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Computational Drug Discovery and Design

Overview of attention for book
Cover of 'Computational Drug Discovery and Design'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Computer-Aided Drug Design: An Overview
  3. Altmetric Badge
    Chapter 2 Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives
  4. Altmetric Badge
    Chapter 3 Practices in Molecular Docking and Structure-Based Virtual Screening
  5. Altmetric Badge
    Chapter 4 Phylogenetic and Other Conservation-Based Approaches to Predict Protein Functional Sites
  6. Altmetric Badge
    Chapter 5 De Novo Design of Ligands Using Computational Methods
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    Chapter 6 Molecular Dynamics Simulation and Prediction of Druggable Binding Sites
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    Chapter 7 Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein–Ligand Docking Method
  9. Altmetric Badge
    Chapter 8 Fragment-Based Ligand Designing
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    Chapter 9 Molecular Dynamics as a Tool for Virtual Ligand Screening
  11. Altmetric Badge
    Chapter 10 Building Molecular Interaction Networks from Microarray Data for Drug Target Screening
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    Chapter 11 Absolute Alchemical Free Energy Calculations for Ligand Binding: A Beginner’s Guide
  13. Altmetric Badge
    Chapter 12 Evaluation of Protein–Ligand Docking by Cyscore
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    Chapter 13 Molecular Dynamics Simulations of Protein–Drug Complexes: A Computational Protocol for Investigating the Interactions of Small-Molecule Therapeutics with Biological Targets and Biosensors
  15. Altmetric Badge
    Chapter 14 Prediction and Optimization of Pharmacokinetic and Toxicity Properties of the Ligand
  16. Altmetric Badge
    Chapter 15 Protein–Protein Docking in Drug Design and Discovery
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    Chapter 16 Automated Inference of Chemical Discriminants of Biological Activity
  18. Altmetric Badge
    Chapter 17 Computational Exploration of Conformational Transitions in Protein Drug Targets
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    Chapter 18 Applications of the NRGsuite and the Molecular Docking Software FlexAID in Computational Drug Discovery and Design
  20. Altmetric Badge
    Chapter 19 Calculation of Thermodynamic Properties of Bound Water Molecules
  21. Altmetric Badge
    Chapter 20 Enhanced Molecular Dynamics Methods Applied to Drug Design Projects
  22. Altmetric Badge
    Chapter 21 AGGRESCAN3D: Toward the Prediction of the Aggregation Propensities of Protein Structures
  23. Altmetric Badge
    Chapter 22 Computational Analysis of Solvent Inclusion in Docking Studies of Protein–Glycosaminoglycan Systems
  24. Altmetric Badge
    Chapter 23 Understanding G Protein-Coupled Receptor Allostery via Molecular Dynamics Simulations: Implications for Drug Discovery
  25. Altmetric Badge
    Chapter 24 Identification of Potential MicroRNA Biomarkers by Meta-analysis
Attention for Chapter 4: Phylogenetic and Other Conservation-Based Approaches to Predict Protein Functional Sites
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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Chapter title
Phylogenetic and Other Conservation-Based Approaches to Predict Protein Functional Sites
Chapter number 4
Book title
Computational Drug Discovery and Design
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7756-7_4
Pubmed ID
Book ISBNs
978-1-4939-7755-0, 978-1-4939-7756-7
Authors

Heval Atas, Nurcan Tuncbag, Tunca Doğan

Abstract

Proteins use their functional regions to exploit various activities, including binding to other proteins, nucleic acids, or drugs. Functional sites of the proteins have a tendency to be more conserved than the rest of the protein surface. Therefore, detection of the conserved residues using phylogenetic analysis is a general approach to predict functionally critical residues. In this chapter, we describe some of the available methods to predict functional sites and demonstrate a complete pipeline with tool alternatives at several steps. We explain the standard procedure and all intermediate stages including homology detection with BLAST search, multiple sequence alignment (MSA) and the construction of a phylogenetic tree for a given query sequence. Additionally, we demonstrate the prediction results of these methods on a case study. Finally, we discuss the possible challenges and bottlenecks throughout the pipeline. Our step-by-step description about the functional site prediction could be a helpful resource for the researchers interested in finding protein functional sites, to be used in drug discovery research.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 38%
Student > Ph. D. Student 3 23%
Unspecified 1 8%
Student > Bachelor 1 8%
Student > Master 1 8%
Other 0 0%
Unknown 2 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 23%
Engineering 2 15%
Unspecified 1 8%
Computer Science 1 8%
Agricultural and Biological Sciences 1 8%
Other 2 15%
Unknown 3 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 June 2018.
All research outputs
#12,775,839
of 23,031,582 outputs
Outputs from Methods in molecular biology
#3,174
of 13,177 outputs
Outputs of similar age
#199,448
of 442,391 outputs
Outputs of similar age from Methods in molecular biology
#258
of 1,499 outputs
Altmetric has tracked 23,031,582 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,177 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 442,391 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.