Unraveling the molecular basis of host cell receptor usage in SARS-CoV-2 and other human pathogenic β-CoVs

Local energetic frustration conservation in protein families and superfamilies

Energetic local frustration offers a biophysical perspective to interpret the effects of sequence variability on protein families. Here we present a methodology to analyze local frustration patterns within protein families and superfamilies that allows us to uncover constraints related to stability and function, and identify differential frustration patterns in families with a common ancestry. We analyze these signals in very well studied protein families such as PDZ, SH3, ɑ and β globins and RAS families. Recent advances in protein structure prediction make it possible to analyze a vast majority of the protein space. An automatic and unsupervised proteome-wide analysis on the SARS-CoV-2 virus demonstrates the potential of our approach to enhance our understanding of the natural phenotypic diversity of protein families beyond single protein instances. We apply our method to modify biophysical properties of natural proteins based on their family properties, as well as perform unsupervised analysis of large datasets to shed light on the physicochemical signatures of poorly characterized proteins such as the ones belonging to emergent pathogens.

Enzyme linked oligonucleotide assay for the sensitive detection of SARS-CoV-2 variants

The exponential spread of COVID-19 has prompted the need to develop a simple and sensitive diagnostic tool. Aptamer-based detection assays like ELONA are promising since they are inexpensive and sensitive. Aptamers have advantages over antibodies in wide modification, small size, in vitro selection, and stability under stringent conditions, which aid in scalable and reliable detection. In this work, we used aptamers against SARS-CoV-2 RBD S protein to design a simple and sensitive ELONA detection tool. Screening CoV2-RBD-1C and CoV2-RBD-4C aptamers and optimizing assay conditions led to the development of a direct ELONA that can detect SARS-CoV-2 RBD S glycoprotein in buffer solution and 0.1 % human nasal fluid with a detection limit of 2.16 ng/mL and 1.02 ng/mL, respectively. We detected inactivated Alpha, Wuhan, and Delta variants of SARS-CoV-2 with the detection limit of 3.73, 5.72, and 6.02 TCID₅₀/mL, respectively. Using the two aptamers as capture and reporter elements, we designed a more sensitive sandwich assay to identify the three SARS-CoV-2 variants employed in this research. As predicted, a lower detection limit was obtained. Sandwich assay LOD was 2.31 TCID₅₀/mL for Alpha, 1.15 TCID₅₀/mL for Wuhan, and 2.96 TCID₅₀/mL for Delta. The sensitivity of sandwich ELONA was validated using Alpha and Wuhan variants spiked in 0.1% human nasal fluid sample condition and were detected in 1.41 and 1.79 TCID₅₀/mL LOD, respectively. SEM was used to visualize the presence of viral particles in the Delta variant sample. The effective detection of SARS-CoV-2 in this study confirms the potential of our aptamer-based technique as a screening tool.

Computational prediction of protein functional sites-Applications in biotechnology and biomedicine

There are many computational approaches for predicting protein functional sites based on different sequence and structural features. These methods are essential to cope with the sequence deluge that is filling databases with uncharacterized protein sequences. They complement the more expensive and time-consuming experimental approaches by pointing them to possible candidate positions. In many cases they are jointly used to characterize the functional sites in proteins of biotechnological and biomedical interest and eventually modify them for different purposes. There is a clear trend towards approaches based on machine learning and those using structural information, due to the recent developments in these areas. Nevertheless, "classic" methods based on sequence and evolutionary features are still playing an important role as these features are strongly related to functionality. In this review, the main approaches for predicting general functional sites in a protein are discussed, with a focus on sequence-based approaches.