Bioinfo Chem

System biology and Infochemistry
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Articles

Bioinfo Chem

Volume 1 Number 1 2019

Bioinfo Chem

Bioinfo Chem

Descriptive Table of Contents


Research Article

(Unspecified)

Research Article | (Unspecified) Published online 30 August 2019
Manuscript-In-Press

In Silico molecular mechanism determination with Falcipain-2 responsible for Antimalarial activity of (N-(5-Chloro-2-hydroxyphenyl)-2-(2-methyl-2-propanyl)-1,3-dioxo-5-isoindolinecarboxamide 

Farhana Mosaddqque A, B, Md Shamsuddin Sultan Khan C

pp. 019-024
Views: 1405 | Downloads: 1

The molecular interactions possessed the reason of being inhibitors of falcipain-2 as antimalarial agent.
 

Abstract | Full Text | Open Access Article

  

Research Article | (Unspecified) Published online 30 August 2019
Manuscript-In-Press

Molecular dynamics simulation and in silico binding study of CBS and IL17A outline modulation of tumor microenvironment

Md Shamsuddin Sultan Khan A*, Aman Shah Abdul Majid B, Amin Malik Shah Abdul Majid B

pp. 006-018
Views: 1411 | Downloads: 0

The tumorigenic reactions were more serious due to senescence of the tumor in the reaction of IL17A, CBS and MHCII molecules. 

Abstract | Full Text | Open Access Article

  

Research Article | (Unspecified) Published online 26 August 2019
Manuscript-In-Press

In silico binding mechanism of Paynantheine, speciogynine and mitragynine with VEGF, TGFB, WNT, and NOTCH for anticancer potency

Aman Shah Abdul Majid B, Md Shamsuddin Sultan Khan A*

pp. 001-005
Views: 1539 | Downloads: 1

Mitragyna alkaloids adopt a binding pose at the VEGF, TGFB, WNT, and NOTCH receptor to produce anticancer potency through angiogenic pathway

Abstract | Full Text | Open Access Article

  

Computational Biology

Research Article | Computational Biology Published online 06 September 2019

Prediction of Protein–Metal Ion-Binding Sites Using Sequence Homology and Machine-Learning Methods

Zihan Tian 1, Cao Wei 1, Yutaka Moriwaki 1, Tohru Terada 1, Shugo Nakamura 1, Kazuya Sumikoshi 1, Fang Chun 1, and Kentaro Shimizu 1*

pp. 025-036
Views: 2264 | Downloads: 5

The sensitivity of the machine-learning method was 0.8239, and many metal ion-binding sites were predicted only by the machine-learning method.

Abstract | Full Text |PDF (2 MB) | Open Access Article