Build new enzymes using SSV

SSV (Structural Signature Variation ) is a method to propose mutations for enzymes used in industrial applications. SSV uses structural signatures to detect patterns, which can improve the activity of enzymes. A real and important application for SSV is the second-generation biofuel production.

Run now!

About

Case study: Second-Generation Biofuel Production

Cellulose is decomposed by the action of three enzymes: endoglucanases, exoglucanases, and β-glucosidases.

β-glucosidases are strongly inhibited by high glucose concentrations (same used for biofuel production).

In addition, it increases cellobiose concentration that inhibites endoglucanases and exoglucanases.

Glucose-tolerant β-glucosidases

β-glucosidases of high resistance to glucose inhibition, also called glucose-tolerants, can help to improve the biofuel production. Also, some mutations can turn non-tolerant in glucose-tolerant β-glucosidases.

SSV Method

SSV method constructs structural signatures for wild and mutant proteins and compares the signature's variation with a manually curated database of glucose-tolerant β-glucosidases, called Betagdb.

Documentation

Basic Flowchart of SSV

Solutions

Python scripts

You can download SSV sourcecode in Python and run on your computer. It requires: Python, MODELLER (if PDB files were not available), MULTIPROT (for structural alignment), and Perl (for running aCSM).

Download Python scripts

Running online

You also can test SSV online. It requires as input PDB files for wild mutant protein. SSV online returns the ΔΔSSV score. If ΔΔSSV > 0, the mutation was not beneficial; if ΔΔSSV < 0, the mutation was beneficial.

Run online now!

Download documentation

Script	Input	Output
extractSite.py	PDBs files inserted in the folder “pdb”.	PDBs files with the catalytic pocket extracted.
dgts3.py	CSV file obtained from aCSM. The first 23 lines must represent the beta-glucosidases from betadb in alphabetical order.	ΔΔGTS and templates for wild and mutant save in the file “result.txt”.

id	File (Wild)	File (Mutant)	Mutation	ΔΔGTS expected	ΔΔGTS score
1	w1.pdb	m1.pdb	H228T	ΔΔGTS < 0	-186.18
2	w2.pdb	m2.pdb	V174C/A404V/L441F	ΔΔGTS < 0	-246.22
3	w3.pdb	m3.pdb	H184F	ΔΔGTS < 0	100.37
4	w4.pdb	m4.pdb	P172L	ΔΔGTS < 0	-6.29
5	w5.pdb	m5.pdb	P172L/F250A	ΔΔGTS < 0	-6.29
6	w6.pdb	m6.pdb	L167W	ΔΔGTS < 0	-602.80
7	w7.pdb	m7.pdb	L167W/P172L	ΔΔGTS < 0	-615.46
8	w8.pdb	m8.pdb	L167W/P172L/P338F	ΔΔGTS < 0	-615.46
9	w9.pdb	m9.pdb	V168Y	ΔΔGTS > 0	330.56
10	w10.pdb	m10.pdb	F225S	ΔΔGTS > 0	-365.07
11	w11.pdb	m11.pdb	Y308F	ΔΔGTS > 0	34.19
12	w12.pdb	m12.pdb	Y308A	ΔΔGTS > 0	-108.62
13	w13.pdb	m13.pdb	I207V	ΔΔGTS < 0	-71.56
14	w14.pdb	m14.pdb	N218H	ΔΔGTS < 0	-230.61
15	w15.pdb	m15.pdb	N273V	ΔΔGTS > 0	-55.26
16	w16.pdb	m16.pdb	F252I	ΔΔGTS > 0	86.70
17	w17.pdb	m17.pdb	F252W	ΔΔGTS > 0	129.97
18	w18.pdb	m18.pdb	F252Y	ΔΔGTS > 0	37.86
19	w19.pdb	m19.pdb	M284N	ΔΔGTS > 0	-127.35
20	w20.pdb	m20.pdb	H276M	ΔΔGTS > 0	-501.32
21	w21.pdb	m21.pdb	V173C	ΔΔGTS > 0	13.59
22	w22.pdb	m22.pdb	M177L	ΔΔGTS > 0	20.86
23	w23.pdb	m23.pdb	D229N	ΔΔGTS > 0	18.11
24	w24.pdb	m24.pdb	H231D	ΔΔGTS > 0	-54.22
25	w25.pdb	m25.pdb	E96K	ΔΔGTS < 0	-31.08
26	w26.pdb	m26.pdb	N223G	ΔΔGTS > 0	39.37
27	w27.pdb	m27.pdb	N223Q	ΔΔGTS > 0	264.34