PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Scope & Guideline

Pioneering Insights into Protein Research

Introduction

Welcome to your portal for understanding PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageEnglish
ISSN0887-3585
PublisherWILEY
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 1986 to 2024
AbbreviationPROTEINS / Proteins
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address111 RIVER ST, HOBOKEN 07030-5774, NJ

Aims and Scopes

The journal "PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS" focuses on the intricate relationships between protein structure, function, and bioinformatics, emphasizing computational methods and experimental validation.
  1. Protein Structure Analysis:
    The journal emphasizes the structural characterization of proteins using techniques like X-ray crystallography, NMR spectroscopy, and cryo-EM, contributing to our understanding of protein function and interactions.
  2. Computational Modeling and Simulation:
    A significant focus on computational approaches, including molecular dynamics simulations, machine learning, and bioinformatics methods, to predict protein structures, dynamics, and interactions.
  3. Protein-Protein and Protein-Ligand Interactions:
    Research often investigates the mechanisms of protein-protein and protein-ligand interactions, including binding affinity predictions and modeling complex formations.
  4. Post-Translational Modifications:
    The journal covers studies on the effects of post-translational modifications on protein function, stability, and interactions, highlighting their biological significance.
  5. Thermodynamics and Stability:
    Research addresses the thermodynamic principles underlying protein stability and folding, often using computational methods to explore stability-enhancing mutations.
  6. Bioinformatics Tools and Resources:
    The journal contributes to the development and application of bioinformatics tools for protein analysis, structure prediction, and functional annotation.
The journal has increasingly highlighted certain emerging themes that reflect the evolving landscape of protein science and bioinformatics, attracting significant attention from researchers.
  1. Machine Learning and AI in Protein Research:
    There is a notable increase in the application of machine learning and AI techniques for predicting protein structures, interactions, and functions, showcasing their potential to revolutionize protein science.
  2. Integration of Structural Biology with Genomics:
    Emerging themes focus on integrating structural biology with genomic data to enhance our understanding of protein functions within broader biological contexts.
  3. Dynamic and Flexible Protein Structures:
    Research on the dynamics and flexibility of proteins, including studies on intrinsically disordered regions, is gaining traction, emphasizing the importance of dynamic behavior in protein function.
  4. Thermostability and Engineering of Proteins:
    There is a growing interest in enhancing protein thermostability through engineering, particularly in the context of biotechnological applications.
  5. Drug Discovery and Protein Targeting:
    Research related to drug discovery, particularly using structural insights for targeting proteins in disease contexts, is increasingly prevalent, reflecting a focus on therapeutic applications.

Declining or Waning

While several themes remain prevalent in the journal, certain areas of research have seen a decline in focus over recent years, suggesting shifts in scientific interest.
  1. Classical Protein Structure Prediction:
    Traditional methods of protein structure prediction based solely on sequence homology have become less prominent, as newer, more accurate approaches like AlphaFold have gained traction.
  2. Single-Method Experimental Approaches:
    Research relying solely on single experimental techniques, such as only X-ray crystallography or NMR without computational support, is waning, as integration with computational methods becomes the norm.
  3. Niche Proteins and Rare Pathways:
    Studies focusing on niche proteins or rare biological pathways have seen less attention, likely due to a broader interest in more universally applicable proteins and pathways.

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