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Computational Design of Peptides as Detectors, Drugs and Biomaterials.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Computational Design of Peptides as Detectors, Drugs and Biomaterials./
Author:	Sarma, Sudeep.
Description:	1 online resource (259 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
Subject:	Crystal structure. -
Online resource:	click for full text (PQDT)
ISBN:	9798380716024

Computational Design of Peptides as Detectors, Drugs and Biomaterials.
Sarma, Sudeep.

Computational Design of Peptides as Detectors, Drugs and Biomaterials. - 1 online resource (259 pages)

Source: Dissertations Abstracts International, Volume: 85-05, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2023.

Includes bibliographical references

Computational design of peptides that can (a) recognize/bind to specific protein interfaces and (b) self-assemble into nano-scale architectures such as β-sheet-based fibrils (amyloid) have potential applications in healthcare and advanced biomaterial fabrication. This dissertation focusses on novel computational protocols and their applications to discover peptides that bind to protein targets and form amyloid structures. The key computational steps involved in these protocols are Monte-Carlo based peptide design algorithms (a peptide binding design algorithm, PepBD and a peptideassembly design algorithm , PepAD) to identify potential protein-binding peptides and self-assembling peptides, respectively. Additionally, atomistic molecular dynamics (MD) simulations are used to assess the stability of the designed peptide fibrils and the binding free energy of the peptide-protein complexes. Discontinuous molecular dynamics (DMD) simulation in conjunction with a coarse-grained model, PRIME20, is employed specifically for designing self-assembling peptides to reveal kinetic pathways for fibril formation.Our first study uses our PepBD algorithm and atomistic MD simulations to design peptides that can bind to the SARS-CoV-2 virus. Peptides capable of binding to the Spike-RBD of the Wuhan-Hu-1 strain were identified. In experimental evaluation, three of the five peptides that were synthesized bound to the Spike-RBD of the Wuhan-Hu-1 strain with dissociation constants in the micromolar range, but none of the peptides could outcompete the ACE2:RBD interactions. One peptide, P4, also bound to the SARS-CoV-2 RBD of Kappa-B.1.617.1 and Delta-B.1.617.2 with micromolar affinity. These results demonstrate our ability to design peptides that can recognize the broad spectrum of SARS-CoV-2 RBD variants.Next, we utilized our computational protocol to design 10-mer peptide inhibitors that block Clostridioides difficile toxin A in intestinal cells. We identified peptides that bind to the catalytic site of the Toxin A glucosyltransferase domain (GTD). Two of our in-silicodesigned peptides (NPA and NPB) exhibited lower binding free energies when bound to the TcdA GTD than a reference peptide, RP. In vitro experiments on human jejunum cells confirmed the toxinneutralizing properties of RP and NPA, but the efficacy of NPB was relatively low.Subsequently, we utilized our computational protocol to design 8-mer peptide inhibitors that block C. diff. toxin A in colon cells. (The 10-mer peptides that we designed previously for C. diff. TcdA neutralized C. diff. TcdA toxicity in small intestinal cells but showed no effect in the colon cells). Here, we developed 8-mer peptide inhibitors that block toxin A in both small intestinal cells and colon epithelial cells. Importantly, the designed peptide, SA1, demonstrated neutralization properties against toxin A toxicity in both the small intestine (SI) and colon; it bound toxin A with affinity binding constant KD= 56 ± 29.8 nM.Next, we developed a peptide assembly design (PepAD) algorithm to design peptides that form amyloid-like structures. DMD simulations were employed to reveal the kinetic pathway of fibril formation taken by the in-silicodiscovered peptides. We focused our efforts on designing peptides that form antiparallel amyloid-like structures, specifically the Class 8 cross-β spine described by Sawaya et al. Twelve 7-mer peptides capable of self-assembling into the desired structure were identified. DMD simulations revealed that eight of these peptides spontaneously form amyloid fibrils. Experimental tests confirmed the formation of antiparallel β-sheets at concentrations between 0.2 mM and 10 mM at room temperature in water.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380716024Subjects--Topical Terms:

1372699
Crystal structure.
Index Terms--Genre/Form:

554714
Electronic books.

Computational Design of Peptides as Detectors, Drugs and Biomaterials.
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502 $a Thesis (Ph.D.)--North Carolina State University, 2023.
504 $a Includes bibliographical references
520 $a Computational design of peptides that can (a) recognize/bind to specific protein interfaces and (b) self-assemble into nano-scale architectures such as β-sheet-based fibrils (amyloid) have potential applications in healthcare and advanced biomaterial fabrication. This dissertation focusses on novel computational protocols and their applications to discover peptides that bind to protein targets and form amyloid structures. The key computational steps involved in these protocols are Monte-Carlo based peptide design algorithms (a peptide binding design algorithm, PepBD and a peptideassembly design algorithm , PepAD) to identify potential protein-binding peptides and self-assembling peptides, respectively. Additionally, atomistic molecular dynamics (MD) simulations are used to assess the stability of the designed peptide fibrils and the binding free energy of the peptide-protein complexes. Discontinuous molecular dynamics (DMD) simulation in conjunction with a coarse-grained model, PRIME20, is employed specifically for designing self-assembling peptides to reveal kinetic pathways for fibril formation.Our first study uses our PepBD algorithm and atomistic MD simulations to design peptides that can bind to the SARS-CoV-2 virus. Peptides capable of binding to the Spike-RBD of the Wuhan-Hu-1 strain were identified. In experimental evaluation, three of the five peptides that were synthesized bound to the Spike-RBD of the Wuhan-Hu-1 strain with dissociation constants in the micromolar range, but none of the peptides could outcompete the ACE2:RBD interactions. One peptide, P4, also bound to the SARS-CoV-2 RBD of Kappa-B.1.617.1 and Delta-B.1.617.2 with micromolar affinity. These results demonstrate our ability to design peptides that can recognize the broad spectrum of SARS-CoV-2 RBD variants.Next, we utilized our computational protocol to design 10-mer peptide inhibitors that block Clostridioides difficile toxin A in intestinal cells. We identified peptides that bind to the catalytic site of the Toxin A glucosyltransferase domain (GTD). Two of our in-silicodesigned peptides (NPA and NPB) exhibited lower binding free energies when bound to the TcdA GTD than a reference peptide, RP. In vitro experiments on human jejunum cells confirmed the toxinneutralizing properties of RP and NPA, but the efficacy of NPB was relatively low.Subsequently, we utilized our computational protocol to design 8-mer peptide inhibitors that block C. diff. toxin A in colon cells. (The 10-mer peptides that we designed previously for C. diff. TcdA neutralized C. diff. TcdA toxicity in small intestinal cells but showed no effect in the colon cells). Here, we developed 8-mer peptide inhibitors that block toxin A in both small intestinal cells and colon epithelial cells. Importantly, the designed peptide, SA1, demonstrated neutralization properties against toxin A toxicity in both the small intestine (SI) and colon; it bound toxin A with affinity binding constant KD= 56 ± 29.8 nM.Next, we developed a peptide assembly design (PepAD) algorithm to design peptides that form amyloid-like structures. DMD simulations were employed to reveal the kinetic pathway of fibril formation taken by the in-silicodiscovered peptides. We focused our efforts on designing peptides that form antiparallel amyloid-like structures, specifically the Class 8 cross-β spine described by Sawaya et al. Twelve 7-mer peptides capable of self-assembling into the desired structure were identified. DMD simulations revealed that eight of these peptides spontaneously form amyloid fibrils. Experimental tests confirmed the formation of antiparallel β-sheets at concentrations between 0.2 mM and 10 mM at room temperature in water.
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