Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis.

Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered "bottom-up" approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of "big data" and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure-when accompanied by high-quality experimental data for model training-has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this.

Developing a SARS-CoV-2 Antigen Test Using Engineered Affinity Proteins.

The ongoing COVID-19 pandemic has clearly established how vital rapid, widely accessible diagnostic tests are in controlling infectious diseases and how difficult and slow it is to scale existing technologies. Here, we demonstrate the use of the rapid affinity pair identification via directed selection (RAPIDS) method to discover multiple affinity pairs for SARS-CoV-2 nucleocapsid protein (N-protein), a biomarker of COVID-19, from in vitro libraries in 10 weeks. The pair with the highest biomarker sensitivity was then integrated into a 10 min, vertical-flow cellulose paper test. Notably, the as-identified affinity proteins were compatible with a roll-to-roll printing process for large-scale manufacturing of tests. The test achieved 40 and 80 pM limits of detection in 1× phosphate-buffered saline (mock swab) and saliva matrices spiked with cell-culture-generated SARS-CoV-2 viruses and is also capable of detection of N-protein from characterized clinical swab samples. Hence, this work paves the way toward the mass production of cellulose paper-based assays which can address the shortages faced due to dependence on nitrocellulose and current manufacturing techniques. Further, the results reported herein indicate the promise of RAPIDS and engineered binder proteins for the timely and flexible development of clinically relevant diagnostic tests in response to emerging infectious diseases.

On the search for COVID-19 therapeutics: identification of potential SARS-CoV-2 main protease inhibitors by virtual screening, pharmacophore modeling and molecular dynamics.

COVID-19 also known as SARS-CoV-2 outbreak in late 2019 and its worldwide pandemic spread has taken the world by surprise. The minute-to-minute increasing coronavirus cases (>85 M) and progressive deaths (≈1.8 M) calls for finding a cure to this devastating pandemic. While there have been many attempts to find biologically active molecules targeting SARS-CoV-2 for treatment of this viral infection, none has found a way to the clinic yet. In this study, a 3-feature structure-based pharmacophore model was designed for SARS-CoV-2 main protease (MPro) that plays a vital role in the viral cellular penetration. High throughput virtual screening of the lead-like ZINC library was then performed to find a potent inhibitor employing the predesigned pharmacophore. In-silico pharmacokinetics/toxicity prediction study was subsequently applied towards the best hits. Finally, a 50 ns molecular dynamics simulation was carried out for the best hit and compared to the co-crystallized ligand where the hit compound displayed high binding and comparable interactions. The results identified new hits for SARS-CoV-2 MPro inhibition showing good docking score, pharmacokinetics and toxicity profile, drug-likeness, high binding energy in addition to a promising synthetic accessibility. Identifying new small compounds as potential leads for inhibiting SARS-CoV-2 is a very important step towards designing a synthesizing of promising drug candidates.Communicated by Ramaswamy H. Sarma.

Natural Products Database Screening for the Discovery of Naturally Occurring SARS-Cov-2 Spike Glycoprotein Blockers.

SARS-CoV-2 coronavirus has been recognized the causative agent of the recent and ongoing pandemic. Effective and specific antiviral agents or vaccines are still missing, despite a large plethora of compounds have been proposed and tested worldwide. New compounds are requested urgently and virtual screening can offer fast and robust predictions to investigate. Moreover, natural compounds were shown to exert antiviral effects and can be endowed with limited side effects and wide availability. Our approach consisted in the validation of a docking protocol able to refine the most suitable candidates, within the 31000 natural compounds of the natural product activity and species source (NPASS) library, interacting with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein. After the refinement process two natural compounds, castanospermine and karuquinone B, were shown to be the best-in-class derivatives in silico able to target an essential structure of the virus and to act in the early stage of infection.