Pregled bibliografske jedinice broj: 308354
Prediction of protein-protein hetero interaction sites from local sequence information using Random Forest
Prediction of protein-protein hetero interaction sites from local sequence information using Random Forest // Book of abstracts, The 2nd Opatija Meeting on Computational Solutions in the Life Sciences / Babić, Darko ; Došlić, Nađa ; Smith, David ; Tomić, Sanja, Vlahoviček, Kristian (ur.).
Opatija: Centre for Computational Solutions in the Life Sciences, IRB, 2007. str. 87-87 (poster, međunarodna recenzija, sažetak, znanstveni)
CROSBI ID: 308354 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Prediction of protein-protein hetero interaction sites from local sequence information using Random Forest
Autori
Šikić, Mile ; Jeren, Branko ; Vlahoviček, Kristian
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni
Izvornik
Book of abstracts, The 2nd Opatija Meeting on Computational Solutions in the Life Sciences
/ Babić, Darko ; Došlić, Nađa ; Smith, David ; Tomić, Sanja, Vlahoviček, Kristian - Opatija : Centre for Computational Solutions in the Life Sciences, IRB, 2007, 87-87
ISBN
978-953-6690-69-5
Skup
The 2nd Opatija Meeting on Computational Solutions in the Life Sciences
Mjesto i datum
Opatija, Hrvatska, 04.09.2007. - 09.09.2007
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
protein ; prediction ; interaction ; sequence ; random forest
Sažetak
Identifying the interface between two interacting proteins provides important clues to the function of a protein, and is becoming increasing relevant to drug discovery. Many studies indicate that the compositions of contacting residues are unique. Here, we describe a method using random forest that identifies protein-protein interfaces from sequence. For prediction we use a non-redundant set of 333 protein complexex. The AUC for random forest classifier is 0.76. When 75% of our predictions were right, we correctly predicted 34% of all interaction sites. In almost all proteins we correctly predicted at least one interaction site. Furthermore, when in prediction we included residues that are up to 5 residues far from our predicted size, we covered 65% of all interaction sites. These results strongly indicate that prediction of interaction sites from sequence alone is possible and comparable with results obtained using structure information. Incorporating predicted structural information like ASA, secondary structure, depth and protrusion index may improve our method.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
POVEZANOST RADA
Projekti:
036-0362214-1987 - Modeliranje kompleksnih sustava (Jeren, Branko, MZO ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb