Daniela Trisciuzzi

Background: In this study, we report a novel series of proline- and pipecolic acid-based small molecules designed as allosteric inhibitors of the NS2B/NS3 serine proteases from dengue and Zika viruses, key targets in antiviral drug discovery. Results: Enzymatic studies revealed that S-proline derivatives bearing electron-withdrawing substituents on the aromatic ring, particularly that with a trifluoromethyl group in meta position (i.e., compound 3, IC₅₀ = 5.0 µM), were the most potent against DENV NS2B/NS3, while nitro-substituted inhibitors were mostly effective only against the ZIKV protease. R-configured pipecolic acid-based derivatives were the only ones active against DENV NS2B/NS3, even if the mid-micromolar range; however, they demonstrated improved cellular efficacy since inhibitors 24 and 27 exhibiting strong activity in a DENV2 protease reporter gene assay (EC₅₀ = 5.2 and 5.1 µM, respectively). All compounds showed no cytotoxicity (CC₅₀ > 100 µM) and were selective for the viral protease over off-target serine proteases. Structure-based approaches were exploited to map the druggable allosteric site close to Asn152. Conclusions: Our findings led us to identify proline and pipecolic acid-based inhibitors as promising leads for the development of selective flaviviral NS2B/NS3 allosteric inhibitors.

Background/Objectives: Studying protein–protein interaction (PPI) networks is crucial in understanding cancer phenotypes and molecular mechanisms. Here, we focus on PPIs involved in 12 different types of cancer (oncoPPIs), highlighting those protein pockets serving as outposts to modulate protein functioning. Methods: To explore these cavities linked to the cancer phenotype changes, we built a comprehensive pocketome of 314 crystallographically solved oncoPPIs. Based on this experimental data, we identified and investigated all ligandable protein pockets by employing 3D geometric and energetic descriptors. These pockets were classified as suitable for designing new oncoPPI modulators or PROTACs. The ligand-bound crystallographic pockets were analyzed to compare their properties across cancer types. Finally, 3D oncoPPI networks were built for each cancer type to identify highly connected proteins acting as hubs. Results: Combining interaction networks with structural pocket data helps identify cancer-relevant proteins and key interacting residues. Using this approach, we present clinical examples (e.g., S100A1, NRP1, CTNNB1, VCP) to show the therapeutic value of targeting ligandable 3D oncoPPIs. We also provide a publicly available reference dataset supporting future research. Conclusions: Notably, this study offers a flexible framework for evaluating and prioritizing novel disease targets.

The whey protein (WP) fraction represents 18–20% of the total milk nitrogen content. It was originally considered a dairy industry waste, but upon its chemical characterization, it was found to be a precious source of bioactive components, growing in popularity as nutritional and functional food ingredients. This has generated a remarkable increase in interest in applications in the different sectors of nutrition, food industry, and pharmaceutics. WPs comprise immunoglobulins and proteins rich in branched and essential amino acids, and peptides endowed with several biological activities (antimicrobial, antihypertensive, antithrombotic, anticancer, antioxidant, opioid, immunomodulatory, and gut microbiota regulation) and technological properties (gelling, water binding, emulsification, and foaming ability). Currently, various process technologies and biotechnological methods are available to recover WPs and convert them into BioActive Peptides (BAPs) for commercial use. Additionally, in silico approaches could have a significant impact on the development of novel foods and/or ingredients and therapeutic agents. This review provides an overview of current and emerging methods for the production, selection, and application of whey peptides, offering insights into bioactivity profiling and potential therapeutic targets. Recent updates in legislation related to commercialized WPs-based products are also presented.

Generative models have revolutionized de novo drug design, allowing to produce molecules on-demand with desired physicochemical and pharmacological properties. String based molecular representations, such as SMILES (Simplified Molecular Input Line Entry System) strings and SELFIES (Self-Referencing Embedded Strings), have played a pivotal role in the success of generative approaches, thanks to their capacity to encode atom- and bond- information and ease-of-generation. However, such ‘atom-level’ string representations have certain limitations, in terms of capturing information on chirality, and synthetic accessibility of the corresponding designs. In this paper, we present fragSMILES, a novel fragment-based molecular representation in the form of string. fragSMILES encode fragments in a ‘chemically-meaningful’ way via a novel graph-reduction approach, allowing to obtain an efficient, interpretable, and expressive molecular representation, which also avoids fragment redundancy. fragSMILES advances the state-of-the-art of fragment-based representations, by reporting fragments and their ‘breaking’ bonds independently, without fragment redundancy. Moreover, fragSMILES also embeds information of molecular chirality, thereby overcoming known limitations of existing string notations. When compared with SMILES and SELFIES for de novo design, the fragSMILES notation showed its promise in generating molecules with desirable biochemical and scaffolds properties.

Thirteen benzothiazolone derivatives (M1–M13) were synthesized and evaluated for their inhibitory activity against cholinesterases (ChEs) and monoamine oxidases (MAOs). All the compounds inhibited ChEs more effectively than MAOs. In addition, most of the compounds showed higher inhibitory activities against butyrylcholinesterase (BChE) than acetylcholinesterase (AChE). Compound M13 most potently inhibited BChE with an IC₅₀ value of 1.21 μM, followed by M2 (IC₅₀ = 1.38 μM). Compound M2 had a higher selectivity index (SI) value for BChE over AChE (28.99) than M13 (4.16). The 6-methoxy indole group of M13 was expected to have a greater effect on BChE inhibitory activity than the other groups. Kinetics and reversibility tests showed that M13 was a reversible noncompetitive BChE inhibitor with a K_i value of 1.14 ± 0.21 μM. In a docking simulation, M13 is predicted to form a hydrogen bond with the backbone carbonyl group of Ser287 of BChE through its methoxy indole moiety and π−π interactions between its benzothiazolone group and the side chain of Trp82 with the five-membered pyrrole ring and with the six-membered benzene ring. From these results, it is suggested that M13 is a BChE inhibitor and a potential candidate agent for the treatment of Alzheimer’s disease.

PLATO (Polypharmacology pLATform predictiOn) is an easy-to-use drug discovery web platform, which has been designed with a two-fold objective: to fish putative protein drug targets and to compute bioactivity values of small molecules. Predictions are based on the similarity principle, through a reverse ligand-based screening, based on a collection of 632,119 compounds known to be experimentally active on 6004 protein targets. An efficient backend implementation allows to speed-up the process that returns results for query in less than 20 s. The graphical user interface is intuitive to give practitioners easy input and transparent output, which is available as a standard report in portable document format. PLATO has been validated on thousands of external data, with performances better than those of other parallel approaches. PLATO is available free of charge (http://plato.uniba.it/ accessed on 13 April 2022).

The fusion oncoprotein Bcr-Abl is an aberrant tyrosine kinase responsible for chronic myeloid leukemia and acute lymphoblastic leukemia. The auto-inhibition regulatory module observed in the progenitor kinase c-Abl is lost in the aberrant Bcr-Abl, because of the lack of the N-myristoylated cap able to bind the myristoyl binding pocket also conserved in the Bcr-Abl kinase domain. A way to overcome the occurrence of resistance phenomena frequently observed for Bcr-Abl orthosteric drugs is the rational design of allosteric ligands approaching the so-called myristoyl binding pocket. The discovery of these allosteric inhibitors although very difficult and extremely challenging, represents a valuable option to minimize drug resistance, mostly due to the occurrence of mutations more frequently affecting orthosteric pockets, and to enhance target selectivity with lower off-target effects. In this perspective, we will elucidate at a molecular level the structural bases behind the Bcr-Abl allosteric control and will show how artificial intelligence can be effective to drive the automated de novo design towards off-patent regions of the chemical space.

In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs.

Among the different aquaporins (AQPs), human aquaporin-4 (hAQP4) has attracted the greatest interest in recent years as a new promising therapeutic target. Such a membrane protein is, in fact, involved in a multiple sclerosis-like immunopathology called Neuromyelitis Optica (NMO) and in several disorders resulting from imbalanced water homeostasis such as deafness and cerebral edema. The gap of knowledge in its functioning and dynamics at the atomistic level of detail has hindered the development of rational strategies for designing hAQP4 modulators. The application, lately, of molecular modeling has proved able to fill this gap providing a breeding ground to rationally address compounds targeting hAQP4. In this review, we give an overview of the important advances obtained in this field through the application of Molecular Dynamics (MD) and other complementary modeling techniques. The case studies presented herein are discussed with the aim of providing important clues for computational chemists and biophysicists interested in this field and looking for new challenges.