Prediction of long-range contacts from sequence profile

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

4 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
Pages938-943
Publication statusPublished - 2007

Publication series

Name
ISSN (Print)1098-7576

Conference

Title2007 International Joint Conference on Neural Networks, IJCNN 2007
PlaceUnited States
CityOrlando, FL
Period12 - 17 August 2007

Abstract

Theoretic study in this paper shows that we can obtain exact long-range contacts by adopting one classifier if the centers of sequence profiles of residue pairs for long-range contacts and non-long-range contacts are known. The adopted classifier, referred to as multiple conditional probability mass function classifier (MCPMFC), can find an optimized transformation of the variables for each of the classes and therefore resulting in K separate classifiers. As a result, about 44.48% long-range contacts are around at the sequence profile (SP) centre for long-range contacts and about 20.9% long-range contacts are correctly predicted when considering the top L/5 (L is the protein sequence length) predicted contacts and the residue pair with 24 apart. The highest cluster result gives us a clue that SP center should be a sound pathway to investigate contact map in protein structures. ©2007 IEEE.

Citation Format(s)

Prediction of long-range contacts from sequence profile. / Chen, Peng; Wang, Bing; Wong, Hau-San et al.
IEEE International Conference on Neural Networks - Conference Proceedings. 2007. p. 938-943 4371084.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review