Protein Secondary and Tertiary Structure Prediction

Steve W. Lockless

steve.lockless@rockefeller.edu

The Sequence of Amino AcidsUnits held together by covalent bonds

Local Folding Maintained byShorter Distance InteractionsUnits held together by sequenceindependent hydrogen bonds

Additional Folding Maintainedby Longer Distance InteractionsUnits held together by sequence dependentinteractions

Want to see the protein we’re studying

Solving structures takes a long time

Not all labs are set up to solve structures

Technical issues prohibit solving structures

Too many protein to attempt to solve

Waste of resources

Why predict protein structure?

Name P(a) P(b) P(turn)Alanine 142 83 66Arginine 98 93 95Aspartic Acid 101 54 146Asparagine 67 89 156Cysteine 70 119 119Glutamic Acid 151 037 74Glutamine 111 110 98Glycine 57 75 156Histidine 100 87 95Isoleucine 108 160 47Leucine 121 130 59Lysine 114 74 101Methionine 145 105 60Phenylalanine 113 138 60Proline 57 55 152Serine 77 75 143Threonine 83 119 96Tryptophan 108 137 96Tyrosine 69 147 114Valine 106 170 50

Helix - Identify regions where 4 out of 6 contiguous residues have P(a-helix) > 100. - Extend the helix in both directions until a set of four contiguous residues that have an average P(a-helix) < 100 is reached. - Helix if P(a-helix) > P(b-sheet) for that segment of 5 or more.

Beta - Identify regions where 3 out of 5 contiguous residues have P(b-sheet) > 100. - Extend the sheet in both directions until a set of four contiguous residues that have an average P(b-sheet) < 100 is reached. - Beta-sheet if the average P(b-sheet) > 105 and P(b-sheet) > P(a-helix).

Chou-Fasman Method

Name f(j) f(j+1) f(j+2) f(j+3)

Alanine 0.06 0.076 0.035 0.058Arginine 0.070 0.106 0.099 0.085Aspartic Acid 0.147 0.110 0.179 0.081Asparagine 0.161 0.083 0.191 0.091Cysteine 0.149 0.050 0.117 0.128Glutamic Acid 0.056 0.060 0.077 0.064Glutamine 0.074 0.098 0.037 0.098Glycine 0.102 0.085 0.190 0.152Histidine 0.140 0.047 0.093 0.054Isoleucine 0.043 0.034 0.013 0.056Leucine 0.061 0.025 0.036 0.070Lysine 0.055 0.115 0.072 0.095Methionine 0.068 0.082 0.014 0.055Phenylalanine 0.059 0.041 0.065 0.065Proline 0.102 0.301 0.034 0.068Serine 0.120 0.139 0.125 0.106Threonine 0.086 0.108 0.065 0.079Tryptophan 0.077 0.013 0.064 0.167Tyrosine 0.082 0.065 0.114 0.125Valine 0.062 0.048 0.028 0.053

To identify a bend at residue number j, calculate the following value

p(t) = f(j)f(j+1)f(j+2)f(j+3)

If: (1) p(t) > 0.000075; (2) the average value for P(turn) > 100 in the tetrapeptide; and (3) the averages for the tetrapeptide are P(a-helix) < P(turn) > P(b-sheet),then a beta-turn is predicted at that location.

Beta-Turn Prediction

Look round the world: contemplate the whole and every part of it: you will find it to benothing but one great machine, subdivided into an infinite number of lesser machines,which again admit of subdivisions to a degree beyond what human senses and facultiescan trace and explain. All these various machines, and even their most minute parts,are adjusted to each other with an accuracy which ravishes into admiration all men whohave ever contemplated them. The curious adapting of means to ends, throughout allnature, resembles exactly, though it much exceeds, the productions of humancontrivance; ... we are led to infer, by all the rules of analogy, … that the Author ofNature is somewhat similar to the mind of man, though possessed of much largerfaculties, proportioned to the grandeur of the work which he has executed.

And what surprise must we feel, when we find him a stupid mechanic, who imitatedothers, and copied an art, which, through a long succession of ages, after multipliedtrials, mistakes, corrections, deliberations, and controversies, had been graduallyimproving? Many worlds might have been botched and bungled, throughout aneternity, ere this system was struck out; much labor lost, many fruitless trials made;and a slow, but continued improvement carried on during infinite ages in the art ofworld-making.

Dialogues Concerning Natural Religion - David Hume (1779)

Cleanthes - Argues for “Intelligent Design”

Philo - Argues for “Evolution”

Protein Design from Basic Principles

Science 1988 vol. 241 pp. 976-978

Science 1993 vol. 262 pp. 1680-1685

LSWF

LSWF

LSWF

L SW FLSW FL SWF LS WF LS WF LWF L L L

E = Inside5 = BoundaryI = TM

HMM Model Example

Where is the Inside -> TM transition most likely to occur?

Model Used by TM-HMM

Nature 1996 vol. 380 (6576) pp. 730-4

Science 1997 vol. 278 (5335) pp. 82-7

Limited Amino Acids at Each PositionLeonard-Jones PotentialSurface PotentialFixed Backbone

Rotamer Library

Dead-End Elimination

Mayo Algorithm

Science 2003 vol. 302 (5649) pp. 1364-8

Rosetta Energy Function

Science 2003 vol. 302 (5649) pp. 1364-8

WrepErep

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Proteins 2001 vol. Suppl 5 pp. 119-26