SPECS is a side-chain-orientation-included protein model-native similarity metric for improved evaluation of protein structural models. SPECS stands for Superposition-based Protein Embedded CA SC score. It combines side-chain orientation and global distance based measures in an integrated framework using the united-residue model of polypeptide conformation for computing model-native similarity. SPECS captures both global and local quality aspects when evaluating structural similarity and is sensitive to minute variations in side-chain, thereby being a robust evaluation metric covering a wide range of modeling scenarios and various aspects of structural similarity. SPECS has the value in (0,1], where 1 indicates a perfect match between two structures.
If you find SPECS useful, please cite our PLOS ONE paper.
- Linux system: SPECS was tested on 64-bit Linux system
- GCC compiler
You can run SPECS from SPECS web-server. However, for large-scale benchmarking, we strongly recommend that you install and run SPECS locally. SPECS is a standalone application. To install, you just need to download and compile as follows:
$ git clone https://github.com/Bhattacharya-Lab/SPECS.git
$ cd SPECS/src
$ g++ -o SPECS SPECS.cpp
To run SPECS, type
$ ./SPECS -h
You should see the following output:
**********************************************************************
* SPECS *
* Superposition-based Protein Embedded CA SC score *
* Range of SPECS: *
* 0.0 <= SPECS <= 1.0, higher scores indicate better similarity *
* For comments, please email to [email protected] *
**********************************************************************
Usage: ./SPECS -m model -n native
-m model : model pdb file
-n native : native pdb file
-h help : this message
Example command to run SPECS
$ ./SPECS -m ../example/model.pdb -n ../example/native.pdb
SPECS provides instantaneous output and computes other structural similarity metrics. For ease of parsing, the output format of SPECS has been kept similar to the currently popular TM-score program. Upon running the above example command, you should see following output:
**********************************************************************
* SPECS-SCORE *
* Superposition-based Protein Embedded CA SC score *
* Range of SPECS: *
* 0.0 <= SPECS <= 1.0, higher scores indicate better similarity *
* For comments, please email to [email protected] *
**********************************************************************
Structure1: ../example/model.pdb Length = 72
Structure2: ../example/native.pdb Length = 72
Number of residues in common = 72
RMSD of common residues = 3.323
SPECS-score = 0.3704 (dCA = 0.4722 rSC = 0.3823 angl = 0.1597 ang2 = 0.3903 tors = 0.1417)
TM-score = 0.6443 (d0 = 2.97)
MaxSub-score = 0.6197 (d0 = 3.50)
GDT-TS-score = 0.6840 %(d<1) = 0.3611 %(d<2) = 0.5556 %(d<4) = 0.8333 %(d<8) = 0.9861
GDT-HA-score = 0.4722 %(d<0.5) = 0.1389 %(d<1) = 0.3611 %(d<2) = 0.5556 %(d<4) = 0.8333
-------- rotation matrix to rotate Chain-1 to Chain-2 ------
i t(i) u(i,1) u(i,2) u(i,3)
1 -23.6888008118 0.7470999956 0.6341999769 0.1993000060
2 12.4891996384 -0.0847999975 -0.2064999938 0.9747999907
3 45.8860015869 0.6593000293 -0.7451000214 -0.1005000025
Superposition in the TM-score: Length(d<5.0) = 62 RMSD = 2.26
(':' denotes the residue pairs of distance < 5.0 Amstrong)
RLALSDAHFRRICQLIYQRAGIVLADHKRDMVYNRLVRRLRALGLDDFGRYLSMLEANQNSAEWQAFINALT
:::::::::::::::::::::: ::::::::::::::::::::::::::::::::: :::: :::
RLALSDAHFRRICQLIYQRAGIVLADHKRDMVYNRLVRRLRALGLDDFGRYLSMLEANQNSAEWQAFINALT
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