System and methods for predicting transmembrane domains in membrane proteins and mining the genome for recognizing G-protein coupled receptors

Abstract

The invention provides computer-implemented methods and apparatus implementing a hierarchical protocol using multiscale molecular dynamics and molecular modeling methods to predict the presence of transmembrane regions in proteins, such as G-Protein Coupled Receptors (GPCR), and protein structural models generated according to the protocol. The protocol features a coarse grain sampling method, such as hydrophobicity analysis, to provide a fast and accurate procedure for predicting transmembrane regions. Methods and apparatus of the invention are useful to screen protein or polynucleotide databases for encoded proteins with transmembrane regions, such as GPCRs.

1 . A method for predicting the transmembrane (TM) region(s) of a TM protein, comprising: 
 (1) obtaining amino acid sequence information and/or sequence alignment information for said TM protein;    (2) plotting a hydrophobic profile using said amino acid sequence information and/or sequence alignment information; and    (3) assigning/identifying initial TM regions based on the global average hydrophobicity of said alignment, or a base_mod within 0.05 thereof,    thereby predicting TM region(s) for said TM protein.    
     
     
         2 . The method of  claim 1 , further comprising: 
 (4) capping each initial TM regions identified in (3) to yield capped TM regions, based on the presence of helix breakers.    
     
     
         3 . The method of  claim 1 , wherein said TM protein is a multipass TM protein.  
     
     
         4 . The method of  claim 3 , wherein said multipass TM protein is an ion channel, a transporter, or a pump.  
     
     
         5 . The method of  claim 3 , wherein said multipass TM protein has two or more TM regions.  
     
     
         6 . The method of  claim 3 , wherein said multipass TM protein is a seven-TM protein.  
     
     
         7 . The method of  claim 6 , wherein said seven-TM protein is a G Protein-Coupled Receptor (GPCR).  
     
     
         8 . The method of  claim 7 , wherein said GPCR is an orphan GPCR or an olfactory receptor (OR).  
     
     
         9 . The method of  claim 7 , wherein said GPCR is a rhodopsin-like receptor selected from: olfactory receptor, adenosine receptor, melanocortin receptor, biogenic amine receptor, vertebrate opsin, neuropeptide receptor, invertebrate opsin, chemokine receptor, chemotactic receptor, somatostatin receptor, opioid receptor, or melatonin receptor; a calcitonin or related receptor selected from: calcitonin receptor, calcitonin-like receptor, CRF receptor, PTH/PTHrP receptor, glucagon receptor, secretin receptor, or latrotoxin receptor; a metabotropic glutamate or related receptor selected from: metabotropic glutamate receptor, calcium receptor, GABA-B receptor, or putative pheromone receptor; a STE2 pheromone receptor; a STE3 pheromone receptor; or a cAMP receptor.  
     
     
         10 . The method of  claim 1 , wherein said TM region comprises one or more potential α-helical region(s).  
     
     
         11 . The method of  claim 10 , wherein each of said a-helical region(s) comprises at least about 21 amino acid residues.  
     
     
         12 . The method of  claim 1 , wherein said hydrophobic profile is based on at least a portion of said TM protein.  
     
     
         13 . The method of  claim 12 , wherein said portion substantially excludes one or more of: the N- or C-terminal region(s) not in contact with lipid bilayers, or inter-TM region loops.  
     
     
         14 . The method of  claim 1 , wherein said hydrophobic profile uses peak signal analysis.  
     
     
         15 . The method of  claim 14 , wherein the generation of said hydrophobic profile uses the Eisenberg hydrophobicity scale.  
     
     
         16 . The method of  claim 15 , wherein said hydrophobic profile is generated by the SeqHyd profile algorithm.  
     
     
         17 . The method of  claim 16 , comprising: 
 (1) obtaining amino acid sequence information and sequence alignment information for said TM protein by: 
 (a) using the sequence of said protein as query, retrieving from a database an ensemble of hit sequences with 20-90% sequence identity, and/or BLAST bit score>200 and E-value>e −100 ;  
 (b) obtaining a multisequence alignment of said hit sequences and the sequence of said protein; and  
 (c) calculating consensus hydrophobicity for every residue position in said alignment;  
   (2) plotting said hydrophobic profile based on said consensus hydrophobicity; and    (3) assigning initial TM regions based on the global average hydrophobicity of said alignment, or a base_mod within 0.05 thereof.    
     
     
         18 . The method of  claim 17 , further comprising: 
 (4) capping each initial TM regions identified in (3) to yield capped TM regions, based on the presence of helix breakers.    
     
     
         19 . The method of  claim 17 , wherein said database is a protein database, or a polynucleotide database translated in at least one of the six reading frames.  
     
     
         20 . The method of  claim 17 , wherein said ensemble of hit sequences have a uniform distribution of sequences over the entire range of sequence identities.  
     
     
         21 . The method of  claim 20 , wherein the lowest sequence identity to said protein within said ensemble of sequences is about 20-40%.  
     
     
         22 . The method of  claim 17 , wherein said multisequence alignment is performed with ClustalW.  
     
     
         23 . The method of  claim 17 , wherein (2) is performed with a window size (WS) between 12-20.  
     
     
         24 . The method of  claim 17 , wherein (3) further comprises identifying additional TM helix region(s) with peak length<23 and peak area<0.8, using local average hydrophobicity more than 0.05 less than said base_mod, if said additional TM helix region(s) are not identified based either on said global average hydrophobicity or said base_mod.  
     
     
         25 . The method of  claim 18 , wherein said helix breakers are Pro (P), Gly (G), Arg (R), His (H), Lys (K), Glu (E), or Asp (D).  
     
     
         26 . The method of  claim 17 , wherein said capped TM regions exclude N- and C-terminal helix breakers.  
     
     
         27 . The method of  claim 17 , wherein the N- and C-termini of said capped TM regions are no more than 6 residues longer or 4 residues shorter than the N- and C-termini of said initial TM regions respectively.  
     
     
         28 . The method of  claim 17 , wherein each residue in each of said capped TM regions has an α-helical conformation.  
     
     
         29 . The method of  claim 28 , wherein said α-helical conformation is characterized by a between −37 and −77 degrees, and a ψ between −27 and −67 degrees.  
     
     
         30 . The method of  claim 17 , wherein said α-helical conformation is checked by verifying φ and ψ using PROCHECK.  
     
     
         31 . The method of  claim 16 , wherein said hydrophobic profile is based on the amino acid sequence information alone of said protein.  
     
     
         32 . The method of  claim 31 , comprising: 
 (1) obtaining amino acid sequence information for said TM protein;    (2) plotting said hydrophobic profile based on the hydrophobicity of each residue within said amino acid sequence; and    (3) assigning/identifying initial TM regions based on the global average hydrophobicity of said alignment, or a base_mod within 0.05 thereof.    
     
     
         33 . The method of  claim 32 , further comprising: 
 (4) capping each initial TM regions identified in (3) to yield capped TM regions, based on the presence of helix breakers.    
     
     
         34 . A method of identifying potential transmembrane (TM) proteins, comprising: 
 (1) obtaining amino acid sequence information for one or more candidate TM proteins;    (2) for each said candidate TM protein, plotting a hydrophobic profile based on the hydrophobicity of each amino acid residue of said candidate TM protein; and    (3) assigning/identifying initial TM regions based on the global average hydrophobicity of said alignment, or a base_mod within 0.05 thereof    
     
     
         35 . The method of  claim 34 , wherein said amino acid sequence information is obtained from a protein database.  
     
     
         36 . The method of  claim 34 , wherein said amino acid sequence information is obtained from a polynucleotide database translated in at least one of the six reading frames.  
     
     
         37 . The method of  claim 36 , wherein said polynucleotide database is based on a cDNA library or a coding sequence (CDS) library predicted from sequenced genomic DNAs.  
     
     
         38 . The method of  claim 36 , wherein said cDNA library is a mouse, rat, or human cDNA library.  
     
     
         39 . The method of  claim 36 , wherein said protein database has more than 20,000 sequences.  
     
     
         40 . The method of  claim 34 , wherein step (3) further comprises identifying additional TM region(s) with peak length<23 and peak area<0.8, using local average hydrophobicity more than 0.05 less than said base_mod, if said additional TM region(s) are not identified based either on said global average hydrophobicity or said base_mod.  
     
     
         41 . The method of  claim 34 , further comprising designating any candidate TM proteins with seven initial TM regions as GPCR, wherein each of said TM regions comprises at least 21 residues.  
     
     
         42 . The method of  claim 41 , further comprising verifying identified GPCR by experimental data selected from x-ray crystallography or nuclear magnetic resonance (NMR), or by first principles procedures.  
     
     
         43 . Computer executable software code stored in a computer readable medium, which upon execution carries out a method for predicting the transmembrane (TM) region(s) of a TM protein, said method comprising: 
 (1) obtaining amino acid sequence information and/or sequence alignment information for said TM protein;    (2) plotting a hydrophobic profile using said amino acid sequence information and/or sequence alignment information; and    (3) assigning/identifying initial TM regions based on the global average hydrophobicity of said alignment, or a base_mod within 0.05 thereof;    thereby predicting TM region(s) for said TM protein.    
     
     
         44 . A system for predicting the transmembrane (TM) region(s) of a TM protein, comprising: 
 (1) a data input module for obtaining amino acid sequence information and/or sequence alignment information for said TM protein;    (2) a profile generation module for plotting a hydrophobic profile using said amino acid sequence information and/or sequence alignment information; and    (3) a TM region identification module for assigning/identifying initial TM regions based on the global average hydrophobicity of said alignment, or a base_mod within 0.05 thereof.    
     
     
         45 . A computational model of the TM regions of a transmembrane protein, the computational model comprising: a computer-readable memory storing data describing one or more predicted TM regions for the transmembrane protein, the predicted TM regions being generated according to the method of  claim 1.