Bioinformatics in Drug Design and Discovery

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Biomolecules biomolecules	4.8	9.2	2011	19.4 Days	CHF 2700
Genes genes	2.8	5.5	2010	14.6 Days	CHF 2600
International Journal of Molecular Sciences ijms	4.9	9.0	2000	20.5 Days	CHF 2900
Marine Drugs marinedrugs	5.4	10.1	2003	13.3 Days	CHF 2900
Scientia Pharmaceutica scipharm	2.5	4.6	1930	38.1 Days	CHF 1000
Molecules molecules	4.6	8.6	1996	16.1 Days	CHF 2700
Journal of Clinical Medicine jcm	2.9	5.2	2012	17.7 Days	CHF 2600

21 pages, 1572 KiB

Open AccessReview

Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing

by Thomas David Daniel Kazmirchuk, Calvin Bradbury-Jost, Taylor Ann Withey, Tadesse Gessese, Taha Azad, Bahram Samanfar, Frank Dehne and Ashkan Golshani

Genes 2023, 14(6), 1194; https://doi.org/10.3390/genes14061194 - 29 May 2023

Cited by 19 | Viewed by 4958

Abstract

Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design through identifying novel therapeutics that exhibit enhanced pharmacokinetic properties [...] Read more.

Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design through identifying novel therapeutics that exhibit enhanced pharmacokinetic properties and reduced toxicity. The process of in-silico peptide design involves the application of molecular docking, molecular dynamics simulations, and machine learning algorithms. Three primary approaches for peptide therapeutic design including structural-based, protein mimicry, and short motif design have been predominantly adopted. Despite the ongoing progress made in this field, there are still significant challenges pertaining to peptide design including: enhancing the accuracy of computational methods; improving the success rate of preclinical and clinical trials; and developing better strategies to predict pharmacokinetics and toxicity. In this review, we discuss past and present research pertaining to the design and development of in-silico peptide therapeutics in addition to highlighting the potential of computation and artificial intelligence in the future of disease therapeutics. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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17 pages, 2817 KiB

Open AccessArticle

In Silico Design of a Chimeric Humanized L-asparaginase

by Alejandro Pedroso, Lisandra Herrera Belén, Jorge F. Beltrán, Rodrigo L. Castillo, Adalberto Pessoa, Enrique Pedroso and Jorge G. Farías

Int. J. Mol. Sci. 2023, 24(8), 7550; https://doi.org/10.3390/ijms24087550 - 20 Apr 2023

Cited by 5 | Viewed by 2827

Abstract

Acute lymphoblastic leukemia (ALL) is the most common cancer among children worldwide, characterized by an overproduction of undifferentiated lymphoblasts in the bone marrow. The treatment of choice for this disease is the enzyme L-asparaginase (ASNase) from bacterial sources. ASNase hydrolyzes circulating L-asparagine in [...] Read more.

Acute lymphoblastic leukemia (ALL) is the most common cancer among children worldwide, characterized by an overproduction of undifferentiated lymphoblasts in the bone marrow. The treatment of choice for this disease is the enzyme L-asparaginase (ASNase) from bacterial sources. ASNase hydrolyzes circulating L-asparagine in plasma, leading to starvation of leukemic cells. The ASNase formulations of E. coli and E. chrysanthemi present notorious adverse effects, especially the immunogenicity they generate, which undermine both their effectiveness as drugs and patient safety. In this study, we developed a humanized chimeric enzyme from E. coli L-asparaginase which would reduce the immunological problems associated with current L-asparaginase therapy. For these, the immunogenic epitopes of E. coli L-asparaginase (PDB: 3ECA) were determined and replaced with those of the less immunogenic Homo sapiens asparaginase (PDB:4O0H). The structures were modeled using the Pymol software and the chimeric enzyme was modeled using the SWISS-MODEL service. A humanized chimeric enzyme with four subunits similar to the template structure was obtained, and the presence of asparaginase enzymatic activity was predicted by protein–ligand docking. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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23 pages, 6913 KiB

Open AccessArticle

In Silico Binding of 2-Aminocyclobutanones to SARS-CoV-2 Nsp13 Helicase and Demonstration of Antiviral Activity

by Thahani S. Habeeb Mohammad, Yash Gupta, Cory T. Reidl, Vlad Nicolaescu, Haley Gula, Ravi Durvasula, Prakasha Kempaiah and Daniel P. Becker

Int. J. Mol. Sci. 2023, 24(6), 5120; https://doi.org/10.3390/ijms24065120 - 7 Mar 2023

Cited by 5 | Viewed by 2762

Abstract

The landscape of viral strains and lineages of SARS-CoV-2 keeps changing and is currently dominated by Delta and Omicron variants. Members of the latest Omicron variants, including BA.1, are showing a high level of immune evasion, and Omicron has become a prominent variant [...] Read more.

The landscape of viral strains and lineages of SARS-CoV-2 keeps changing and is currently dominated by Delta and Omicron variants. Members of the latest Omicron variants, including BA.1, are showing a high level of immune evasion, and Omicron has become a prominent variant circulating globally. In our search for versatile medicinal chemistry scaffolds, we prepared a library of substituted ɑ-aminocyclobutanones from an ɑ-aminocyclobutanone synthon (11). We performed an in silico screen of this actual chemical library as well as other virtual 2-aminocyclobutanone analogs against seven SARS-CoV-2 nonstructural proteins to identify potential drug leads against SARS-CoV-2, and more broadly against coronavirus antiviral targets. Several of these analogs were initially identified as in silico hits against SARS-CoV-2 nonstructural protein 13 (Nsp13) helicase through molecular docking and dynamics simulations. Antiviral activity of the original hits as well as ɑ-aminocyclobutanone analogs that were predicted to bind more tightly to SARS-CoV-2 Nsp13 helicase are reported. We now report cyclobutanone derivatives that exhibit anti-SARS-CoV-2 activity. Furthermore, the Nsp13 helicase enzyme has been the target of relatively few target-based drug discovery efforts, in part due to a very late release of a high-resolution structure accompanied by a limited understanding of its protein biochemistry. In general, antiviral agents initially efficacious against wild-type SARS-CoV-2 strains have lower activities against variants due to heavy viral loads and greater turnover rates, but the inhibitors we are reporting have higher activities against the later variants than the wild-type (10–20X). We speculate this could be due to Nsp13 helicase being a critical bottleneck in faster replication rates of the new variants, so targeting this enzyme affects these variants to an even greater extent. This work calls attention to cyclobutanones as a useful medicinal chemistry scaffold, and the need for additional focus on the discovery of Nsp13 helicase inhibitors to combat the aggressive and immune-evading variants of concern (VOCs). Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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19 pages, 12464 KiB

Open AccessArticle

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug–Drug Interactions

by Jing Zhang, Meng Chen, Jie Liu, Dongdong Peng, Zong Dai, Xiaoyong Zou and Zhanchao Li

Molecules 2023, 28(3), 1490; https://doi.org/10.3390/molecules28031490 - 3 Feb 2023

Cited by 14 | Viewed by 4609

Abstract

The identification of drug–drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (KGCNs) with neural factorization machines (NFMs). A KGCN [...] Read more.

The identification of drug–drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (KGCNs) with neural factorization machines (NFMs). A KGCN is used to learn the embedding representation containing high-order structural information and semantic information in the knowledge graph (KG). The embedding and the Morgan molecular fingerprint of drugs are then used as input of NFMs to predict DDIs. The performance and effectiveness of the current method have been evaluated and confirmed based on the two real-world datasets with different sizes, and the results demonstrate that KGCN_NFM outperforms the state-of-the-art algorithms. Moreover, the identified interactions between topotecan and dantron by KGCN_NFM were validated through MTT assays, apoptosis experiments, cell cycle analysis, and molecular docking. Our study shows that the combination therapy of the two drugs exerts a synergistic anticancer effect, which provides an effective treatment strategy against lung carcinoma. These results reveal that KGCN_NFM is a valuable tool for integrating heterogeneous information to identify potential DDIs. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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19 pages, 4529 KiB

Open AccessArticle

The Tumorigenic Effect of the High Expression of Ladinin-1 in Lung Adenocarcinoma and Its Potential as a Therapeutic Target

by Lei Hu, Yu Liu, Changfang Fu, Jiarong Zhao, Qianwen Cui, Qiuyan Sun, Hongqiang Wang, Li Lu, Haiming Dai, Xiaohui Xu and Wulin Yang

Molecules 2023, 28(3), 1103; https://doi.org/10.3390/molecules28031103 - 22 Jan 2023

Cited by 6 | Viewed by 2819

Abstract

The oncogenic role of Ladinin-1 (LAD1), an anchoring filament protein, is largely unknown. In this study, we conducted a series of studies on the oncogenic role of LAD1 in lung adenocarcinoma (LUAD). Firstly, we analyzed the aberrant expression of LAD1 in LUAD and [...] Read more.

The oncogenic role of Ladinin-1 (LAD1), an anchoring filament protein, is largely unknown. In this study, we conducted a series of studies on the oncogenic role of LAD1 in lung adenocarcinoma (LUAD). Firstly, we analyzed the aberrant expression of LAD1 in LUAD and its correlation with patient survival, tumor immune infiltration, and the activation of cancer signaling pathways. Furthermore, the relationship between LAD1 expression and K-Ras and EGF signaling activation, tumor cell proliferation, migration, and colony formation was studied by gene knockout/knockout methods. We found that LAD1 was frequently overexpressed in LUAD, and high LAD1 expression predicts a poor prognosis. LAD1 exhibits promoter hypomethylation in LUAD, which may contribute to its mRNA upregulation. Single-sample gene set enrichment analysis (ssGSEA) showed that acquired immunity was negatively correlated with LAD1 expression, which was verified by the downregulated GO terms of “Immunoglobulin receptor binding” and “Immunoglobulin complex circulating” in the LAD1 high-expression group through Gene Set Variation Analysis (GSVA). Notably, the Ras-dependent signature was the most activated signaling in the LAD1 high-expression group, and the phosphorylation of downstream effectors, such as ERK and c-jun, was strongly inhibited by LAD1 deficiency. Moreover, we demonstrated that LAD1 depletion significantly inhibited the proliferation, migration, and cell-cycle progression of LUAD cells and promoted sensitivity to Gefitinib, K-Ras inhibitor, and paclitaxel treatments. We also confirmed that LAD1 deficiency remarkably retarded tumor growth in the xenograft model. Conclusively, LAD1 is a critical prognostic biomarker for LUAD and has potential as an intervention target. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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30 pages, 12740 KiB

Open AccessArticle

Identification of Potential Druggable Targets and Structure-Based Virtual Screening for Drug-like Molecules against the Shrimp Pathogen Enterocytozoon hepatopenaei

by Prasenjit Paria and Anchalee Tassanakajon

Int. J. Mol. Sci. 2023, 24(2), 1412; https://doi.org/10.3390/ijms24021412 - 11 Jan 2023

Cited by 6 | Viewed by 3112

Abstract

Enterocytozoon hepatopenaei (EHP) causes slow growth syndrome in shrimp, resulting in huge economic losses for the global shrimp industry. Despite worldwide reports, there are no effective therapeutics for controlling EHP infections. In this study, five potential druggable targets of EHP, namely, aquaporin (AQP), [...] Read more.

Enterocytozoon hepatopenaei (EHP) causes slow growth syndrome in shrimp, resulting in huge economic losses for the global shrimp industry. Despite worldwide reports, there are no effective therapeutics for controlling EHP infections. In this study, five potential druggable targets of EHP, namely, aquaporin (AQP), cytidine triphosphate (CTP) synthase, thymidine kinase (TK), methionine aminopeptidase2 (MetAP2), and dihydrofolate reductase (DHFR), were identified via functional classification of the whole EHP proteome. The three-dimensional structures of the proteins were constructed using the artificial-intelligence-based program AlphaFold 2. Following the prediction of druggable sites, the ZINC15 and ChEMBL databases were screened against targets using docking-based virtual screening. Molecules with affinity scores ≥ 7.5 and numbers of interactions ≥ 9 were initially selected and subsequently enriched based on their ADMET properties and electrostatic complementarities. Five compounds were finally selected against each target based on their complex stabilities and binding energies. The compounds CHEMBL3703838, CHEMBL2132563, and CHEMBL133039 were selected against AQP; CHEMBL1091856, CHEMBL1162979, and CHEMBL525202 against CTP synthase; CHEMBL4078273, CHEMBL1683320, and CHEMBL3674540 against TK; CHEMBL340488, CHEMBL1966988, and ZINC000828645375 against DHFR; and CHEMBL3913373, ZINC000016682972, and CHEMBL3142997 against MetAP2.The compounds exhibited high stabilities and low binding free energies, indicating their abilities to suppress EHP infections; however, further validation is necessary for determining their efficacy. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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17 pages, 5514 KiB

Open AccessArticle

Exploration of Flavonoids as Lead Compounds against Ewing Sarcoma through Molecular Docking, Pharmacogenomics Analysis, and Molecular Dynamics Simulations

by Muhammad Yasir, Jinyoung Park, Eun-Taek Han, Won Sun Park, Jin-Hee Han, Yong-Soo Kwon, Hee-Jae Lee, Mubashir Hassan, Andrzej Kloczkowski and Wanjoo Chun

Molecules 2023, 28(1), 414; https://doi.org/10.3390/molecules28010414 - 3 Jan 2023

Cited by 19 | Viewed by 3731

Abstract

Ewing sarcoma (ES) is a highly malignant carcinoma prevalent in children and most frequent in the second decade of life. It mostly occurs due to t(11;22) (q24;q12) translocation. This translocation encodes the oncogenic fusion protein EWS/FLI (Friend leukemia integration 1 transcription factor), which [...] Read more.

Ewing sarcoma (ES) is a highly malignant carcinoma prevalent in children and most frequent in the second decade of life. It mostly occurs due to t(11;22) (q24;q12) translocation. This translocation encodes the oncogenic fusion protein EWS/FLI (Friend leukemia integration 1 transcription factor), which acts as an aberrant transcription factor to deregulate target genes essential for cancer. Traditionally, flavonoids from plants have been investigated against viral and cancerous diseases and have shown some promising results to combat these disorders. In the current study, representative flavonoid compounds from various subclasses are selected and used to disrupt the RNA-binding motif of EWS, which is required for EWS/FLI fusion. By blocking the RNA-binding motif of EWS, it might be possible to combat ES. Therefore, molecular docking experiments validated the binding interaction patterns and structural behaviors of screened flavonoid compounds within the active region of the Ewing sarcoma protein (EWS). Furthermore, pharmacogenomics analysis was used to investigate potential drug interactions with Ewing sarcoma-associated genes. Finally, molecular dynamics simulations were used to investigate the stability of the best selected docked complexes. Taken together, daidzein, kaempferol, and genistein exhibited a result comparable to ifosfamide in the proposed in silico study and can be further analyzed as possible candidate compounds in biological in vitro studies against ES. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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18 pages, 3126 KiB

Open AccessArticle

Predicting the Assembly of the Transmembrane Domains of Viral Channel Forming Proteins and Peptide Drug Screening Using a Docking Approach

by Ta-Chou Huang and Wolfgang B. Fischer

Biomolecules 2022, 12(12), 1844; https://doi.org/10.3390/biom12121844 - 10 Dec 2022

Viewed by 2044

Abstract

A de novo assembly algorithm is provided to propose the assembly of bitopic transmembrane domains (TMDs) of membrane proteins. The algorithm is probed using, in particular, viral channel forming proteins (VCPs) such as M2 of influenza A virus, E protein of severe acute [...] Read more.

A de novo assembly algorithm is provided to propose the assembly of bitopic transmembrane domains (TMDs) of membrane proteins. The algorithm is probed using, in particular, viral channel forming proteins (VCPs) such as M2 of influenza A virus, E protein of severe acute respiratory syndrome corona virus (SARS-CoV), 6K of Chikungunya virus (CHIKV), SH of human respiratory syncytial virus (hRSV), and Vpu of human immunodeficiency virus type 2 (HIV-2). The generation of the structures is based on screening a 7-dimensional space. Assembly of the TMDs can be achieved either by simultaneously docking the individual TMDs or via a sequential docking. Scoring based on estimated binding energies (EBEs) of the oligomeric structures is obtained by the tilt to decipher the handedness of the bundles. The bundles match especially well for all-atom models of M2 referring to an experimentally reported tetrameric bundle. Docking of helical poly-peptides to experimental structures of M2 and E protein identifies improving EBEs for positively charged (K,R,H) and aromatic amino acids (F,Y,W). Data are improved when using polypeptides for which the coordinates of the amino acids are adapted to the Cα coordinates of the respective experimentally derived structures of the TMDs of the target proteins. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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13 pages, 2624 KiB

Open AccessReview

Bridging the Gap in Malaria Parasite Resistance, Current Interventions, and the Way Forward from in Silico Perspective: A Review

by Ransford Oduro Kumi, Belinda Oti, Nader E. Abo-Dya, Mohamed Issa Alahmdi and Mahmoud E. S. Soliman

Molecules 2022, 27(22), 7915; https://doi.org/10.3390/molecules27227915 - 16 Nov 2022

Cited by 8 | Viewed by 7179

Abstract

The past decade has seen most antimalarial drugs lose their clinical potency stemming from parasite resistance. Despite immense efforts by researchers to mitigate this global scourge, a breakthrough is yet to be achieved, as most current malaria chemotherapies suffer the same fate. Though [...] Read more.

The past decade has seen most antimalarial drugs lose their clinical potency stemming from parasite resistance. Despite immense efforts by researchers to mitigate this global scourge, a breakthrough is yet to be achieved, as most current malaria chemotherapies suffer the same fate. Though the etiology of parasite resistance is not well understood, the parasite’s complex life has been implicated. A drug-combination therapy with artemisinin as the central drug, artemisinin-based combination therapy (ACT), is currently the preferred malaria chemotherapy in most endemic zones. The emerging concern of parasite resistance to artemisinin, however, has compromised this treatment paradigm. Membrane-bound Ca²⁺-transporting ATPase and endocytosis pathway protein, Kelch13, among others, are identified as drivers in plasmodium parasite resistance to artemisinin. To mitigate parasite resistance to current chemotherapy, computer-aided drug design (CADD) techniques have been employed in the discovery of novel drug targets and the development of small molecule inhibitors to provide an intriguing alternative for malaria treatment. The evolution of plasmepsins, a class of aspartyl acid proteases, has gained tremendous attention in drug discovery, especially the non-food vacuole. They are expressed at multi-stage of the parasite’s life cycle and involve in hepatocytes’ egress, invasion, and dissemination of the parasite within the human host, further highlighting their essentiality. In silico exploration of non-food vacuole plasmepsin, PMIX and PMX unearthed the dual enzymatic inhibitory mechanism of the WM382 and 49c, novel plasmepsin inhibitors presently spearheading the search for potent antimalarial. These inhibitors impose structural compactness on the protease, distorting the characteristic twist motion. Pharmacophore modeling and structure activity of these compounds led to the generation of hits with better affinity and inhibitory prowess towards PMIX and PMX. Despite these headways, the major obstacle in targeting PM is the structural homogeneity among its members and to human Cathepsin D. The incorporation of CADD techniques described in the study at early stages of drug discovery could help in selective inhibition to augment malaria chemotherapy. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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26 pages, 29008 KiB

Open AccessArticle

Cytotoxic Activity and Docking Studies of 2-arenoxybenzaldehyde N-acyl Hydrazone and 1,3,4-Oxadiazole Derivatives against Various Cancer Cell Lines

by Esranur Aydın, Ahmet Mesut Şentürk, Hatice Başpınar Küçük and Mustafa Güzel

Molecules 2022, 27(21), 7309; https://doi.org/10.3390/molecules27217309 - 27 Oct 2022

Cited by 8 | Viewed by 2365

Abstract

To understand whether previously synthesized novel hydrazone and oxadiazole derivatives have promising anticancer effects, docking studies and in vitro toxicity assays were performed on A-549, MDA-MB-231, and PC-3 cell lines. The antiproliferative properties of the compounds were investigated using molecular docking experiments. Each [...] Read more.

To understand whether previously synthesized novel hydrazone and oxadiazole derivatives have promising anticancer effects, docking studies and in vitro toxicity assays were performed on A-549, MDA-MB-231, and PC-3 cell lines. The antiproliferative properties of the compounds were investigated using molecular docking experiments. Each compound’s best-docked poses, binding affinity, and receptor-ligand interaction were evaluated. Compounds’ molecular weights, logPs, TPSAs, abilities to pass the blood-brain barrier, GI absorption qualities, and CYPP450 inhibition have been given. When the activities of these molecules were examined in vitro, for the A-549 cell line, hydrazone 1e had the minimum IC₅₀ value of 13.39 μM. For the MDA-MB-231 cell line, oxadiazole 2l demonstrated the lowest IC₅₀ value, with 22.73 μM. For PC-3, hydrazone 1d showed the lowest C50 value of 9.38 μM. The three most promising compounds were determined as compounds 1e, 1d, and 2a based on their minimum IC₅₀ values, and an additional scratch assay was performed for A-549 and MDA-MB-231 cells, which have high migration capacity, for the three most potent molecules; it was determined that these molecules did not show a significant antimetastatic effect. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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18 pages, 4548 KiB

Open AccessArticle

Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches

by Linfeng Zheng, Xiangyang Qin, Jiao Wang, Mengying Zhang, Quanlin An, Jinzhi Xu, Xiaosheng Qu, Xin Cao and Bing Niu

Biomolecules 2022, 12(10), 1470; https://doi.org/10.3390/biom12101470 - 13 Oct 2022

Cited by 5 | Viewed by 2545

Abstract

Alzheimer’s disease (AD) is the most common type of dementia and is a serious disruption to normal life. Monoamine oxidase-B (MAO-B) is an important target for the treatment of AD. In this study, machine learning approaches were applied to investigate the identification model [...] Read more.

Alzheimer’s disease (AD) is the most common type of dementia and is a serious disruption to normal life. Monoamine oxidase-B (MAO-B) is an important target for the treatment of AD. In this study, machine learning approaches were applied to investigate the identification model of MAO-B inhibitors. The results showed that the identification model for MAO-B inhibitors with　K-nearest neighbor(KNN) algorithm had a prediction accuracy of 94.1% and 88.0% for the 10-fold cross-validation test and the independent test set, respectively. Secondly, a quantitative activity prediction model for MAO-B was investigated with the Topomer CoMFA model. Two separate cutting mode approaches were used to predict the activity of MAO-B inhibitors. The results showed that the cut model with q² = 0.612 (cross-validated correlation coefficient) and r² = 0.824 (non-cross-validated correlation coefficient) were determined for the training and test sets, respectively. In addition, molecular docking was employed to analyze the interaction between MAO-B and inhibitors. Finally, based on our proposed prediction model, 1-(4-hydroxyphenyl)-3-(2,4,6-trimethoxyphenyl)propan-1-one (LB) was predicted as a potential MAO-B inhibitor and was validated by a multi-spectroscopic approach including fluorescence spectra and ultraviolet spectrophotometry. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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16 pages, 3141 KiB

Open AccessArticle

Comprehensive Bioinformatics Analysis Combined with Wet-Lab Experiments to Find Target Proteins of Chinese Medicine Monomer

by Xiaohui Xu, Yunyi Zhu, Changling Yue, Qianwen Yang and Zhaohuan Zhang

Molecules 2022, 27(18), 6105; https://doi.org/10.3390/molecules27186105 - 19 Sep 2022

Cited by 5 | Viewed by 2983

Abstract

How to use bioinformatics methods to quickly and accurately locate the effective targets of traditional Chinese medicine monomer (TCM) is still an urgent problem needing to be solved. Here, we used high-throughput sequencing to identify the genes that were up-regulated after cells were [...] Read more.

How to use bioinformatics methods to quickly and accurately locate the effective targets of traditional Chinese medicine monomer (TCM) is still an urgent problem needing to be solved. Here, we used high-throughput sequencing to identify the genes that were up-regulated after cells were treated with TCM monomers and used bioinformatics methods to analyze which transcription factors activated these genes. Then, the binding proteins of these transcription factors were analyzed and cross-analyzed with the docking proteins predicted by small molecule reverse docking software to quickly and accurately determine the monomer’s targets. Followeding this method, we predicted that the TCM monomer Daphnoretin (DT) directly binds to JAK2 with a binding energy of −5.43 kcal/mol, and activates the JAK2/STAT3 signaling transduction pathway. Subsequent Western blotting and in vitro binding and kinase experiments further validated our bioinformatics predictions. Our method provides a new approach for quickly and accurately locating the effective targets of TCM monomers, and we also have discovered for the first time that TCM monomer DT is an agonist of JAK2. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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9 pages, 2263 KiB

Open AccessArticle

Molecular Dynamic Simulation Reveals Structure Differences in APOL1 Variants and Implication in Pathogenesis of Chronic Kidney Disease

by Richard Mayanja, Christopher Kintu, Oudou Diabate, Opeyemi Soremekun, Olugbenga Oluseun Oluwagbemi, Mamadou Wele, Robert Kalyesubula, Daudi Jjingo, Tinashe Chikowore and Segun Fatumo

Genes 2022, 13(8), 1460; https://doi.org/10.3390/genes13081460 - 16 Aug 2022

Cited by 3 | Viewed by 3347

Abstract

Background: According to observational studies, two polymorphisms in the apolipoprotein L1 (APOL1) gene have been linked to an increased risk of chronic kidney disease (CKD) in Africans. One polymorphism involves the substitution of two amino-acid residues (S342G and I384M; known as [...] Read more.

Background: According to observational studies, two polymorphisms in the apolipoprotein L1 (APOL1) gene have been linked to an increased risk of chronic kidney disease (CKD) in Africans. One polymorphism involves the substitution of two amino-acid residues (S342G and I384M; known as G1), while the other involves the deletion of two amino-acid residues in a row (N388 and Y389; termed G2). Despite the strong link between APOL1 polymorphisms and kidney disease, the molecular mechanisms via which these APOL1 mutations influence the onset and progression of CKD remain unknown. Methods: To predict the active site and allosteric site on the APOL1 protein, we used the Computed Atlas of Surface Topography of Proteins (CASTp) and the Protein Allosteric Sites Server (PASSer). Using an extended molecular dynamics simulation, we investigated the characteristic structural perturbations in the 3D structures of APOL1 variants. Results: According to CASTp’s active site characterization, the topmost predicted site had a surface area of 964.892 Å² and a pocket volume of 900.792 Å³. For the top three allosteric pockets, the allostery probability was 52.44%, 46.30%, and 38.50%, respectively. The systems reached equilibrium in about 125 ns. From 0–100 ns, there was also significant structural instability. When compared to G1 and G2, the wildtype protein (G0) had overall high stability throughout the simulation. The root-mean-square fluctuation (RMSF) of wildtype and variant protein backbone Cα fluctuations revealed that the Cα of the variants had a large structural fluctuation when compared to the wildtype. Conclusion: Using a combination of different computational techniques, we identified binding sites within the APOL1 protein that could be an attractive site for potential inhibitors of APOL1. Furthermore, the G1 and G2 mutations reduced the structural stability of APOL1. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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13 pages, 2911 KiB

Open AccessArticle

Molecular Basis of Inhibitory Mechanism of Naltrexone and Its Metabolites through Structural and Energetic Analyses

by Martiniano Bello

Molecules 2022, 27(15), 4919; https://doi.org/10.3390/molecules27154919 - 2 Aug 2022

Cited by 2 | Viewed by 2377

Abstract

Naltrexone is a potent opioid antagonist with good blood–brain barrier permeability, targeting different endogenous opioid receptors, particularly the mu-opioid receptor (MOR). Therefore, it represents a promising candidate for drug development against drug addiction. However, the details of the molecular interactions of naltrexone and [...] Read more.

Naltrexone is a potent opioid antagonist with good blood–brain barrier permeability, targeting different endogenous opioid receptors, particularly the mu-opioid receptor (MOR). Therefore, it represents a promising candidate for drug development against drug addiction. However, the details of the molecular interactions of naltrexone and its derivatives with MOR are not fully understood, hindering ligand-based drug discovery. In the present study, taking advantage of the high-resolution X-ray crystal structure of the murine MOR (mMOR), we constructed a homology model of the human MOR (hMOR). A solvated phospholipid bilayer was built around the hMOR and submitted to microsecond (µs) molecular dynamics (MD) simulations to obtain an optimized hMOR model. Naltrexone and its derivatives were docked into the optimized hMOR model and submitted to µs MD simulations in an aqueous membrane system. The MD simulation results were submitted to the molecular mechanics–generalized Born surface area (MMGBSA) binding free energy calculations and principal component analysis. Our results revealed that naltrexone and its derivatives showed differences in protein–ligand interactions; however, they shared contacts with residues at TM2, TM3, H6, and TM7. The binding free energy and principal component analysis revealed the structural and energetic effects responsible for the higher potency of naltrexone compared to its derivatives. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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9 pages, 5441 KiB

Open AccessArticle

cpxDeepMSA: A Deep Cascade Algorithm for Constructing Multiple Sequence Alignments of Protein–Protein Interactions

by Zi Liu and Dong-Jun Yu

Int. J. Mol. Sci. 2022, 23(15), 8459; https://doi.org/10.3390/ijms23158459 - 30 Jul 2022

Cited by 5 | Viewed by 2453

Abstract

Protein–protein interactions (PPIs) are fundamental to many biological processes. The coevolution-based prediction of interacting residues has made great strides in protein complexes that are known to interact. A multiple sequence alignment (MSA) is the basis of coevolution analysis. MSAs have recently made significant [...] Read more.

Protein–protein interactions (PPIs) are fundamental to many biological processes. The coevolution-based prediction of interacting residues has made great strides in protein complexes that are known to interact. A multiple sequence alignment (MSA) is the basis of coevolution analysis. MSAs have recently made significant progress in the protein monomer sequence analysis. However, no standard or efficient pipelines are available for the sensitive protein complex MSA (cpxMSA) collection. How to generate cpxMSA is one of the most challenging problems of sequence coevolution analysis. Although several methods have been developed to address this problem, no standalone program exists. Furthermore, the number of built-in properties is limited; hence, it is often difficult for users to analyze sequence coevolution according to their desired cpxMSA. In this article, we developed a novel cpxMSA approach (cpxDeepMSA. We used different protein monomer databases and incorporated the three strategies (genomic distance, phylogeny information, and STRING interaction network) used to join the monomer MSA results of protein complexes, which can prevent using a single method fail to the joint two-monomer MSA causing the cpxMSA construction failure. We anticipate that the cpxDeepMSA algorithm will become a useful high-throughput tool in protein complex structure predictions, inter-protein residue-residue contacts, and the biological sequence coevolution analysis. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery)

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