Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.8
5.6
Journals
IJMS
Special Issues
Deep Learning for Modeling the Structure, Dynamics, and Function of...
ijms-logo
Submit to IJMS Review for IJMS Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Deep Learning for Modeling the Structure, Dynamics, and Function of Small and Large Molecules

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 1095

Special Issue Editor

Prof. Dr. Amarda Shehu

E-Mail Website
Guest Editor
Department of Computer Science, College of Engineering and Computing, George Mason University, Fairfax Campus, Fairfax, VA 22030, USA
Interests: artificial intelligence; stochastic optimization; machine learning; deep learning; optimization for deep learning; generative models; language models; bioinformatics; computational biophysics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rising algorithmic sophistication of deep learning frameworks is allowing us to make increasingly rapid discoveries and real headways in many long-standing, hallmark problems in computational biology and bioinformatics. In molecular biology, these platforms are now facilitating our ability to make connections among information across various modalities, such as molecular sequence, structure, dynamics, and function. Integrating such knowledge is leading to novel deep learning methods that are situated in molecular biology and biophysics and are leading to prediction of tertiary structure and structure ensembles, modeling of structural dynamics, design of novel proteins, optimization and in-silico generation of small molecules for novel therapeutics and biotechnology applications, design of novel energy functions, prediction of variant effects on structure, stability, and function, prediction of function at varying levels of granularity, prediction and design of binding sites, and much more. The purpose of this special issue is to bring together the increasingly diverse and growing community of researchers across artificial intelligence, machine deep learning, bioinformatics, biophysics, and molecular biology. Authors are invited to submit original research and review articles so that as a community organized around this special issue we summarize the state of the art and push further the boundary of our knowledge and understanding.

Prof. Dr. Amarda Shehu
Guest Editor

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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • structure, structure ensembles, or structural dynamics
  • optimization, design, and generation
  • variant effects
  • stability, binding, function
  • scoring functions
  • evolutionary history and dynamics

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

13 pages, 2356 KiB  
Article
DeepSub: Utilizing Deep Learning for Predicting the Number of Subunits in Homo-Oligomeric Protein Complexes
by Rui Deng, Ke Wu, Jiawei Lin, Dehang Wang, Yuanyuan Huang, Yang Li, Zhenkun Shi, Zihan Zhang, Zhiwen Wang, Zhitao Mao, Xiaoping Liao and Hongwu Ma
Int. J. Mol. Sci. 2024, 25(9), 4803; https://doi.org/10.3390/ijms25094803 - 28 Apr 2024
Viewed by 242
Abstract
The molecular weight (MW) of an enzyme is a critical parameter in enzyme-constrained models (ecModels). It is determined by two factors: the presence of subunits and the abundance of each subunit. Although the number of subunits (NS) can potentially be obtained from UniProt, [...] Read more.
The molecular weight (MW) of an enzyme is a critical parameter in enzyme-constrained models (ecModels). It is determined by two factors: the presence of subunits and the abundance of each subunit. Although the number of subunits (NS) can potentially be obtained from UniProt, this information is not readily available for most proteins. In this study, we addressed this gap by extracting and curating subunit information from the UniProt database to establish a robust benchmark dataset. Subsequently, we propose a novel model named DeepSub, which leverages the protein language model and Bi-directional Gated Recurrent Unit (GRU), to predict NS in homo-oligomers solely based on protein sequences. DeepSub demonstrates remarkable accuracy, achieving an accuracy rate as high as 0.967, surpassing the performance of QUEEN. To validate the effectiveness of DeepSub, we performed predictions for protein homo-oligomers that have been reported in the literature but are not documented in the UniProt database. Examples include homoserine dehydrogenase from Corynebacterium glutamicum, Matrilin-4 from Mus musculus and Homo sapiens, and the Multimerins protein family from M. musculus and H. sapiens. The predicted results align closely with the reported findings in the literature, underscoring the reliability and utility of DeepSub. Full article
(This article belongs to the Special Issue Deep Learning for Modeling the Structure, Dynamics, and Function of Small and Large Molecules)
►▼ Show Figures

Figure 1

27 pages, 19904 KiB  
Article
Elucidating the Role of Wildtype and Variant FGFR2 Structural Dynamics in (Dys)Function and Disorder
by Yiyang Lian, Dale Bodian and Amarda Shehu
Int. J. Mol. Sci. 2024, 25(8), 4523; https://doi.org/10.3390/ijms25084523 - 20 Apr 2024
Viewed by 268
Abstract
The fibroblast growth factor receptor 2 (FGFR2) gene is one of the most extensively studied genes with many known mutations implicated in several human disorders, including oncogenic ones. Most FGFR2 disease-associated gene mutations are missense mutations that result in constitutive activation [...] Read more.
The fibroblast growth factor receptor 2 (FGFR2) gene is one of the most extensively studied genes with many known mutations implicated in several human disorders, including oncogenic ones. Most FGFR2 disease-associated gene mutations are missense mutations that result in constitutive activation of the FGFR2 protein and downstream molecular pathways. Many tertiary structures of the FGFR2 kinase domain are publicly available in the wildtype and mutated forms and in the inactive and activated state of the receptor. The current literature suggests a molecular brake inhibiting the ATP-binding A loop from adopting the activated state. Mutations relieve this brake, triggering allosteric changes between active and inactive states. However, the existing analysis relies on static structures and fails to account for the intrinsic structural dynamics. In this study, we utilize experimentally resolved structures of the FGFR2 tyrosine kinase domain and machine learning to capture the intrinsic structural dynamics, correlate it with functional regions and disease types, and enrich it with predicted structures of variants with currently no experimentally resolved structures. Our findings demonstrate the value of machine learning-enabled characterizations of structure dynamics in revealing the impact of mutations on (dys)function and disorder in FGFR2. Full article
(This article belongs to the Special Issue Deep Learning for Modeling the Structure, Dynamics, and Function of Small and Large Molecules)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop