↓ Skip to main content

Data Mining Techniques for the Life Sciences

Overview of attention for book
Cover of 'Data Mining Techniques for the Life Sciences'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Data Mining Techniques for the Life Sciences
  3. Altmetric Badge
    Chapter 2 Protein Structure Databases.
  4. Altmetric Badge
    Chapter 3 The MIntAct Project and Molecular Interaction Databases.
  5. Altmetric Badge
    Chapter 4 Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.
  6. Altmetric Badge
    Chapter 5 Classification and Exploration of 3D Protein Domain Interactions Using Kbdock.
  7. Altmetric Badge
    Chapter 6 Data Mining of Macromolecular Structures.
  8. Altmetric Badge
    Chapter 7 Criteria to Extract High-Quality Protein Data Bank Subsets for Structure Users.
  9. Altmetric Badge
    Chapter 8 Homology-Based Annotation of Large Protein Datasets.
  10. Altmetric Badge
    Chapter 9 Data Mining Techniques for the Life Sciences
  11. Altmetric Badge
    Chapter 10 Improving the Accuracy of Fitted Atomic Models in Cryo-EM Density Maps of Protein Assemblies Using Evolutionary Information from Aligned Homologous Proteins.
  12. Altmetric Badge
    Chapter 11 Systematic Exploration of an Efficient Amino Acid Substitution Matrix: MIQS.
  13. Altmetric Badge
    Chapter 12 Promises and Pitfalls of High-Throughput Biological Assays.
  14. Altmetric Badge
    Chapter 13 Data Mining Techniques for the Life Sciences
  15. Altmetric Badge
    Chapter 14 Predicting Conformational Disorder.
  16. Altmetric Badge
    Chapter 15 Classification of Protein Kinases Influenced by Conservation of Substrate Binding Residues.
  17. Altmetric Badge
    Chapter 16 Spectral-Statistical Approach for Revealing Latent Regular Structures in DNA Sequence.
  18. Altmetric Badge
    Chapter 17 Protein Crystallizability.
  19. Altmetric Badge
    Chapter 18 Data Mining Techniques for the Life Sciences
  20. Altmetric Badge
    Chapter 19 Data Mining Techniques for the Life Sciences
  21. Altmetric Badge
    Chapter 20 Functional Analysis of Metabolomics Data.
  22. Altmetric Badge
    Chapter 21 Data Mining Techniques for the Life Sciences
  23. Altmetric Badge
    Chapter 22 A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-synonymous Variants.
  24. Altmetric Badge
    Chapter 23 Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.
  25. Altmetric Badge
    Chapter 24 Protein Residue Contacts and Prediction Methods.
  26. Altmetric Badge
    Chapter 25 The Recipe for Protein Sequence-Based Function Prediction and Its Implementation in the ANNOTATOR Software Environment.
  27. Altmetric Badge
    Chapter 26 Data Mining Techniques for the Life Sciences
  28. Altmetric Badge
    Chapter 27 Data Mining Techniques for the Life Sciences
Attention for Chapter 4: Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.
Altmetric Badge

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
22 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.
Chapter number 4
Book title
Data Mining Techniques for the Life Sciences
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3572-7_4
Pubmed ID
Book ISBNs
978-1-4939-3570-3, 978-1-4939-3572-7
Authors

M. Michael Gromiha, P. Anoosha, Liang-Tsung Huang

Editors

Oliviero Carugo, Frank Eisenhaber

Abstract

Protein stability is the free energy difference between unfolded and folded states of a protein, which lies in the range of 5-25 kcal/mol. Experimentally, protein stability is measured with circular dichroism, differential scanning calorimetry, and fluorescence spectroscopy using thermal and denaturant denaturation methods. These experimental data have been accumulated in the form of a database, ProTherm, thermodynamic database for proteins and mutants. It also contains sequence and structure information of a protein, experimental methods and conditions, and literature information. Different features such as search, display, and sorting options and visualization tools have been incorporated in the database. ProTherm is a valuable resource for understanding/predicting the stability of proteins and it can be accessed at http://www.abren.net/protherm/ . ProTherm has been effectively used to examine the relationship among thermodynamics, structure, and function of proteins. We describe the recent progress on the development of methods for understanding/predicting protein stability, such as (1) general trends on mutational effects on stability, (2) relationship between the stability of protein mutants and amino acid properties, (3) applications of protein three-dimensional structures for predicting their stability upon point mutations, (4) prediction of protein stability upon single mutations from amino acid sequence, and (5) prediction methods for addressing double mutants. A list of online resources for predicting has also been provided.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
Unknown 21 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 27%
Researcher 4 18%
Student > Master 3 14%
Student > Postgraduate 2 9%
Professor 2 9%
Other 4 18%
Unknown 1 5%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 27%
Agricultural and Biological Sciences 5 23%
Computer Science 2 9%
Chemistry 2 9%
Immunology and Microbiology 2 9%
Other 3 14%
Unknown 2 9%