Avner Schlessinger

Ligand discovery from a dopamine D3 receptor homology model and crystal structure

G-Protein coupled receptors (GPCRs) are intensely studied as drug targets and for their role in signaling. With the determination of the first crystal structures, interest in structure-based ligand discovery has increased. Unfortunately, most GPCRs lack experimental structures. The determination of the D3 receptor structure, and a community challenge to predict it, enabled a fully prospective comparison of ligand discovery from a modeled structure versus that of the subsequently released crystal structure. Over 3.3 million molecules were docked against a homology model, and 26 of the highest ranking were tested for binding. Six had affinities from 0.2 to 3.1μM. Subsequently, the crystal structure was released and the docking screen repeated. Of the 25 compounds selected, five had affinities from 0.3 to 3.0μM. One of the novel ligands from the homology model screen was optimized for affinity to 81nM. The feasibility of docking screens against modeled GPCRs more generally is considered.

Function of human Rh based on structure of RhCG at 2.1 Å

In humans, NH3 transport across cell membranes is facilitated by the Rh (rhesus) family of proteins. Human Rh C glycoprotein (RhCG) forms a trimeric complex that plays an essential role in ammonia excretion and renal pH regulation. The X-ray crystallographic structure of human RhCG, determined at 2.1 Å resolution, reveals the mechanism of ammonia transport. Each monomer contains 12 transmembrane helices, one more than in the bacterial homologs. Reconstituted into proteoliposomes, RhCG conducts NH3 to raise internal pH. Models of the erythrocyte Rh complex based on our RhCG structure suggest that the erythrocytic Rh complex is composed of stochastically assembled heterotrimers of RhAG, RhD, and RhCE.

Improved Disorder Prediction by Combination of Orthogonal Approaches

Disordered proteins are highly abundant in regulatory processes such as transcription and cell-signaling. Different methods have been developed to predict protein disorder often focusing on different types of disordered regions. Here, we present MD, a novel META-Disorder prediction method that molds various sources of information predominantly obtained from orthogonal prediction methods, to significantly improve in performance over its constituents. In sustained cross-validation, MD not only outperforms its origins, but it also compares favorably to other state-of-the-art prediction methods in a variety of tests that we applied. Availability: http://www.rostlab.org/services/md/

Crystal structure of a eukaryotic phosphate transporter

Phosphate is crucial for structural and metabolic needs, including nucleotide and lipid synthesis, signalling and chemical energy storage. Proton-coupled transporters of the major facilitator superfamily (MFS) are essential for phosphate uptake in plants and fungi, and also have a function in sensing external phosphate levels as transceptors. Here we report the 2.9 Å structure of a fungal (Piriformospora indica) high-affinity phosphate transporter, PiPT, in an inward-facing occluded state, with bound phosphate visible in the membrane-buried binding site. The structure indicates both proton and phosphate exit pathways and suggests a modified asymmetrical ‘rocker-switch’ mechanism of phosphate transport. PiPT is related to several human transporter families, most notably the organic cation and anion transporters of the solute carrier family (SLC22), which are implicated in cancer-drug resistance. We modelled representative cation and anion SLC22 transporters based on the PiPT structure to surmise the structural basis for substrate binding and charge selectivity in this important family. The PiPT structure demonstrates and expands on principles of substrate transport by the MFS transporters and illuminates principles of phosphate uptake in particular.

Protein flexibility and rigidity predicted from sequence

Structural flexibility has been associated with various biological processes such as molecular recognition and catalytic activity. In silico studies of protein flexibility have attempted to characterize and predict flexible regions based on simple principles. B‐values derived from experimental data are widely used to measure residue flexibility. Here, we present the most comprehensive large‐scale analysis of B‐values. We used this analysis to develop a neural network–based method that predicts flexible–rigid residues from amino acid sequence. The system uses both global and local information (i.e., features from the entire protein such as secondary structure composition, protein length, and fraction of surface residues, and features from a local window of sequence‐consecutive residues). The most important local feature was the evolutionary exchange profile reflecting sequence conservation in a family of related proteins. To illustrate its potential, we applied our method to 4 different case studies, each of which related our predictions to aspects of function. The first 2 were the prediction of regions that undergo conformational switches upon environmental changes (switch II region in Ras) and the prediction of surface regions, the rigidity of which is crucial for their function (tunnel in propeller folds). Both were correctly captured by our method. The third study established that residues in active sites of enzymes are predicted by our method to have unexpectedly low B‐values. The final study demonstrated how well our predictions correlated with NMR order parameters to reflect motion. Our method had not been set up to address any of the tasks in those 4 case studies. Therefore, we expect that this method will assist in many attempts at inferring aspects of function. Proteins 2005.

Role of Organic Cation Transporter 3 (SLC22A3) and Its Missense Variants in the Pharmacologic Action of Metformin

Objectives The goals of this study were to determine the role of organic cation transporter 3 (OCT3) in the pharmacological action of metformin and to identify and functionally characterize genetic variants of OCT3 with respect to the uptake of metformin and monoamines. Methods For pharmacological studies, we evaluated metformin-induced activation of AMP-activated protein kinase, a molecular target of metformin. We used quantitative PCR and immunostaining to localize the transporter and isotopic uptake studies in cells transfected with OCT3 and its nonsynonymous genetic variants for functional analyses. Results Quantitative PCR and immunostaining showed that OCT3 was expressed high on the plasma membrane of skeletal muscle and liver, target tissues for metformin action. Both the OCT inhibitor, cimetidine, and OCT3-specific short hairpin RNA significantly reduced the activating effect of metformin on AMP-activated protein kinase. To identify genetic variants in OCT3, we used recent data from the 1000 Genomes and the Pharmacogenomics of Membrane Transporters projects. Six novel missense variants were identified. In functional assays, using various monoamines and metformin, three variants, T44M (c.131C>T), T400I (c.1199C>T) and V423F (c.1267G>T) showed altered substrate specificity. Notably, in cells expressing T400I and V423F, the uptakes of metformin and catecholamines were significantly reduced, but the uptakes of metformin, 1-methyl-4-phenylpyridinium and histamine by T44M were significantly increased more than 50%. Structural modeling suggested that these two variants may be located in the pore lining (T400) or proximal (V423) membrane-spanning helixes. Conclusion Our study suggests that OCT3 plays a role in the therapeutic action of metformin and that genetic variants of OCT3 may modulate metformin and catecholamine action.

A common 5′-UTR variant in MATE2-K is associated with poor response to metformin

Multidrug and toxin extrusion 2 (MATE2‐K (SLC47A2)), a polyspecific organic cation exporter, facilitates the renal elimination of the antidiabetes drug metformin. In this study, we characterized genetic variants of MATE2‐K, determined their association with metformin response, and elucidated their impact by means of a comparative protein structure model. Four nonsynonymous variants and four variants in the MATE2‐K basal promoter region were identified from ethnically diverse populations. Two nonsynonymous variants—c.485C>T and c.1177G>A—were shown to be associated with significantly lower metformin uptake and reduction in protein expression levels. MATE2‐K basal promoter haplotypes containing the most common variant, g.−130G>A (>26% allele frequency), were associated with a significant increase in luciferase activities and reduced binding to the transcriptional repressor myeloid zinc finger 1 (MZF‐1). Patients with diabetes who were homozygous for g.−130A had a significantly poorer response to metformin treatment, assessed as relative change in glycated hemoglobin (HbA1c) (−0.027 (−0.076, 0.033)), as compared with carriers of the reference allele, g.−130G (−0.15 (−0.17, −0.13)) (P = 0.002). Our study showed that MATE2‐K plays a role in the antidiabetes response to metformin.

Natively unstructured loops differ from other loops.

Natively unstructured or disordered protein regions may increase the functional complexity of an organism; they are particularly abundant in eukaryotes and often evade structure determination. Many computational methods predict unstructured regions by training on outliers in otherwise well-ordered structures. Here, we introduce an approach that uses a neural network in a very different and novel way. We hypothesize that very long contiguous segments with nonregular secondary structure (NORS regions) differ significantly from regular, well-structured loops, and that a method detecting such features could predict natively unstructured regions. Training our new method, NORSnet, on predicted information rather than on experimental data yielded three major advantages: it removed the overlap between testing and training, it systematically covered entire proteomes, and it explicitly focused on one particular aspect of unstructured regions with a simple structural interpretation, namely that they are loops. Our hypothesis was correct: well-structured and unstructured loops differ so substantially that NORSnet succeeded in their distinction. Benchmarks on previously used and new experimental data of unstructured regions revealed that NORSnet performed very well. Although it was not the best single prediction method, NORSnet was sufficiently accurate to flag unstructured regions in proteins that were previously not annotated. In one application, NORSnet revealed previously undetected unstructured regions in putative targets for structural genomics and may thereby contribute to increasing structural coverage of large eukaryotic families. NORSnet found unstructured regions more often in domain boundaries than expected at random. In another application, we estimated that 50%–70% of all worm proteins observed to have more than seven protein–protein interaction partners have unstructured regions. The comparative analysis between NORSnet and DISOPRED2 suggested that long unstructured loops are a major part of unstructured regions in molecular networks.

Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET

The norepinephrine transporter (NET) transports norepinephrine from the synapse into presynaptic neurons, where norepinephrine regulates signaling pathways associated with cardiovascular effects and behavioral traits via binding to various receptors (e.g., β2-adrenergic receptor). NET is a known target for a variety of prescription drugs, including antidepressants and psychostimulants, and may mediate off-target effects of other prescription drugs. Here, we identify prescription drugs that bind NET, using virtual ligand screening followed by experimental validation of predicted ligands. We began by constructing a comparative structural model of NET based on its alignment to the atomic structure of a prokaryotic NET homolog, the leucine transporter LeuT. The modeled binding site was validated by confirming that known NET ligands can be docked favorably compared to nonbinding molecules. We then computationally screened 6,436 drugs from the Kyoto Encyclopedia of Genes and Genomes (KEGG DRUG) against the NET model. Ten of the 18 high-scoring drugs tested experimentally were found to be NET inhibitors; five of these were chemically novel ligands of NET. These results may rationalize the efficacy of several sympathetic (tuaminoheptane) and antidepressant (tranylcypromine) drugs, as well as side effects of diabetes (phenformin) and Alzheimer’s (talsaclidine) drugs. The observations highlight the utility of virtual screening against a comparative model, even when the target shares less than 30% sequence identity with its template structure and no known ligands in the primary binding site.

A Conserved Coatomer-related Complex Containing Sec13 and Seh1 Dynamically Associates With the Vacuole in Saccharomyces cerevisiae

The presence of multiple membrane-bound intracellular compartments is a major feature of eukaryotic cells. Many of the proteins required for formation and maintenance of these compartments share an evolutionary history. Here, we identify the SEA (Seh1-associated) protein complex in yeast that contains the nucleoporin Seh1 and Sec13, the latter subunit of both the nuclear pore complex and the COPII coating complex. The SEA complex also contains Npr2 and Npr3 proteins (upstream regulators of TORC1 kinase) and four previously uncharacterized proteins (Sea1–Sea4). Combined computational and biochemical approaches indicate that the SEA complex proteins possess structural characteristics similar to the membrane coating complexes COPI, COPII, the nuclear pore complex, and, in particular, the related Vps class C vesicle tethering complexes HOPS and CORVET. The SEA complex dynamically associates with the vacuole in vivo. Genetic assays indicate a role for the SEA complex in intracellular trafficking, amino acid biogenesis, and response to nitrogen starvation. These data demonstrate that the SEA complex is an additional member of a family of membrane coating and vesicle tethering assemblies, extending the repertoire of protocoatomer-related complexes.