Software for designing Bio-therapeutic molecules
Software Name Description Usage
CellPPD CellPPD is an in silico method, which is developed to predict and design efficient cell penetrating peptides (CPPs). The main dataset used in this method consists of 708 experimentally validated CPPs. One of the major features of server is that it also calculates various physicochemical properties. Peptide analogues can be displayed in sorting order based upon desired properties. cellppd.pl -i /gpsr/examples/example_cellppd.fasta -o out -t 0.5 -m 1
-i Sequence in FASTA format
-o out put file
-t SVM threshold
-m Method (1 for Binary based and 2 for Binary+Motif (Hybrid method) based)
ToxinPred ToxinPred is an in silico method, which is developed to predict and design toxic/non-toxic peptides. The main dataset used in this method consists of 1805 toxic peptides (less than or equal to 35 residues). toxinpred.pl -i /gpsr/examples/example_toxinpred.fasta -o out -t 0.5 -m 1
-i Sequence in FASTA format
-o out put file
-t SVM threshold
-m Method (1 for Dipeptide based (Swiss), 2 for Dipeptide+Motif (Swiss), 3 for Dipeptide based (Trembl),4 for Dipeptide+Motif (Trembl), 5 for Monopeptide (Quantitative Matrix, Swiss), 6 for Monopeptide (Quantitative Matrix, Trembl), 7 for Dipeptide (Quantitative Matrix, Swiss) and 8 for Dipeptide (Quantitative Matrix, Trembl)
DesiRM Short interfering RNA (siRNA) has become a major tool in basic sciences for functional gene knockdown and in molecular medicine to suppress aberrant gene expression. In natural system it is not always possible to get highly effective siRNA against a target. Thus it is important to design highly effective siRNA by making minimum mutation in it against a given target. This study can be divided into two parts. In the first part we developed a support vector machine model for predicting siRNA efficacy on existing datasets. The performance of our model is as good as other well-known methods when tested on independent data. In the second part we developed a strategy where one can design mutant siRNA of desired efficacy. In this approach we mutated a given siRNA on all possible sites with all possible nucleotides. Efficacy of mutated siRNAs is predicted using SVM model. It is well known from literature that making mismatches between siRNA and target affect the silencing efficacy. Thus we have incorporated the rules derived from base mismatches experimental data to find out over all efficacy of mutated siRNAs. Finally we have developed this webserver, desiRm, which is freely accessible for academic user.
./desirm_sirna.pl -i input_file -t threshold_of_svm -o output_file
-i: input file in fasta format
-t: SVM Threshold (ranges from -1 to +1 with default value of 0)
-o: output file
AntiBP Antibp server predicts the antibacterial peptides in a protein sequence.Prediction can be done by using QM,ANN and SVM based methods using binary patterns of peptide sequences and overall accuracy of this server is ~92.11%.This server can help in finding and designing of peptides based antibiotics.
antibp.pl -i input_file -o output -p path
-i Sequence in FASTA format
-o name of out put file
AntiBP2 AntiBP2 server predicts the antibacterial peptides in a protein sequence. Prediction can be done by using Support Vector Machine (SVM) based method using coposition of peptide sequences and overall accuracy of this server is ~92.14%. This server can also predict the source of these antibacterial peptides with ~98.52% accuracy. If the source of these antibacterial peptides are insect, frog or mammal then it gives the information of its family also. This server can help in finding and designing of peptides based antibiotics.
antibp2.pl -i input_file -o output -p path
-i Sequence in FASTA format
-o name of out put file
