Comprehensive analysis of commercial fragment libraries

Screening of fragment libraries is a valuable approach to the drug discovery process. The quality of the library is one of the keys to success, and more particularly the design...

A drug discovery-oriented non-invasive protocol for protein crystal cryoprotection by dehydration, with application for crystallization screening.

In X-ray macromolecular crystallography, cryoprotection of crystals mounted on harvesting loops is achieved when the water in the sample solvent transitions to vitreous ice before crystalline ice forms. This is achieved by rapid cooling in liquid nitrogen or propane. Protocols for protein crystal cryoprotection are based on either increasing environmental pressure or reducing the water fraction in the solvent. This study presents a new protocol for cryoprotecting crystals. It is based on vapour diffusion dehydration of the crystal drop to reduce the water fraction in the solvent by adding a highly concentrated salt solution, 13 M potassium formate (KF13), directly to the reservoir. Cryoprotection by the KF13 protocol is non-invasive to the crystal, high throughput, not labour intensive, can benefit diffraction resolution and ligand binding, and is very useful in cases with high redundancy such as drug discovery projects which utilize very large compound or fragment libraries. Moreover, an application of KF13 to discover new crystal hits from clear drops of equilibrated crystallization screening plates is also shown.

Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit

While transcriptome- and proteome-wide technologies to assess processes in protein biogenesis are now widely available, we still lack global approaches to assay post-ribosomal biogenesis events, in particular those occurring in the eukaryotic secretory system. We here develop a method, SECRiFY, to simultaneously assess the secretability of >105 protein fragments by two yeast species, S. cerevisiae and P. pastoris, using custom fragment libraries, surface display and a sequencing-based readout. Screening human proteome fragments with a median size of 50–100 amino acids, we generate datasets that enable datamining into protein features underlying secretability, revealing a striking role for intrinsic disorder and chain flexibility. The SECRiFY methodology generates sufficient amounts of annotated data for advanced machine learning methods to deduce secretability patterns. The finding that secretability is indeed a learnable feature of protein sequences provides a solid base for application-focused studies.

Mother’s Milk Microbiome Shaping Fecal and Skin Microbiota in Infants with Food Allergy and Atopic Dermatitis: A Pilot Analysis

The child microbiome, including gut and skin communities, is shaped by a multitude of factors, and breastfeeding is one of the most essential. Food allergy (FA) and atopic dermatitis (AD) are among the most common diseases in pediatrics, with the prevalence of each up to 6% and 20%, respectively. Therefore, we aimed at finding differences between the fecal and skin microbiomes of FA and AD patients in the context of breastfeeding, by means of the Illumina sequencing of 16S rRNA gene fragment libraries amplified from the total DNA isolated from samples collected from allergic and healthy infants. We also analyzed milk samples from the mothers of the examined children and searched for patterns of incidence suggesting milk influence on an infant’s allergy status. Here we show that a mother’s milk influences her child’s fecal and skin microbiomes and identify Acinetobacter as the taxon whose abundance is correlated with milk and child-derived samples. We demonstrate that breastfeeding makes allergic children's fecal and skin communities more similar to those of healthy infants than in the case of formula-feeding. We also identify signature taxa that might be important in maintaining health or allergy development.

Complementing sequence-derived features with structural information extracted from fragment libraries for protein structure prediction

Background Fragment libraries play a key role in fragment-assembly based protein structure prediction, where protein fragments are assembled to form a complete three-dimensional structure. Rich and accurate structural information embedded in fragment libraries has not been systematically extracted and used beyond fragment assembly. Methods To better leverage the valuable structural information for protein structure prediction, we extracted seven types of structural information from fragment libraries. We broadened the usage of such structural information by transforming fragment libraries into protein-specific potentials for gradient-descent based protein folding and encoding fragment libraries as structural features for protein property prediction. Results Fragment libraires improved the accuracy of protein folding and outperformed state-of-the-art algorithms with respect to predicted properties, such as torsion angles and inter-residue distances. Conclusion Our work implies that the rich structural information extracted from fragment libraries can complement sequence-derived features to help protein structure prediction.

Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model

Fragment libraries are often used in protein structure prediction, simulation and design as a means to significantly reduce the vast conformational search space. Current state-of-the-art methods for fragment library generation do not properly account for aleatory and epistemic uncertainty, respectively due to the dynamic nature of proteins and experimental errors in protein structures. Additionally, they typically rely on information that is not generally or readily available, such as homologous sequences, related protein structures and other complementary information. To address these issues, we developed BIFROST, a novel take on the fragment library problem based on a Deep Markov Model architecture combined with directional statistics for angular degrees of freedom, implemented in the deep probabilistic programming language Pyro. BIFROST is a probabilistic, generative model of the protein backbone dihedral angles conditioned solely on the amino acid sequence. BIFROST generates fragment libraries with a quality on par with current state-of-the-art methods at a fraction of the run-time, while requiring considerably less information and allowing efficient evaluation of probabilities.

Exploring protein hotspots by optimized fragment pharmacophores

Fragment-based drug design has introduced a bottom-up process for drug development, with improved sampling of chemical space and increased effectiveness in early drug discovery. Here, we combine the use of pharmacophores, the most general concept of representing drug-target interactions with the theory of protein hotspots, to develop a design protocol for fragment libraries. The SpotXplorer approach compiles small fragment libraries that maximize the coverage of experimentally confirmed binding pharmacophores at the most preferred hotspots. The efficiency of this approach is demonstrated with a pilot library of 96 fragment-sized compounds (SpotXplorer0) that is validated on popular target classes and emerging drug targets. Biochemical screening against a set of GPCRs and proteases retrieves compounds containing an average of 70% of known pharmacophores for these targets. More importantly, SpotXplorer0 screening identifies confirmed hits against recently established challenging targets such as the histone methyltransferase SETD2, the main protease (3CLPro) and the NSP3 macrodomain of SARS-CoV-2.

Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement

COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors—S/Ace2, Tmprss2, Cathepsins L and K, and Mpro—to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.

Small-molecule inhibitors of carboxylesterase Notum

Notum has recently been identified as a negative regulator of Wnt signaling through the removal of an essential palmitoleate group from Wnt proteins. There are emerging reports that Notum plays a role in human disease, with published data suggesting that targeting Notum could represent a new therapeutic approach for treating cancer, osteoporosis and neurodegenerative disorders. Complementary hit-finding strategies have been applied with successful approaches that include high-throughput screening, activity-based protein profiling, screening of fragment libraries and virtual screening campaigns. Structural studies are accelerating the discovery of new inhibitors of Notum. Three fit-for-purpose examples are LP-922056, ABC99 and ARUK3001185. The application of these small-molecule inhibitors is helping to further advance an understanding of the role Notum plays in human disease.