Biology subjects

Aina, A.

Publications and source records attributed to Aina, A..

4 recordsLinked to original sources

bioRxiv2023

De novo design of a β-helix tau protein scaffold: An oligomer-selective vaccine immunogen candidate for Alzheimer's disease

Tau pathology is associated with many neurodegenetive disorders, including Alzheimers disease (AD), where the spatio-temporal pattern of tau neurofibrillary tangles strongly correlates with disease progression, which motivates therapeutics selective for misfolded tau. Here, we introduce a new avidity-enhanced, multi-epitope approach for protein misfolding immunogen design, which is predicted to mimic the conformational state of an exposed epitope in toxic tau oligomers. A predicted oligomer-selective tau epitope 343KLDFK347 was scaffolded by designing a {beta}-helix structure that incorporated multiple instances of the 16-residue tau fragment 339VKSEKLDFKDRVQSKI354. Largescale conformational ensemble analyses involving Jensen-Shannon Divergence and the embedding depth [D] showed that the multi-epitope scaffolding approach, employed in designing the {beta}-helix scaffold, was predicted to better discriminate toxic tau oligomers than other "monovalent" strategies utilizing a single instance of an epitope for vaccine immunogen design. Using Rosetta, 10,000 sequences were designed and screened for the linker portions of the {beta}-helix scaffold, along with a C-terminal stabilizing -helix that interacts with the linkers, to optimize the folded structure and stability of the scaffold. Structures were ranked by energy, and the lowest 1% (82 unique sequences) were verified using AlphaFold. Several selection criteria involving AlphaFold are implemented to obtain a lead designed sequence. The structure was further predicted to have free energetic stability by using Hamiltonian replica exchange molecular dynamics (MD) simulations. The synthesized {beta}-helix scaffold showed direct binding in surface plasmon resonance (SPR) experiments to several antibodies that were raised to the structured epitope using a designed cyclic peptide. Moreover the strength of binding of these antibodies to in vitro tau oligomers correlated with the strength of binding to the {beta}-helix construct, suggesting that the construct presents an oligomer-like conformation and may thus constitute an effective oligomer-selective immunogen.

bioengineering↗

bioRxiv2023

PROTHON: A Local Order Parameter-Based Method for Efficient Comparison of Protein Ensembles

The comparison of protein conformational ensembles is of central importance in structural biology. However, there are few computational methods for ensemble comparison, and those that are readily available, such as ENCORE, utilize methods that are sufficiently computationally expensive to be prohibitive for large ensembles. Here, a new method is presented for efficient representation and comparison of protein conformational ensembles. The method is based on the representation of a protein ensemble as a vector of probability distribution functions (pdfs), with each pdf representing the distribution of a local structural property such as the number of contacts between C{beta} atoms. Dissimilarity between two conformational ensembles is quantified by the Jensen Shannon distance between the corresponding set of probability distribution functions. The method is validated for conformational ensembles generated by molecular dynamics simulations of ubiquitin, as well as experimentally derived conformational ensembles of a 130 amino acid truncated form of human tau protein. In the ubiquitin ensemble dataset, the method was up to 88 times faster than the existing ENCORE software, while simultaneously utilizing 48 times fewer computing cores. We make the method available as a Python package, called PROTHON, and provide a GitHub page with the Python source code at https://github.com/PlotkinLab/Prothon.

biophysics↗

bioRxiv2022

Accelerated ensemble generation for cyclic peptides using a Reservoir-REMD implementation in GROMACS

Cyclic peptides naturally occur as antibiotics, fungicides, and immunosuppressants, and have been adapted for use as potential therapeutics. Scaffolded cyclic peptide antigens have many protein characteristics such as reduced toxicity, increased stability over linear peptides, and conformational selectivity, but with fewer amino acids than whole proteins. The profile of shapes presented by a cyclic peptide modulates its therapeutic efficacy, and is represented by the ensemble of its sampled conformations. Although some algorithms excel in creating a diverse ensemble of cyclic peptide conformations, they seldom address the entropic contribution of flexible conformations, and they often have significant practical difficulty producing an ensemble with converged and reliable thermodynamic properties. In this study, an accelerated molecular dynamics (MD) method, reservoir replica exchange MD (R-REMD or Res-REMD), was implemented in GROMACS-4.6.7, and benchmarked on three small cyclic peptide model systems: a cyclized segment of A{beta} (cyclo-(CGHHQKLVG)), a cyclized furin cleavage site of SARS-CoV-2 spike (cyclo-(CGPRRARSG)), and oxytocin (disulfide bonded CY-IQNCPLG). Additionally, we also benchmarked Res-REMD on Alanine dipeptide and Trpzip2 to demonstrate its validity and efficiency over REMD. Compared to REMD, Res-REMD significantly accelerated the ensemble generation of cyclo-(CGHHQKLVG), but not cyclo-(CGPRRARSG) or oxytocin. This difference is due to the longer auto-correlation time of torsional angles in cyclo-(CGHHQKLVG) v s. the latter two cyclic peptide systems; The randomly seeded reservoir in Res-REMD thus accelerates sampling and convergence. The auto-correlation time of the torsional angles can thus be used to determine whether Res-REMD is preferable to REMD for cyclic peptides. We provide a github page with modified GROMACS source code for running Res-REMD at https://github.com/PlotkinLab/Reservoir-REMD.

biophysics↗

bioRxiv2021

Epitope scaffolding using alpha-synuclein cyclic peptides to generate oligomer-selective antibodies for Parkinson's disease

Effectively presenting epitopes on immunogens, in order to raise conformationally selective antibodies through active immunization, is a central problem in treating protein misfolding diseases, particularly neurodegenerative diseases such as Alzheimers disease or Parkinsons disease. We seek to selectively target conformations enriched in toxic, oligomeric propagating species while sparing the healthy forms of the protein that are often more abundant. To this end, we computationally modelled scaffolded epitopes in cyclic peptides by inserting/deleting a variable number of flanking glycines ("glycindels"), to best mimic a misfolding-specific conformation of an epitope of -synuclein enriched in the oligomer ensemble, as characterized by a region most readily disordered and solvent-exposed in a stressed, partially denatured protofibril. We screen and rank the cyclic peptide scaffolds of -synuclein in silico based on their ensemble overlap properties with the fibril, oligomer-model, and isolated monomer ensembles. We present experimental data of seeded aggregation that supports nucleation rates consistent with computationally predicted cyclic peptide conformational similarity. We also introduce a method for screening against structured off-pathway targets in the human proteome, by selecting scaffolds with minimal conformational similarity between their epitope and the same solvent-exposed primary sequence in structured human proteins. Different cyclic peptide scaffolds with variable numbers of glycines are predicted computationally to have markedly different conformational ensembles. Ensemble comparison and overlap was quantified by the Jensen-Shannon Divergence, and a new measure introduced here--the embedding depth, which determines the extent to which a given ensemble is subsumed by another ensemble, and which may be a more useful measure in developing immunogens that confer conformational-selectivity to an antibody. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/460126v2_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@2d5927org.highwire.dtl.DTLVardef@138292dorg.highwire.dtl.DTLVardef@1769c8org.highwire.dtl.DTLVardef@121c984_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗