Abstract
Machine learning algorithms have wide range of applications in bioinformatics and computational biology such as prediction of protein secondary structures, solvent accessibility, binding site residues in protein complexes, protein folding rates, stability of mutant proteins, and discrimination of proteins based on their structure and function. In this work, we focus on two aspects of predictions: (i) protein folding rates and (ii) stability of proteins upon mutations. We briefly introduce the concepts of protein folding rates and stability along with available databases, features for prediction methods and measures for prediction performance. Subsequently, the development of structure based parameters and their relationship with protein folding rates will be outlined. The structure based parameters are helpful to understand the physical basis for protein folding and stability. Further, basic principles of major machine learning techniques will be mentioned and their applications for predicting protein folding rates and stability of mutant proteins will be illustrated. The machine learning techniques could achieve the highest accuracy of predicting protein folding rates and stability. In essence, statistical methods and machine learning algorithms are complimenting each other for understanding and predicting protein folding rates and the stability of protein mutants. The available online resources on protein folding rates and stability will be listed.
Keywords: Protein folding rates, protein stability, structure based parameters, machine learning techniques, three-dimensional structure, polypeptide chain, protein folding problem, magnitude, spectroscopy, Folding rate
Current Protein & Peptide Science
Title: Machine Learning Algorithms for Predicting Protein Folding Rates and Stability of Mutant Proteins: Comparison with Statistical Methods
Volume: 12 Issue: 6
Author(s): M. Michael Gromiha and Liang-Tsung Huang
Affiliation:
Keywords: Protein folding rates, protein stability, structure based parameters, machine learning techniques, three-dimensional structure, polypeptide chain, protein folding problem, magnitude, spectroscopy, Folding rate
Abstract: Machine learning algorithms have wide range of applications in bioinformatics and computational biology such as prediction of protein secondary structures, solvent accessibility, binding site residues in protein complexes, protein folding rates, stability of mutant proteins, and discrimination of proteins based on their structure and function. In this work, we focus on two aspects of predictions: (i) protein folding rates and (ii) stability of proteins upon mutations. We briefly introduce the concepts of protein folding rates and stability along with available databases, features for prediction methods and measures for prediction performance. Subsequently, the development of structure based parameters and their relationship with protein folding rates will be outlined. The structure based parameters are helpful to understand the physical basis for protein folding and stability. Further, basic principles of major machine learning techniques will be mentioned and their applications for predicting protein folding rates and stability of mutant proteins will be illustrated. The machine learning techniques could achieve the highest accuracy of predicting protein folding rates and stability. In essence, statistical methods and machine learning algorithms are complimenting each other for understanding and predicting protein folding rates and the stability of protein mutants. The available online resources on protein folding rates and stability will be listed.
Export Options
About this article
Cite this article as:
Michael Gromiha M. and Huang Liang-Tsung, Machine Learning Algorithms for Predicting Protein Folding Rates and Stability of Mutant Proteins: Comparison with Statistical Methods, Current Protein & Peptide Science 2011; 12 (6) . https://dx.doi.org/10.2174/138920311796957630
DOI https://dx.doi.org/10.2174/138920311796957630 |
Print ISSN 1389-2037 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5550 |
Call for Papers in Thematic Issues
Advancements in Proteomic and Peptidomic Approaches in Cancer Immunotherapy: Unveiling the Immune Microenvironment
The scope of this thematic issue centers on the integration of proteomic and peptidomic technologies into the field of cancer immunotherapy, with a particular emphasis on exploring the tumor immune microenvironment. This issue aims to gather contributions that illustrate the application of these advanced methodologies in unveiling the complex interplay ...read more
Artificial Intelligence for Protein Research
Protein research, essential for understanding biological processes and creating therapeutics, faces challenges due to the intricate nature of protein structures and functions. Traditional methods are limited in exploring the vast protein sequence space efficiently. Artificial intelligence (AI) and machine learning (ML) offer promising solutions by improving predictions and speeding up ...read more
Nutrition and Metabolism in Musculoskeletal Diseases
The musculoskeletal system consists mainly of cartilage, bone, muscles, tendons, connective tissue and ligaments. Balanced metabolism is of vital importance for the homeostasis of the musculoskeletal system. A series of musculoskeletal diseases (for example, sarcopenia, osteoporosis) are resulted from the dysregulated metabolism of the musculoskeletal system. Furthermore, metabolic diseases (such ...read more
Protein Folding, Aggregation and Liquid-Liquid Phase Separation
Protein folding, misfolding and aggregation remain one of the main problems of interdisciplinary science not only because many questions are still open, but also because they are important from the point of view of practical application. Protein aggregation and formation of fibrillar structures, for example, is a hallmark of a ...read more
Related Journals
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
Related Articles
-
Paliperidone Use in the Elderly
Current Drug Safety The Retinal Pigment Epithelium in Health and Disease
Current Molecular Medicine Resveratrol, a Phytochemical Inducer of Multiple Cell Death Pathways: Apoptosis, Autophagy and Mitotic Catastrophe
Current Medicinal Chemistry Drug Therapy in Schizophrenia
Current Pharmaceutical Design The Tortuous Road to an Ideal CGRP Function Blocker for the Treatment of Migraine
Current Topics in Medicinal Chemistry Mechanisms and Medical Management of Exercise Intolerance in Hypertrophic Cardiomyopathy
Current Pharmaceutical Design Cardiovascular Risk and Endothelial Dysfunction: The Preferential Route for Atherosclerosis
Current Pharmaceutical Biotechnology Impact of Viscosity and Refractive Index on Droplet Size and Zeta Potential of Model O/W and W/O Nanoemulsion
Current Nanoscience Universal Nature of Spondyloarthropathy as a Reactive Disease, Reflecting Differential Sensitivities
Current Rheumatology Reviews Recent Advances in the Chemistry of Phthalimide Analogues and their Therapeutic Potential
Mini-Reviews in Medicinal Chemistry Patent Selections:
Recent Patents on Food, Nutrition & Agriculture Inactivation of Parathyroid Hormone: Perspectives of Drug Discovery to Combating Hyperparathyroidism
Current Molecular Pharmacology Understanding the Role of Hypoxia Inducible Factor During Neurodegeneration for New Therapeutics Opportunities
Current Neuropharmacology Zinc as an Appetite Stimulator - The Possible Role of Zinc in the Progression of Diseases Such as Cachexia and Sarcopenia
Recent Patents on Food, Nutrition & Agriculture Ginkgo biloba Extract EGb761 Attenuates Hyperhomocysteinemia-induced AD Like Tau Hyperphosphorylation and Cognitive Impairment in Rats
Current Alzheimer Research Oxidative Stress and Antioxidants in Neurological Diseases: Is There Still Hope?
Current Drug Targets Protein Conformational Diseases: From Mechanisms to Drug Designs
Current Drug Discovery Technologies Effects of LPA and S1P on the Nervous System and Implications for Their Involvement in Disease
Current Drug Targets The G Protein Signal-Biased Compound TRV130; Structures, Its Site of Action and Clinical Studies
Current Topics in Medicinal Chemistry Diagnostic and Therapeutic Uses of Nanomaterials in the Brain
Current Medicinal Chemistry