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Symmetry
Special Issues
Applications of Symmetry in Computational Biology
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Applications of Symmetry in Computational Biology

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Life Sciences".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 781

Special Issue Editors

Dr. Siqi Liang

E-Mail Website
Guest Editor
1. Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
2. Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
Interests: computational biology; machine learning; bioinformatics
Dr. Siwei Chen

E-Mail Website
Guest Editor
1. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
2. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
Interests: bioinformatics; disease association; translational research; omics data; genetics and genomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this Special Issue of Symmetry, we delve into the intricate role symmetry plays within the field of computational biology. Symmetry, a recurring theme in biological structures and processes, provides a unique perspective through which computational biological techniques can be applied to study a variety of biological problems. This issue aims to showcase innovative research where symmetry principles are applied to solve complex biological problems, ranging from molecular simulations to system-level analyses. The convergence of computational power and symmetrical models has the potential to unlock new understandings in areas such as macromolecular structures, genetic networks, mathematical biology, developmental biology, and evolution. Through original research articles as well as review articles, we invite contributors to explore the transformative impact of symmetry in computational biology research, fostering a deeper comprehension of life's underlying patterns and processes.

We are looking for submissions that cover a wide range of topics at the intersection of symmetry and computational biology, including but not limited to:

  • Computational studies of macromolecular structures (protein, RNA, etc.) involving structural symmetry or asymmetry;
  • Computational methods for molecular drug discovery leveraging the symmetry of molecules and transition states;
  • Genomic symmetry and asymmetry in gene regulation and genome evolution;
  • Symmetry-based modeling of the development and evolution of biological systems, such as the organization of cellular structures and the development of organisms;
  • Application of group theory in molecular and cellular network dynamics;
  • Symmetrical structures in biological neural networks and their relationship to functional connectivity and brain functions;
  • Synthetic engineering of symmetrical biological circuits and systems to create robust biological functions.

Dr. Siqi Liang
Dr. Siwei Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • symmetry
  • asymmetry
  • computational biology
  • macromolecular structure
  • drug discovery
  • genomics
  • developmental biology
  • group theory
  • network dynamics
  • computational neuroscience
  • synthetic biology

Published Papers (1 paper)

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Research

15 pages, 3212 KiB  
Article
UniproLcad: Accurate Identification of Antimicrobial Peptide by Fusing Multiple Pre-Trained Protein Language Models
by Xiao Wang, Zhou Wu, Rong Wang and Xu Gao
Symmetry 2024, 16(4), 464; https://doi.org/10.3390/sym16040464 - 11 Apr 2024
Viewed by 613
Abstract
Antimicrobial peptides (AMPs) are vital components of innate immunotherapy. Existing approaches mainly rely on either deep learning for the automatic extraction of sequence features or traditional manual amino acid features combined with machine learning. The peptide sequence contains symmetrical sequence motifs or repetitive [...] Read more.
Antimicrobial peptides (AMPs) are vital components of innate immunotherapy. Existing approaches mainly rely on either deep learning for the automatic extraction of sequence features or traditional manual amino acid features combined with machine learning. The peptide sequence contains symmetrical sequence motifs or repetitive amino acid patterns, which may be related to the function and structure of the peptide. Recently, the advent of large language models has significantly boosted the representational power of sequence pattern features. In light of this, we present a novel AMP predictor called UniproLcad, which integrates three prominent protein language models—ESM-2, ProtBert, and UniRep—to obtain a more comprehensive representation of protein features. UniproLcad utilizes deep learning networks, encompassing the bidirectional long and short memory network (Bi-LSTM) and one-dimensional convolutional neural networks (1D-CNN), while also integrating an attention mechanism to enhance its capabilities. These deep learning frameworks, coupled with pre-trained language models, efficiently extract multi-view features from antimicrobial peptide sequences and assign attention weights to them. Through ten-fold cross-validation and independent testing, UniproLcad demonstrates competitive performance in the field of antimicrobial peptide identification. This integration of diverse language models and deep learning architectures enhances the accuracy and reliability of predicting antimicrobial peptides, contributing to the advancement of computational methods in this field. Full article
(This article belongs to the Special Issue Applications of Symmetry in Computational Biology)
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