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Ab initio modelling of protein structure

Guest Editors:
Luigi Donato: University of Messina, Italy & IEMEST, Italy
Mohamed Hammad: Menoufia University, Egypt & Prince Sultan University, Saudi Arabia
Saurav Mallik: University of Arizona, USA
Shoba Ranganathan: Macquarie University, Australia
Leyi Wei: Shandong University, China


BMC Bioinformatics called for submissions to our Collection on "Ab initio modelling of protein structure".  This Collection includes but is not limited to:

•    Ab initio protein structure prediction methodologies based on genetic algorithms/metaheuristic searching strategies;
•    Development and implementation of ab initio computational modelling strategies based around specific experimental techniques;
•    Ab initio modelling methods using low-resolution experimental data;
•    Methods for (ab initio) bead modelling of proteins 
•    Enhancement of the fragment-based ab initio protein structure assembly through the prediction of low-accuracy contact-map;
•    Evolutionary computation-based approaches for ab initio protein structure modelling;
•    Advances on methods for ab initio modelling and folding of small single-domain proteins via iterative TASSER simulations (threading/assembly/refinement);
•    Integrating DL and coevolution-oriented contact-maps via replica-exchange-based Monte Carlo fragment assembly simulation experiments.
 

Meet the Guest Editors

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Luigi Donato: University of Messina, Italy & IEMEST, Italy

Luigi Donato, researcher in IEMEST of Palermo and affiliated to University of Messina, published more than 80 papers in reputed journals and participated in more than 60 national and international congresses. Today, he is a member of the Executive Committee of Low Vision Academy (LVA). Moreover, he was a member of ARVO, and he is actually a member of AIBG and SIBS. He joined the Editorial Board of several journals, such as Heliyon, BMC Bioinformatics and Cell Cycle. In 2020 he was the Editor and an author of the book "Advances in Bioinformatics, Biostatistics and Omic Sciences". His main research fields are focused on rare diseases, in particular retinal dystrophies, and omics approaches.

Mohamed Hammad: Menoufia University, Egypt & Prince Sultan University, Saudi Arabia

Dr. Hammad is an Assistant Professor in the Faculty of Computers and Information, Menoufia University, Egypt. His research interests include Biomedical Imaging, Bioinformatics, IoT, Computer Vision, Machine Learning, Deep Learning, Pattern Recognition, and Biometrics. Dr. Hammad has published more than 40 papers in international SCI-IF journals. Furthermore, he serves as an an Editor Board member in BMC Bioinformatics. He is listed in the top 2% of scientists worldwide (According to the recently released list by Stanford university USA in 2022.

Saurav Mallik: University of Arizona, USA

Dr. Saurav Mallik is currently working as Research Scientist in the Department of Pharmacology and Toxicology, University of Arizona, USA. Previously, he worked as Postdoctoral Fellow in Harvard University, University of Texas, and University of Miami, USA. He obtained his PhD degree in Computer Science & Engineering from Jadavpur University, India in 2017. He previously worked in Indian Statistical Institute, India as Junior Research Fellow. He is the recipient of Research Associate from Council of Scientific and Industrial Research, MHRD, India (2017), "Emerging Researcher In Bioinformatics" award from Bioclues, India (2020), "Young scientist award" from ISAO from India (2021) and two travel grant awards from USA. His research areas include data mining, computational biology, bioinformatics, biostatistics and machine learning.

Shoba Ranganathan: Macquarie University, Australia

Prof. Shoba Ranganathan, Ph.D., has worked in bioinformatics since 1983, when the field was known as theoretical biochemistry and subsequently as biocomputing. Her research has multiple areas of bioinformatics, including structure prediction. Shoba currently serves as the Chair, Board of Directors of APBioNet Ltd. and as a consultant to the National SuperComputing Centre (NSCC), Singapore. Recent honours include the 2018 Honorary Senior Fellow award of the Australian Bioinformatics and Computational Biology Society (ABACBS) and the 2023 Outstanding Contributions to ISCB award of the International Society for Computational Biology (ISCB).

Leyi Wei: Shandong University, China

Prof. Leyi Wei is a full Professor at the School of Software, Shandong University, China. His research interests include bioinformatics and artificial intelligence. He has published 100+ peer-reviewed papers, and his work has been recognized through the reception of awards, including “Highly Cited Researcher” (Clarivate Analytics, 2021,2022), World’s Top 2% Scientists (released by Stanford University 2021, ACM SIGBIO Rising Star Award 2021, and many others.  In addition, he serves as Associate Editor and Editorial Board member for a number of well-known journals, such as Frontiers in Genetics, Methods, BMC Bioinformatics, etc.

About the collection

Protein structure characterisation has been at the forefront of scientific efforts in all aspects of human health, due to its importance in understanding the mode of action of both infectious and non-infectious diseases.

Structure determination not only allows to characterise protein function but is also key in developing effective treatment.

In silico modelling has emerged as a preferred method for pre-experimental analysis and characterisation of particularly challenging proteins. Homology modelling, which creates structure predictions based on existing structures of proteins of similar sequence, has historically been the focus of many currently available tools. Ab initio modelling, on the other hand, is an emerging field. The structure prediction is entirely independent of a priori knowledge and entirely based on sequence or alternative experimental data. While these methods have been employed for some time in selected biophysical contexts (such as small angle scattering), they have recently gained popularity thanks to the development and implementation of AlphaFold and are of particular interest for non-homologous proteins.

This Collection includes (but is not limited to) various algorithms/approaches of machine learning (ML), deep learning (DL) and evolutionary computation that are applied to predict protein folding. In addition, different improved version of C-QUARK based algorithms that merge multiple DL and coevolution-dependent contact-maps to guide the related replica exchange Monte Carlo fragment assembly-based simulations are also considered.

This Collection welcomes submissions on all aspects of ab initio protein modelling, including but not limited to:

•    Ab initio protein structure prediction methodologies based on genetic algorithms/metaheuristic searching strategies;
•    Development and implementation of ab initio computational modelling strategies based around specific experimental techniques;
•    Ab initio modelling methods using low-resolution experimental data;
•    Methods for (ab initio) bead modelling of proteins 
•    Enhancement of the fragment-based ab initio protein structure assembly through the prediction of low-accuracy contact-map;
•    Evolutionary computation-based approaches for ab initio protein structure modelling;
•    Advances on methods for ab initio modelling and folding of small single-domain proteins via iterative TASSER simulations (threading/assembly/refinement);
•    Integrating DL and coevolution-oriented contact-maps via replica-exchange-based Monte Carlo fragment assembly simulation experiments.

Image credit: © molekuul.be / stock.adobe.com

  1. Recently, significant progress has been made in the field of protein structure prediction by the application of artificial intelligence techniques, as shown by the results of the CASP13 and CASP14 (Critical As...

    Authors: Irena Roterman, Katarzyna Stapor and Leszek Konieczny
    Citation: BMC Bioinformatics 2023 24:425
    Content type: Research Published on:

  2. The aqueous environment directs the protein folding process towards the generation of micelle-type structures, which results in the exposure of hydrophilic residues on the surface (polarity) and the concentrat...

    Authors: Irena Roterman, Katarzyna Stapor and Leszek Konieczny
    Citation: BMC Bioinformatics 2023 24:418
    Content type: Research Published on:

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select ["ab initio modelling of protein structure"] from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.