Iterative Focused Screening with Biological Fingerprints Identifies Selective Asc-1 Inhibitors Distinct from Traditional High Throughput Screening

Structure and mechanisms of transport of human Asc1/CD98hc amino acid transporter

Recent cryoEM studies elucidated details of the structural basis for the substrate selectivity and translocation of heteromeric amino acid transporters. However, Asc1/CD98hc is the only neutral heteromeric amino acid transporter that can function through facilitated diffusion, and the only one that efficiently transports glycine and D-serine, and thus has a regulatory role in the central nervous system. Here we use cryoEM, ligand-binding simulations, mutagenesis, transport assays, and molecular dynamics to define human Asc1/CD98hc determinants for substrate specificity and gain insights into the mechanisms that govern substrate translocation by exchange and facilitated diffusion. The cryoEM structure of Asc1/CD98hc is determined at 3.4-3.8 Å resolution, revealing an inward-facing semi-occluded conformation. We find that Ser 246 and Tyr 333 are essential for Asc1/CD98hc substrate selectivity and for the exchange and facilitated diffusion modes of transport. Taken together, these results reveal the structural bases for ligand binding and transport features specific to human Asc1.

Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries

Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein-protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF's 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 μM.

Identification and characterization of a novel SNAT2 (SLC38A2) inhibitor reveals synergy with glucose transport inhibition in cancer cells

SNAT2 (SLC38A2) is a sodium-dependent neutral amino acid transporter, which is important for the accumulation of amino acids as nutrients, the maintenance of cellular osmolarity, and the activation of mTORC1. It also provides net glutamine for glutaminolysis and consequently presents as a potential target to treat cancer. A high-throughput screening assay was developed to identify new inhibitors of SNAT2 making use of the inducible nature of SNAT2 and its electrogenic mechanism. Using an optimized FLIPR membrane potential (FMP) assay, a curated scaffold library of 33934 compounds was screened to identify 3-(N-methyl (4-methylphenyl)sulfonamido)-N-(2-trifluoromethylbenzyl)thiophene-2-carboxamide as a potent inhibitor of SNAT2. In two different assays an IC₅₀ of 0.8–3 µM was determined. The compound discriminated against the close transporter homologue SNAT1. MDA-MB-231 breast cancer and HPAFII pancreatic cancer cell lines tolerated the SNAT2 inhibitor up to a concentration of 100 µM but in combination with tolerable doses of the glucose transport inhibitor Bay-876, proliferative growth of both cell lines was halted. This points to synergy between inhibition of glycolysis and glutaminolysis in cancer cells.

Maximizing the integration of virtual and experimental screening in hit discovery

INTRODUCTION

Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approaches.

AREAS COVERED

After reviewing the recent trends in both experimental and virtual screening, the authors discuss different integration strategies from parallel, focused, sequential, and iterative screening. Strategic considerations are demonstrated in a number of real-life case studies.

EXPERT OPINION

Experimental and virtual screening are complementary approaches that should be integrated in lead discovery settings. Virtual screening can access extremely large synthetically feasible chemical space that can be effectively searched on GPU clusters or cloud architectures. Experimental screening provides reliable datasets by quantitative HTS applications, and DNA-encoded libraries (DEL) have enlarged the chemical space covered by these technologies. These developments, together with the use of artificial intelligence methods, represent new options for their efficient integration. The case studies discussed here demonstrate the benefits of complementary strategies, such as focused and iterative screening.

Increased/Targeted Brain (Pro)Drug Delivery via Utilization of Solute Carriers (SLCs)

Membrane transporters have a crucial role in compounds’ brain drug delivery. They allow not only the penetration of a wide variety of different compounds to cross the endothelial cells of the blood–brain barrier (BBB), but also the accumulation of them into the brain parenchymal cells. Solute carriers (SLCs), with nearly 500 family members, are the largest group of membrane transporters. Unfortunately, not all SLCs are fully characterized and used in rational drug design. However, if the structural features for transporter interactions (binding and translocation) are known, a prodrug approach can be utilized to temporarily change the pharmacokinetics and brain delivery properties of almost any compound. In this review, main transporter subtypes that are participating in brain drug disposition or have been used to improve brain drug delivery across the BBB via the prodrug approach, are introduced. Moreover, the ability of selected transporters to be utilized in intrabrain drug delivery is discussed. Thus, this comprehensive review will give insights into the methods, such as computational drug design, that should be utilized more effectively to understand the detailed transport mechanisms. Moreover, factors, such as transporter expression modulation pathways in diseases that should be taken into account in rational (pro)drug development, are considered to achieve successful clinical applications in the future.

The characterization of protein interactions - what, how and how much?

Protein interactions underlie most molecular events in biology. Many methods have been developed to identify protein partners, to measure the affinity with which these biomolecules interact and to characterise the structures of the complexes. Each approach has its own advantages and limitations, and it can be difficult for the newcomer to determine which methodology would best suit their system. This review provides an overview of many of the techniques most widely used to identify protein partners, assess stoichiometry and binding affinity, and determine low-resolution models for complexes. Key methods covered include: yeast two-hybrid analysis, affinity purification mass spectrometry and proximity labelling to identify partners; size-exclusion chromatography, scattering methods, native mass spectrometry and analytical ultracentrifugation to estimate stoichiometry; isothermal titration calorimetry, biosensors and fluorometric methods (including microscale thermophoresis, anisotropy/polarisation, resonance energy transfer, AlphaScreen, and differential scanning fluorimetry) to measure binding affinity; and crosslinking and hydrogen-deuterium exchange mass spectrometry to probe the structure of complexes.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

Heteromeric Amino Acid Transporters in Brain: from Physiology to Pathology

In humans, more than 50 transporters are responsible for the traffic and balance of amino acids within and between cells and tissues, and half of them have been associated with disease [1]. Covering all common amino acids, Heteromeric Amino acid Transporters (HATs) are one class of such transporters. This review first highlights structural and functional studies that solved the atomic structure of HATs and revealed molecular clues on substrate interaction. Moreover, this review focuses on HATs that have a role in the central nervous system (CNS) and that are related to neurological diseases, including: (i) LAT1/CD98hc and its role in the uptake of branched chain amino acids trough the blood brain barrier and autism. (ii) LAT2/CD98hc and its potential role in the transport of glutamine between plasma and cerebrospinal fluid. (iii) y⁺LAT2/CD98hc that is emerging as a key player in hepatic encephalopathy. xCT/CD98hc as a potential therapeutic target in glioblastoma, and (iv) Asc-1/CD98hc as a potential therapeutic target in pathologies with alterations in NMDA glutamate receptors.

Augmenting Hit Identification by Virtual Screening Techniques in Small Molecule Drug Discovery

Two orthogonal approaches for hit identification in drug discovery are large-scale in vitro and in silico screening. In recent years, due to the emergence of new targets and a rapid increase in the size of the readily synthesizable chemical space, there is a growing emphasis on the integration of the two techniques to improve the hit finding efficiency. Here, we highlight three examples of drug discovery projects at Merck & Co., Inc., Kenilworth, NJ, USA in which different virtual screening (VS) techniques, each specifically tailored to leverage knowledge available for the target, were utilized to augment the selection of high-quality chemical matter for in vitro assays and to enhance the diversity and tractability of hits. Central to success is a fully integrated workflow combining in silico and experimental expertise at every stage of the hit identification process. We advocate that workflows encompassing VS as part of an integrated hit finding plan should be widely adopted to accelerate hit identification and foster cross-functional collaborations in modern drug discovery.

Asc-1 Transporter (SLC7A10): Homology Models And Molecular Dynamics Insights Into The First Steps Of The Transport Mechanism

The alanine-serine-cysteine transporter Asc-1 regulates the synaptic availability of d-serine and glycine (the two co-agonists of the NMDA receptor) and is regarded as an important drug target. To shuttle the substrate from the extracellular space to the cytoplasm, this transporter undergoes multiple distinct conformational states. In this work, homology modeling, substrate docking and molecular dynamics simulations were carried out to learn more about the transition between the “outward-open” and “outward-open occluded” states. We identified a transition state involving the highly-conserved unwound TM6 region in which the Phe243 flips close to the d-serine substrate without major movements of TM6. This feature and those of other key residues are proposed to control the binding site and substrate translocation. Competitive inhibitors ACPP, LuAE00527 and SMLC were docked and their binding modes at the substrate binding site corroborated the key role played by Phe243 of TM6. For ACPP and LuAE00527, strong hydrophobic interactions with this residue hinder its mobility and prevent the uptake and the efflux of substrates. As for SMLC, the weaker interactions maintain the flexibility of Phe243 and the efflux process. Overall, we propose a molecular basis for the inhibition of substrate translocation of the Asc-1 transporter that should be valuable for rational drug design.

Targeting RNA with Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space

Although the potential value of RNA as a target for new small molecule therapeutics is becoming increasingly credible, the physicochemical properties required for small molecules to selectively bind to RNA remain relatively unexplored. To investigate the druggability of RNAs with small molecules, we have employed affinity mass spectrometry, using the Automated Ligand Identification System (ALIS), to screen 42 RNAs from a variety of RNA classes, each against an array of chemically diverse drug-like small molecules (~50,000 compounds) and functionally annotated tool compounds (~5100 compounds). The set of RNA–small molecule interactions that was generated was compared with that for protein–small molecule interactions, and naïve Bayesian models were constructed to determine the types of specific chemical properties that bias small molecules toward binding to RNA. This set of RNA-selective chemical features was then used to build an RNA-focused set of ~3800 small molecules that demonstrated increased propensity toward binding the RNA target set. In addition, the data provide an overview of the specific physicochemical properties that help to enable binding to potential RNA targets. This work has increased the understanding of the chemical properties that are involved in small molecule binding to RNA, and the methodology used here is generally applicable to RNA-focused drug discovery efforts.

Amino Acid Transporters as Disease Modifiers and Drug Targets

Amino acids perform a variety of functions in cells and organisms, particularly in the synthesis of proteins, as energy metabolites, neurotransmitters, and precursors for many other molecules. Amino acid transport plays a key role in all these functions. Inhibition of amino acid transport is pursued as a therapeutic strategy in several areas, such as diabetes and related metabolic disorders, neurological disorders, cancer, and stem cell biology. The role of amino acid transporters in these disorders and processes is well established, but the implementation of amino acid transporters as drug targets is still in its infancy. This is at least in part due to the underdeveloped pharmacology of this group of membrane proteins. Recent advances in structural biology, membrane protein expression, and inhibitor screening methodology will see an increased number of improved and selective inhibitors of amino acid transporters that can serve as tool compounds for further studies.

Inhibition of the Alanine-Serine-Cysteine-1 Transporter by BMS-466442

Experiment and modeling were combined to understand inhibition of the alanine-serine-cysteine-1 (asc1) transporter. The structure-activity relationship (SAR) was explored with synthesis of analogues of BMS-466442. Direct target interaction and binding site location between TM helices 6 and 10 were confirmed via site directed mutagenesis. Computational modeling suggested the inhibitor binds via competitive occupation of the orthosteric site while also blocking the movement of TM helices that are required for transport.

Prospective Assessment of Virtual Screening Heuristics Derived Using a Novel Fusion Score

High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.