Hi all, I was using the residue depth module from Bio.PDB and extracting the residue depth of proteins. However, I randomly input a protein and found out that the residue depth are too similar among residues (1.5-1.9A).I tried different proteins and had similar results. I expected to have a bigger deviation and larger residue depth. Anyone has any idea on how to interpret this seemingly incorrect results.
Coding:
parser = MMCIFParser() structure = parser.get_structure('test', 'PDB/1qmo.cif') model = structure[0] chain = model['A'] surface = get_surface(model) for each in range(1,100): resi = chain[each] rd = ResidueDepth(resi, surface) resi_depth,ca_depth=rd[rd.keys()[0]] print(resi.get_resname(),each,"resi_depth:",resi_depth,"ca_depth:",ca_depth)
Output:
ALA 1 resi_depth: 1.76746537894
GLN 2 resi_depth: 1.75285448018
SER 3 resi_depth: 1.73959646157
LEU 4 resi_depth: 1.82207581001
SER 5 resi_depth: 1.73955300311
PHE 6 resi_depth: 1.8067918142
SER 7 resi_depth: 1.73951724545
PHE 8 resi_depth: 1.80683567125
THR 9 resi_depth: 1.77673089694
LYS 10 resi_depth: 1.76756483127
PHE 11 resi_depth: 1.80688191791
ASP 12 resi_depth: 1.67205727412
PRO 13 resi_depth: 1.83379116059
ASN 14 resi_depth: 1.72208197147
GLN 15 resi_depth: 1.7529624168
GLU 16 resi_depth: 1.70843598225
ASP 17 resi_depth: 1.67203387621
LEU 18 resi_depth: 1.8220660772
ILE 19 resi_depth: 1.85456052148
PHE 20 resi_depth: 1.80679806743
GLN 21 resi_depth: 1.75289893794
GLY 22 resi_depth: 1.70963404182
HIS 23 resi_depth: 1.75363235972
ALA 24 resi_depth: 1.76757734259
THR 25 resi_depth: 1.77668049
SER 26 resi_depth: 1.73960713832
THR 27 resi_depth: 1.77673852224
ASN 28 resi_depth: 1.72209344522
ASN 29 resi_depth: 1.72204425256
VAL 30 resi_depth: 1.83378782042
LEU 31 resi_depth: 1.82205552799
GLN 32 resi_depth: 1.75291631342
VAL 33 resi_depth: 1.83386014702
THR 34 resi_depth: 1.77672868555
LYS 35 resi_depth: 1.76759233854
LEU 36 resi_depth: 1.82211062771
ASP 37 resi_depth: 1.67206662754
SER 38 resi_depth: 1.73951018538
ALA 39 resi_depth: 1.76750116214
GLY 40 resi_depth: 1.70948746419
ASN 41 resi_depth: 1.72215038037
PRO 42 resi_depth: 1.8338081986
VAL 43 resi_depth: 1.83392179311
SER 44 resi_depth: 1.73966523175
SER 45 resi_depth: 1.739524853
SER 46 resi_depth: 1.73944782784
ALA 47 resi_depth: 1.76764147231
GLY 48 resi_depth: 1.70945286317
ARG 49 resi_depth: 1.80681924879
VAL 50 resi_depth: 1.83390929813
LEU 51 resi_depth: 1.82205562777
TYR 52 resi_depth: 1.77962717968
SER 53 resi_depth: 1.7395572628
ALA 54 resi_depth: 1.76755923724
PRO 55 resi_depth: 1.83388677583
LEU 56 resi_depth: 1.82205880098
ARG 57 resi_depth: 1.80685753138
LEU 58 resi_depth: 1.82203206138
TRP 59 resi_depth: 1.78952236069
GLU 60 resi_depth: 1.70846381898
ASP 61 resi_depth: 1.67212575224
SER 62 resi_depth: 1.73949591106
ALA 63 resi_depth: 1.76749955914
VAL 64 resi_depth: 1.83385779002
LEU 65 resi_depth: 1.82206430838
THR 66 resi_depth: 1.77673042203
SER 67 resi_depth: 1.73957876291
PHE 68 resi_depth: 1.80681401187
ASP 69 resi_depth: 1.67210216411
THR 70 resi_depth: 1.77667940948
ILE 71 resi_depth: 1.85456746315
ILE 72 resi_depth: 1.85452470948
ASN 73 resi_depth: 1.7220897346
PHE 74 resi_depth: 1.80684148809
GLU 75 resi_depth: 1.7084363719
ILE 76 resi_depth: 1.85454488214
SER 77 resi_depth: 1.73950168541
THR 78 resi_depth: 1.77665741506
PRO 79 resi_depth: 1.8337830683
TYR 80 resi_depth: 1.77954354275
THR 81 resi_depth: 1.77672941066
SER 82 resi_depth: 1.7394912958
ARG 83 resi_depth: 1.76752844782
ILE 84 resi_depth: 1.8545700946
ALA 85 resi_depth: 1.76750466164
ASP 86 resi_depth: 1.67206078958
GLY 87 resi_depth: 1.70953721952
LEU 88 resi_depth: 1.82205449959
ALA 89 resi_depth: 1.76757115457
PHE 90 resi_depth: 1.80682955618
PHE 91 resi_depth: 1.8068518812
ILE 92 resi_depth: 1.85456522545
ALA 93 resi_depth: 1.76751459015
PRO 94 resi_depth: 1.83381213235
PRO 95 resi_depth: 1.83391253076
ASP 96 resi_depth: 1.67205295932
SER 97 resi_depth: 1.73953674539
VAL 98 resi_depth: 1.83378935217
ILE 99 resi_depth: 1.85448345625
