Retraction notice to “A comparison among five equations of state in predicting the inversion curve of some fluids” [Cryogenics 43(7) (2003) 393–398]

Cryogenics ◽  
2021 ◽  
Vol 114 ◽  
pp. 103277
Author(s):  
Behzad Haghighi ◽  
Mohammad Reza Laee ◽  
Naser Seyed Matin
Keyword(s):  
Equations Of State ◽  
Inversion Curve ◽  
Retraction Notice

Prediction of the Joule–Thomson inversion curve of air from cubic equations of state

Cryogenics ◽  
1998 ◽  
Vol 38 (7) ◽  
pp. 721-728 ◽  
Author(s):  
Coray M Colina ◽  
Claudio Olivera-Fuentes
Keyword(s):  
Equations Of State ◽  
Inversion Curve

RETRACTED: A comparison among five equations of state in predicting the inversion curve of some fluids

Cryogenics ◽  
2003 ◽  
Vol 43 (7) ◽  
pp. 393-398 ◽  
Author(s):  
Behzad Haghighi ◽  
Mohammad Reza Laee ◽  
Naser Seyed Matin
Keyword(s):  
Equations Of State ◽  
Inversion Curve

TPT2 and SAFTD equations of state for mixtures of hard chain copolymers

2000 ◽  
Vol 98 (24) ◽  
pp. 2045-2052
Author(s):  
Keshawa P. Shukla, Walter G. Chapman
Keyword(s):  
Equations Of State

RETRACTION NOTICE

2020 ◽  
Vol 06 (02) ◽  
pp. 130-130
Author(s):  
Isha Patel
Keyword(s):  
Retraction Notice

Computation of flows with arbitrary equations of state

AIAA Journal ◽  
1998 ◽  
Vol 36 ◽  
pp. 515-521 ◽  
Author(s):  
Charles L. Merkle ◽  
Philip E. O. Buelow ◽  
Sankaran Venkateswaran ◽  
Jennifer Y. Sullivan
Keyword(s):  
Equations Of State

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

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