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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 2100

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 (4 papers)

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Research

13 pages, 23846 KiB  
Article
Protein–Protein Interfaces: A Graph Neural Network Approach
by Niccolò Pancino, Caterina Gallegati, Fiamma Romagnoli, Pietro Bongini and Monica Bianchini
Int. J. Mol. Sci. 2024, 25(11), 5870; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms25115870 - 28 May 2024
Viewed by 178
Abstract
Protein–protein interactions (PPIs) are fundamental processes governing cellular functions, crucial for understanding biological systems at the molecular level. Compared to experimental methods for PPI prediction and site identification, computational deep learning approaches represent an affordable and efficient solution to tackle these problems. Since [...] Read more.
Protein–protein interactions (PPIs) are fundamental processes governing cellular functions, crucial for understanding biological systems at the molecular level. Compared to experimental methods for PPI prediction and site identification, computational deep learning approaches represent an affordable and efficient solution to tackle these problems. Since protein structure can be summarized as a graph, graph neural networks (GNNs) represent the ideal deep learning architecture for the task. In this work, PPI prediction is modeled as a node-focused binary classification task using a GNN to determine whether a generic residue is part of the interface. Biological data were obtained from the Protein Data Bank in Europe (PDBe), leveraging the Protein Interfaces, Surfaces, and Assemblies (PISA) service. To gain a deeper understanding of how proteins interact, the data obtained from PISA were assembled into three datasets: Whole, Interface, and Chain, consisting of data on the whole protein, couples of interacting chains, and single chains, respectively. These three datasets correspond to three different nuances of the problem: identifying interfaces between protein complexes, between chains of the same protein, and interface regions in general. The results indicate that GNNs are capable of solving each of the three tasks with very good performance levels. Full article
(This article belongs to the Special Issue Deep Learning for Modeling the Structure, Dynamics, and Function of Small and Large Molecules)
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15 pages, 471 KiB  
Article
Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features
by Aman Sawhney, Jiefu Li and Li Liao
Int. J. Mol. Sci. 2024, 25(10), 5247; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms25105247 - 11 May 2024
Viewed by 323
Abstract
Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact [...] Read more.
Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact maps primarily as an intermediate step for 3D structure prediction, contact prediction methods have limited themselves exclusively to sequential features. Now that AlphaFold2 predicts 3D structures with good accuracy in general, we examine (1) how well predicted 3D structures can be directly used for deciding residue contacts, and (2) whether features from 3D structures can be leveraged to further improve residue contact prediction. With a well-known benchmark dataset, we tested predicting inter-helical residue contact based on AlphaFold2’s predicted structures, which gave an 83% average precision, already outperforming a sequential features-based state-of-the-art model. We then developed a procedure to extract features from atomic structure in the neighborhood of a residue pair, hypothesizing that these features will be useful in determining if the residue pair is in contact, provided the structure is decently accurate, such as predicted by AlphaFold2. Training on features generated from experimentally determined structures, we leveraged knowledge from known structures to significantly improve residue contact prediction, when testing using the same set of features but derived using AlphaFold2 structures. Our results demonstrate a remarkable improvement over AlphaFold2, achieving over 91.9% average precision for a held-out subset and over 89.5% average precision in cross-validation experiments. Full article
(This article belongs to the Special Issue Deep Learning for Modeling the Structure, Dynamics, and Function of Small and Large Molecules)
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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://0-doi-org.brum.beds.ac.uk/10.3390/ijms25094803 - 28 Apr 2024
Viewed by 521
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)
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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://0-doi-org.brum.beds.ac.uk/10.3390/ijms25084523 - 20 Apr 2024
Viewed by 436
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)
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