Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 5 de 5
Filter
1.
Mathematics ; 10(19):3614, 2022.
Article in English | MDPI | ID: covidwho-2066232

ABSTRACT

The world is still trying to recover from the devastation caused by the wide spread of COVID-19, and now the monkeypox virus threatens becoming a worldwide pandemic. Although the monkeypox virus is not as lethal or infectious as COVID-19, numerous countries report new cases daily. Thus, it is not surprising that necessary precautions have not been taken, and it will not be surprising if another worldwide pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application may be implemented to diagnose monkeypox as it invades the human skin. An image can be acquired and utilized to further diagnose the condition. In this paper, two algorithms are proposed for improving the classification accuracy of monkeypox images. The proposed algorithms are based on transfer learning for feature extraction and meta-heuristic optimization for feature selection and optimization of the parameters of a multi-layer neural network. The GoogleNet deep network is adopted for feature extraction, and the utilized meta-heuristic optimization algorithms are the Al-Biruni Earth radius algorithm, the sine cosine algorithm, and the particle swarm optimization algorithm. Based on these algorithms, a new binary hybrid algorithm is proposed for feature selection, along with a new hybrid algorithm for optimizing the parameters of the neural network. To evaluate the proposed algorithms, a publicly available dataset is employed. The assessment of the proposed optimization of feature selection for monkeypox classification was performed in terms of ten evaluation criteria. In addition, a set of statistical tests was conducted to measure the effectiveness, significance, and robustness of the proposed algorithms. The results achieved confirm the superiority and effectiveness of the proposed methods compared to other optimization methods. The average classification accuracy was 98.8%.

2.
RSC advances ; 11(26):16026-16033, 2021.
Article in English | EuropePMC | ID: covidwho-1812711

ABSTRACT

In the present era, there are many efforts trying to face the emerging and successive waves of the COVID-19 pandemic. This has led to considering new and unusual targets for SARS CoV-2. 2′-O-Methyltransferase (nsp16) is a key and attractive target in the SARS CoV-2 life cycle since it is responsible for the viral RNA protection via a cap formation process. In this study, we propose a new potential inhibitor for SARS COV-2 2′-O-methyltransferase (nsp16). A fragment library was screened against the co-crystal structure of the SARS COV-2 2′-O-methyltransferase complexed with Sinefungin (nsp16 – PDB ID: 6WKQ), and consequently the best proposed fragments were linked via a de novo approach to build molecule AP-20. Molecule AP-20 displayed a superior docking score to Sinefungin and reproduced the key interactions in the binding site of 2′-O-methyltransferase. Three molecular dynamic simulations of the 2′-O-methyltransferase apo structure and its complexed forms with AP-20 and Sinefungin were performed for 150 nano-seconds to provide insights on the dynamic nature of such setups and to assess the stability of the proposed AP-20/enzyme complex. AP-20/enzyme complex demonstrated better stability for the ligand–enzyme complex compared to Sinefungin in a respective setup. Furthermore, MM-PBSA binding free energy calculations showed a better profile for AP-20/enzyme complex compared to Sinefungin/enzyme complex emphasizing the potential inhibitory effect of AP-20 on SARS COV-2 2′-O-methyltransferase. We endorse our designed molecule AP-20 to be further explored via experimental evaluations to confront the spread of the emerging COVID-19. Also, in silico ADME profiling has ascribed to AP-20 an excellent safety and metabolic stability profile. The identification of AP-20 as a potential SARS COV-2 2′-O-methyltransferase inhibitor: fragment-based screening approach and MM-PBSA calculations.

3.
Processes ; 9(6):1004, 2021.
Article in English | MDPI | ID: covidwho-1259568

ABSTRACT

Since December 2019, the world has been facing the outbreak of the SARS-CoV-2 pandemic that has infected more than 149 million and killed 3.1 million people by 27 April 2021, according to WHO statistics. Safety measures and precautions taken by many countries seem insufficient, especially with no specific approved drugs against the virus. This has created an urgent need to fast track the development of new medication against the virus in order to alleviate the problem and meet public expectations. The SARS-CoV-2 3CL main protease (Mpro) is one of the most attractive targets in the virus life cycle, which is responsible for the processing of the viral polyprotein and is a key for the ribosomal translation of the SARS-CoV-2 genome. In this work, we targeted this enzyme through a structure-based drug design (SBDD) protocol, which aimed at the design of a new potential inhibitor for Mpro. The protocol involves three major steps: fragment-based drug design (FBDD), covalent docking and molecular dynamics (MD) simulation with the calculation of the designed molecule binding free energy at a high level of theory. The FBDD step identified five molecular fragments, which were linked via a suitable carbon linker, to construct our designed compound RMH148. The mode of binding and initial interactions between RMH148 and the enzyme active site was established in the second step of our protocol via covalent docking. The final step involved the use of MD simulations to test for the stability of the docked RMH148 into the Mpro active site and included precise calculations for potential interactions with active site residues and binding free energies. The results introduced RMH148 as a potential inhibitor for the SARS-CoV-2 Mpro enzyme, which was able to achieve various interactions with the enzyme and forms a highly stable complex at the active site even better than the co-crystalized reference.

4.
RSC Adv ; 11(26): 16026-16033, 2021 Apr 26.
Article in English | MEDLINE | ID: covidwho-1236099

ABSTRACT

In the present era, there are many efforts trying to face the emerging and successive waves of the COVID-19 pandemic. This has led to considering new and unusual targets for SARS CoV-2. 2'-O-Methyltransferase (nsp16) is a key and attractive target in the SARS CoV-2 life cycle since it is responsible for the viral RNA protection via a cap formation process. In this study, we propose a new potential inhibitor for SARS COV-2 2'-O-methyltransferase (nsp16). A fragment library was screened against the co-crystal structure of the SARS COV-2 2'-O-methyltransferase complexed with Sinefungin (nsp16 - PDB ID: 6WKQ), and consequently the best proposed fragments were linked via a de novo approach to build molecule AP-20. Molecule AP-20 displayed a superior docking score to Sinefungin and reproduced the key interactions in the binding site of 2'-O-methyltransferase. Three molecular dynamic simulations of the 2'-O-methyltransferase apo structure and its complexed forms with AP-20 and Sinefungin were performed for 150 nano-seconds to provide insights on the dynamic nature of such setups and to assess the stability of the proposed AP-20/enzyme complex. AP-20/enzyme complex demonstrated better stability for the ligand-enzyme complex compared to Sinefungin in a respective setup. Furthermore, MM-PBSA binding free energy calculations showed a better profile for AP-20/enzyme complex compared to Sinefungin/enzyme complex emphasizing the potential inhibitory effect of AP-20 on SARS COV-2 2'-O-methyltransferase. We endorse our designed molecule AP-20 to be further explored via experimental evaluations to confront the spread of the emerging COVID-19. Also, in silico ADME profiling has ascribed to AP-20 an excellent safety and metabolic stability profile.

5.
J Enzyme Inhib Med Chem ; 36(1): 727-736, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1123193

ABSTRACT

The novel coronavirus disease COVID-19, caused by the virus SARS CoV-2, has exerted a significant unprecedented economic and medical crisis, in addition to its impact on the daily life and health care systems all over the world. Regrettably, no vaccines or drugs are currently available for this new critical emerging human disease. Joining the global fight against COVID-19, in this study we aim at identifying a potential novel inhibitor for SARS COV-2 2'-O-methyltransferase (nsp16) which is one of the most attractive targets in the virus life cycle, responsible for the viral RNA protection via a cap formation process. Firstly, nsp16 enzyme bound to Sinefungin was retrieved from the protein data bank (PDB ID: 6WKQ), then, a 3D pharmacophore model was constructed to be applied to screen 48 Million drug-like compounds of the Zinc database. This resulted in only 24 compounds which were subsequently docked into the enzyme. The best four score-ordered hits from the docking outcome exhibited better scores compared to Sinefungin. Finally, three molecular dynamics (MD) simulation experiments for 150 ns were carried out as a refinement step for our proposed approach. The MD and MM-PBSA outputs revealed compound 11 as the best potential nsp16 inhibitor herein identified, as it displayed a better stability and average binding free energy for the ligand-enzyme complex compared to Sinefungin.


Subject(s)
Antiviral Agents/chemistry , Enzyme Inhibitors/chemistry , SARS-CoV-2/enzymology , Viral Nonstructural Proteins/chemistry , Adenosine/analogs & derivatives , Adenosine/chemistry , Adenosine/metabolism , Antiviral Agents/metabolism , Binding Sites , Crystallography, X-Ray , Databases, Pharmaceutical , Databases, Protein , Drug Stability , Enzyme Inhibitors/metabolism , High-Throughput Screening Assays , Humans , Kinetics , Methyltransferases , Molecular Docking Simulation , Molecular Dynamics Simulation , Protein Binding , Protein Conformation, alpha-Helical , Protein Conformation, beta-Strand , Protein Interaction Domains and Motifs , SARS-CoV-2/chemistry , Thermodynamics , Viral Nonstructural Proteins/antagonists & inhibitors
SELECTION OF CITATIONS
SEARCH DETAIL