JBrowseMSA

Hemoglobin’s two subunits and the interfaces between them

Hemoglobin is a tetramer of two alpha and two beta chains, and the residues that hold it together sit on two kinds of interface: alpha1beta1, the packing contacts that keep a dimer together, and alpha1beta2, the sliding contacts that move when the tetramer binds oxygen. The viewer holds one sequence per row, so this page gives each species one row with its alpha and its beta concatenated, and draws every residue pair in contact between two chains of PDB 2HHB as an arc from the alpha block to the beta block. Eleven vertebrates, 22 RefSeq proteins, and the last section reads how conserved each interface is against the residues on the surface, which touch nothing.

Prerequisites

Every figure below links to the live view it captured.

Where the data comes from

Twenty-two globin proteins as NCBI RefSeq holds them, and one crystal structure from RCSB.

1. Name the rows

Write one species per line: the label the viewer draws, then the RefSeq protein for the alpha subunit and for the beta subunit. The label becomes the FASTA defline, the tree tip, the GFF seq_id and the row of every layer.

Human	NP_000549.1	NP_000509.1
Macaque	NP_001038189.1	NP_001157900.1
Mouse	NP_032244.2	NP_001188320.1
Rat	NP_001007723.1	NP_942071.2
Cow	NP_001070890.2	NP_776342.1
Pig	NP_001432111.1	NP_001138313.1
Horse	NP_001078901.1	NP_001157490.1
Platypus	XP_028905054.1	XP_028913684.1
Chicken	NP_001004376.1	NP_990820.1
Zebrafish	NP_571332.3	NP_001003431.2
Trout	NP_001118023.1	XP_021413554.1

Seven placental mammals, a monotreme, a bird and two fish. The human pair is HBA1 and HBB; each other species contributes its adult alpha and adult beta chain.

2. Fetch the sequences

One efetch request takes every accession, and a short script splits the result into one FASTA per subunit, with the species label as the defline so the two files pair up by name:

ids=$(awk -F'\t' '{printf "%s%s,%s", sep, $2, $3; sep=","}' rows.tsv)
curl -sf "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=protein&id=$ids&rettype=fasta&retmode=text" \
  -o globins-ncbi.fasta
Human      alpha NP_000549.1     142 aa   beta NP_000509.1     147 aa
Macaque    alpha NP_001038189.1  142 aa   beta NP_001157900.1  147 aa
Mouse      alpha NP_032244.2     142 aa   beta NP_001188320.1  147 aa
Rat        alpha NP_001007723.1  142 aa   beta NP_942071.2     147 aa
Cow        alpha NP_001070890.2  142 aa   beta NP_776342.1     145 aa
Pig        alpha NP_001432111.1  142 aa   beta NP_001138313.1  147 aa
Horse      alpha NP_001078901.1  142 aa   beta NP_001157490.1  147 aa
Platypus   alpha XP_028905054.1  141 aa   beta XP_028913684.1  147 aa
Chicken    alpha NP_001004376.1  142 aa   beta NP_990820.1     147 aa
Zebrafish  alpha NP_571332.3     143 aa   beta NP_001003431.2  147 aa
Trout      alpha NP_001118023.1  143 aa   beta XP_021413554.1  147 aa

Every alpha is 141 to 143 residues and every beta 145 to 147, initiator methionine included. The Cow beta is two residues short at the N terminus, and the two fish alphas carry one extra.

3. Align each subunit on its own

Alpha and beta are paralogs, and an aligner given both would put them in shared columns. Each subunit gets its own alignment, and the two are joined per species afterwards:

for sub in alpha beta; do
  clustalw -INFILE=$sub.fasta -ALIGN -TYPE=PROTEIN -OUTORDER=INPUT \
    -OUTPUT=FASTA -OUTFILE=$sub.afa
done

-OUTORDER=INPUT keeps the rows in the table’s order, so the two files line up by position as well as by name. Concatenating them is a loop over the labels:

alpha = read('alpha.afa')
beta = read('beta.afa')
with open('hemoglobin.afa', 'w') as fh:
    for name, a in alpha.items():
        fh.write(f'>{name}\n{a}{beta[name]}\n')
alpha: 143 columns, beta: 147 columns
concatenated: 290 columns, beta starts at column 144
Eleven rows of 290 columns, the alpha block to the left of column 144 and the beta block to its right, with the tree inferred from the concatenation in step 5.

4. Mark the subunits

A GFF gives every row its two spans, in that row’s own residue numbering, so the boundary lands on the right residue in the rows whose alpha is one residue shorter or longer. A color= attribute on each line sets the overlay’s color:

alen = len(a.replace('-', ''))
blen = len(b.replace('-', ''))
g.write(f'{name}\tRefSeq\tpolypeptide_region\t1\t{alen}\t.\t.\t.\t'
        f'Name=alpha;signature_desc=hemoglobin subunit alpha;color=%234e79a7\n')
g.write(f'{name}\tRefSeq\tpolypeptide_region\t{alen + 1}\t{alen + blen}\t.\t.\t.\t'
        f'Name=beta;signature_desc=hemoglobin subunit beta;color=%23e15759\n')
Human	RefSeq	polypeptide_region	1	142	.	.	.	Name=alpha;signature_desc=hemoglobin subunit alpha;color=%234e79a7
Human	RefSeq	polypeptide_region	143	289	.	.	.	Name=beta;signature_desc=hemoglobin subunit beta;color=%23e15759
The two spans as the overlay draws them, blue for alpha and red for beta. The Platypus row has a one-column gap at the start of its alpha, and the Cow row a two-column gap at the start of its beta.
The boundary at residue resolution. The last alpha residue is R in every row, the first beta residues are MV in ten of them, and the Cow row has two gap columns where its beta chain starts two residues later.

5. Infer a tree

clustalw -INFILE=hemoglobin.afa -TREE -TYPE=PROTEIN -OUTPUTTREE=phylip
tr -d '[:space:]' < hemoglobin.ph > hemoglobin.nwk
(((Human:0.02366,Macaque:0.02133):0.03786,((Mouse:0.04913,Rat:0.11350):0.04393,
(Platypus:0.14248,(Chicken:0.15042,(Zebrafish:0.18011,Trout:0.16126):0.13737):0.03453)
:0.02900):0.01972):0.00663,(Cow:0.07886,Pig:0.07794):0.00138,Horse:0.07648);

The two fish sit together at the end of the longest branch, Chicken joins them next, then Platypus, and the seven placentals fill the rest of the tree.

6. Read the contacts off the structure

2HHB is the human deoxy tetramer at 1.74 Å. Chains A and C are alpha, B and D are beta; chain A touches B across the alpha1beta1 interface and D across alpha1beta2. Biopython’s NeighborSearch finds every residue pair with a heavy atom of each within 4 Å:

from Bio.PDB import MMCIFParser, NeighborSearch

def contacts(c1, c2, cutoff=4.0):
    ns = NeighborSearch([a for r in c2 for a in r])
    pairs = set()
    for r in c1:
        for atom in r:
            for other in ns.search(atom.coord, cutoff):
                pairs.add((r.id[1], other.get_parent().id[1]))
    return sorted(pairs)

model = MMCIFParser(QUIET=True).get_structure('2HHB', '2hhb.cif')[0]
alpha1beta1 = contacts(model['A'], model['B'])
alpha1beta2 = contacts(model['A'], model['D'])

The RefSeq rows keep the initiator methionine and the mature chains in the crystal do not, so the script finds each chain’s sequence inside the Human row before trusting a single number:

chain A (141 aa) is row residues 2-142 of Human, chain B (146 aa) is 144-289
the Human row is 289 residues: alpha 1-142, beta 143-289
alpha1beta1: 32 residue pairs within 4.0 A, 14 alpha residues and 17 beta residues
alpha1beta2: 26 residue pairs within 4.0 A, 14 alpha residues and 14 beta residues

Chain residue n is row residue n + 1 within its subunit, and beta residue n is row residue 143 + n. Every pair becomes an arc from its alpha column to its beta column, colored by interface, in an arc column track over the Human row. A second, text track gives every residue of that row a class: 1 and 2 for the two interfaces, and for the rest, B where the residue is buried in the assembled tetramer, E where it is exposed, and O between. The exposure comes from Biopython’s ShrakeRupley over the whole tetramer, divided by the residue type’s maximum accessible area:

residue classes on the Human row: 29 at alpha1beta1, 28 at alpha1beta2, 73 buried, 107 exposed, 52 in between

The script also writes two residueMappings records for the Human row, one per chain, so a host can look any residue of the row up in the structure.

58 arcs from the alpha block to the beta block, blue for alpha1beta1 and red for alpha1beta2. Under them, the class track: blue and red on the interface columns, grey where the residue is buried, yellow where it is exposed.

7. How conserved each class is

For every residue of the Human row, the share of the ten other rows carrying the same letter in that column, averaged per class:

alpha1beta1 interface     29 residues, mean identity 0.77, 10 identical in all 11 rows
alpha1beta2 interface     28 residues, mean identity 0.92, 20 identical in all 11 rows
buried, no interface      73 residues, mean identity 0.78, 21 identical in all 11 rows
exposed, no interface    107 residues, mean identity 0.69, 22 identical in all 11 rows
23 of 58 contact pairs have both residues identical in all 11 rows

The alpha1beta2 residues are the most conserved class in the row: 20 of the 28 are identical from human to trout, and the mean identity is above the buried core’s. The alpha1beta1 residues match the core, and the exposed residues, which touch no other chain, are the lowest of the four.

The alpha1beta2 interface at residue resolution, every row read against the Human row: a dot is the same residue, a letter a different one. The boxed column is alpha Tyr42, whose hydroxyl reaches Asp99 of the beta chain across the interface, and it is a dot in every row.
The alpha1beta1 interface at the same resolution. The boxed column is alpha 111, the residue of that interface whose contact pair varies most across the rows, and it reads a different letter in most of them.

8. Check it against the raw data

The script prints the letters every row carries at one pair per interface, the Tyr42 to Asp99 pair and the alpha1beta1 pair that varies most:

alpha1beta2 Tyr42-Asp99: row residues 43 and 242, columns 43 and 243
  Human      Y D
  Macaque    Y D
  Mouse      Y D
  Rat        Y D
  Cow        Y D
  Pig        Y D
  Horse      Y D
  Platypus   Y D
  Chicken    Y D
  Zebrafish  Y D
  Trout      Y D
alpha1beta1, most variable: row residues 112 and 258, columns 113 and 259
  Human      A A
  Macaque    A A
  Mouse      S G
  Rat        C G
  Cow        S A
  Pig        A A
  Horse      V A
  Platypus   R A
  Chicken    I A
  Zebrafish  M A
  Trout      I A

Alpha 111 and beta 115 pack against each other in the crystal, and across the eleven rows the alpha side reads seven different letters.

9. Open the whole thing

The view combines three hosted files and three layers. The files go behind URLs, and the layers go in the link, because they take a few kilobytes:

{
  "msaview": {
    "type": "MsaView",
    "colWidth": 4.4,
    "rowHeight": 22,
    "colorSchemeName": "clustalx_protein_dynamic",
    "msaFilehandle": { "uri": "https://example.org/hemoglobin.afa" },
    "treeFilehandle": { "uri": "https://example.org/hemoglobin.nwk" },
    "gffFilehandle": { "uri": "https://example.org/hemoglobin-subunits.gff" },
    "showDomains": false
  }
}

Add the columnTracks and residueMappings entries from hemoglobin-layers.json beside those, URL-encode the whole object, and hang it off the app as ?data=.

The final view: the contact arcs and the class track above, the alignment below, the subunit spans loaded with the overlay off so the residue colors show through.

A host that loads this view can look a residue of the Human row up in either chain. The two mappings share an entry id, so the reverse lookup needs the chain:

model.structureResidue('Human', 43) // 2HHB chain A, residue 42
model.structureResidue('Human', 242) // 2HHB chain B, residue 99
model.structureResidue('Human', 143) // undefined: the beta initiator, in no chain
model.rowResidue('2HHB', 42) // undefined: both chains have a residue 42
model.rowResidue('2HHB', 42, 'A') // Human, residue 43

Reproduce it end to end

curl -O https://raw.githubusercontent.com/GMOD/JBrowseMSA/main/docs/tutorials/scripts/build_protein_complex.sh
bash build_protein_complex.sh

With no arguments the script writes the row table above and builds all four files beside it, printing every number on this page. Pass your own table to do the same for another complex and another structure:

bash build_protein_complex.sh my-rows.tsv out/

See also

References