Finding the needle in the haystack: towards solving the protein-folding problem computationally. (2nd January 2018)
- Record Type:
- Journal Article
- Title:
- Finding the needle in the haystack: towards solving the protein-folding problem computationally. (2nd January 2018)
- Main Title:
- Finding the needle in the haystack: towards solving the protein-folding problem computationally
- Authors:
- Li, Bian
Fooksa, Michaela
Heinze, Sten
Meiler, Jens - Abstract:
- Abstract: Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem, " has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with aAbstract: Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem, " has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions. … (more)
- Is Part Of:
- Critical reviews in biochemistry and molecular biology. Volume 53:Number 1(2018)
- Journal:
- Critical reviews in biochemistry and molecular biology
- Issue:
- Volume 53:Number 1(2018)
- Issue Display:
- Volume 53, Issue 1 (2018)
- Year:
- 2018
- Volume:
- 53
- Issue:
- 1
- Issue Sort Value:
- 2018-0053-0001-0000
- Page Start:
- 1
- Page End:
- 28
- Publication Date:
- 2018-01-02
- Subjects:
- Protein-folding problem -- protein-folding simulation -- protein structure prediction -- conformational sampling algorithms -- protein energy approximations -- sparse experimental data
Biochemistry -- Periodicals
Molecular biology -- Periodicals
Biochemistry -- Periodicals
Molecular Biology -- Periodicals
Review Literature -- Periodicals
572 - Journal URLs:
- http://informahealthcare.com/loi/bmg ↗
http://informahealthcare.com ↗ - DOI:
- 10.1080/10409238.2017.1380596 ↗
- Languages:
- English
- ISSNs:
- 1040-9238
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3487.471500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 5526.xml