Korean
Albanian
Arabic
Armenian
Azerbaijani
Belarusian
Bengali
Bosnian
Catalan
Czech
Danish
Deutsch
Dutch
English
Estonian
Finnish
Français
Greek
Haitian Creole
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Korean
Latvian
Lithuanian
Macedonian
Mongolian
Norwegian
Persian
Polish
Portuguese
Romanian
Russian
Serbian
Slovak
Slovenian
Spanish
Swahili
Swedish
Turkish
Ukrainian
Vietnamese
Български
中文(简体)
中文(繁體)
Biochimica et Biophysica Acta - General Subjects 2017-03

Structure prediction and network analysis of chitinases from the Cape sundew, Drosera capensis.

등록 된 사용자 만 기사를 번역 할 수 있습니다.
로그인 / 가입
링크가 클립 보드에 저장됩니다.
Megha H Unhelkar
Vy T Duong
Kaosoluchi N Enendu
John E Kelly
Seemal Tahir
Carter T Butts
Rachel W Martin

키워드

요약

Carnivorous plants possess diverse sets of enzymes with novel functionalities applicable to biotechnology, proteomics, and bioanalytical research. Chitinases constitute an important class of such enzymes, with future applications including human-safe antifungal agents and pesticides. Here, we compare chitinases from the genome of the carnivorous plant Drosera capensis to those from related carnivorous plants and model organisms.

Using comparative modeling, in silico maturation, and molecular dynamics simulation, we produce models of the mature enzymes in aqueous solution. We utilize network analytic techniques to identify similarities and differences in chitinase topology.

Here, we report molecular models and functional predictions from protein structure networks for eleven new chitinases from D. capensis, including a novel class IV chitinase with two active domains. This architecture has previously been observed in microorganisms but not in plants. We use a combination of comparative and de novo structure prediction followed by molecular dynamics simulation to produce models of the mature forms of these proteins in aqueous solution. Protein structure network analysis of these and other plant chitinases reveal characteristic features of the two major chitinase families.

This work demonstrates how computational techniques can facilitate quickly moving from raw sequence data to refined structural models and comparative analysis, and to select promising candidates for subsequent biochemical characterization. This capability is increasingly important given the large and growing body of data from high-throughput genome sequencing, which makes experimental characterization of every target impractical.

페이스 북
페이지에 가입하세요

과학이 뒷받침하는 가장 완벽한 약초 데이터베이스

  • 55 개 언어로 작동
  • 과학이 뒷받침하는 약초 치료제
  • 이미지로 허브 인식
  • 인터랙티브 GPS지도-위치에 허브 태그 지정 (출시 예정)
  • 검색과 관련된 과학 출판물 읽기
  • 효과로 약초 검색
  • 관심사를 정리하고 뉴스 연구, 임상 실험 및 특허를 통해 최신 정보를 확인하세요.

증상이나 질병을 입력하고 도움이 될 수있는 약초에 대해 읽고 약초를 입력하고 사용되는 질병과 증상을 확인합니다.
* 모든 정보는 발표 된 과학 연구를 기반으로합니다.

Google Play badgeApp Store badge