Person:
Paravastu, Anant K.

Associated Organization(s)
ORCID
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Publication Search Results

Now showing 1 - 3 of 3
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    Sequence patterns and signatures: computational and experimental discovery of amyloid-forming peptides dataset
    (Georgia Institute of Technology, 2022-11) Xiao, Xinqing ; Robang, Alicia S. ; Sarma, Sudeep ; Le, Justin V. ; Helmicki, Michael E. ; Lambert, Matthew J. ; Guerrero-Ferreira, Ricardo ; Arboleda-Echavarria, Johana ; Paravastu, Anant K. ; Hall, Carol K.
    Screening amino acid sequence space via experiments to discover peptides that self-assemble into amyloid fibrils is challenging. We have developed a computational peptide assembly design (PepAD) algorithm, that enables the discovery of amyloid-forming peptides. Discontinuous molecular dynamics (DMD) simulation with the PRIME20 force field combined with the FoldAmyloid tool is used to examine the fibrilization kinetics of PepAD-generated peptides. PepAD screening of ∼10,000 7-mer peptides resulted in twelve top-scoring peptides with two distinct hydration properties. Our studies revealed that eight of the twelve in-silico discovered peptides spontaneously form amyloid fibrils in the DMD simulations and that all eight have at least five residues that the FoldAmyloid tool classifies as being aggregation-prone. Based on these observations, we re-examined the PepAD-generated peptides in the sequence pool returned by PepAD and extracted five sequence patterns as well as associated sequence signatures for the 7-mer amyloid-forming peptides. Experimental results from Fourier transform infrared spectroscopy (FTIR), thioflavin T (ThT) fluorescence, circular dichroism (CD), and transmission electron microscopy (TEM) indicate that all the peptides predicted to assemble in-silico assemble into antiparallel β-sheet nanofibers in a concentration-dependent manner. This is the first attempt to use a computational approach to search for amyloid-forming peptides based on customized settings. Our efforts facilitate the identification of β-sheet-based self-assembling peptides, and contribute insights towards answering a fundamental scientific question: “What does it take, sequence-wise, for a peptide to self-assemble?”
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    De Novo Design of Peptides that Co-assemble into β-sheet Based Nanofibrils Dataset
    (Georgia Institute of Technology, 2021) Xiao, Xingqing ; Wang, Yiming ; Seroski, Dillon T. ; Wong, Kong M. ; Liu, Renjie ; Paravastu, Anant K. ; Hudalla, Gregory A. ; Hall, Carol K.
    Peptides’ hierarchical co-assembly into nanostructures enables controllable fabrication of multicomponent biomaterials. In this work, we describe a novel computational and experimental approach to design pairs of charge-complementary peptides that selectively co-assemble into β-sheet nanofibers when mixed together, but remain unassembled when isolated separately. The key advance is a pep_tide _c_o-_a_ssembly _d_esign (PepCAD) algorithm that searches for pairs of co-assembling peptides. Six peptide pairs are identified from a pool of ~106 candidates via the PepCAD algorithm and then subjected to DMD/PRIME20 simulations to examine their co-/self-association kinetics. The five pairs that spontaneously aggregate in kinetic simulations selectively co-assemble in biophysical experiments, with four forming β-sheet nanofibers, and one forming a stable non-fibrillar aggregate. Solid-state NMR, which is applied to characterize the co-assembling pairs, suggests that the _in-silico peptides exhibit a higher degree of structural order than the previously reported CATCH(+/-) peptides.
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    Anatomy of a Selectively Coassembled Beta-sheet Peptide Nanofiber Dataset
    (Georgia Institute of Technology, 2020-01) Shao, Qing ; Wong, Kong M. ; Seroski, Dillon T. ; Wang, Yiming ; Liu, Renjie ; Paravastu, Anant K. ; Hudalla, Gregory A. ; Hall, Carol K.
    Peptide self-assembly, wherein molecule A associates with other A molecules to form fibrillar β-sheet structures, is common in nature and widely used to fabricate synthetic biomaterials. Selective coassembly of peptide pairs A and B with complementary partial charges is gaining interest due to its potential for expanding the form and function of biomaterials that can be realized. It has been hypothesized that charge-complementary peptides organize into alternating ABAB-type arrangements within assembled β-sheets, but no direct molecular-level evidence exists to support this interpretation. We report a computational and experimental approach to characterize molecular-level organization of the established peptide pair, CATCH. Discontinuous molecular dynamics simulations predict that CATCH(+) and CATCH(−) peptides coassemble but do not self-assemble. Two-layer β-sheet amyloid structures predominate, but off-pathway β-barrel oligomers are also predicted. At low concentration, transmission electron microscopy and dynamic light scattering identified nonfibrillar ∼20-nm oligomers, while at high concentrations elongated fibers predominated. Thioflavin T fluorimetry estimates rapid and near-stoichiometric coassembly of CATCH(+) and CATCH(−) at concentrations ≥100 μM. Natural abundance 13C NMR and isotope-edited Fourier transform infrared spectroscopy indicate that CATCH(+) and CATCH(−) coassemble into two-component nanofibers instead of self-sorting. However, 13C–13C dipolar recoupling solid-state NMR measurements also identify nonnegligible AA and BB interactions among a majority of AB pairs. Collectively, these results demonstrate that strictly alternating arrangements of β-strands predominate in coassembled CATCH structures, but deviations from perfect alternation occur. Off-pathway β-barrel oligomers are also suggested to occur in coassembled β-strand peptide systems.