Clustering protein environments for function prediction: finding PROSITE motifs in 3D-4

<p><b>Copyright information:</b></p><p>Taken from "Clustering protein environments for function prediction: finding PROSITE motifs in 3D"</p><p>http://www.biomedcentral.com/1471-2105/8/S4/S10</p><p>BMC Bioinformatics 2007;8(Suppl 4):S10-S10.</p><p>Published online 22 May 2007</p><p>PMCID:PMC1892080.</p><p></p>he text are shown. The structures were oriented by superimposing the PROSITE patterns, and the arrows indicate the atoms around which the microenvironments were centered. All residues containing atoms within the 7.5-Angstrom environment are depicted. The three comparisons show varying degrees of similarities among environments in the same cluster, ranging from nearly identical (a) to somewhat diverse (c). (a) The environments in the cluster containing residues from the PROTEIN_KINASE_TYR PROSITE motif are quite similar (top: PDB identifier 1fvr; bottom: 1luf). (b, c) The UBIQUITIN_CONJUGAT_1 (top: 1ayz; bottom: 1wzv) and STAPH_STREP_TOXIN_2 (top: 1aw7; bottom: 1ck1) clusters show greater degrees of structural variability. These images were produced using PyMol [28]

Clustering protein environments for function prediction: finding PROSITE motifs in 3D-1

<p><b>Copyright information:</b></p><p>Taken from "Clustering protein environments for function prediction: finding PROSITE motifs in 3D"</p><p>http://www.biomedcentral.com/1471-2105/8/S4/S10</p><p>BMC Bioinformatics 2007;8(Suppl 4):S10-S10.</p><p>Published online 22 May 2007</p><p>PMCID:PMC1892080.</p><p></p>, and weights the distance computed between two FEATURE vectors. The values of are calculated from the distribution of ones and zeros in feature over the entire two million FEATURE vectors used. This heat map shows the values of over all 44 features in each of the 6 shells

Clustering protein environments for function prediction: finding PROSITE motifs in 3D-0

<p><b>Copyright information:</b></p><p>Taken from "Clustering protein environments for function prediction: finding PROSITE motifs in 3D"</p><p>http://www.biomedcentral.com/1471-2105/8/S4/S10</p><p>BMC Bioinformatics 2007;8(Suppl 4):S10-S10.</p><p>Published online 22 May 2007</p><p>PMCID:PMC1892080.</p><p></p> value (S). A silhouette value represents the clustering quality for each object in a cluster as a continuous number between +1 (perfectly clustered) and -1 (the opposite). In order to evaluate the performance of various distance metrics, we calculated the silhouette values for each object in 15 training clusters based on previously validated FEATURE models. (a) The Euclidean distance gave a negative median silhouette value (-0.117), indicating that it is not suitable for clustering FEATURE vectors. The distance was calculated using FEATURE vectors in their original representations before any preprocessing occurred. (b) After converting the FEATURE vectors in the 15 clusters into their binary representations, we obtained better separation between clusters (median silhouette value of 0.362). (c) The weighted Hamming distance (called F-distance) produced an even better result (median silhouette value of 0.414) than the unweighted Hamming distance and was thus selected for clustering of the entire dataset of binary FEATURE vectors

Clustering protein environments for function prediction: finding PROSITE motifs in 3D-3

<p><b>Copyright information:</b></p><p>Taken from "Clustering protein environments for function prediction: finding PROSITE motifs in 3D"</p><p>http://www.biomedcentral.com/1471-2105/8/S4/S10</p><p>BMC Bioinformatics 2007;8(Suppl 4):S10-S10.</p><p>Published online 22 May 2007</p><p>PMCID:PMC1892080.</p><p></p>n each of the five validation clusters described in the text. The descriptions of the features are listed in Table 1. Green cells indicate the features that are significantly overrepresented with respect to the background of all two million feature vectors (P < 0.001). Red cells indicate features that are underrepresented. We used the two-sample test for binomial proportions for p-value computation. The rows and columns correspond to feature numbers (see Table 1) and the shell numbers, respectively

Clustering protein environments for function prediction: finding PROSITE motifs in 3D-2

<p><b>Copyright information:</b></p><p>Taken from "Clustering protein environments for function prediction: finding PROSITE motifs in 3D"</p><p>http://www.biomedcentral.com/1471-2105/8/S4/S10</p><p>BMC Bioinformatics 2007;8(Suppl 4):S10-S10.</p><p>Published online 22 May 2007</p><p>PMCID:PMC1892080.</p><p></p>e mean and median sizes are 437.2 and 232, respectively, and the standard deviation is 589.8. As discussed in the text, the long tail may represent internal hydrophobic environments

Doping-Free Complementary Logic Gates Enabled by Two-Dimensional Polarity-Controllable Transistors

Atomically thin two-dimensional (2D) materials belonging to transition metal dichalcogenides, due to their physical and electrical properties, are an exceptional vector for the exploration of next-generation semiconductor devices. Among them, due to the possibility of ambipolar conduction, tungsten diselenide (WSe₂) provides a platform for the efficient implementation of polarity-controllable transistors. These transistors use an additional gate, named polarity gate, that, due to the electrostatic doping of the Schottky junctions, provides a device-level dynamic control of their polarity, that is, n- or p-type. Here, we experimentally demonstrate a complete doping-free standard cell library realized on WSe₂ without the use of either chemical or physical doping. We show a functionally complete family of complementary logic gates (INV, NAND, NOR, 2-input XOR, 3-input XOR, and MAJ) and, due to the reconfigurable capabilities of the single devices, achieve the realization of highly expressive logic gates, such as exclusive-OR (XOR) and majority (MAJ), with fewer transistors than possible in conventional complementary metal-oxide-semiconductor logic. Our work shows a path to enable doping-free low-power electronics on 2D semiconductors, going beyond the concept of unipolar physically doped devices, while suggesting a road to achieve higher computational densities in two-dimensional electronics

Conformal Deposition of Conductive Single-Crystalline Cobalt Silicide Layer on Si Wafer via a Molecular Approach

The realization of metal–semiconductor contacts plays a significant role in ultrascaled integrated circuits. Here, we establish a low-temperature molecular approach for the conformal deposition of a 20 nm Co-rich layer on Si (100) wafers by reaction in solution of Co₂(CO)₈ with SiH₄. Postannealing at 850 °C under vacuum (∼10^–5 mbar) yields a crystalline CoSi₂ film with a lower surface roughness (<i>R</i>_rms = 5.3 nm) by comparison with the conventional physical method; this layer exhibiting a metallic conductive behavior (ohmic behavior) with a low resistivity (ρ = 11.6 μΩ cm) according to four-point probe measurement. This approach is applicable to trench-structured wafers, showing the conformal layer deposition on 3D structures and showcasing the potential of this approach in modern transistor technology