Biochemistry Online: An Approach Based on Chemical Logic

Biochemistry Online




Last Update:  3/9/16

Learning Goals/Objectives for Chapter 2G:  After class and this reading, students will be able to:

  • find web based proteomics protein to analyze protein sequences and structures
  • describe the basis for methods used to predict the secondary structure and hydrophobic structures of proteins
  • analyze secondary structure and hydropathy plots from web-based proteomics programs.
  • describe differences between integral and peripheral membranes proteins, and how each could be purified.
  • explain how hydropathy and secondary structure plots can be used to predict membrane spanning sequences of proteins
  • describe in general the theoretical and empirically based methods to predict protein tertiary structure from a primary sequence
  • describe possible early intermediates in protein folding as determined by theoretical methods

G1.  Introduction to Bioinformatics, Computational Biology and Proteomics

With the solving of the human genome, intensive effort has been devoted to analysis of the human genome to determine the number and transcriptional regulation of the encoded genes.  Much has been learned from comparative genomics, as genomes from mice, rats, chimpanzees, and a variety of prokaryotes are compared in an effort to help understand the nature of genes and their transcriptional regulation.  The vast amount of genomic data that has to be "mined" has required the development of computational and computer programs to enable the analysis.  Two relatively new fields have subsequently arisen:  bioinformatics and computational biology.  (In a personal note, the words computational biology seem somewhat restrictive since the field of computational chemistry, which has a longer history, has significant overlap with "computational biology".  I prefer computational biochemistry).  These fields have significant overlap (as do physical chemistry/chemical physics and biochemistry/molecular biology/chemical biology), so I defer to others to define them. 

The NIH Biomedical Information Science and Technology Initiative Consortium:   "This consortium has agreed on the following definitions of bioinformatics and computational biology, recognizing that no definition could completely eliminate overlap with other activities or preclude variations in interpretation by different individuals and organizations.

Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data.

Computational Biology: The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems."

This web book has been developed as a first semester biochemistry text and choices have been made to limit the scope of the material to exclude content covered in detail in a molecular biology/genetics class.  Hence, this text will not discuss in significant detail the genome and transcriptome, and mechanisms of replication, transcription, or translation.   However, with its emphasis on protein structure and function, proteomics, the characterization of structure and function of all proteins within a cell, is a logical  candidate for inclusion.

In the last several years, computational biology/chemistry and web-based programs have become available for the systematic analysis of individual proteins, and for the comparative analysis of many proteins, based on either their DNA or amino acid sequence.  Clearly the ultimate goal in the description of a protein would be to determine, from the amino acid or nucleotide sequence, the three dimensional structure of a protein and its biological function, including all its binding partners.  

Here is a list of proteome web resources and tutorials

Voluminous databases of biomolecule sequence and structural data, as well as analysis software packages, are available at a variety of web sites, including:

The NCBI has an extensive array of available tools (free), including:

A summary of three important sites:

NCBI-Protein: The Protein database is a collection of sequences from several sources, including translations from annotated coding regions in GenBank, RefSeq and TPA, as well as records from SwissProt, PIR, PRF, and PDB. Protein sequences are the fundamental determinants of biological structure and function
Uniprot: The UniProt Knowledgebase (UniProtKB) is the central hub for the collection of functional information on proteins, with accurate, consistent and rich annotation. In addition to capturing the core data mandatory for each UniProtKB entry (mainly, the amino acid sequence, protein name or description, taxonomic data and citation information), as much annotation information as possible is added.
Gene Card: GeneCards is a searchable, integrative database that provides comprehensive, user-friendly information on all annotated and predicted human genes. It automatically integrates gene-centric data from ~125 web sources, including genomic, transcriptomic, proteomic, genetic, clinical and functional information

The table below (directly taken from Wikipedia) shows some of the incredible information available the proteome and genome of each human chromosome. 

Table:  Human proteome and genome from Wikipedia
(Data source: Ensembl genome browser release 68, July 2012)

Chromsome Length (mm) BP Variations Confirmed Proteins Putative Proteins Pseudogenes miRNA rRNA snRNA snoRNA misc ncRNA Links
1 85 249,250,621 4,401,091 2,012 31 1,130 134 66 221 145 106 EBI
2 83 243,199,373 4,607,702 1,203 50 948 115 40 161 117 93 EBI
3 67 198,022,430 3,894,345 1,040 25 719 99 29 138 87 77 EBI
4 65 191,154,276 3,673,892 718 39 698 92 24 120 56 71 EBI
5 62 180,915,260 3,436,667 849 24 676 83 25 106 61 68 EBI
6 58 171,115,067 3,360,890 1,002 39 731 81 26 111 73 67 EBI
7 54 159,138,663 3,045,992 866 34 803 90 24 90 76 70 EBI
8 50 146,364,022 2,890,692 659 39 568 80 28 86 52 42 EBI
9 48 141,213,431 2,581,827 785 15 714 69 19 66 51 55 EBI
10 46 135,534,747 2,609,802 745 18 500 64 32 87 56 56 EBI
11 46 135,006,516 2,607,254 1,258 48 775 63 24 74 76 53 EBI
12 45 133,851,895 2,482,194 1,003 47 582 72 27 106 62 69 EBI
13 39 115,169,878 1,814,242 318 8 323 42 16 45 34 36 EBI
14 36 107,349,540 1,712,799 601 50 472 92 10 65 97 46 EBI
15 35 102,531,392 1,577,346 562 43 473 78 13 63 136 39 EBI
16 31 90,354,753 1,747,136 805 65 429 52 32 53 58 34 EBI
17 28 81,195,210 1,491,841 1,158 44 300 61 15 80 71 46 EBI
18 27 78,077,248 1,448,602 268 20 59 32 13 51 36 25 EBI
19 20 59,128,983 1,171,356 1,399 26 181 110 13 29 31 15 EBI
20 21 63,025,520 1,206,753 533 13 213 57 15 46 37 34 EBI
21 16 48,129,895 787,784 225 8 150 16 5 21 19 8 EBI
22 17 51,304,566 745,778 431 21 308 31 5 23 23 23 EBI
X 53 155,270,560 2,174,952 815 23 780 128 22 85 64 52 EBI
Y 20 59,373,566 286,812 45 8 327 15 7 17 3 2 EBI
mtDNA 0.0054 16,569 929 13 0 0 0 2 0 0 22 EBI

This chapter will describe programs that allow predictions of secondary and tertiary structures of proteins.  Specific exercises using web-based bioinformatics programs can be found at the end.  

G2.  Prediction of Secondary Structure

As we have seen previously, amino acids vary in their propensity to be found in alpha helices, beta strands, or reverse turns (beta bends, beta turns).    These difference can be rationalized from the structure of each amino acid, as described before. 

Figure:  Amino Acid Structure and propensity for secondary structure

From the data bases, propensities can be calculated to determine the likelihood that a given amino acid will be in one of those structures. Glycine for example would have a high propensity to be in reverse turns, while Pro, a helix breaker,  would have a low propensity to be in an alpha helix.   A number is assigned to each amino acid for each category of secondary structure. High numbers favor the likelihood that that amino acid would be in that structure.  One of the earliest propensity scales was from Chou-Fasman, where H indicates high propensity for secondary structure, h intermediate propensity, i is inhibitory, b is a intermediate breaker, and  B is a significant breaker of secondary structure.  

Chou-Fasman Amino Acid Propensities

A.A. Helix Sheet
Designation P Designation P
Ala H 1.42 i 0.83
Cys i 0.70 h 1.19
Asp I 1.01 B 0.54
Glu H 1.51 B 0.37
Phe h 1.13 h 1.38
Gly B 0.57 b 0.75
His I 1.00 h 0.87
Ile h 1.08 H 1.60
Lys h 1.16 b 0.74
Leu H 1.21 h 1.30
Met H 1.45 h 1.05
Asn b 0.67 b 0.89
Pro B 0.57 B 0.55
Gln h 1.11 h 1.10
Arg i 0.98 i 0.93
Ser i 0.77 b 0.75
Thr i 0.83 h 1.19
Val h 1.06 H 1.70
Trp h 1.08 h 1.37
Tyr b 0.69 H 1.47

Next a stretch or "window" of amino acids about 7 amino acids is taken, starting from the N-terminal of the protein. First the average alpha helical propensities for amino acids 1-7 are determined and assigned, let's say, to the middle (4th) amino acid in that sequence. Then alpha helical propensities for amino acids 2-8  (the next window) are averaged and assigned to the middle (5) amino acid in that range. The window slide down the protein sequence until all but the first and last few amino acids have an average value assigned to them. If a contiguous stretch of amino acids has high average propensity, they are probably in an alpha helix in the native protein. This process is repeated using beta strand and reverse turn propensities. The final assignments of most probably secondary structure are made. Of course this system was tested against proteins whose tertiary structure was known. See the results for secondary structure prediction for one protein. In this example, the average propensity for four contiguous amino acids is calculated (starting with amino acids 1-4, then amino acids 5-8, etc, and continuing to the end of the polypeptide).  Next this process is repeated for contiguous stretches 2-5, 6-9, etc, and continuing to the end.  The original Chou Fasman propensities have been updated using known protein structure to give better predictions.

Additional information about putative helices can be obtained by determining if they are amphiphilic (one side of the helix containing mostly hydrophobic side chains, with the opposite side containing polar or charged side chains.  A helical wheel projection can be made. In this a circle is draw representing a downward cross-sectional view of the helix axis.

Figure:  Helical wheel projection

The side chains are placed on the outside of the circle, staggered in a fashion determined by the fact that there are 3.6 amino acids per turn of the helix. If one side of the wheel contains predominantly nonpolar side chains while the other side has polar side chains, the helix is amphiphilic. Imagine how such helices might be packed in a protein.

G3.  Prediction of Hydrophobicity

In a completely analogous fashion, a hydrophobic propensity or hydopathy can be calculated. In this system, empirical measures of the hydrophobic nature of the side chains are used to assign a number to a given amino acid. Many hydropathy scales are used. Several are based on the Dmo transfer of the side chains from water to a nonpolar solvent. Two commonly used scales are the Kyte-Doolittle Hydropathy and Hopp-Woods scales (used more like a hydrophilicity index to predict surface or water accessible structures that might be recognized by the immune system)

  Hydrophobicity Indices for Amino Acids

Amino Acid












Aspartic acid









Glutamic acid










































For a water-soluble protein, a  continuous stretch of amino acids found to have a high average hydropathy is probably buried in the interior of the protein.  Consider the example of bovine a-chymotrypsinogen, a 245 amino acid protein, whose sequence is shown below in single letter code.


A hydrophathy plot for chymotrypsinogen (sum of hydropathies of seven consecutive residues) shows many stretches that are presumably buried in the interior of the protein.

Figure:  hydrophathy plot for chymotrypsinogen

G4.  Prediction of Membrane Protein Structure

So far we have discussed predominantly globular proteins that are soluble in water.  Proteins are also found associated with membranes.   Two major classes of membrane proteins are found in nature.

Figure:  Types of membrane proteins

In some of these integral membrane proteins, large extracellullar and intracellular domains of the protein are present, connected by the intramembrane regions. The intramembrane spanning region often consists of either a single alpha helix, or 7 different helical regions which zig-zag through the membrane. These  transmembrane sequences can readily be determined through hydropathy calculations.  For example, consider the integral membrane bovine protein rhodopsin.  Its 348 amino acid sequence  (in single letter code) is shown below:


 Rhodopsin hydropathy plot calculations shows that is contains seven transmembrane helices which wind through the membrane in a serpentine fashion..  

Figure:   Rhodopsin hydropathy plot

Figure:  seven transmembrane helices

Rhodopsin Hydropathy Results

No. N terminal transmembrane region C terminal type length

In summary, hydropathy plots are hence useful in finding buried regions in water soluble proteins, transmembrane helices in integral membrane proteins as well as short stretches of polar/charged amino acids that might form surface loops recognizable by immune system antibodies.  The window size used in hydropathy plots would obviously affect the calculated results.  Windows of 20 amino acids are useful to determine transmembrane helices while windows of 5-7 amino acids are used to find surface-exposed hydrophilic sites.

Membrane proteins call be solubilized by addition of single chain amphiphiles (detergents).  The nonpolar tails of the detergents interact with the hydrophobic transmembrane domain of the membrane protein forming a "mixed" micelle-like structure.  Nonionic detergents like Triton X-100 and octyl-glucoside are often used to solubilize membrane proteins in their near native state.  In contrast, ionic detergents like sodium dedecyl sulfate (with a negatively charged head group) denature proteins during the solubilization process.  To study membrane proteins in a more native-like environment, proteins solubilized by nonionic detergent can be reconstituted into bilayer liposome structures using methods similar to those from Lab 1 in which you prepared dye-capsulated large unilamellar vesicles (LUVs).  However, it can be difficult to study the intra- and extracellular domains of membrane proteins in liposomes, given that one of those domains is hidden inside the liposome.  A novel technique that removes this barrier was recently developed by Sligar.  He created an amphiphilic protein disc with an opening in the center.  The inner opening is lined with nonpolar residues, while the outer surface of the disc is polar.  When the discs were added to phosphlipids, small bilayers formed inside the disc.  Membrane proteins like the b-2 adrenergic receptor could be reconstituted in the nanodisc bilayers, allowing solvent exposure of both the intracellular and extracellular domains of the receptor protein.

Figure:  Nanodisc with membrane protein

G5.  Prediction of Protein Tertiary Structure

We are getting closer to predicting the tertiary structure of a protein, but as we have seen from molecular mechanics and dynamics calculations, it is a huge computational task.  There are two basic approaches which are often combined.

Many mechanisms of the actual folding process have been postulated, most of which have some experimental support.  In one, a hydrophobic collapse of the protein produces a seed structure upon which secondary structure and final tertiary collapse results.  Alternatively, initial formation of an alpha helix might serve as the seed structure.  A combination of the two is likely.  In one scenario, two small amphiphilic helices might form which interact through their nonpolar faces to produce the initial seed structure.

Many studies have been done on a domain of the protein villin.  A company at Stanford University (Folding at Home) actually allows you to process protein folding data on your own computer when you're not using it (an example of distributed computing).  The example below shows one simulation of length greater than 1 ms.  In the simulation, it collapses to a near native-like state then unfolds again as it iteratively probes conformational space as it "seeks" the global energy minimum.

Zhou and Karplus simulated the folding of residues 10-55 of Staphylococcus aureus protein A which form a 3-helix bundle structure.

Figure:   3-helix bundle

Using molecular dynamics, they carried out 100 folding simulations. Two types of folding trajectories were noted.

Figure:  helices form early

Figure:  simultaneous and quick partial helix formation and collapse

The Fersht lab has been combining experimental and theoretical approaches to the folding/unfolding of another three helix bundle protein, Engrailed homeodomain. 

Figure:  Engrailed homeodomain

This protein is among the fastest folding and unfolding proteins known (ms time scale).  This time frame is now also amenable to study through molecular dynamics simulations.  Both sets of data support a folding pathway in which the unfolded state (U) collapses in a microsecond to an intermediate state (I) characterized by significant native secondary structure and mobile side chains that is less compact than the native state (N).  The I state hence resembles the molten globule state.  To more clearly understand the unfolded state, they generated a mutant (Leu16Ala) which was only marginally stable at room temperature (2.5 kcal/mol).  Spectroscopic measurements (CD, NMR) showed this state to resemble the intermediate (I) state, with much native secondary structure and a 33% greater radius of gyration than the N state.  In effect they could study the transient intermediate of the wild type protein more easily by making that state more stable through mutagenesis.  These studies showed that the intermediate is on the folding pathway and not inhibitory to the process.  Using molecular dynamic simulations, the intermediate to native state transition was shown to proceed via a transition state (TS) in which the native secondary structure is almost all present and the helices are engaged in the final packing process.

Figure:  Complete Folding Pathway of Engrailed Homeodomain by Experiment and Simulation

Bradley et al (2005) have taken another step forward in prediction of tertiary structure for small proteins
(< 85 amino acids).  They describe the two biggest stumbling blocks to such predictions as the huge number of conformations which must be explored (i.e. all of conformational space) and accurate determination of the energy of the solvated structures.  Searching conformational space is difficult since the energy landscape around the global energy minimum can be very steep and sharp,  since modest side chain displacements arising from subtle main chain movements cause significant side chain packing and energy changes.  The narrowness of the energy well makes it difficult to find the global minimum in stochastic conformational search processes.  Energy calculations also require better (more realistic) energy functions (force fields) which show the native state to be clearly differentiated as the global minimum from the denatured (non-native) states.  They conducted energy calculations on many different small proteins and produced for each protein a low resolution model. To reach this low resolution model for a given protein, they found many sequence homologs of the given target protein.   These homologs were naturally occurring sequence variants found by a relatively conservative BLAST sequence search, with sequence identities of 30-60 percent.  They also contained insertions and deletions compared to the target sequence, which probably are involved in surface loop structures.   The target and homolog sequences were folded, generating a more diverse population of low-resolution models as starting points for all-atom refinement of the structure.   Then, using a new force field that stressed short range interactions (van der Waals, H-bonding), which would expected to be more important for final folding of the low resolution models than long range electrostatic forces), they were able to refine the models and condense to a final low energy that was very close in main and side chain packing to the experimental crystal structure (resolution < 1. angstroms). 

The holy grail in protein folding research has always been to predict the tertiary structure of a protein given its primary sequence.  A similar but conceptually easier problem is to design a protein which will fold to a given structure with predicted secondary structure.  Many possible sequences could be designed to fold to the desired structure, which makes this problem easier compared to the folding of a given sequence to just one native state.  Kuhlman et al. have recently accomplished such a feat for a synthetic protein of 93 amino acids which they designed to fold to a unique topology not yet observed in nature.  This represents a significant advance over earlier attempts in which mimics of known proteins were made.   Such structures would be expected to fold in analogous fashions to the parent protein because of the necessary constraints placed by the need to fold to a compact state.

Jmol:  Updated  Top7 - A designed 93 amino acid protein with a novel fold    Jmol14 (Java) |  JSMol  (HTML5)    

Several web sites exist that allow users to download protein folding software onto their own PC.  By distributing folding calculations to many home PC, their untapped computational power can be linked to provide the vast computational time needed to perform these calculations.

Additional links:

G6.  Proteomics Problem Set 1

You will study a signal transduction protein and their interaction domains using a variety of web-based proteomics programs. For most of these programs you will need to input the amino acid sequence in FASTA format. Select a PDB code for a protein from the table at the end of this section.  You could also use these programs to study any protein in the PDB.

Getting the FASTA sequence
1. First go to the PDB. Input the name of your protein (which has an interaction domain) in the search box. Limit the search to homo sapiens. Pick from the list of protein structure files the most appropriate one. The example below is for the 2YYN pdb code.

2. Select the Download Files dropdown and save the FASTA sequence to your home directory. Download the file as a Wordpad. You might have to remove recurring sections that don’t correspond to the single letter amino acid sequence or identical sequences if the structure consists of identical subunits To see if that might be the case, select JSmol (see figure above), rotate the structure with your mouse to see if there are multiple chains, and hover the mouse over the chains to see how the amino acids in that chain are labeled. You might see [TRP]33A: for example, where A indicates a separate A chain. Move to other chains. Then go to the Wordpad version of the FASTA sequences. You can examine the chains to see if the chains are identical. If so delete all but the first. See the above FASTA link for help.

I. Prediction of Protein Properties from Sequence Data
Use the following programs to gain information about your protein. Snip (with snipping tool for example) and paste a bit of relevant info from each program (using Snipping Tool) into this DOCX file and save it into the folder and upload it into Sharepoint. Name the file Lastname_LastName_FirstInitial_WebInteraction. If you have any problem with any of the programs (lots of error messages), skip that particular program. Several of them do the same type of analyzes. Compare the result. Snip and paste sufficient content to show that you complete the question. Write answers when asked to interpret the output.

a. Sequence Manipulation Suite: Determine the molecular weight of the protein.

b. Eukaryotic Linear Motif: Linear motifs are short, evolutionarily intrinsically disordered section of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. The Eukaryotic Linear Motif (ELM) provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences. Snip and paste the top of the output that shows the IUPRED showing the disorder/order graph.

c. TargetP 1.1  : predicts the subcellular location of eukaryotic protein. Snip and paste the results. Interpret them based on this link. Where is your protein likely found?

d. NET-NES 1.1 Server:: predicts leucine-rich nuclear export signals (NES) in eukaryotic protein This link will help you explain the output. Does yours?

e. NLSdb -- Database of nuclear localization signals: Search for information on nuclear localization signals (NLSs) and nuclear proteins. Select Query. Input the PDB code and select NL. Does yours?

f. NetPhos 2.0 server: produces neural network predictions for serine, threonine and tyrosine phosphorylation sites in eukaryotic proteins. (other cool prediction programs from this site)

g. TMPRED: The TMpred program makes a prediction of membrane-spanning regions and their orientation. The algorithm is based on the statistical analysis of TMbase, a database of naturally occurring transmembrane proteins. The prediction is made using a combination of several weight-matrices for scoring. Paste in your FASTA sequence but remove the header before running. Does it have a transmembrane helix?

h. TopPred 1.1 – Topology predictor for membrane proteins at the Pasteur Institute. You will have to input your email address. Paste in the entire FASTA file. Does it have transmembrane helices? (Part of Mobyle)

Save the first graph (PNG graphic file) of the output, open it with Adobe Photoshop, and paste the image into your report. Does the graph show alternating hydrophobic (+ values)/hydrophilic (- values) sections consistent with transmembrane helices (for example you would expect to see 7 hydrophobic stretches for GPCR)?

i. PFAM – multiple analyses of Protein FAMilies. This program looks at the domain organization of a protein sequence. Input the pdb code. When finished, select “sequences” in the list below.

PFAM Sequence Bar
Then select the human sequence. Snip the resulting diagram and legend showing the domain structure of the protein. You can also click on each domain in the diagram to get more info on the domain. Does the protein have the domain suggested in the beginning table? 

j. Prosite: Input your FASTA sequence in the Quick Scan mode. Select Exclude motifs with a high probability of occurrence from the scan. Snip and Paste the Hits by Proifle domain structure. Sometimes you might need a different code number, the UniProtKB: Accession number. Get this from the PDB web page as shown below:

k. eFindSite: is a ligand binding site prediction and virtual screening algorithm that detects common ligand binding sites. Put in the PDB code and then the pdb file you downloaded.

l. eFindSitePPI: detects protein binding sites and residues using meta-threading. It also predicts interfacial geometry and specific interactions stabilizing protein-protein complexes, such as hydrogen bonds, salt bridges, aromatic and hydrophobic interactions

m. NCBI Standard Protein BLAST: Input the FASTA file. The output shows the domain and domain superfamily followed by other protein sequences nearly identical to your protein. The results are graphical followed by descriptive. Snip domain structure with the closest aligned sequences.  Then select under PDB structures the pdb code (example below 1xww).
You will see a window similar to below. Select under domain 1xwwwA00 (as an example). :
Then select the UniProtKB accession number. Confirm the many of the predictions you made above.

n. Predict Protein Open: Physiochemical properties of your protein. You will have to provide your email address. When complete you can access much of what you learned above by the links to the left under the Dashboard.

II. Visualizing Protein Interactions

It is important to be able to visualize the binding interactions between the targeted domain and the ligand (small molecule, PTM modified protein, protein or DNA). Here are some programs that allow that. Note: which programa you select will depend on if your protein is bound to a small ligand or to another protein or other macromolecule, in which case you need to explore protein interaction interfaces.


Assignment: You will study the interaction of Protein Kinase C (PKC) with the ligand phorbal ester, which is a mimic of the 2nd messenger diacylglyceride with two programs: Ligand Explorer and Protein-Ligand Interaction Profilers

a. Ligand Explorer is a Java-based program. It probably will NOT work on a Mac running Safari. You will need the latest version of Java to run it. Try the various computer labs around campus as well. Go to the PDB page for your protein. After you input the pdb code, scroll down to Small Molecules section in the middle of the displayed page for that complex. There are links for both 2D and 3D visualization of the interactions. .

Select the 2D plot showing the interactions. The select Jsmol to see the ligand with a binding surface) interacting with contact residues in the protein. You can select a white background and toggle on and off H bonds. SNIP and PASTE.

Now select Ligand Explorer for a more detailed view. Make sure to select the correct ligand (see table below). You may be prompted to allow pop-ups form the site. If so, allow it. You may have to reselect Ligand Explorer again to start the program. Keep giving permissions and following prompts until Ligand Explorer is open. Once launched, select open this link in a new tab or window and instructions will open in a browser. Use the mouse to help find the best view of the interactions.
Select in turn hydrogen bond, hydrophobic, bridged H bond (mediated by a water molecule) and metal interaction (shown on the left hand side. Select Label Interactions by Distance. Take a cropped screenshot of each interaction (see instructions below).
For the final rendering, move the toggle on the Select Surfaces to opaque. Then change the distance in the best way to show the cavity in which the ligand binds. Color by hydrophobicity which gives two colors representing nonpolar and polar parts of cavity. Select solid surfaces

b. Protein-Ligand Interaction Profilers: Input the name of your PDB file. After the run is complete, select SMALL MOLECULE and then the appropriate ligand. You will get a 2D representation you can snip and paste. Then select Pymol 3D view (first 5 computers in ASC 135). You will see an interactive rendering of a small bound ligand and the protein residues it contacts in the complex. You can get a free student download of Pymol for your own computer. Snip and Paste relevant info.


You will study protein:protein interactions between a Src domain and small phospho-Tyr peptide using InterProSurf and COCOMAPS.

a. InterProSurf: Reports numbers of surface and buried atoms for each chain, and areas for each residue deemed to be in the interface. Select PDB Complex in the top menu tabs and input your pdb file. This gives numerical data only. Snip and Paste relevant info.

b. COCOMAPS: analyzes and visualizes interfaces in biological complexes (such as protein-protein, protein-DNA and protein-RNA complexes). Input the PDB file name and then the chains within the PDB file that you wish to see the interaction surface. Put in the letter for one of the interacting chains you selected into the first input box and the second letter into the second box. Detailed results will appear in graphical and tabular form.

A great way to visualize the binding interface is to download the new .pdb and .pml files and open the pdb file in Pymol .  Once the PDB file is opened in Pymol, select file -> run -> script_name.pml.  Snip and Paste relevant info.

Table:   Signaling Proteins for Analysis

Domains in Signaling Molecules


Binding Target

Cellular Process

Example protein

Pdb file



Chromatin reg.





Plasma memb recruitment




Phospholipid (Ca dependent)

Membrane targeting, vesicle trafficking




Homotypic interactins






Chromo reg, gene txn



Death (DD)

Homotypic inter.





Homotypic inter.


Caspase 8



Memb, GPCRs

Sig trans, prot trafficking


human dishevelled 2



Arf/Art G prot

Golgi traffic

Golgin-97 (Golga5)



C-term peptide motifs

Diverse, scaffolding


Or discs large homolog 4




Membrane recuirt






Y kinase signaling

Shc 1

SHC-transforming protein 1





GTP binding pocket of Galpha

Sig trans









Pro-rich sequence

Diverse, cytoskelet





Cytokine and immune




TNF signaling

Cell survival













Protein Ligand and Protein Protein Interactions

Protein (PKC) :Ligand  (phorbal ester mimic of 2nd messenger diacylglyceride with Ligand Explorer and
Protein-Ligand Interaction Profilers


Protein (Chain E-Src fragment) : Protein (Chain I – phospho-peptide) with COCOMAPS


H-Ras-GppNHp bound to the Ras binding domain (RBD) of Raf Kinase

GppNHp binding with Ligand Explorer and Protein-Ligand Interaction Profilers

Protein (Ras, chain A):Protein (RBD-Raf, Chain B) interactions with COCOMAPS


G7.  Proteomics Problem Set 2

You will study a protein, Myelin Regulatory Factor (MYRF), which may be a transcription factor. One way to learn more about the features and likely function of the MYRF protein is to explore the structure of the 1,139 amino acid sequence in silico.

You will analyze the protein sequence using a variety of web-based proteomics programs.  For most of these programs you will need to input the amino acid sequence in FASTA format.  Here is the FASTA amino acid sequence (in single letter amino acid code).     

Use these programs to gain information about this protein.  If you have any problem with any of the programs (lots of error messages), skip that particular program.

a.      Sequence Manipulation Suite: Determine the molecular weight of the protein.

b.      Eukaryotic Linear Motif :  Linear motifs are short, evolutionarily plastic components of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. Given their importance, our understanding of motifs is surprisingly limited, largely as a result of the difficulty of discovery, both experimentally and computationally. The Eukaryotic Linear Motif (ELM) provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences.

c.       PSORT II: programs for prediction of eukaryotic sequence subcellular localization as well as other datasets and resources relevant to cellular localization prediction.  After running it, examine the link shown as PSORT features and traditional PSORTII prediction.
You might get an error message saying the protein does not begin with an N (Met).  Met is the first amino acid encoded from a gene sequence in eukaryotes (using the codon AUG).  It is usually removed after or during protein synthesis.  Don’t’ worry about it.  Either way, the output shows you the number of homologous proteins found and where they are located (cyto, nuc, secreted, etc). Go to the Details link and the protein are listed.  The ones on top are most homologous to the MYRF.

d.       NucPred: analyses a eukaryotic protein sequence and predicts if the protein spends at least some time in the nucleus or spends no time in the nucleus

 e.      TMPRED:  The TMpred program makes a prediction of membrane-spanning regions and their orientation. The algorithm is based on the statistical analysis of TMbase, a database of naturally occurring transmembrane proteins. The prediction is made using a combination of several weight-matrices for scoring 

f.        CCTOP  - Prediction of transmembrane helices and topology of proteins.  Select the advanced tab.  This program might not work.   In the output under each amino acid you will see I (inside), O (outside), H for transmembrane helical region, and i of indeterminate.

g.      Das-TMfilter:  might have to remove nonsequence part of fasta file

h.      TopPred 1.1 – Topoloyg predictor for membrane proteins at the Pasteur Institute.  You will have to input your email address.

i.        PFAM – multiple analyses of Protein FAMilies.   View a sequence. Look at the domain organization of a protein sequence.  Input MRF_Mouse.  Click on the various domains discovered based on sequence homology.

j.        Prosite:  input your sequence in the fast scan region.  Prosite  can determine the likely function of the protein MYRF based on presence of "patterns, motifs, or signatures " in the protein sequences which are characteristic of a specific biological function, such as ligand binding, catalysis, in vivo chemical modification.  We will only use it to probe for post-translational modification sites.   Select Scan a sequence against PROSITE patterns and profiles, and see possible sites for in vivo chemical modification of the protein. In Prosite Tools uncheck exclude patterns of high probability of occurrence. 

k.       HHPRED will give you homology detection and structure prediction, returning domain information and alignment with other proteins of known function.  Select the input link (FASTA format) to input your sequence.

l.        NCBI Standard Protein BLAST:  

m.    m. Use CATH  (Protein Structure Classification - Class, Architecture, Topology, homology Superfamilies) to determine its domain structure and the superfamily it resides in.  Select Search and type in 1XWW in the ID/Key Word box.   Select return.  Determine its class, architecture, topology and homologous Superfamily classifications.  After search, select the BLAST tab, then select CATH Code OR click CATH Code Superfamily (whichever works)Go to  

n.n. UniProt and input the mouse MYRF sequence (accession number Q3UR85)for a trove of information which you have probably just discovered.   Do the in silico analysis support the fact that the protein is a transcription factor?

G8.  General Links and References

  1. Bradley, P. et al. Toward high-resolution de novo structure prediction for small proteins.  Science. 309, 1868 (2005)
  2. Boyle J. A. Bioinformatics in Undergraduate Education.  Biochemistry and Molecular Biology Education. 32, 236 (2004)
  3. Feig, A. L., & Jabri, E. . Incorporation of Bioinformatics Exercises into the Undergraduate Biochemistry.  Biochemistry and Molecular Biology Education. 30, 224 (2002)
  4. Mayor et al. The complete folding pathway of a protein from nanoseconds to microseconds. Nature 421, pg 863 (2003)
  5. Zhou and Karplus. Interpreting the folding kinetics of helical proteins.  Nature 401, pg 400(1999) 


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