related to higher sequence identity.

10. Sequence Analyses: RNA Codons (3 RNA bases in sequence) determine each amino acid that will build the protein expressed Many amino acids are encoded by more than 1 codon (change in 3 rd base).  Change of single base may not be significant.

11. Comparing protein sequences Comparing protein sequences usually more informative than nucleotide sequences. – Changing base at 3 rd position in codon does not always change AA (Ex: Both UUU and UUC encode for phenylalanine)Ex: Both UUU and UUC encode for phenylalanine – Different AAs may share similar chemical properties (Ex: hydrophobic residues A, V, L, I) – Relationships between related but mismatched AAs in sequence analysis can be accounted for using scoring systems (matrices). – Protein sequence comparisons can ID sequence homologies from proteins sharing a common ancestor as far back as 1 × 10 9 years ago (vs. 600 × 10 6 for DNA).

12. Amino acids by similar biophysical properties http://kimwootae.com.ne.kr/apbiology/chap2.htm

13. Amino acids by similar biophysical properties http://kimwootae.com.ne.kr/apbiology/chap2.htm These have useful fluorescent properties

14. Amino acids by similar biophysical properties http://kimwootae.com.ne.kr/apbiology/chap2.htm

15. Amino acids by similar biophysical properties http://kimwootae.com.ne.kr/apbiology/chap2.htm

16. Amino acids by similar biophysical properties http://kimwootae.com.ne.kr/apbiology/chap2.htm

17. Sequence Identity and Similarity Identity: How closely two sequences match one another. – Unlike homology, identity can be measured quantitatively Similarity: Pairs of residues that are structurally or functionally related (conservative substitutions). Pevsner, Bioinformatics and Functional Genomics, 2009 >lcln|28245 3CLN:A|PDBID|CHAIN|SEQUENCE Length=148 Score = 268 bits (684), Expect = 3e-97, Method: Compositional matrix adjust. Identities = 130/148 (88%), Positives = 143/148 (97%), Gaps = 0/148 (0%) Query 1 AEQLTEEQIAEFKEAFALFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGN 60 A+QLTEEQIAEFKEAF+LFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGN Sbjct 1 ADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGN 60 Query 61 GTIDFPEFLSLMARKMKEQDSEEELIEAFKVFDRDGNGLISAAELRHVMTNLGEKLTDDE 120 GTIDFPEFL++MARKMK+ DSEEE+ EAF+VFD+DGNG ISAAELRHVMTNLGEKLTD+E Sbjct 61 GTIDFPEFLTMMARKMKDTDSEEEIREAFRVFDKDGNGYISAAELRHVMTNLGEKLTDEE 120 Query 121 VDEMIREADIDGDGHINYEEFVRMMVSK 148 VDEMIREA+IDGDG +NYEEFV+MM +K Sbjct 121 VDEMIREANIDGDGQVNYEEFVQMMTAK 148 88% of sequences include the same amino acids (Identities). This increases to 97% (Positives) when you include amino acids that are different, but with similar properties.

18. Sequence Homology Homology: Two sequences are homologous if they share a common ancestor. No “degrees of homology”: only homologous or not Almost always share similar 3D structure – Ex. myoglobin and beta globin – Sequences can change significantly over time, but 3D structure changes more slowly Pevsner, Bioinformatics and Functional Genomics, 2009 Beta-globin sub-unit of adult hemoglobin (2H35.pdb, in blue), superimposed over myoglobin (3RGK.pdb, in red). These sequences probably separated 600 million years ago.

19. Percent Identity and Homology For an alignment of 70 amino acids, 40% sequence identity is a reasonable threshold for homology. Above 20% (more than 70 amino acids) may indicate homology. Below 20% probably indicates chance alignment. Pevsner, Bioinformatics and Functional Genomics, 2009

20. Orthologs and Paralogs Orthologs: Homologous sequences in different species that arose from a common ancestral gene during speciation. – Ex. Humans and rats diverged around 80 million years ago  divergence of myoglobin genes occurred. – Orthologs frequently have similar biological functions. Human and rat myoglobin (oxygen transport) Human and rat CaM Paralogs: Homologous sequences that arose by a mechanism such as gene duplication. Within same organism/species Ex. Myoglobin and beta globin are paralogs – Have distinct but related functions. Pevsner, Bioinformatics and Functional Genomics, 2009

21. Conservative Substitutions in Matrices Scoring may also vary based on conserved substitutions of amino acids: i.e., amino acids with similar properties will not lose as many points as AAs with very different properties. Basic AAs: K, R, H Acidic AAs: D, E Hydroxylated AAs: S, T Hydrophobic AAs: G, A, V, L, I, M, F, P, W, Y Pevsner, Bioinformatics and Functional Genomics, 2009 These relationships would be considered when calculating “Positives” in BLAST alignment.

22. Dayhoff Model: Building a Scoring Matrix  1978, Margaret Dayhoff provided one of the first models of a scoring matrix  Model was based on rules by which evolutionary changes occur in proteins  Catalogued 1000’s of proteins, considered which specific amino acid substitutions occurred when 2 homologous proteins aligned  Assumes substitution patterns in closely-related proteins can be extrapolated to more distantly-related proteins  An accepted point mutation (PAM) is an AA replacement accepted by natural selection  Based on observed mutations, not necessarily on related AA properties  Probable mutations are rewarded, while unlikely mutations are penalized  Scores for comparison of 2 residues (i, j) based on the following equation: Here, q i,j is the probability of an observed substitution (from mutation probability matrix), while p is the likelihood of observing the replacement AA (i) as a result of chance (normalized frequency of AA table). Pevsner, Bioinformatics and Functional Genomics, 2009

23. PAM250 Mutation Probability Matrix Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val A R N D C Q E G H I L K M F P S T W Y V Ala A 13 6 9 9 5 8 9 12 6 8 6 7 7 4 11 11 11 2 4 9 Arg R 3 17 4 3 2 5 3 2 6 3 2 9 4 1 4 4 3 7 2 2 Asn N 4 4 6 7 2 5 6 4 6 3 2 5 3 2 4 5 4 2 3 3 Asp D 5 4 8 11 1 7 10 5 6 3 2 5 3 1 4 5 5 1 2 3 Cys C 2 1 1 1 52 1 1 2 2 2 1 1 1 1 2 3 2 1 4 2 Gln Q 3 5 5 6 1 10 7 3 7 2 3 5 3 1 4 3 3 1 2 3 Glu E 5 4 7 11 1 9 12 5 6 3 2 5 3 1 4 5 5 1 2 3 Gly G 12 5 10 10 4 7 9 27 5 5 4 6 5 3 8 11 9 2 3 7 His H 2 5 5 4 2 7 4 2 15 2 2 3 2 2 3 3 2 2 3 2 Ile I 3 2 2 2 2 2 2 2 2 10 6 2 6 5 2 3 4 1 3 9 Leu L 6 4 4 3 2 6 4 3 5 15 34 4 20 13 5 4 6 6 7 13 Lys K 6 18 10 8 2 10 8 5 8 5 4 24 9 2 6 8 8 4 3 5 Met M 1 1 1 1 0 1 1 1 1 2 3 2 6 2 1 1 1 1 1 2 Phe F 2 1 2 1 1 1 1 1 3 5 6 1 4 32 1 2 2 4 20 3 Pro P 7 5 5 4 3 5 4 5 5 3 3 4 3 2 20 6 5 1 2 4 Ser S 9 6 8 7 7 6 7 9 6 5 4 7 5 3 9 10 9 4 4 6 Thr T 8 5 6 6 4 5 5 6 4 6 4 6 5 3 6 8 11 2 3 6 Trp W 0 2 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 55 1 0 Tyr Y 1 1 2 1 3 1 1 1 3 2 2 1 2 15 1 2 2 3 31 2 Val V 7 4 4 4 4 4 4 4 5 4 15 10 4 10 5 5 5 72 4 17 Think of these values as percentages (columns sum to 100). For example, there is an 18% (0.18) probability of R being replaced by K. This probability matrix needs to be converted into a scoring matrix. Original AA Replacement AA http://www.icp.ucl.ac.be/~opperd/private/pam250.html

24. Normalized Frequencies of Amino Acids http://www.icp.ucl.ac.be/~opperd/private/pam250.html **How often a given amino acid appears in a protein (determined by empirical analyses)

25. Purpose of PAM Matrices Derive a scoring system to determine relatedness of 2 sequences. PAM mutation probability matrix must be converted to a scoring matrix (log odds matrix).

26. PAM250 Log-Odds Matrix Cys C 12 Ser S 0 2 Thr T -2 1 3 Pro P -3 1 0 6 Ala A -2 1 1 1 2 Gly G -3 1 0 -1 1 5 Asn N -4 1 0 -1 0 0 2 Asp D -5 0 0 -1 0 1 2 4 Glu E -5 0 0 -1 0 0 1 3 4 Gln Q -5 -1 -1 0 0 -1 1 2 2 4 His H -3 -1 -1 0 -1 -2 2 1 1 3 6 Arg R -4 0 -1 0 -2 -3 0 -1 -1 1 2 8 Lys K -5 0 0 -1 -1 -2 1 0 0 1 0 3 5 Met M -5 -2 -1 -2 -1 -3 -2 -3 -2 -1 -2 0 0 6 Ile I -2 -1 0 -2 -1 -3 -2 -2 -2 -2 -2 -2 -2 2 5 Leu L -8 -3 -2 -3 -2 -4 -3 -4 -3 -2 -2 -3 -3 4 2 8 Val V -2 -1 0 -1 0 -1 -2 -2 -2 -2 -2 -2 -2 2 4 2 4 Phe F -4 -3 -3 -5 -4 -5 -4 -6 -5 -5 -2 -4 -5 0 1 2 -1 9 Tyr Y 0 -3 -3 -5 -3 -5 -2 -4 -4 -4 0 -4 -4 -2 -1 -1 -2 7 10 Trp W -8 -2 -5 -6 -6 -7 -4 -7 -7 -5 -3 2 -3 -4 -5 -2 -6 0 0 17 C S T P A G N D E Q H R K M I L V F Y W Cys Ser Thr Pro Ala Gly Asn Asp Glu Gln His Arg Lys Met Ile Leu Val Phe Tyr Trp This is the PAM250 scoring matrix, calculated as follows: http://www.icp.ucl.ac.be/~opperd/private/pam250.html

27. Pairwise Alignment and Homology Think of PAM value as total number of mutations. This included multiple mutations over time at a single position. Currently, we accept that once the percent distance reaches ~85%, homology is indeterminate. PAM250 works best for more distantly related protein sequences. http://www.icp.ucl.ac.be/~opperd/private/pam.html Seq1 AGDFWYGGDGEYLLV Seq2 AGQFWYGGEGEKLLV Seq3 AGEFWYGGEGEKLLV Seq1 and Seq2 separated by 3 units, while Seq1 and Seq3 separated by 4 PAM units

28. Practical Lessons from the Dayhoff Model  Less mutable amino acids likely play more important structural and functional roles  Mutable amino acids fulfill functions that can be filled by other amino acids with similar properties  Common substitutions tend to require only a single nucleotide change in codon  Amino acids that can be created from more than 1 codon are more likely to be created as a substitute (See p. 63, textbook) Changes to sequence that do not alter structure and function of protein likely to be more tolerated in nature Pevsner, Bioinformatics and Functional Genomics, 2009

29. BLOSUM62 Scoring Matrix  BLOck SUbstitution Matrix  By Henikoff and Henikoff (1992)  Default scoring matrix for pairwise alignment of sequences using BLAST (local alignments)  Based on empirical observations of distantly- related proteins organized into blocks Pevsner, Bioinformatics and Functional Genomics, 2009 In BLOSUM62, proteins are arranged in blocks sharing at least 62% identity

30. General Trends in Scoring Matrices Less divergent More divergent BLOSUM90 PAM30 BLOSUM45 PAM250 BLOSUM62 PAM120 Human vs. chimp Human vs. bacteria Choose a matrix that is consistent with the level sequence identity you are investigating. I.E., if you are looking at/for more closely related sequences, use BLOSUM90. If you are not sure, use BLOSUM62.

31. Sequence Alignments: General Concepts Global Alignment: Tries to match the entire length of the sequence. Local Alignment: Tries to find the longest section that matches. Both are examples of dynamic programming: precise but slow

32. Global Alignment Input: two sequences over the same alphabet (either nucleotide or amino acid sequences) Output: The alignment of the sequences Example: GADEGYFGPVILAADGEVA and GGAEGDYFGPAIAEGEVA A possible alignment might look like this: ins del mut -GADEG-YFGPVILAADGEVA GGA-EGDYFGPAI--AEGEVA

33. Each position is scored independently: Match: +1 Mismatch: -1 Insertions or deletions (gaps):-2 The alignment score is the sum of the position scores Global Alignment – A Simple Scoring Scheme -GADEG-YFGPVILAADGEVA GGA-EGDYFGPAI--AEGEVA Global Alignment Score: (14 ×(+1)) + (5 × (-2)) + (2 × (-1)) = 2 -----GADEG-YFGPVILAADGEVA--- DLGNVGA-EGDYFGPAI-- AEGEVARPL Global Alignment Score: (14 ×(+1)) + (12 × (-2)) + (2 × (-1)) = -12 -----GADEG-YFGPVILAADGEVA--- dlgnvGA-EGDYFGPAI--AEGEVArpl Local Alignment Score: (14 ×(+1)) + (4 × (-2)) + (2 × (-1)) = 4

34. Matrices and Gap Costs The raw score of an alignment is the sum of the scores for aligning pairs of residues and the scores for gaps. Gapped BLAST and PSI-BLAST use "affine gap costs" which charge the score -a for the existence of a gap, and the score -b for each residue in the gap. Thus a gap of k residues receives a total score of -(a+bk); specifically, a gap of length 1 receives the score -(a+b). Your total raw score for the alignment is reduced when you introduce gaps into the query sequence. Calculate the score in BLOSUM-62 for a gap with 7 residues… http://www.ncbi.nlm.nih.gov/BLAST/blastcgihelp.shtml#Matrix/

35. Global Sequence Alignments Global Alignment: Entire sequence of each protein or DNA. Needleman and Wunsch (1970) Reduces problem to series of smaller alignments on a residue- by-residue basis. How this approach works 1.Setting up a matrix 2.Score the matrix 3.ID the optimal alignment

36. Local Sequence Alignment Local Alignment: Longest matching regions (subsets) between 2 sequences. Smith and Waterman Algorithm (1981) Scoring is similar to global alignment 1.Set up a matrix 2.Score the matrix No negative values allowed: If negative values are the only choices, then answer defaults to zero (0). Mismatches and gaps at ends score 0. 3.ID the optimal alignment More sensitive but much slower than heuristic methods (FASTA, BLAST)

37. Heuristic (word or k-tuple based) algorithms Uses initial query to make reasonable guesses about sequence alignments, then evaluates those considered “most likely” Alignment then extended until: – One of the sequences ends – Score falls below some threshold In BLAST, search depends on word size KENFDKARFSGTWYAMAKKDPEG 50 RBP (query) MKGLDIQKVAGTWYSLAMAASD. 44 lactoglobulin (hit) Hit! extend

38. FASTA (Pearson and Lippman 1988) Combines Smith and Waterman algorithm with word (k-tup) search  faster, heuristic approach Query sequence divided into small words (usually k=2 for proteins) – Words used to initially compare and match sequences – If words located on same diagonal, surrounding region is then selected for analysis Seq 1 FYGKLHMEGD Seq 2 FWGKLHMEGSNE Seq 1 Search words (k-tup = 2) FY YG GK KL LH HM ME EG GD http://www.incogen.com/bioinfo_tutorials/Bioinfo-Lecture_2-pairwise-align.html

39. Statistical Measures of Algorithms Objective of alignment algorithms is to maximize sensitivity and specificity of alignments. Sensitivity: Measure of how well algorithm correctly predicts sequences that are related. Specificity: Measure of how well algorithm correctly predicts sequences that are unrelated. TP: Positive identified as positive FP: Negative identified as positive TN: Negative identified as negative FN: Positive identified as negative

40. Relationships between biological sequences Biological sequences tend to occur in families –These may be related genes within an organism (paralogs) or between species (orthologs) –Presumably derived from common ancestor Nucleotides corresponding to coding regions are typically less well conserved than proteins due to degeneracy of genetic code –More difficult to align Sequences evolve faster than structures, but homologous sequences tend to retain similar structure and function (e.g., rat vs. human CaM)

41. Multiple sequence alignments Homology can be observed through multiple sequence alignments (MSA) MSA: 3 or more protein (or nucleic acid) sequences that are partially or completely aligned Homologous residues are aligned in columns across the length of the sequences

42. Multiple sequence alignments MSAs are powerful because they can reveal relationships between 2 sequences that can only be observed by their relationships with a third sequence AVGYDFGEKMLSGADDW LVGERADLTGAEIDE AVGYDFGEKMLSGA--DDW LVGYDRADK-LTGAE-DD- LVG-ERAD--LTGAEIDE- Seq 1 Seq 2 Seq 1 Seq 3 Seq 2

43. How MSAs are determined? MSAs can be determined based on: Presence of highly-conserved residues such as cysteine Conserved motifs and domains Conserved features of protein secondary structure Regions showing consistent patterns of insertions or deletions C-terminal domain of CaM (from 3cln.pdb) Conserved 2° structure (α-helices)

44. 1.Color coding indicates AA property class 2.* Indicates 100% conserved over entire alignment 3.: Conservative mutations 4.. Less conservative mutations 5.[blank] gap or least conserved mutations ClustalW Output for CD2 Protein 12345

45. Ca: EF-HandCa: Non-EF-Hand Pb: Ligand Distribution (Kirberger, Wang et al. 2008; Kirberger and Yang 2008; Glusker et al. 1998) Statistical Analysis of PDB Data: Ca 2+ vs. Pb 2+ Holo- and Hemi-directed geometries Pentagonal bipyramidal geometry

46. Develop Algorithms/Programs to Address Specific Problems Identify calcium-binding proteins by matching patterns of known calcium-binding sites in sequences.

47. Protein Engineering by Rational Design 1. Computer aided design – May include statistical & structural parameters 2. Site-directed mutagenesis – changing one or more nucleic acids in plasmid to change AA in protein 3. Transformation – Alteration of bacterial cell through introduction of exogenous genetic material 4. Protein expression – Manufacturing the protein(s) 5. Protein purification – separate target protein from other biomolecules 6. Biochemical testing

48. Engineered Proteins: Therapy Abatacept: Fusion protein composed of the Fc region of the immunoglobulin IgG1 fused to the extracellular domain of CTLA-4. Abatacept binds to the CD80 and CD86 molecule, and inhibits T cell activation by blocking signal from antigen presenting cell. Prevents immune response. Developed by Bristol-Myers Squibb and is licensed in the United States for the treatment of rheumatoid arthritis.

49. Engineered Proteins: Research Ca.CD2 is a protein engineered by Dr. Jenny Yang’s research group at Georgia State University. Cell Adhesion Molecule CD2 was modified by insertion of a calcium binding site. The binding site was observed to bind calcium selectively over other mono- and di- valent biological metals, and to bind several other metals including lanthanum and terbium, while still retaining the ability to bind it’s natural target molecule. The objectives of this research were to see if a metal binding site could be engineered into a small protein without significantly altering the protein, to study an isolated calcium binding site, and to develop a model for the development of proteins with specific functions. Design of a calcium-binding protein with desired structure in a cell adhesion molecule, JACS, 2005. 1T6W

50. Engineered Proteins: Research GFP and other FP’s, fused to other proteins, have found a variety of uses in cellular and tissue imaging. http://www.conncoll.edu/ccacad/zimmer/GFP-ww/prasher.html

51. Mutations to GFP produce different colors http://zeiss-campus.magnet.fsu.edu/articles/probes/jellyfishfps.html The availability of a different FP colors has also enabled researchers to develop methods to probe whether two proteins are within a distance of less than 10 nm of each other using the phenomenon of Förster (or fluorescence) resonance energy transfer (FRET) (Förster 1948). FRET is the distance- and orientation- dependent radiationless transfer of excitation energy from a donor fluorophore to an acceptor chromophore.

52. Protein Design Algorithms Two major classes: Exact algorithms (e.g., Dead-end elimination), provided optimal solutions but long run times Heuristic algorithms (e.g., Monte Carlo), faster run times but may not provide optimal solutions.

53. DEE Algorithm (Exact) The DEE (dead-end elimination): Compares all possible side chain rotamers on fixed protein backbone and removes those that cannot be part of the global lowest energy conformation (GMEC). DEE cannot guarantee convergence. If, after a certain number of iterations, DEE cannot remove any more rotamers, then either rotamers have to be merged or another search algorithm must be used to search the remaining search space. In such cases, the dead-end elimination acts as a pre-filtering algorithm to reduce the search space. https://www.cs.duke.edu/brd/papers/Proteins12/

54. Branch and Bound Algorithms (Exact) The protein design conformational space can be represented as a tree, where the protein residues are ordered in an arbitrary way, and the tree branches at each of the rotamers in a residue. Branch and bound algorithms use this representation to efficiently explore the conformation tree: At each branching, branch and bound algorithms bound the conformation space and explore only the promising branches. Tests multiple conformational changes (global changes), retaining lowest energy conformations. Can be very slow process.

55. Monte Carlo and Simulated Annealing Algorithm (Heuristic) A starting structure is needed for a molecular dynamics calculation, which is generated from all constraints for the molecular structure, such as bond-lengths and bond-angles. This starting structure may be any conformation such as an extended strand or an already folded protein. Starting at theoretical high temperatures (meaning energy put into system) approximately 20 different random, simulated protein folds are allowed to “cool” to lowest localized energies, to observe folding. These results are used for another set of iterations with different input parameters, until energy can no longer be minimized (global energy minimum is achieved). Simons, JMB, 1997