RAS-inhibiting biologics identify and probe druggable pockets including an SII-α3 allosteric site

RAS mutations are the most common oncogenic drivers across human cancers, but there remains a paucity of clinically-validated pharmacological inhibitors of RAS, as druggable pockets have proven difficult to identify. Here, we identify two RAS-binding Affimer proteins, K3 and K6, that inhibit nucleotide exchange and downstream signaling pathways with distinct isoform and mutant profiles. Affimer K6 binds in the SI/SII pocket, whilst Affimer K3 is a non-covalent inhibitor of the SII region that reveals a conformer of wild-type RAS with a large, druggable SII/α3 pocket. Competitive NanoBRET between the RAS-binding Affimers and known RAS binding small-molecules demonstrates the potential to use Affimers as tools to identify pharmacophores. This work highlights the potential of using biologics with small interface surfaces to select unseen, druggable conformations in conjunction with pharmacophore identification for hard-to-drug proteins.

Genetic and structural analyses of ssRNA viruses pave the way for the discovery of novel antiviral pharmacological targets

In the era of big data and artificial intelligence a lot of new discoveries have influenced the field of antiviral drug design and pharmacophore identification. Viruses have always been a...

RAS-inhibiting biologics identify and probe druggable pockets including an SII-α3 allosteric site

ABSTRACTRAS mutations are the most common oncogenic drivers across human cancers, but there remains a paucity of clinically-validated pharmacological inhibitors of RAS, as druggable pockets have proven difficult to identify. We have identified two RAS-binding Affimer proteins, K3 and K6, that inhibit nucleotide exchange and downstream signalling pathways with distinct isoform and mutant profiles. Affimer K6 is the first biologic to bind in the SI/SII pocket, whilst Affimer K3 is the first non-covalent inhibitor of the SII region, revealing a novel RAS conformer with a large, druggable SII/α3 pocket. This work demonstrates the potential of using biologics with small interface surfaces to select novel druggable conformations in conjunction with pharmacophore identification for hard-to-drug proteins.

Computational Methodologies in the Exploration of Marine Natural Product Leads

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.