Consensus protein sequences are useful for numerous applications. Often, mutating a protein to be more like the consensus of homologs will increase the stability of a protein, allowing it to function at higher temperatures, and have better soluble expression when expressed recombinatly. Consensus Finder identifies a consensus sequence and predicts potentially stabilizing mutations.
Consensus Finder starts from your protein sequence, finds similar sequences from the NCBI database, aligns them, removes redundant/highly similar sequences, trims alignments to the size of the original query, and analyzes consensus. Output is a trimmed alignment, consensus sequence, frequency and count tables for amino acids at each position, as well as a list of suggested mutations to consensus that may be stabilizing.
For help and further instruction, please visit the help guide
Download Consensus Finder for local use
To cite Consensus Finder:
B. J. Jones, H. Y. Lim, J. Huang, R. J. Kazlauskas (2017) Comparison of five protein engineering strategies to stabilize an α/β-hydrolase. Biochemistry 56, 6521–32; doi:10.1021/acs.biochem.7b00571
Consensus Finder uses the following tools:
blastp (2.2.31+): C. Camacho, G. Coulouris, V. Avagyan, N. Ma, J. Papadopoulos, K. Bealer, T. L. Madden (2008) BLAST+: architecture and applications. BMC Bioinformatics 10, 421;doi:/10.1186/1471-2105-10-421
CD-HIT (4.6.4): W. Li, L. Jaroszewski, A. Godzik (2001) Clustering of highly homologous sequences to reduce the size of large protein database. Bioinformatics, 17, 282-3; doi:10.1093/bioinformatics/17.3.282 Id. (2002) Tolerating some redundancy significantly speeds up clustering of large protein databases. Bioinformatics 18, 77-82; doi:10.1093/bioinformatics/18.1.77
Clustal Omega (1.2.0): F. Sievers et al. (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539; doi:10.1038/msb.2011.75