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ItemSequence 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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ItemDe 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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ItemAnatomy 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.
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ItemAnalysis of Multicomponent Ionic Mixtures using Blind Source Separation - a Processing Case Study Dataset(Georgia Institute of Technology, 2019-08) Maggioni, Giovanni MariaManagement and remediation of complex nuclear waste solutions require identification and quantification of multiple species. Some of the species forming the solution are unknown and they can be different from vessel to vessel, thus limiting the utility of standard calibration approaches. To cope with such limited information, we propose a procedure based on blind source separation (BSS) techniques, in particular independent component analysis and multivariate curve resolution, with a one-point calibration library. Here we show the applicability and reliability of our procedure for on-line measurements of aqueous ionic solutions by proposing an automatic procedure to identify the number of species in the mixture, estimate the spectra of the pure species, and label the spectra with respect to a library of reference components. We test our procedure against simulated and experimental data for mixtures with six species (water plus five sodium salts) for the case of Raman and ATR-FTIR spectroscopy.
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ItemRaw Data for the Elastic Modulus of Sporopollenin(Georgia Institute of Technology, 2017-10-27) Qu, Zihao ; Meredith, J. CarsonSporopollenin, the polymer comprising the exine (outer solid shell) of pollens, is recognized as one of the most chemically- and mechanically-stable naturally-occurring organic substances. The elastic modulus of sporopollenin is of great importance to understanding the adhesion, transport, and protective functions of pollen grains. In addition, this fundamental mechanical property is of significant interest in using pollen exine as materials for drug delivery, reinforcing fillers, sensors, and adhesives. Yet, the literature reports of sporopollenin modulus are very limited. We provide the first report of the elastic modulus of sporopollenin of pollen particles from three plant species: ragweed (Ambrosia artemisiifolia), pecan (Carya illinoinensis) and Kentucky bluegrass (Poa pratensis). Modulus was determined with atomic force microscopy by using direct nanomechanical mapping of the pollen shell surface. The moduli were atypically high for noncrystalline organic biomaterials, with average values of 16 ± 2.5 GPa (ragweed), 9.5 ± 2.3 GPa (pecan) and 16 ± 4.0 GPa (Kentucky bluegrass). The amorphous pollen exine has a modulus exceeding all non-crystalline biomaterials, such as lignin (6.7 GPa) and actin (1.8 GPa). In addition to native pollens, we have investigated the effects of exposure to a common preparative acid-base chemical treatment and elevated humidity on modulus. Acid-base treatment reduced the ragweed modulus by up to 58% and water vapor exposure at 90% relative humidity reduced the modulus by 54% (pecan) and 72% (Kentucky bluegrass).
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ItemTraining and Validation Data for Automated Head versus Tail Classification and Cell Identification in C. elegans(Georgia Institute of Technology, 2015) Zhan, Mei ; Crane, Matthew Muria ; Lu, Hang ; Ch'ng, QueeLim ; Entchev, Eugeni ; Caballero, Antonio ; de Abreu, Diana Andrea FernandesWe demonstrate the utility of a generalizable classification routine for biological image processing by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. These data files contain all of the training and validation data used for the construction and validation of these classifiers.