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Background: The global outbreak of the 2019 novel Coronavirus Disease (COVID-19) caused by the infection with the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), which appeared in China at the end of 2019, signifies a major public health issue at the current time. Objective: The objective of the present study is to characterize the physicochemical properties of the SARS-CoV-2 proteins at a residues level, and to generate a “bioinformatics fingerprint” in the form of a “PIM® profile” created for each sequence utilizing the Polarity Index Method® (PIM®), suitable for the identification of these proteins. Methods: Two different bioinformatics approaches were used to analyze sequence characteristics of these proteins at the residues level, an in-house bioinformatics system PIM®, and a set of the commonly used algorithms for the predic-tion of protein intrinsic disorder predisposition, such as PONDR® VLXT, PONDR® VL3, PONDR® VSL2, PONDR® FIT, IUPred_short and IUPred_long. The PIM® profile was generated for four SARS-CoV-2 structural proteins and compared with the corresponding profiles of the SARS-CoV-2 non-structural proteins, SARS-CoV-2 putative proteins, SARS-CoV proteins, MERS-CoV proteins, sets of bacterial, fungal, and viral proteins, cell-penetrating peptides, and a set of intrinsically disordered proteins. We also searched for the UniProt proteins with PIM® profiles similar to those of SARS-CoV-2 structural, non-structural, and putative proteins. Results: We show that SARS-CoV-2 structural, non-structural, and putative proteins are characterized by a unique PIM® profile. A total of 1736 proteins were identified from the 562,253 “reviewed” proteins from the UniProt database, whose PIM® profile was similar to that of the SARS-CoV-2 structural, non-structural, and putative proteins. Conclusion: The PIM® profile represents an important characteristic that might be useful for the identification of proteins similar to SARS-CoV-2 proteins.

Background: Selective Cationic Amphipathic Antibacterial Peptides (SCAAPs) occupy a prominent place in the production of new drugs on account of their high toxicity towards bacteria and low toxicity towards mammalian cells, low hemolytic activity, and contribution to the protection of the human immune system. Aim: their number in nature is very low, and experimental tests are very protracted and costly. Therefore, it would be useful to have bioinformatics tools that would identify them in the existing databases and also propose new synthetic SCAAPs. Methods: In order to reduce the costs of identification and/or chemical synthesis and to know the physicochemical characteristics of SCAAPs at a residues level and to obtain a “bioiformatics fingerprint” suitable for their selection, we have modified the Polarity Index Method® (PIM®) and the α-helical configuration of each sequence is included in determining their individual “PIM® profile”. We have also used a set of the computer program to determine their “Intrinsic Disorder Predisposition”. This information was then compared with other protein groups, such as bacteria, fungi, virus and Cell-Penetrating Peptides (CPP) from the UniProt database and a set of intrinsically disordered proteins. Once the “fingerprint” of SCAAPs was obtained, it was used for searching among the 559228 “reviewed” proteins from the UniProt database and a set of synthetic SCAAPs characterized by the predefined “PIM® profile” selected. Results: Our results showed that the metric named “PIM® profile” can identify, with a high level of accuracy, a group of bacterial SCAAPs. This bioinformatics study was supported at residues level, using the in-house bioinformatics system Polarity Index Method the commonly used algorithm for the prediction of intrinsic disorder predisposition, PONDR® FIT. Conclusions: The Polarity Index Method seems highly efficient identifying SCAAP candidates.

Background: The female Aedes aegypti mosquito is a vector of several arthropod-borne viruses, such as Mayaro, Dengue, Chikungunya, Yellow Fever, and Zika. These viruses cause the death of at least 600000 people a year and temporarily disable several million more around the world. Up to date, there are no effective prophylactic measures that would prevent the contact and bite of this arthropod and, therefore, its consequential contagion. Objective: The objective of the present study was to search for the regularities of the proteins expressed by these five viruses, at residues level, and obtain a “bioinformatic fingerprint” to select them. Methods: We used two bioinformatic systems, our in-house bioinformatic system named Polarity Index Method® (PIM®) supported at residues level, and the commonly used algorithm for the prediction of intrinsic disorder predisposition, PONDR® FIT. We applied both programs to the 29 proteins that express the five groups of arboviruses studied, and we calculated for each of them their Polarity Index Method® profile and their intrinsic disorder predisposition. This information was then compared with analogous information for other protein groups, such as proteins from bacteria, fungi, viruses, and cell-penetrating peptides from the UniProt database, and a set of intrinsically disordered proteins. Once the “fingerprint” of each group of arboviruses was obtained, these “fingerprints” were searched among the 559228 “reviewed” proteins from the UniProt database. Results: In total, 1736 proteins were identified from the 559228 “reviewed” proteins from the UniProt database, with similar “PIM® profile” to the 29 mutated proteins that express the five groups of arboviruses. Conclusion: We propose that the “PIM® profile” of characterization of proteins might be useful for the identification of proteins expressed by arthropod-borne viruses transmitted by Aedes aegypti mosquito.