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The recent outbreak of coronavirus disease (COVID‐19) in China caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has led to worldwide human infections and deaths. The nucleocapsid (N) protein of coronaviruses (CoVs) is a multifunctional RNA binding protein necessary for viral RNA replication and transcription. Therefore, it is a potential antiviral drug target, serving multiple critical functions during the viral life cycle. This study addresses the potential to repurpose antiviral compounds approved or in development for treating human CoV induced infections against SARS‐CoV‐2 N. For this purpose, we used the docking methodology to better understand the inhibitory mechanism of this protein with the existing 34 antiviral compounds. The results of this analysis indicate that rapamycin, saracatinib, camostat, trametinib, and nafamostat were the top hit compounds with binding energy (−11.87, −10.40, −9.85, −9.45, −9.35 kcal/mol, respectively). This analysis also showed that the most common residues that interact with the compounds are Phe66 , Arg68 , Gly69 , Tyr123 , Ile131 , Trp132 , Val133 , and Ala134 . Subsequently, protein‐ligand complex stability was examined with molecular dynamics simulations for these five compounds, which showed the best binding affinity. According to the results of this study, the interaction between these compounds and crucial residues of the target protein were maintained. These results suggest that these residues are potential drug targeting sites for the SARS‐CoV‐2 N protein. This study information will contribute to the development of novel compounds for further in vitro and in vivo studies of SARS‐CoV‐2, as well as possible new drug repurposing strategies to treat COVID‐19 disease.

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No significant improvement was found in the mean points of the total group. In the second year, only 16.4 of the instructors were affected positively.

Data-centric approaches have been utilized to develop predictive methods for elucidating uncharacterized aspects of proteins such as their functions, biophysical properties, subcellular locations and interactions. However, studies indicate that the performance of these methods should be further improved to effectively solve complex problems in biomedicine and biotechnology. A data representation method can be defined as an algorithm that calculates numerical feature vectors for samples in a dataset, to be later used in quantitative modelling tasks. Data representation learning methods do this by training and using a model that employs statistical and machine/deep learning algorithms. These novel methods mostly take inspiration from the data-driven language models that have yielded ground-breaking improvements in the field of natural language processing. Lately, these learned data representations have been applied to the field of protein informatics and have displayed highly promising results in terms of extracting complex traits of proteins regarding sequence-structure-function relations. In this study, we conducted a detailed investigation over protein representation learning methods, by first categorizing and explaining each approach, and then conducting benchmark analyses on; (i) inferring semantic similarities between proteins, (ii) predicting ontology-based protein functions, and (iii) classifying drug target protein families. We examine the advantages and disadvantages of each representation approach over the benchmark results. Finally, we discuss current challenges and suggest future directions. We believe the conclusions of this study will help researchers in applying machine/deep learning-based representation techniques on protein data for various types of predictive tasks. Furthermore, we hope it will demonstrate the potential of machine learning-based data representations for protein science and inspire the development of novel methods/tools to be utilized in the fields of biomedicine and biotechnology.

Bu araştırmadaki temel amaç Türkiye'de yürütülmekte olan fizyoterapi lisans eğitim programlarının son sınıf öğrencileri ile programı yürüten öğretim elemanları tarafından değerlendirilmesiydi. Diğer amaçlar öğrencilerin ve öğretim elemanlarının eğitim programıyla ilgili memnuniyet düzeylerini karşılaştırmak ve öğrencilerin öğretim elemanlarından memnuniyet düzeylerini belirlemekti. Yöntem: Bu çalışmaya fizyoterapist mezun etmekte olan beş üniversitedeki lisans programına kayıtlı 348 son sınıf öğrencisi (yaş ortalaması: 22,86±1,56 yıl) ve bu programlarda görevli 69 öğretim elemanı (yaş ortalaması: 37,38±2,86 yıl) dahil edildi. Katılımcıların programlarındaki eğitim hizmetleri ile ilgili görüşleri iki anketle sorgulandı. Anket-1 (18 madde) eğitim programının içeriği, fiziksel ortam gibi alanlarla ilgili geribildirimleri yansıtan ifadelerden oluşmaktaydı. Anket-2 (20 madde) ise, öğrencilerin öğretim elemanlarını değerlendirdikleri anketti. Sonuçlar: Anket-1'in sonuçlarına göre, öğrencilerin ve öğretim elemanlarının memnuniyet düzeyleri, eğitim programının içeriği, stajların etkinliği ve genel memnuniyet kategorilerinde en yüksekti. Fiziksel ortam, araç-gereç yeterliliği ve sınav kategorilerinde en düşüktü. Anket-2'nin sonuçlarına göre, öğrencilerin memnuniyet düzeyleri iletişim becerileri, sınav ve not verme kategorilerinde en düşüktü. Ders hazırlığı, mesleki bilgilendirme ve konu anlatım becerisi kategorilerinde en yüksekti. Tartışma: Sonuçlar, Türkiye'de fizyoterapi eğitiminin kalitesini artırmak için son sınıf öğrencilerinin ve öğretim elemanlarının geri bildirimlerinin dikkate alınması gerektiğini ortaya koymuştur.

The recent outbreak of coronavirus disease (COVID-19) in China caused by SARS-CoV-2 virus continually lead to worldwide human infections and deaths. It is currently no specific viral protein targeted therapeutics yet. The nucleocapsid (N) protein of coronaviruses (CoVs) is a multifunctional RNA-binding protein necessary for viral RNA replication and transcription. Therefore, it is a potential antiviral drug target, serving multiple critical functions during the viral life cycle. Herein, we focus here on the potential to repurpose antiviral compounds approved or in development for treating infections caused by human CoVs. For this purpose, we used the docking methodology to better understand the inhibition mechanism of SARS-CoV-2 N protein with this existing 34 antiviral compounds. The results of this analysis were showed that Nafamostat, Rapamycin, Saracatinib, Imatinib and Camostat are the top hit compounds with binding energy (-10.24 kcal/mol, -9.88 kcal/mol, -9.66 kcal/mol, -9.23 kcal/mol, -9.07 kcal/mol) and K i (0.0313 mM, 0.05736 mM, 0.08304 mM, 0.17224 mM, 0.22413 mM). In addition, this analysis also showed that the most common residues that interact with the compounds are Lys65, Phe66, Arg 68, Glu69, Tyr123, Gly124, Lys127, Ile 130, Val133 and Ala134. These results suggest that these residues are potential drug targeting sites for the SARS-CoV-2 N protein. Subsequently, protein-ligand complex stability was examined with Molecular Dynamics (MD) simulations for the Nafamostat compound, which showed the best binding affinity. According to the results of this study, the interaction between the compound and the crucial residues of the target were maintained. Based on this information, we propose guidelines to develop novel N protein-based antiviral agents that target CoVs.