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Computational Design of Peptides as Detectors, Drugs and Biomaterials- [electronic resource]
Computational Design of Peptides as Detectors, Drugs and Biomaterials - [electronic resour...
Contents Info
Computational Design of Peptides as Detectors, Drugs and Biomaterials- [electronic resource]
Material Type  
 학위논문
 
0016935179
Date and Time of Latest Transaction  
20240214101902
ISBN  
9798380716024
DDC  
540
Author  
Sarma, Sudeep.
Title/Author  
Computational Design of Peptides as Detectors, Drugs and Biomaterials - [electronic resource]
Publish Info  
[S.l.] : North Carolina State University., 2023
Publish Info  
Ann Arbor : ProQuest Dissertations & Theses, 2023
Material Info  
1 online resource(259 p.)
General Note  
Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
General Note  
Advisor: Hall, Carol K.
학위논문주기  
Thesis (Ph.D.)--North Carolina State University, 2023.
Restrictions on Access Note  
This item must not be sold to any third party vendors.
Abstracts/Etc  
요약Computational 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.
Subject Added Entry-Topical Term  
Crystal structure.
Subject Added Entry-Topical Term  
Infections.
Subject Added Entry-Topical Term  
Toxicity.
Subject Added Entry-Topical Term  
Severe acute respiratory syndrome coronavirus 2.
Subject Added Entry-Topical Term  
Cytotoxicity.
Subject Added Entry-Topical Term  
Mutation.
Subject Added Entry-Topical Term  
Solvents.
Subject Added Entry-Topical Term  
Glucose.
Subject Added Entry-Topical Term  
Decomposition.
Subject Added Entry-Topical Term  
Amino acids.
Subject Added Entry-Topical Term  
Design.
Subject Added Entry-Topical Term  
Energy.
Subject Added Entry-Topical Term  
COVID-19.
Subject Added Entry-Topical Term  
Disease transmission.
Subject Added Entry-Topical Term  
Cellular biology.
Subject Added Entry-Topical Term  
Toxicology.
Added Entry-Corporate Name  
North Carolina State University.
Host Item Entry  
Dissertations Abstracts International. 85-05B.
Host Item Entry  
Dissertation Abstract International
Electronic Location and Access  
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소장사항  
202402 2024
Control Number  
joongbu:641108
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