HIV-1 gp120 V3 loop : CCR5 Complex Structure;

<p>Molecular graphics image of the entire simulation system corresponding to the complex with the lowest average binding free energy. The V3 loop is shown in tube and transparent surface representation in red color, and the residue moiety 16–20 is shown in fat tube representation. The CCR5 is shown in cartoon representation, and the coloring used for different protein domains is as follows: (i) N-terminal domain is colored in blue, (ii) Transmembrane helix 1 (TH1) is colored in green; (iii) Intracellular loop 1 (ICL1) is colored in light gray; (iv) TH2 is colored in purple, (v) Extracellular loop 1 (ECL1) is colored in light gray; (vi) TH3 is colored in yellow; (vii) ICL2 is colored in light gray; (viii) TH4 is colored in gray; (ix) ECL2 is colored in ochre; (x) TH5 is colored in pink; (xi) ICL3 is colored in light gray; (xii) TH6 is colored in cyan; (xiii) ECL3 is colored in lime; (xiv) TH7 is colored in orange; (xv) C-terminal domain is colored in light gray. The N-terminal Cα atom of CCR5 is shown in a small van der Waals sphere and the V3 loop disulfide bridge is shown in fat transparent licorice representation. The definition of CCR5 and V3 loop domains is presented in <i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095767#pone.0095767.s004" target="_blank">Information S4</a></i>.</p

Elucidating a Key Component of Cancer Metastasis: CXCL12 (SDF-1α) Binding to CXCR4

The chemotactic signaling induced by the binding of chemokine CXCL12 (SDF-1α) to chemokine receptor CXCR4 is of significant biological importance and is a potential therapeutic axis against HIV-1. However, as CXCR4 is overexpressed in certain cancer cells, the CXCL12:CXCR4 signaling is involved in tumor metastasis, progression, angiogenesis, and survival. Motivated by the pivotal role of the CXCL12:CXCR4 axis in cancer, we employed a comprehensive set of computational tools, predominantly based on free energy calculations and molecular dynamics simulations, to obtain insights into the molecular recognition of CXCR4 by CXCL12. We report, what is to our knowledge, the first computationally derived CXCL12:CXCR4 complex structure which is in remarkable agreement with experimental findings and sheds light into the functional role of CXCL12 and CXCR4 residues which are associated with binding and signaling. Our results reveal that the CXCL12 N-terminal domain is firmly bound within the CXCR4 transmembrane domain, and the central 24–50 residue domain of CXCL12 interacts with the upper N-terminal domain of CXCR4. The stability of the CXCL12:CXCR4 complex structure is attributed to an abundance of nonpolar and polar intermolecular interactions, including salt bridges formed between positively charged CXCL12 residues and negatively charged CXCR4 residues. The success of the computational protocol can mainly be attributed to the nearly exhaustive docking conformational search, as well as the heterogeneous dielectric implicit water-membrane-water model used to simulate and select the optimum conformations. We also recently utilized this protocol to elucidate the binding of an HIV-1 gp120 V3 loop in complex with CXCR4, and a comparison between the molecular recognition of CXCR4 by CXCL12 and the HIV-1 gp120 V3 loop shows that both CXCL12 and the HIV-1 gp120 V3 loop share the same CXCR4 binding pocket, as they mostly interact with the same CXCR4 residues

Important Intermolecular Polar Interactions;

<p>Molecular graphics image of important polar interactions corresponding to the complex with the lowest average binding free energy. The figure shows the salt bridges and specific important hydrogen bonds. The V3 loop is shown in tube and in red color, and the residue moiety 16–20 is shown in fat tube representation. The CCR5 is shown in light gray transparent tube representation. The salt bridge and hydrogen bonds are denoted in dashed lines and the participating V3 loop and CCR5 residue moieties are shown in licorice; V3 loop and CCR5 residues are annotated in red and black, color respectively. Hydrogen atoms are omitted for clarity, and the V3 loop disulfide bridge is shown in fat transparent licorice representation.</p

Intermolecular Interaction Free Energies of V3 loop Residues in Complex with CCR5/CXCR4;

<p>Average intermolecular interaction free energies (y-axis) of V3 loop residues (x-axis). The intermolecular interaction free energies for every V3 loop residue are summed up for all interacting residues of CCR5 (first bar per residue) and CXCR4 (second bar per residue). The polar contribution is denoted in red and green color, for CCR5 and CXCR4, respectively, and the non-polar contribution is denoted in blue and black color, for CCR5 and CXCR4, respectively. The total interaction free energy of each V3 loop residue corresponds to the sum of polar and non-polar contributions.</p

<em>β</em>-sheet Topology Prediction with High Precision and Recall for <em>β</em> and Mixed <em>α/β</em> Proteins

<div><p>The prediction of the correct -sheet topology for pure and mixed proteins is a critical intermediate step toward the three dimensional protein structure prediction. The predicted beta sheet topology provides distance constraints between sequentially separated residues, which reduces the three dimensional search space for a protein structure prediction algorithm. Here, we present a novel mixed integer linear optimization based framework for the prediction of -sheet topology in and mixed proteins. The objective is to maximize the total strand-to-strand contact potential of the protein. A large number of physical constraints are applied to provide biologically meaningful topology results. The formulation permits the creation of a rank-ordered list of preferred -sheet arrangements. Finally, the generated topologies are re-ranked using a fully atomistic approach involving torsion angle dynamics and clustering. For a large, non-redundant data set of 2102 and mixed proteins with at least 3 strands taken from the PDB, the proposed approach provides the top 5 solutions with average precision and recall greater than 78%. Consistent results are obtained in the -sheet topology prediction for blind targets provided during the CASP8 and CASP9 experiments, as well as for actual and predicted secondary structures. The -sheet topology prediction algorithm, BeST, is available to the scientific community at <a href="http://selene.princeton.edu/BeST/">http://selene.princeton.edu/BeST/</a>.</p> </div

Molecular Recognition of CCR5 by an HIV-1 gp120 V3 Loop

<div><p>The binding of protein HIV-1 gp120 to coreceptors CCR5 or CXCR4 is a key step of the HIV-1 entry to the host cell, and is predominantly mediated through the V3 loop fragment of HIV-1 gp120. In the present work, we delineate the molecular recognition of chemokine receptor CCR5 by a dual tropic HIV-1 gp120 V3 loop, using a comprehensive set of computational tools predominantly based on molecular dynamics simulations and free energy calculations. We report, what is to our knowledge, the first complete HIV-1 gp120 V3 loop : CCR5 complex structure, which includes the whole V3 loop and the N-terminus of CCR5, and exhibits exceptional agreement with previous experimental findings. The computationally derived structure sheds light into the functional role of HIV-1 gp120 V3 loop and CCR5 residues associated with the HIV-1 coreceptor activity, and provides insights into the HIV-1 coreceptor selectivity and the blocking mechanism of HIV-1 gp120 by maraviroc. By comparing the binding of the specific dual tropic HIV-1 gp120 V3 loop with CCR5 and CXCR4, we observe that the HIV-1 gp120 V3 loop residues 13–21, which include the tip, share nearly identical structural and energetic properties in complex with both coreceptors. This result paves the way for the design of dual CCR5/CXCR4 targeted peptides as novel potential anti-AIDS therapeutics.</p></div

Important intermolecular polar and non-polar interaction free energies, hydrogen bonds, salt bridges, between V3 loop and CCR5 residue pairs within the MD simulation of the complex with the lowest average binding free energy (see <i>Methods</i>).

<p>Important intermolecular polar and non-polar interaction free energies, hydrogen bonds, salt bridges, between V3 loop and CCR5 residue pairs within the MD simulation of the complex with the lowest average binding free energy (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095767#s2" target="_blank"><i>Methods</i></a>).</p

The number of motifs possible for a protein with strands ().

<p>The number of motifs possible for a protein with strands ().</p

Synthesis of flexible heat exchanger networks for multiperiod operation

Technical Repor

A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: II. Probabilistic Guarantees on Constraint Satisfaction

Probabilistic guarantees on constraint satisfaction for robust counterpart optimization are studied in this paper. The robust counterpart optimization formulations studied are derived from box, ellipsoidal, polyhedral, “interval+ellipsoidal”, and “interval+polyhedral” uncertainty sets (Li, Z.; Ding, R.; Floudas, C.A. A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear and Robust Mixed Integer Linear Optimization. Ind. Eng. Chem. Res. 2011, 50, 10567). For those robust counterpart optimization formulations, their corresponding probability bounds on constraint satisfaction are derived for different types of uncertainty characteristic (i.e., bounded or unbounded uncertainty, with or without detailed probability distribution information). The findings of this work extend the results in the literature and provide greater flexibility for robust optimization practitioners in choosing tighter probability bounds so as to find less conservative robust solutions. Extensive numerical studies are performed to compare the tightness of the different probability bounds and the conservatism of different robust counterpart optimization formulations. Guiding rules for the selection of robust counterpart optimization models and for the determination of the size of the uncertainty set are discussed. Applications in production planning and process scheduling problems are presented