Bioinformatics in Drug Design and Discovery, 2nd Volume

Topic Information

Dear Colleagues,

With the development of modern sequencing technology, this decade has witnessed the expansion of huge biomedical data advances which has opened a new window for the clinical diagnoses and therapeutics of complex disease. Bioinformatics can extract, analyze, and communicate hidden information from sequences and structures as well as functional knowledge of nucleic acids and proteins in order to discover and identify new drug targets. This can potentially guide the design of therapeutic drugs that can activate or block the biological functions of biomolecules and help to construct various prediction models to aid virtual bioactive screening. This will, in turn, help to design and discover safer and more efficient therapeutic drugs that can either activate or block the biological functions of biomolecules.

Thus, there is a need to fundamentally address all of the above-mentioned issues in the application of bioinformatics techniques and the development of novel drugs. Here, we seek original research papers and reviews for a Special Issue on the theme of bioinformatics in drug design and discovery. Dr. Bing Niu Dr. Suren Rao Sooranna Dr. Pufeng Du Topic Editors

Dr. Bing Niu
Dr. Suren Rao Sooranna
Dr. Pufeng Du
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Biomolecules biomolecules	5.5	8.3	2011	16.9 Days	CHF 2700	Submit
International Journal of Molecular Sciences ijms	5.6	7.8	2000	16.3 Days	CHF 2900	Submit
Marine Drugs marinedrugs	5.4	9.6	2003	14 Days	CHF 2900	Submit
Molecules molecules	4.6	6.7	1996	14.6 Days	CHF 2700	Submit
Scientia Pharmaceutica scipharm	2.5	6.4	1930	22.7 Days	CHF 1000	Submit
Genes genes	3.5	5.1	2010	16.5 Days	CHF 2600	Submit
Pharmaceutics pharmaceutics	5.4	6.9	2009	14.2 Days	CHF 2900	Submit
Crystals crystals	2.7	3.6	2011	10.6 Days	CHF 2600	Submit

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Published Papers (3 papers)

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All Biomolecules IJMS Marine Drugs Molecules Sci. Pharm. Genes Pharmaceutics Crystals

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Open AccessArticle

An Approach for Engineering Peptides for Competitive Inhibition of the SARS-COV-2 Spike Protein

by Ana Paula de Abreu, Frederico Chaves Carvalho, Diego Mariano, Luana Luiza Bastos, Juliana Rodrigues Pereira Silva, Leandro Morais de Oliveira, Raquel C. de Melo-Minardi and Adriano de Paula Sabino

Molecules 2024, 29(7), 1577; https://doi.org/10.3390/molecules29071577 - 01 Apr 2024

Viewed by 648

Abstract

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, [...] Read more.

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent. However, designing peptides that bind efficiently is still an open challenge. Here, we show an algorithm for peptide engineering. Our strategy consists of starting with a peptide whose structure is similar to the interaction region of the human ACE2 protein with the SPIKE protein, which is important for SARS-COV-2 infection. Our methodology is based on a genetic algorithm performing systematic steps of random mutation, protein–peptide docking (using the PyRosetta library) and selecting the best-optimized peptides based on the contacts made at the peptide–protein interface. We performed three case studies to evaluate the tool parameters and compared our results with proposals presented in the literature. Additionally, we performed molecular dynamics (MD) simulations (three systems, 200 ns each) to probe whether our suggested peptides could interact with the spike protein. Our results suggest that our methodology could be a good strategy for designing peptides. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery, 2nd Volume)

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Open AccessArticle

Semaglutide as a Possible Calmodulin Binder: Ligand-Based Computational Analyses and Relevance to Its Associated Reward and Appetitive Behaviour Actions

by Giuseppe Floresta, Davide Arillotta, Valeria Catalani, Gabriele Duccio Papanti Pelletier, John Martin Corkery, Amira Guirguis and Fabrizio Schifano

Sci. Pharm. 2024, 92(2), 17; https://doi.org/10.3390/scipharm92020017 - 22 Mar 2024

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Abstract

Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, has gained considerable attention as a therapeutic agent for type 2 diabetes mellitus and obesity. Despite its clinical success, the precise mechanisms underlying its pharmacological effects remain incompletely understood. In this study, we employed ligand-based drug [...] Read more.

Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, has gained considerable attention as a therapeutic agent for type 2 diabetes mellitus and obesity. Despite its clinical success, the precise mechanisms underlying its pharmacological effects remain incompletely understood. In this study, we employed ligand-based drug design strategies to investigate potential off-target interactions of semaglutide. Through a comprehensive in silico screening of semaglutide’s structural properties against a diverse panel of proteins, we have identified calmodulin (CaM) as a putative novel target of semaglutide. Molecular docking simulations revealed a strong interaction between semaglutide and CaM, characterized by favourable binding energies and a stable binding pose. Further molecular dynamics simulations confirmed the stability of the semaglutide–CaM complex, emphasizing the potential for a physiologically relevant interaction. In conclusion, our ligand-based drug design approach has uncovered calmodulin as a potential novel target of semaglutide. This discovery sheds light on the complex pharmacological profile of semaglutide and offers a promising direction for further research into the development of innovative therapeutic strategies for metabolic disorders. The CaM, and especially so the CaMKII, system is central in the experience of both drug- and natural-related reward. It is here hypothesized that, due to semaglutide binding, the reward pathway-based calmodulin system may be activated, and/or differently regulated. This may result in the positive semaglutide action on appetitive behaviour. Further studies are required to confirm these findings. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery, 2nd Volume)

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Open AccessArticle

CONSMI: Contrastive Learning in the Simplified Molecular Input Line Entry System Helps Generate Better Molecules

by Ying Qian, Minghua Shi and Qian Zhang

Molecules 2024, 29(2), 495; https://doi.org/10.3390/molecules29020495 - 19 Jan 2024

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Abstract

In recent years, the application of deep learning in molecular de novo design has gained significant attention. One successful approach involves using SMILES representations of molecules and treating the generation task as a text generation problem, yielding promising results. However, the generation of [...] Read more.

In recent years, the application of deep learning in molecular de novo design has gained significant attention. One successful approach involves using SMILES representations of molecules and treating the generation task as a text generation problem, yielding promising results. However, the generation of more effective and novel molecules remains a key research area. Due to the fact that a molecule can have multiple SMILES representations, it is not sufficient to consider only one of them for molecular generation. To make up for this deficiency, and also motivated by the advancements in contrastive learning in natural language processing, we propose a contrastive learning framework called CONSMI to learn more comprehensive SMILES representations. This framework leverages different SMILES representations of the same molecule as positive examples and other SMILES representations as negative examples for contrastive learning. The experimental results of generation tasks demonstrate that CONSMI significantly enhances the novelty of generated molecules while maintaining a high validity. Moreover, the generated molecules have similar chemical properties compared to the original dataset. Additionally, we find that CONSMI can achieve favorable results in classifier tasks, such as the compound–protein interaction task. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery, 2nd Volume)

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