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Recent Advances in Computational Structural Bioinformatics

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 7280

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Special Issue Editor

Prof. Dr. Amarda Shehu

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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 complex relationship between molecular sequence, structure, and function continues to present many challenges and opportunities for computational treatments of small and large molecules. In particular, the rapid growth in molecular databases is now presenting unique opportunities for novel methodologies at the intersection of artificial intelligence, machine learning, deep learning, and molecular biology that are finally able to digest and distill the information present over sequences, structures, and phenotypical properties for novel insight and discoveries. The 15th Computational Structural Biology Workshop (CSBW) provided an interdisciplinary forum for researchers of various communities to present and discuss state-of-the-art computational approaches for the advancement of computational structural biology.

This Special Issue invites extended manuscripts from the 15th CSBW, as well as original research that meets the programmatic focus of this Special Issue. We are thankful to the Co-Chairs of the 15th CSBW, Dr. Chinwe Ekenna and Dr. Bruna Jacobson, for their great support to this Special Issue.

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 representation, prediction, and alignment
modeling of molecular interactions and docking
modeling of molecular dynamics
molecular visualization
representation learning over sequence, structure, and function data
structural genomics
high-performance computing in molecular modeling
graph theory applied to molecular structure
molecule generation and optimization
structure-based drug design

Published Papers (4 papers)

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Research

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15 pages, 1306 KiB

Open AccessArticle

Molecular Descriptors Property Prediction Using Transformer-Based Approach

by Tuan Tran and Chinwe Ekenna

Int. J. Mol. Sci. 2023, 24(15), 11948; https://doi.org/10.3390/ijms241511948 - 26 Jul 2023

Cited by 2 | Viewed by 1802

Abstract

In this study, we introduce semi-supervised machine learning models designed to predict molecular properties. Our model employs a two-stage approach, involving pre-training and fine-tuning. Particularly, our model leverages a substantial amount of labeled and unlabeled data consisting of SMILES strings, a text representation system for molecules. During the pre-training stage, our model capitalizes on the Masked Language Model, which is widely used in natural language processing, for learning molecular chemical space representations. During the fine-tuning stage, our model is trained on a smaller labeled dataset to tackle specific downstream tasks, such as classification or regression. Preliminary results indicate that our model demonstrates comparable performance to state-of-the-art models on the chosen downstream tasks from MoleculeNet. Additionally, to reduce the computational overhead, we propose a new approach taking advantage of 3D compound structures for calculating the attention score used in the end-to-end transformer model to predict anti-malaria drug candidates. The results show that using the proposed attention score, our end-to-end model is able to have comparable performance with pre-trained models. Full article

(This article belongs to the Special Issue Recent Advances in Computational Structural Bioinformatics)

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17 pages, 1675 KiB

Open AccessArticle

A New Tool to Study the Binding Behavior of Intrinsically Disordered Proteins

by Aakriti Upadhyay and Chinwe Ekenna

Int. J. Mol. Sci. 2023, 24(14), 11785; https://doi.org/10.3390/ijms241411785 - 22 Jul 2023

Viewed by 880

Abstract

Understanding the binding behavior and conformational dynamics of intrinsically disordered proteins (IDPs) is crucial for unraveling their regulatory roles in biological processes. However, their lack of stable 3D structures poses challenges for analysis. To address this, we propose an algorithm that explores IDP binding behavior with protein complexes by extracting topological and geometric features from the protein surface model. Our algorithm identifies a geometrically favorable binding pose for the IDP and plans a feasible trajectory to evaluate its transition to the docking position. We focus on IDPs from Homo sapiens and Mus-musculus, investigating their interaction with the Plasmodium falciparum (PF) pathogen associated with malaria-related deaths. We compare our algorithm with HawkDock and HDOCK docking tools for quantitative (computation time) and qualitative (binding affinity) measures. Our results indicated that our method outperformed the compared methods in computation performance and binding affinity in experimental conformations. Full article

(This article belongs to the Special Issue Recent Advances in Computational Structural Bioinformatics)

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21 pages, 1942 KiB

Open AccessArticle

An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders

by Xiangwen Wang, Yonggang Lu, Xianghong Lin, Jianwei Li and Zequn Zhang

Int. J. Mol. Sci. 2023, 24(9), 8380; https://doi.org/10.3390/ijms24098380 - 06 May 2023

Cited by 1 | Viewed by 1664

Abstract

Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as proteins in different functional states. Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity problem in single-particle cryo-EM. The majority of heterogeneous projection image classification methods are developed using supervised learning technology or require a large amount of a priori knowledge, such as the orientations or common lines of the projection images, which leads to certain limitations in their practical applications. In this paper, an unsupervised heterogeneous cryo-EM projection image classification algorithm based on autoencoders is proposed, which only needs to know the number of heterogeneous 3D structures in the dataset and does not require any labeling information of the projection images or other a priori knowledge. A simple autoencoder with multi-layer perceptrons trained in iterative mode and a complex autoencoder with residual networks trained in one-pass learning mode are implemented to convert heterogeneous projection images into latent variables. The extracted high-dimensional features are reduced to two dimensions using the uniform manifold approximation and projection dimensionality reduction algorithm, and then clustered using the spectral clustering algorithm. The proposed algorithm is applied to two heterogeneous cryo-EM datasets for heterogeneous 3D reconstruction. Experimental results show that the proposed algorithm can effectively extract category features of heterogeneous projection images and achieve high classification and reconstruction accuracy, indicating that the proposed algorithm is effective for heterogeneous 3D reconstruction in single-particle cryo-EM. Full article

(This article belongs to the Special Issue Recent Advances in Computational Structural Bioinformatics)

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Review

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23 pages, 6329 KiB

Open AccessReview

Economic Separations of Organic Acidic or Basic Enantiomeric Mixtures—A Protocol Suggestion

by Emese Pálovics, János Madarász, György Pokol, Elemér Fogassy and Dorottya Fruzsina Bánhegyi

Int. J. Mol. Sci. 2023, 24(1), 846; https://doi.org/10.3390/ijms24010846 - 03 Jan 2023

Cited by 2 | Viewed by 1826

Abstract

In this review, we aim to present new concepts for the revisited separation of enantiomers from racemic compounds and a protocol worth to be followed in designing the preparation of pure enantiomers. We have taken into account not only the influence of the properties (eutectic composition) and characteristics of the reactants (racemic compound, resolving agent), but also the behavior of the resulting diastereomers and the different conditions (e.g., crystallization time, solvents used, solvate-forming compounds, achiral additives, etc.). The examples discussed are resolutions developed by our research team, through which we will try to illustrate the impact of all these considerations, presenting the methodological investigations interpreting recent discoveries and observations. Some special solid-state analytical and structural investigations assisting us in the elucidation and invention design of the resolution processes of some active pharmaceutical ingredients, such as Tetramisole, tofisopam, and Amlodipine, are also shown. Full article

(This article belongs to the Special Issue Recent Advances in Computational Structural Bioinformatics)

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