ACPred


ACPred is a webserver for the prediction of adenylyl cyclase functional centers (ACCs) from amino acid sequence. ACC is a novel class of functional centers typically consisting of short 14 amino acids and is found in complex multi-domain proteins. They are different from canonical AC domains hence ACPred is not valid for the prediction of such domains as well as transmembrane regions.

Instructions:
The software takes a single or multiple amino acid sequence and returns predicted ACCs each accompanied by ACC values 0-1, where 1 is closest to the mean of experimentally validated ACCs.

Please enter single letter amino acid codes in FASTA format and select if co-factor binding (Mg2+/Mn2+) is required. The amino acid implicated in co-factor binding is found at 0-3 positions downstream of the ACC.

Requirement for Mg2+/Mn2+ co-factor

For example
>sp|Q9FY75|POT7_ARATH Potassium transporter 7 OS=Arabidopsis thaliana OX=3702 GN=POT7 PE=1 SV=2
MAEESSMEGSEKEEIDSSGGGFGDMASMDSIESRWVIQDDDDSEIGVDDD NDGFDGTGLESDEDEIPEHRLIRTGPRVDSFDVEALEVPGAPRNDYEDLT VGRKVLLAFQTLGVVFGDVGTSPLYTFSVMFSKSPVQEKEDVIGALSLVL YTLLLVPLIKYVLVVLWANDDGEGGTFALYSLISRHAKISLIPNQLRSDT RISSFRLKVPCPELERSLKLKEKLENSLILKKILLVLVLAGTSMVIADGV VTPAMSVMSAVGGLKVGVDVVEQDQVVMISVAFLVILFSLQKYGTSKMGL VVGPALLIWFCSLAGIGIYNLIKYDSSVYRAFNPVHIYYFFKRNSINAWY ALGGCILCATGSEALFADLCYFSVRSVQLTFVCLVLPCLMLGYMGQAAYL MENHADASQAFFSSVPGSAFWPVLFIANIAALIASRTMTTATFSCIKQST ALGCFPRLKIIHTSRKFMGQIYIPVLNWFLLAVCLVVVCSISSIDEIGNA YGMAELGVMMTTTILVTLIMLLIWQINIVIVIAFLVVFLGVELVFFSSVI ASVGDGSWIILVFAVIMFGIMYIWNYGSKLRYETEVEQKLSMDLMRELGC NLGTIRAPGIGLLYNELVKGVPAIFGHFLTTLPAIHSMVIFVCIKYVPVP VVPQNERFLFRRVCTKSYHLFRCIARYGYKDARKETHQAFEQLLIESLEK FIRREAQERSLESDGNDDSDSEEDFPGSRVVIGPNGSMYSMGVPLLSEYR DLNKPIMEMNTSSDHTNHHPFDTSSDSSVSEAEQSLERELSFIHKAKESG VVYLLGHGDIRARKDSWFIKKLVINYFYTFLRKNCRRGIANLSVPQSHLM QVGMTYMV



If you find ACPred useful for your work, please cite:
Xu N, Zhang C, Lim LL & Wong A (2018). Proceedings of ACM-BCB, DOI: 10.1145/3233547.3233549

ACPred was built based on previous work published in:
Xu N, Fu D, Li S, Wang Y & Wong A (2018). Bioinformatics, DOI: 10.1093/bioinformatics/bty067
Wong A, Tian X, Gehring C & Marondedze C (2018). Comput Struct Biotechnol J, 16 (2018), pp. 70-76
Wong A, Gehring C & Irving HR (2015). Front Bioeng Biotechnol 82, 1-8
Al-Younis I, Wong A & Gehring C (2015). FEBS Lett, 589 (2015), pp. 3848-3852
Wong A & Gehring C (2013). Methods Mol Biol, 1016, 195-205, Humana Press
Ruzvidzo O, Dikobe BT, Kawadza DT, Mabadahanye GH, Chatukuta P & Kwezi L (2013). Methods Mol Biol, 1016, 13-25, Humana Press
Gehring C (2010). Cell Commun Signal, 8 (2010), p. 15
Chatukuta P, Dikobe TB, Kawadza DT, Sehlabane KS, Takundwa MM, Wong A, Gehring C & Ruzvidzo O (2018). Biomolecules, 23(2), E15

This tool is still under development and the ACC values will be revised as new experimental data becomes available. Additional features and improvements are forthcoming.

Please contact us at alwong@kean.edu if you have any questions or comments.