Native, engineered and de novo designed ligands targeting the SARS-CoV-2 spike protein.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the deadly coronavirus disease 2019 (Covid-19) and is a concerning hazard to public health. This virus infects cells by establishing a contact between its spike protein (S-protein) and host human angiotensin-converting enzyme 2 (hACE2) receptor, subsequently initiating viral fusion. The inhibition of the interaction between the S-protein and hACE2 has immediately drawn attention amongst the scientific community, and the S-protein was considered the prime target to design vaccines and to develop affinity ligands for diagnostics and therapy. Several S-protein binders have been reported at a fast pace, ranging from antibodies isolated from immunised patients to de novo designed ligands, with some binders already yielding promising in vivo results in protecting against SARS-CoV-2. Natural, engineered and designed affinity ligands targeting the S-protein are herein summarised, focusing on molecular recognition aspects, whilst identifying preferred hot spots for ligand binding. This review serves as inspiration for the improvement of already existing ligands or for the design of new affinity ligands towards SARS-CoV-2 proteins. Lessons learnt from the Covid-19 pandemic are also important to consolidate tools and processes in protein engineering to enable the fast discovery, production and delivery of diagnostic, prophylactic, and therapeutic solutions in future pandemics.

Discovery of phosphotyrosine-binding oligopeptides with supramolecular target selectivity.

We demonstrate phage-display screening on self-assembled ligands that enables the identification of oligopeptides that selectively bind dynamic supramolecular targets over their unassembled counterparts. The concept is demonstrated through panning of a phage-display oligopeptide library against supramolecular tyrosine-phosphate ligands using 9-fluorenylmethoxycarbonyl-phenylalanine-tyrosine-phosphate (Fmoc-FpY) micellar aggregates as targets. The 14 selected peptides showed no sequence consensus but were enriched in cationic and proline residues. The lead peptide, KVYFSIPWRVPM-NH2 (P7) was found to bind to the Fmoc-FpY ligand exclusively in its self-assembled state with K D = 74 ± 3 µM. Circular dichroism, NMR and molecular dynamics simulations revealed that the peptide interacts with Fmoc-FpY through the KVYF terminus and this binding event disrupts the assembled structure. In absence of the target micellar aggregate, P7 was further found to dynamically alternate between multiple conformations, with a preferred hairpin-like conformation that was shown to contribute to supramolecular ligand binding. Three identified phages presented appreciable binding, and two showed to catalyze the hydrolysis of a model para-nitro phenol phosphate substrate, with P7 demonstrating conformation-dependent activity with a modest k cat/K M = 4 ± 0.3 × 10-4 M-1 s-1.

Free Marine Natural Products Databases for Biotechnology and Bioengineering.

Marine organisms and micro-organisms are a source of natural compounds with unique chemical features. These chemical properties are useful for the discovery of new functions and applications of marine natural products (MNPs). To extensively exploit the potential implementations of MNPs, they are gathered in chemical databases that allow their study and screening for applications of biotechnological interest. However, the classification of MNPs is currently poor in generic chemical databases. The present availability of free-access-focused MNP databases is scarce and the molecular diversity of these databases is still very low when compared to the paid-access ones. In this review paper, the current scenario of free-access MNP databases is presented as well as the hindrances involved in their development, mainly compound dereplication. Examples and opportunities for using freely accessible MNP databases in several important areas of biotechnology are also assessed. The scope of this paper is, as well, to notify the latent potential of these information sources for the discovery and development of new MNPs in biotechnology, and push future efforts to develop a public domain MNP database freely available for the scientific community.

Designed affinity ligands to capture human serum albumin.

Human serum albumin (HSA) in an important therapeutic agent and disease biomarker, with an increasing market demand. By proteins and drugs that bind to HSA as inspiration, a combinatorial library of 64 triazine-based ligands was rationally designed and screened for HSA binding at physiological conditions. Two triazine-based lead ligands (A3A2 and A6A5), presenting more than 50% HSA bound and high enrichment factors, were selected for further studies. Binding and elution conditions for HSA purification from human plasma were optimized for both ligands. The A6A5 adsorbent yielded a purified HSA sample with 98% purity at 100% recovery yield under mild binding and elution conditions.

Discovery of new FXR agonists based on 6-ECDCA binding properties by virtual screening and molecular docking.

FXR is a member of the nuclear receptor superfamily, which regulates the expression of various genes involved in bile acid, lipid and glucose metabolism. Targeting FXR with small molecules has been exploited to treat lipid-related disorders and diseases such as cholestasis, gallstones and hepatic disorders. In this work, we expand the existing pool of known FXR agonists using a fast hit-to-lead structure-based pharmacophore and docking screening protocol. A set of 25 molecules was selected after screening a large database of commercial chemicals, and experimental tests were carried out to demonstrate their ability to activate FXR. Three novel FXR agonists are reported, namely, one full agonist, more efficient than the endogenous ligand chenodeoxycholic acid, and two partial agonists.

Protein- and Peptide-Based Biosensors in Artificial Olfaction.

Animals' olfactory systems rely on proteins, olfactory receptors (ORs) and odorant-binding proteins (OBPs), as their native sensing units to detect odours. Recent advances demonstrate that these proteins can also be employed as molecular recognition units in gas-phase biosensors. In addition, the interactions between odorant molecules and ORs or OBPs are a source of inspiration for designing peptides with tunable odorant selectivity. We review recent progress in gas biosensors employing biological units (ORs, OBPs, and peptides) in light of future developments in artificial olfaction, emphasizing examples where biological components have been employed to detect gas-phase analytes.

Integration of Molecular Dynamics Based Predictions into the Optimization of De Novo Protein Designs: Limitations and Benefits.

Recent advances in de novo protein design have gained considerable insight from the intrinsic dynamics of proteins, based on the integration of molecular dynamics simulations protocols on the state-of-the-art de novo protein design protocols used nowadays. With this protocol we illustrate how to set up and run a molecular dynamics simulation followed by a functional protein dynamics analysis. New users will be introduced to some useful open-source computational tools, including the GROMACS molecular dynamics simulation software package and ProDy for protein structural dynamics analysis.

Inhibition of human dyskerin as a new approach to target ribosome biogenesis.

The product of the DKC1 gene, dyskerin, is required for both ribosome biogenesis and telomerase complex stabilization. Targeting these cellular processes has been explored for the development of drugs to selectively or preferentially kill cancer cells. Presently, intense research is conducted involving the identification of new biological targets whose modulation may simultaneously interfere with multiple cellular functions that are known to be hyper-activated by neoplastic transformations. Here, we report, for the first time, the computational identification of small molecules able to inhibit dyskerin catalytic activity. Different in silico techniques were applied to select compounds and analyze the binding modes and the interaction patterns of ligands in the human dyskerin catalytic site. We also describe a newly developed and optimized fast real-time PCR assay that was used to detect dyskerin pseudouridylation activity in vitro. The identification of new dyskerin inhibitors constitutes the first proof of principle that the pseudouridylation activity can be modulated by means of small molecule agents. Therefore, the presented results, obtained through the usage of computational tools and experimental validation, indicate an alternative therapeutic strategy to target ribosome biogenesis pathway.

Comparative analysis of pharmacophore screening tools.

The pharmacophore concept is of central importance in computer-aided drug design (CADD) mainly because of its successful application in medicinal chemistry and, in particular, high-throughput virtual screening (HTVS). The simplicity of the pharmacophore definition enables the complexity of molecular interactions between ligand and receptor to be reduced to a handful set of features. With many pharmacophore screening softwares available, it is of the utmost interest to explore the behavior of these tools when applied to different biological systems. In this work, we present a comparative analysis of eight pharmacophore screening algorithms (Catalyst, Unity, LigandScout, Phase, Pharao, MOE, Pharmer, and POT) for their use in typical HTVS campaigns against four different biological targets by using default settings. The results herein presented show how the performance of each pharmacophore screening tool might be specifically related to factors such as the characteristics of the binding pocket, the use of specific pharmacophore features, and the use of these techniques in specific steps/contexts of the drug discovery pipeline. Algorithms with rmsd-based scoring functions are able to predict more compound poses correctly as overlay-based scoring functions. However, the ratio of correctly predicted compound poses versus incorrectly predicted poses is better for overlay-based scoring functions that also ensure better performances in compound library enrichments. While the ensemble of these observations can be used to choose the most appropriate class of algorithm for specific virtual screening projects, we remarked that pharmacophore algorithms are often equally good, and in this respect, we also analyzed how pharmacophore algorithms can be combined together in order to increase the success of hit compound identification. This study provides a valuable benchmark set for further developments in the field of pharmacophore search algorithms, e.g., by using pose predictions and compound library enrichment criteria.