Coronavirus SARS-CoV–2, previously known as 2019-nCoV, is a recently discovered single- stranded RNA (ssRNA) betacoronavirus, responsible for a severe pathological condition known as coronavirus disease 2019 (COVID–19).1 Since it was first identified in December 2019, this novel coronavirus has infected about 80000 people around the world, of whom more than 2000 have lost their lives due to a severe respiratory illness.2
The first outbreak of this new disease originally took place in the city of Wuhan (China), rapidly spreading in the southeast of Asia and, recently, in other continents like Europe, North America and Africa.1 The astonishing rate at which COVID is expanding compared to previous coronavirus related diseases (SARS-CoV and MERS-CoV), in conjunction with the absence of approved drugs or effective therapeutic approaches for its treatment, have gathered the attention of the international community, which is promoting a cooperative effort to face this emergency situation.3,4 On January 2020 indeed, the International Health Regulations Emergency Committee of the World Health Organization declared the outbreak a “public health emergency of international concern” in responding to SARS-CoV–2.
Unfortunately, the timeline characterizing a typical drug discovery process badly couples with the urgency of finding a cure for the already infected patients as rapidly as possible. In this kind of scenario, it is of paramount importance to accelerate the early stages of the drug discovery process for COVID–19 treatment, and for all possible future emergencies.5
The early isolation of the SARS-CoV–2 genome from ill patients represented a first crucial outcome, making it possible to highlight an important sequence identity (~80% of conserved nucleotides) with respect to the original SARS-CoV epidemic virus.6 In light of this similarity, some therapeutic strategies could be inherited from other genetically related CoV diseases.
A possible target is for example represented by structural viral proteins, therefore interfering with the assembly and the internalization of the pathogen into the host, which was shown to occur also in this case through the Angiotensin-converting enzyme II (ACE2) receptor. From this perspective, the development of a vaccine is desirable, and it is foreseen that the first candidates will be advanced to clinical phase I around mid–2020.7–9
In the meantime, however, a great effort involves the targeting of non-structural viral proteins which are instead essential for the viral replication and the maturation processes, thus representing a crucial and specific target for anti-COVID drug development.3,10 In this regard, the crystallographic structure of the SARS-CoV–2 main protease (Mpro), also known as C30 Endopeptidase, was elucidated and made available to the scientific community with impressive timing, just a few weeks after the first COVID–19 outbreak (PDB ID: 6LU7). The structural characterization of the protease, which shares 96.1% of its sequence with those of SARS-CoV, has revealed a highly conserved architecture of the catalytic binding site.
As a result, Structure-Based Drug Discovery techniques (SBDD) can now be applied to efficiently speed up the rational identification of putative Mpro inhibitors or to drive the repurposing process of known therapy. This latter route is particularly attractive, as it allows to significantly shrink the time required to access the first phases of clinical trials, without compromising patient safety. A multitude of research groups has begun to apply computational approaches, such as molecular docking based virtual screening (VS), to evaluate already approved FDA approved drugs against the aforementioned viral protease.11–14 Many of these studies have found convergence in suggesting compounds inhibitors of the human immunodeficiency viruses (HIV) as possible anti-COVID candidates; this is surprising considering the important structural differences exiting among these two homologous enzymes. The repositioning of HIV antiviral drugs for the treatment of coronavirus infections found, however, a foundation in the scientific literature of the past 20 years. Some of these compounds have therefore been experimentally investigated, showing promising activity, both in the case of SARS-CoV and MERS-CoV outbreak.15,16
Moreover, at least three randomized clinical trials are currently been held in China in order to evaluate the therapeutic efficacy of Lopinavir and Ritonavir, a combination of HIV protease inhibitors, in COVID–19 treatment.7 In this perspective and preliminary computational research, we took advantage of the recently solved crystallographic structure of SARS-CoV–2 Mpro to perform a cutting edge in-silico investigation. (Figure 1)
Supervised Molecular Dynamics (SuMD), an emerging technique allowing to investigate at an atomic level of detail the molecular recognition process, was exploited to characterize the putative binding mechanism of three HIV protease inhibitors.17–19 In detail, along with the aforementioned combination of Lopinavir and Ritonavir, also Nelfinavir was taken into consideration, due to the promising in-vitro activity shown by this compound against the structurally related SARS-CoV protease.20 SuMD protocol implements a tabu-like algorithm that controls the sampling of short unbiased MD trajectories, each of which hundreds of picoseconds (ps) long. In detail, simulation steps are accepted only when describing a ligand approaching a known binding site, otherwise, the simulation is discharged and restarted from the previous coordinate set. The combination of all productive SuMD simulation steps represents, therefore, a putative molecular recognition trajectory collected, differently from brute force MD, in a very competitive computational time not exceeding the nanoseconds (ns) timescale. Contrary to molecular docking, SuMD simulations fully consider both the flexibility characterizing the protein target during the binding event and the contribution played by water molecules during the recognition. Moreover, the study is not limited to a possible final state but allows peeking dynamically at the whole process of recognition, also identifying putative metastable binding sites.