Impaired Amino Acid Transport at the Blood Brain Barrier Is a Cause of Autism Spectrum Disorder.

Autism spectrum disorders (ASD) are a group of genetic disorders often overlapping with other neurological conditions. We previously described abnormalities in the branched-chain amino acid (BCAA) catabolic pathway as a cause of ASD. Here, we show that the solute carrier transporter 7a5 (SLC7A5), a large neutral amino acid transporter localized at the blood brain barrier (BBB), has an essential role in maintaining normal levels of brain BCAAs. In mice, deletion of Slc7a5 from the endothelial cells of the BBB leads to atypical brain amino acid profile, abnormal mRNA translation, and severe neurological abnormalities. Furthermore, we identified several patients with autistic traits and motor delay carrying deleterious homozygous mutations in the SLC7A5 gene. Finally, we demonstrate that BCAA intracerebroventricular administration ameliorates abnormal behaviors in adult mutant mice. Our data elucidate a neurological syndrome defined by SLC7A5 mutations and support an essential role for the BCAA in human brain function.

Targeting SLC transporters: small molecules as modulators and therapeutic opportunities.

Solute carrier (SLCs) transporters mediate the transport of a broad range of solutes across biological membranes. Dysregulation of SLCs has been associated with various pathologies, including metabolic and neurological disorders, as well as cancer and rare diseases. SLCs are therefore emerging as key targets for therapeutic intervention with several recently approved drugs targeting these proteins. Unlocking this large and complex group of proteins is essential to identifying unknown SLC targets and developing next-generation SLC therapeutics. Recent progress in experimental and computational techniques has significantly advanced SLC research, including drug discovery. Here, we review emerging topics in therapeutic discovery of SLCs, focusing on state-of-the-art approaches in structural, chemical, and computational biology, and discuss current challenges in transporter drug discovery.

Haploinsufficiency underlies the neurodevelopmental consequences of SLC6A1 variants.

Heterozygous variants in SLC6A1, encoding the GAT-1 GABA transporter, are associated with seizures, developmental delay, and autism. The majority of affected individuals carry missense variants, many of which are recurrent germline de novo mutations, raising the possibility of gain-of-function or dominant-negative effects. To understand the functional consequences, we performed an in vitro GABA uptake assay for 213 unique variants, including 24 control variants. De novo variants consistently resulted in a decrease in GABA uptake, in keeping with haploinsufficiency underlying all neurodevelopmental phenotypes. Where present, ClinVar pathogenicity reports correlated well with GABA uptake data; the functional data can inform future reports for the remaining 72% of unscored variants. Surface localization was assessed for 86 variants; two-thirds of loss-of-function missense variants prevented GAT-1 from being present on the membrane while GAT-1 was on the surface but with reduced activity for the remaining third. Surprisingly, recurrent de novo missense variants showed moderate loss-of-function effects that reduced GABA uptake with no evidence for dominant-negative or gain-of-function effects. Using linear regression across multiple missense severity scores to extrapolate the functional data to all potential SLC6A1 missense variants, we observe an abundance of GAT-1 residues that are sensitive to substitution. The extent of this missense vulnerability accounts for the clinically observed missense enrichment; overlap with hypermutable CpG sites accounts for the recurrent missense variants. Strategies to increase the expression of the wild-type SLC6A1 allele are likely to be beneficial across neurodevelopmental disorders, though the developmental stage and extent of required rescue remain unknown.

SLC16 Family: From Atomic Structure to Human Disease.

The solute carrier 16 (SLC16) family represents a diverse group of membrane proteins mediating the transport of monocarboxylates across biological membranes. Family members show a variety of functional roles ranging from nutrient transport and intracellular pH regulation to thyroid hormone homeostasis. Changes in the expression levels and transport function of certain SLC16 transporters are manifested in severe health disorders including cancer, diabetes, and neurological disorders. L-Lactate-transporting SLC16 family members play essential roles in the metabolism of certain tumors and became validated drug targets. This review illuminates the SLC16 family under a new light using structural information obtained from a SLC16 homolog. Furthermore, the role of these transporters in cancer metabolism and how their inhibition can contribute to anticancer therapy are discussed.

Functional genomics of OCTN2 variants informs protein-specific variant effect predictor for Carnitine Transporter Deficiency.

Genetic variants in SLC22A5, encoding the membrane carnitine transporter OCTN2, cause the rare metabolic disorder Carnitine Transporter Deficiency (CTD). CTD is potentially lethal but actionable if detected early, with confirmatory diagnosis involving sequencing of SLC22A5. Interpretation of missense variants of uncertain significance (VUSs) is a major challenge. In this study, we sought to characterize the largest set to date (n = 150) of OCTN2 variants identified in diverse ancestral populations, with the goals of furthering our understanding of the mechanisms leading to OCTN2 loss-of-function (LOF) and creating a protein-specific variant effect prediction model for OCTN2 function. Uptake assays with 14C-carnitine revealed that 105 variants (70%) significantly reduced transport of carnitine compared to wild-type OCTN2, and 37 variants (25%) severely reduced function to less than 20%. All ancestral populations harbored LOF variants; 62% of green fluorescent protein (GFP)-tagged variants impaired OCTN2 localization to the plasma membrane of human embryonic kidney (HEK293T) cells, and subcellular localization significantly associated with function, revealing a major LOF mechanism of interest for CTD. With these data, we trained a model to classify variants as functional (>20% function) or LOF (<20% function). Our model outperformed existing state-of-the-art methods as evaluated by multiple performance metrics, with mean area under the receiver operating characteristic curve (AUROC) of 0.895 ± 0.025. In summary, in this study we generated a rich dataset of OCTN2 variant function and localization, revealed important disease-causing mechanisms, and improved upon machine learning-based prediction of OCTN2 variant function to aid in variant interpretation in the diagnosis and treatment of CTD.

Identification of discriminative gene-level and protein-level features associated with pathogenic gain-of-function and loss-of-function variants.

Identifying whether a given genetic mutation results in a gene product with increased (gain-of-function; GOF) or diminished (loss-of-function; LOF) activity is an important step toward understanding disease mechanisms because they may result in markedly different clinical phenotypes. Here, we generated an extensive database of documented germline GOF and LOF pathogenic variants by employing natural language processing (NLP) on the available abstracts in the Human Gene Mutation Database. We then investigated various gene- and protein-level features of GOF and LOF variants and applied machine learning and statistical analyses to identify discriminative features. We found that GOF variants were enriched in essential genes, for autosomal-dominant inheritance, and in protein binding and interaction domains, whereas LOF variants were enriched in singleton genes, for protein-truncating variants, and in protein core regions. We developed a user-friendly web-based interface that enables the extraction of selected subsets from the GOF/LOF database by a broad set of annotated features and downloading of up-to-date versions. These results improve our understanding of how variants affect gene/protein function and may ultimately guide future treatment options.

Rational design of ASCT2 inhibitors using an integrated experimental-computational approach.

ASCT2 (SLC1A5) is a sodium-dependent neutral amino acid transporter that controls amino acid homeostasis in peripheral tissues. In cancer, ASCT2 is up-regulated where it modulates intracellular glutamine levels, fueling cell proliferation. Nutrient deprivation via ASCT2 inhibition provides a potential strategy for cancer therapy. Here, we rationally designed stereospecific inhibitors exploiting specific subpockets in the substrate binding site using computational modeling and cryo-electron microscopy (cryo-EM). The final structures combined with molecular dynamics simulations reveal multiple pharmacologically relevant conformations in the ASCT2 binding site as well as a previously unknown mechanism of stereospecific inhibition. Furthermore, this integrated analysis guided the design of a series of unique ASCT2 inhibitors. Our results provide a framework for future development of cancer therapeutics targeting nutrient transport via ASCT2, as well as demonstrate the utility of combining computational modeling and cryo-EM for solute carrier ligand discovery.

Protein structure-based gene expression signatures.

Gene expression signatures (GES) connect phenotypes to differential messenger RNA (mRNA) expression of genes, providing a powerful approach to define cellular identity, function, and the effects of perturbations. The use of GES has suffered from vague assessment criteria and limited reproducibility. Because the structure of proteins defines the functional capability of genes, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from multiple levels of protein structure (e.g., domain and fold) encoded by the mRNAs in GES. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, and mRNA expression of drug effects on cardiomyocytes show that sGES are useful for characterizing biological phenomena. sGES enable phenotypic characterization across experimental platforms, facilitates interoperability of expression datasets, and describe drug action on cells.

PredictProtein - Predicting Protein Structure and Function for 29 Years.

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.

Describing inhibitor specificity for the amino acid transporter LAT1 from metainference simulations.

The human L-type amino acid transporter 1 (LAT1; SLC7A5) is a membrane transporter of amino acids, thyroid hormones, and drugs such as the Parkinson's disease drug levodopa (L-Dopa). LAT1 is found in the blood-brain barrier, testis, bone marrow, and placenta, and its dysregulation has been associated with various neurological diseases, such as autism and epilepsy, as well as cancer. In this study, we combine metainference molecular dynamics simulations, molecular docking, and experimental testing, to characterize LAT1-inhibitor interactions. We first conducted a series of molecular docking experiments to identify the most relevant interactions between LAT1's substrate-binding site and ligands, including both inhibitors and substrates. We then performed metainference molecular dynamics simulations using cryoelectron microscopy structures in different conformations of LAT1 with the electron density map as a spatial restraint, to explore the inherent heterogeneity in the structures. We analyzed the LAT1 substrate-binding site to map important LAT1-ligand interactions as well as newly described druggable pockets. Finally, this analysis guided the discovery of previously unknown LAT1 ligands using virtual screening and cellular uptake experiments. Our results improve our understanding of LAT1-inhibitor recognition, providing a framework for rational design of future lead compounds targeting this key drug target.

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge.

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ('polypharmacology'). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Structures of ligand-occupied ß-Klotho complexes reveal a molecular mechanism underlying endocrine FGF specificity and activity.

The three members of the endocrine fibroblast growth factor (FGF) family designated FGF19, FGF21, and FGF23 mediate their pleiotropic cellular effects by binding to and activating binary complexes composed of an FGF receptor (FGFR) bound to either α-Klotho or ß-Klotho receptors. Structural analyses of ligand-occupied Klotho extracellular domains have provided important insights concerning mechanisms underlying the binding specificities of FGF21 and FGF23 to ß-Klotho or α-Klotho, respectively. They have also demonstrated that Klotho proteins function as primary high-affinity receptors while FGFRs function as the catalytic subunits that mediate intracellular signaling. Here we describe the crystal structure the C-terminal tail of FGF19 (FGF19CT) bound to sKLB and demonstrate that FGF19CT and FGF21CT bind to the same binding site on sKLB, via a multiturn D-P motif to site 1 and via a S-P-S motif to the pseudoglycoside hydrolase region (site 2). Binding affinities to sKLB and cellular stimulatory activities of FGF19CT, FGF21CT, and a variety of chimeric mutants to cells expressing ß-Klotho together with FGFR1c or FGFR4 were also analyzed. These experiments as well as detailed comparison of the structures of free and ligand-occupied sKLB to the structure of ligand-occupied sKLA reveal a general mechanism for recognition of endocrine FGFs by Klotho proteins and regulatory interactions with FGFRs that control their pleiotropic cellular responses.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Type II Binders Targeting the "GLR-Out" Conformation of the Pseudokinase STRADα.

Pseudokinases play important roles in signal transduction and cellular processes similar to those of catalytically competent kinases. However, pseudokinase pharmacological tractability and conformational space accessibility are poorly understood. Pseudokinases have only recently been suggested to adopt "inactive" conformations or interact with conformation-specific kinase inhibitors (e.g., type II compounds). In this work, the heavily substituted pseudokinase STRADα, which possesses a DFG → GLR substitution in the catalytic site that permits nucleotide binding while impairing divalent cation coordination, is used as a test case to demonstrate the potential applicability of conformation-specific, type II compounds to pseudokinase pharmacology. Integrated structural modeling is employed to generate a "GLR-out" conformational ensemble. Likely interacting type II compounds are identified through virtual screening against this ensemble model. Biophysical validation of compound binding is demonstrated through protein thermal stabilization and ATP competition. Localization of a top-performing compound through surface methylation strongly suggests that STRADα can adopt the "GLR-out" conformation and interact with compounds that comply with the standard type II pharmacophore. These results suggest that, despite a loss of catalytic function, some pseudokinases, including STRADα, may retain the conformational switching properties of conventional protein kinases.

Maternal health around pregnancy and autism risk: a diagnosis-wide, population-based study.

BACKGROUND: Many studies have reported an increased risk of autism spectrum disorder (ASD) associated with some maternal diagnoses in pregnancy. However, such associations have not been studied systematically, accounting for comorbidity between maternal disorders. Therefore our aim was to comprehensively test the associations between maternal diagnoses around pregnancy and ASD risk in offspring. METHODS: This exploratory case-cohort study included children born in Israel from 1997 to 2008, and followed up until 2015. We used information on all ICD-9 codes received by their mothers during pregnancy and the preceding year. ASD risk associated with each of those conditions was calculated using Cox proportional hazards regression, adjusted for the confounders (birth year, maternal age, socioeconomic status and number of ICD-9 diagnoses during the exposure period). RESULTS: The analytic sample consisted of 80 187 individuals (1132 cases, 79 055 controls), with 822 unique ICD-9 codes recorded in their mothers. After extensive quality control, 22 maternal diagnoses were nominally significantly associated with offspring ASD, with 16 of those surviving subsequent filtering steps (permutation testing, multiple testing correction, multiple regression). Among those, we recorded an increased risk of ASD associated with metabolic [e.g. hypertension; HR = 2.74 (1.92-3.90), p = 2.43 × 10-8], genitourinary [e.g. non-inflammatory disorders of cervix; HR = 1.88 (1.38-2.57), p = 7.06 × 10-5] and psychiatric [depressive disorder; HR = 2.11 (1.32-3.35), p = 1.70 × 10-3] diagnoses. Meanwhile, mothers of children with ASD were less likely to attend prenatal care appointment [HR = 0.62 (0.54-0.71), p = 1.80 × 10-11]. CONCLUSIONS: Sixteen maternal diagnoses were associated with ASD in the offspring, after rigorous filtering of potential false-positive associations. Replication in other cohorts and further research to understand the mechanisms underlying the observed associations with ASD are warranted.

Interaction of the neutral amino acid transporter ASCT2 with basic amino acids.

Glutamine transport across cell membranes is performed by a variety of transporters, including the alanine serine cysteine transporter 2 (ASCT2). The substrate-binding site of ASCT2 was proposed to be specific for small amino acids with neutral side chains, excluding basic substrates such as lysine. A series of competitive inhibitors of ASCT2 with low µM affinity were developed previously, on the basis of the 2,4-diaminobutyric acid (DAB) scaffold with a potential positive charge in the side chain. Therefore, we tested whether basic amino acids with side chains shorter than lysine can interact with the ASCT2 binding site. Molecular docking of L-1,3-diaminopropionic acid (L-DAP) and L-DAB suggested that these compounds bind to ASCT2. Consistent with this prediction, L-DAP and L-DAB, but not ornithine, lysine or D-DAP, elicited currents when applied to ASCT2-expressing cells. The currents were carried by anions and showed the hallmark properties of ASCT2 currents induced by transported substrates. The L-DAP response could be eliminated by a competitive ASCT2 inhibitor, suggesting that binding occurs at the substrate binding site. The KM for L-DAP was weakly voltage dependent. Furthermore, the pH dependence of the L-DAP response showed that the compound can bind in several protonation states. Together, these results suggest that the ASCT2 binding site is able to recognize L-amino acids with short, basic side chains, such as the L-DAP derivative ß-N-methylamino-l-Alanine (BMAA), a well-studied neurotoxin. Our results expand the substrate specificity of ASCT2 to include amino acid substrates with positively charged side chains.

KinaMetrix: a web resource to investigate kinase conformations and inhibitor space.

Protein kinases are among the most explored protein drug targets. Visualization of kinase conformations is critical for understanding structure-function relationship in this family and for developing chemically unique, conformation-specific small molecule drugs. We have developed Kinformation, a random forest classifier that annotates the conformation of over 3500 protein kinase structures in the Protein Data Bank. Kinformation was trained on structural descriptors derived from functionally important motifs to automatically categorize kinases into five major conformations with pharmacological relevance. Here we present KinaMetrix (http://KinaMetrix.com), a web resource enabling researchers to investigate the protein kinase conformational space as well as a subset of kinase inhibitors that exhibit conformational specificity. KinaMetrix allows users to classify uploaded kinase structures, as well as to derive structural descriptors of protein kinases. Uploaded structures can then be compared to atomic structures of other kinases, enabling users to identify kinases that occupy a similar conformational space to their uploaded structure. Finally, KinaMetrix also serves as a repository for both small molecule substructures that are significantly associated with each conformation type, and for homology models of kinases in inactive conformations. We expect KinaMetrix to serve as a resource for researchers studying kinase structural biology or developing conformation-specific kinase inhibitors.

High-Resolution Structure and Inhibition of the Schizophrenia-Linked Pseudokinase ULK4.

The ULK (UNC51-like) enzymes are a family of mammalian kinases that have critical roles in autophagy and development. While ULK1, ULK2, and ULK3 have been characterized, very little is known about ULK4. However, recently, deletions in ULK4 have been genetically linked to increased susceptibility to developing schizophrenia, a devastating neuropsychiatric disease with high heritability but few genes identified. Interestingly, ULK4 is a pseudokinase with some unusual mutations in the kinase catalytic motifs. Here, we report the first structure of the human ULK4 kinase at high resolution and show that although ULK4 has no apparent phosphotransfer activity, it can strongly bind ATP. We find an unusual mechanism for binding ATP in a Mg2+-independent manner, including a rare hydrophobic bridge in the active site. In addition, we develop two assays for ATP binding to ULK4, perform a virtual and experimental screen to identify small-molecule binders of ULK4, and identify several novel scaffolds that bind ULK4 and can lead the way to more selective small molecules that may help shed light on the function of this enigmatic protein.

SLC13A3 variants cause acute reversible leukoencephalopathy and α-ketoglutarate accumulation.

OBJECTIVE: SLC13A3 encodes the plasma membrane Na+ /dicarboxylate cotransporter 3, which imports inside the cell 4 to 6 carbon dicarboxylates as well as N-acetylaspartate (NAA). SLC13A3 is mainly expressed in kidney, in astrocytes, and in the choroid plexus. We describe two unrelated patients presenting with acute, reversible (and recurrent in one) neurological deterioration during a febrile illness. Both patients exhibited a reversible leukoencephalopathy and a urinary excretion of α-ketoglutarate (αKG) that was markedly increased and persisted over time. In one patient, increased concentrations of cerebrospinal fluid NAA and dicarboxylates (including αKG) were observed. Extensive workup was unsuccessful, and a genetic cause was suspected. METHODS: Whole exome sequencing (WES) was performed. Our teams were connected through GeneMatcher. RESULTS: WES analysis revealed variants in SLC13A3. A homozygous missense mutation (p.Ala254Asp) was found in the first patient. The second patient was heterozygous for another missense mutation (p.Gly548Ser) and an intronic mutation affecting splicing as demonstrated by reverse transcriptase polymerase chain reaction performed in muscle tissue (c.1016 + 3A > G). Mutations and segregation were confirmed by Sanger sequencing. Functional studies performed on HEK293T cells transiently transfected with wild-type and mutant SLC13A3 indicated that the missense mutations caused a marked reduction in the capacity to transport αKG, succinate, and NAA. INTERPRETATION: SLC13A3 deficiency causes acute and reversible leukoencephalopathy with marked accumulation of αKG. Urine organic acids (especially αKG and NAA) and SLC13A3 mutations should be screened in patients presenting with unexplained reversible leukoencephalopathy, for which SLC13A3 deficiency is a novel differential diagnosis. ANN NEUROL 2019;85:385-395.

A whole-animal platform to advance a clinical kinase inhibitor into new disease space.

Synthetic tailoring of approved drugs for new indications is often difficult, as the most appropriate targets may not be readily apparent, and therefore few roadmaps exist to guide chemistry. Here, we report a multidisciplinary approach for accessing novel target and chemical space starting from an FDA-approved kinase inhibitor. By combining chemical and genetic modifier screening with computational modeling, we identify distinct kinases that strongly enhance ('pro-targets') or limit ('anti-targets') whole-animal activity of the clinical kinase inhibitor sorafenib in a Drosophila medullary thyroid carcinoma (MTC) model. We demonstrate that RAF-the original intended sorafenib target-and MKNK kinases function as pharmacological liabilities because of inhibitor-induced transactivation and negative feedback, respectively. Through progressive synthetic refinement, we report a new class of 'tumor calibrated inhibitors' with unique polypharmacology and strongly improved therapeutic index in fly and human MTC xenograft models. This platform provides a rational approach to creating new high-efficacy and low-toxicity drugs.