Figure 1

<p>The TDI Model of Online Collaboration</p

Human Concentrative Nucleoside Transporter 3 (hCNT3, SLC28A3) Forms a Cyclic Homotrimer

Many anticancer and antiviral drugs are purine or pyrimidine analogues, which use membrane transporters to cross cellular membranes. Concentrative nucleoside transporters (CNTs) mediate the salvage of nucleosides and the transport of therapeutic nucleoside analogues across plasma membranes by coupling the transport of ligands to the sodium gradient. Of the three members of the human CNT family, CNT3 has the broadest selectivity and the widest expression profile. However, the molecular mechanisms of the transporter, including how it interacts with and translocates structurally diverse nucleosides and nucleoside analogues, are unclear. Recently, the crystal structure of vcCNT showed that the prokaryotic homologue of CNT3 forms a homotrimer. In this study, we successfully expressed and purified the wild type human homologue, hCNT3, demonstrating the homotrimer by size exclusion profiles and glutaraldehyde cross-linking. Further, by creating a series of cysteine mutants at highly conserved positions guided by comparative structure models, we cross-linked hCNT3 protomers in a cell-based assay, thus showing the existence of hCNT3 homotrimers in human cells. The presence and absence of cross-links at specific locations along TM9 informs us of important structural differences between vcCNT and hCNT3. Comparative modeling of the trimerization domain and sequence coevolution analysis both indicate that oligomerization is critical to the stability and function of hCNT3. In particular, trimerization appears to shorten the translocation path for nucleosides across the plasma membrane and may allow modulation of the transport function via allostery

Human Concentrative Nucleoside Transporter 3 (hCNT3, SLC28A3) Forms a Cyclic Homotrimer

Many anticancer and antiviral drugs are purine or pyrimidine analogues, which use membrane transporters to cross cellular membranes. Concentrative nucleoside transporters (CNTs) mediate the salvage of nucleosides and the transport of therapeutic nucleoside analogues across plasma membranes by coupling the transport of ligands to the sodium gradient. Of the three members of the human CNT family, CNT3 has the broadest selectivity and the widest expression profile. However, the molecular mechanisms of the transporter, including how it interacts with and translocates structurally diverse nucleosides and nucleoside analogues, are unclear. Recently, the crystal structure of vcCNT showed that the prokaryotic homologue of CNT3 forms a homotrimer. In this study, we successfully expressed and purified the wild type human homologue, hCNT3, demonstrating the homotrimer by size exclusion profiles and glutaraldehyde cross-linking. Further, by creating a series of cysteine mutants at highly conserved positions guided by comparative structure models, we cross-linked hCNT3 protomers in a cell-based assay, thus showing the existence of hCNT3 homotrimers in human cells. The presence and absence of cross-links at specific locations along TM9 informs us of important structural differences between vcCNT and hCNT3. Comparative modeling of the trimerization domain and sequence coevolution analysis both indicate that oligomerization is critical to the stability and function of hCNT3. In particular, trimerization appears to shorten the translocation path for nucleosides across the plasma membrane and may allow modulation of the transport function via allostery

Unexpected evolutionary relationships within the rapamycin family.

<p><b>a</b>, Distinct scaffolds produced by pathways from related BGCs. The scatter plot shows the relationship between the sequence homology of a pair of BGCs (x-axis) and the structural homology of their small molecule products (y-axis), compared to rapamycin and its BGC. Each circle represents a gene cluster and its small molecule product. Meridamycin and FK520 are closely related to rapamycin, as are their BGCs. While the pladienolide BGC is closely related to the rapamycin BGC, the structure of pladienolide itself is not very similar to that of rapamycin. In particular, pladienolide has a much smaller macrocycle and lacks shikimate- or pipecolate-derived moieties, and, as a result, binds to a distinct protein target. Structural similarity is estimated by the Tanimoto coefficient using linear-path fingerprints (FP2) from Open Babel <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004016#pcbi.1004016-OBoyle1" target="_blank">[67]</a>, while sequence homology is represented as the Jaccard index defined on pairs of Pfam domains that share sequence identities within the top 10^th percentile of all-pair sequence identities. The number of domain pairs that share sequence identities within the top 10^th percentile and sequence identity of all domain pairs are shown as point sizes and colors, respectively. <b>b</b>, The role of concerted evolution in homogenizing domains within a BGC. Phylogenetic trees of KS and AT domains from the rapamycin, FK520, meridamycin, and pladienolide BGCs are shown (for detailed trees with accession numbers and bootstrap values, see <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004016#pcbi.1004016.s011" target="_blank">Figure S11</a></b>). The KS and AT sequences largely cluster into BGC-specific clades; for the AT domains, this is even the case for two different clusters encoding the same compound (meridamycin), showing the ability of concerted evolution to homogenize domains within a BGC. <b>c</b>, Chemical structures of rapamycin, meridamycin, FK520 and pladienolide. The sub-structure shared among rapamycin, meridamycin and FK520 is colored red, and the domains responsible for the biosynthesis of this sub-structure in each molecule are indicated with red circles in <b>b</b>.</p

The rapid and dynamic evolution of BGCs differs from the evolution of ribosomal gene clusters and primary metabolism.

<p><b>a</b>, Distributions of the best matching sequence homologs with respect to organism similarity (based on 16S rRNA) for predicted BGCs and histidine operons suggest significant differences in the ways they evolve. <b>b</b>, Number of detected rearrangements, indels and duplications plotted against the average percent identity in the aligned gene cluster pairs from which the events were deduced for predicted BGCs (top) and ribosomal gene clusters (bottom). Ribosomal gene clusters were selected for comparison based on their relatively large sizes (∼10–15 kb) compared to primary metabolic operons; to obtain a fair comparison with BGCs, only gene clusters of sizes 5–15 kb were taken into account. Counts are based on a systematic comparison of all gene clusters in our data set that share regions of >1000 bp with >70% identity, in which events were inferred from alignments of such 1000 bp blocks. Of the 10,096 BGC pairs meeting these criteria, 1,750 had a rearrangement, 1,140 had an indel, and 135 had a duplication, each of which were far more common than the corresponding evolutionary events in gene clusters encoding the translation apparatus. Interestingly, while indels and rearrangements could be detected in ∼16% and ∼19% of BGCs of all sizes, duplications are found far more commonly in gene clusters with sizes of >40 kb (7.6%) than in gene clusters with sizes of 10–20 kb (0.3%), suggesting a possible role for duplication and divergence in the evolution of large gene clusters. <b>c</b>, Size distribution of inserted/deleted fragments during recent gene cluster evolution, based on the indel analysis.</p

Spatial Distribution of Predicted Deleterious and Neutral Missense Variants in the BRCA1 BRCT Domains

<div><p>(A) Ribbon representation of the two domains with labeled helices (α1, α2, etc.) and strands (β1, β2, etc.). Recreation of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030026#pcbi-0030026-g001" target="_blank">Figure 1</a>A [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030026#pcbi-0030026-b064" target="_blank">64</a>].</p><p>(B) BRCA1 BRCT missense variants reported as neutral (blue) and deleterious (red) in the mammalian transactivation assay shown mapped onto the BRCA1 BRCT X-ray crystal structure (1t29).</p><p>(C) Consensus predictions of Random Forest, Naïve Bayes, and Support Vector Machine for 54 BRCA1 BRCT VUS in the Breast Information Core database (<a href="http://research.nhgri.nih.gov/bic/BIC/" target="_blank">http://research.nhgri.nih.gov/bic/BIC/</a>) mapped onto the same structure, with predicted neutral shown in blue and predicted deleterious in red.</p></div

Identification of a Putative Novel Binding Site in BRCA1 BRCT Domains

<div><p>Two surface variants found to be deleterious to BRCA1 activity in our companion paper (R1753T and T1685I) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030026#pcbi-0030026-b014" target="_blank">14</a>] lie at a highly conserved patch of amino acid residues, forming a groove on the protein surface, possibly a heretofore uncharacterized binding site of BRCA1 with a protein partner or nucleotide ligand.</p><p>(A) Surface representation of BRCA1 BRCT domains colored by conservation in our multiple sequence alignment of orthologs. Red, 100% conserved; white, 39% conserved; blue, 0% conserved.</p><p>(B) Two hydrogen-bonding networks are shown in ball-and-stick format.</p><p>(C) Changes in the electrostatic surface potential of the putative binding site upon mutation of R1753 to T1753. The electrostatic surface potential of the groove changes from primarily positive (greater than 10 <i>k</i>T) and neutral (0 <i>k</i>T), depicted as blue and white, to negative (less than −10 <i>k</i>T), depicted as red. This change may weaken the binding of protein partner(s) or nucleic acid ligand(s) necessary for BRCA1′s transactivation activity. Electrostatic surface potential calculated by DELPHI, visualized by CHIMERA in GRASP format [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030026#pcbi-0030026-b039" target="_blank">39</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030026#pcbi-0030026-b040" target="_blank">40</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030026#pcbi-0030026-b043" target="_blank">43</a>].</p><p>(D) Multiple sequence alignment of BRCT domains in BRCA1 orthologs. Primary groove residues are shaded in black, and their hydrogen-bonding partners are shaded in gray.</p></div

Computational Classifications of 36 BRCA1 BRCT Variants Functionally Characterized by the Transactivation Assay

<div><p>For each variant, the local protein structure environment is represented by secondary structure type and whether the amino acid residue is buried (<i>normalized solvent accessibility < 0.2</i>) or exposed (<i>normalized solvent accessibility ≥ 0.2</i>). Labels (“1655 S->F”) are colored according to whether the variant was functional in the assays (blue) or nonfunctional (red). Computational classifications in agreement with the assay are indicated by filled circles. Computational classifications not in agreement with the assay are indicated by outlined circles. Computational classifications yielding “unclassified” are indicated by an outlined black circle. The variant D1692N is fully functional as a transcriptional activator but results in incorrect splicing in vivo. Results from variant M1775K are unpublished (Foulkes et al.).</p><p>A, Ancestral Sequence; B, Rule-based decision tree; D, Decision Tree; F, SIFT; MCC, Matthews correlation coefficient; N, Naïve Bayes; R, Random Forest; S, Support Vector Machine; T, Align-GVGD Tnig; U, Align-GVGD Spur.</p></div

Sensitivity versus 1-Specificity of Classifiers That Use a Numerical Score to Predict the Functional Impact of 34 BRCA1 BRCT UCVs

<div><p>Comparison of four supervised machine learning methods, trained on 618 biochemically characterized missense variants in the human transcription factor TP53 and two sequence analysis methods that consider evolutionary conservation and physiochemical properties of amino acids (SIFT and Align-GVGD Tnig based on alignment of eight placental mammals, marsupial, chicken, frog, and pufferfish). Align-GVGD Spur, using an alignment that includes these species plus sea urchin, performs slightly worse than Align-GVGD Tnig in terms of ROC analysis and is not shown. Plot created with ROCR [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030026#pcbi-0030026-b086" target="_blank">86</a>].</p><p>DT, decision tree; NB, Naïve Bayes; RF, random forest; SVM, support vector machine.</p></div

Computational Classifications of 54 Uncharacterized Variants Found in BIC

<div><p>For each variant, the local protein structure environment is represented by secondary structure type and whether the amino acid residue is buried (<i>normalized solvent accessibility < 0.2</i>) or exposed (<i>normalized solvent accessibility ≥ 0.2</i>). For the 54 uncharacterized variants, labels (“1652 M->T”) are colored according to consensus prediction from Naïve Bayes, Support Vector Machine, and Random Forest. Predictions of each method are indicated by filled circles (blue, neutral; red, deleterious).</p><p>N, Naïve Bayes. R, Random Forest; S, Support Vector Machine. </p></div