World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
10981
World Ranking
9981
National Ranking
624

Overview

Liam J. McGuffin is affiliated with the University of Reading in the United Kingdom. Their research primarily focuses on the field of Biochemistry, Genetics, and Molecular Biology, with a particular emphasis on Molecular Biology. The scientist's scholarly output includes a notable number of publications related to protein structures and functions, machine learning applications in bioinformatics, and microbial metabolic engineering.

The main topics covered in their work include:

  • Protein Structure and Dynamics
  • Enzyme Structure and Function
  • Microbial Metabolic Engineering and Bioproduction
  • Machine Learning in Bioinformatics
  • Bioinformatics and Genomic Networks
  • Computational Drug Discovery Methods
  • RNA and protein synthesis mechanisms

McGuffin has contributed research published in several scientific venues, with frequent contributions to:

  • Methods in Molecular Biology
  • Nucleic Acids Research
  • Proteins Structure Function and Bioinformatics
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Cell Communication and Signaling

Recent research papers authored or co-authored by McGuffin include:

  • ModFOLD8: accurate global and local quality estimates for 3D protein models, 2021, Nucleic Acids Research
  • Prediction of protein structures, functions and interactions using the IntFOLD7, MultiFOLD and ModFOLDdock servers, 2023, Nucleic Acids Research

Frequent co-authors working alongside McGuffin include:

  • Recep Adiyaman
  • Nicholas S. Edmunds
  • Ahmet G Genc
  • Shuaa M. A. Alharbi
  • Jianlin Cheng

The range and focus of McGuffin's published work reflect an integration of computational methods with biochemical and molecular biology techniques. Their ongoing association with the University of Reading situates them within a research environment contributing to advances in understanding protein modeling, structure prediction, and bioinformatics tool development.

Best Publications

  • The PSIPRED protein structure prediction server.

    Liam J. McGuffin;Kevin Bryson;David T. Jones

  • Protein structure prediction servers at University College London

    Kevin Bryson;Liam J. McGuffin;Russell L. Marsden;Jonathan J. Ward

  • The DISOPRED server for the prediction of protein disorder

    Jonathan J. Ward;Liam J. Mcguffin;Kevin Bryson;Bernard F. Buxton

  • The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

    Naihui Zhou;Yuxiang Jiang;Timothy R. Bergquist;Alexandra J. Lee

  • Improvement of the GenTHREADER method for genomic fold recognition

    Liam J. McGuffin;David T. Jones

  • Secondary structure prediction with support vector machines.

    Jonathan J. Ward;Liam J. McGuffin;Bernard F. Buxton;David T. Jones

  • Rapid protein domain assignment from amino acid sequence using predicted secondary structure

    Russell L. Marsden;Liam J. McGuffin;David T. Jones

  • Predicting Metal-binding Site Residues in Low-resolution Structural Models

    Jaspreet Singh Sodhi;Kevin Bryson;Liam J. McGuffin;Jonathan J. Ward

  • The ModFOLD server for the quality assessment of protein structural models

    Liam J. McGuffin

  • Intrinsic disorder prediction from the analysis of multiple protein fold recognition models

    Liam J. McGuffin

  • The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction

    Daniel B. Roche;Maria T. Buenavista;Stuart J. Tetchner;Liam J. McGuffin

  • IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences

    Liam J. McGuffin;Jennifer D. Atkins;Bajuna R. Salehe;Ahmad N. Shuid

  • The ModFOLD4 server for the quality assessment of 3D protein models

    Liam J. McGuffin;Maria T. Buenavista;Daniel B. Roche

  • IntFOLD: an integrated web resource for high performance protein structure and function prediction.

    Liam J McGuffin;Recep Adiyaman;Ali H A Maghrabi;Ahmad N Shuid

  • ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models.

    Ali H. A. Maghrabi;Liam J. McGuffin

  • Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments

    Liam J. McGuffin;Daniel B. Roche

  • Benchmarking consensus model quality assessment for protein fold recognition

    Liam J McGuffin

  • Assembling novel protein folds from super-secondary structural fragments.

    David T. Jones;Liam J. McGuffin

  • Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.

    Daniel Barry Roche;Danielle Allison Brackenridge;Liam James McGuffin

  • Prediction of novel and analogous folds using fragment assembly and fold recognition

    D. T. Jones;K. Bryson;A. Coleman;Liam J. McGuffin

  • Estimation of model accuracy in CASP13.

    Jianlin Cheng;Myong‐Ho Choe;Arne Elofsson;Kun‐Sop Han

  • Improving sequence-based fold recognition by using 3D model quality assessment

    Chris S. Pettitt;Liam J. Mcguffin;David T. Jones

Frequent Co-Authors

David T. Jones
David T. Jones University College London
Christophe Dessimoz
Christophe Dessimoz University College London
Patrick A. Lewis
Patrick A. Lewis University College London
Jianlin Cheng
Jianlin Cheng University of Missouri
Tapio Salakoski
Tapio Salakoski University of Turku
Daisuke Kihara
Daisuke Kihara Purdue University West Lafayette
Peter H. Sugden
Peter H. Sugden University of Reading
Arne Elofsson
Arne Elofsson Science for Life Laboratory
Yang Zhang
Yang Zhang University of Michigan–Ann Arbor
Angela Clerk
Angela Clerk University of Reading

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Pursuing Computer Science in the USA is flexible, with many online pathways available to suit different goals and budgets. For those eager to accelerate their careers, you can explore the quickest cheapest masters degree options. These programs offer speed and affordability, letting you earn a respected credential without breaking the bank.

Not sure which area to specialize in? Discover insights on what masters program should i do to maximize your job opportunities and salary potential. If you prefer to start with a foundational qualification, earning an online associate's degree can be a smart entry point, leading directly to tech roles or further studies.

Affordability matters too. If budget is a concern, look into the cheapest online degrees for quality education at lower tuition rates. With these flexible online options, you can start or advance your Computer Science journey from anywhere, on your schedule.

Best Scientists Citing Liam J. McGuffin

Trending Scientists