A method for probing the mutational landscape of amyloid structure

Motivation: Proteins of all kinds can self-assemble into highly ordered β-sheet aggregates known as amyloid fibrils, important both biologically and clinically. However, the specific molecular structure of a fibril can vary dramatically depending on sequence and environmental conditions, and mutations can drastically alter amyloid function and pathogenicity. Experimental structure determination has proven extremely difficult with only a handful of NMR-based models proposed, suggesting a need for computational methods. Results: We present AmyloidMutants, a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability. Tested on non-mutant, full-length amyloid structures with known chemical shift data, AmyloidMutants offers roughly 2-fold improvement in prediction accuracy over existing tools. Moreover, AmyloidMutants is the only method to predict complete super-secondary structures, enabling accurate discrimination of topologically dissimilar amyloid conformations that correspond to the same sequence locations. Applied to mutant prediction, AmyloidMutants identifies a global conformational switch between Aβ and its highly-toxic ‘Iowa’ mutant in agreement with a recent experimental model based on partial chemical shift data. Predictions on mutant, yeast-toxic strains of HET-s suggest similar alternate folds. When applied to HET-s and a HET-s mutant with core asparagines replaced by glutamines (both highly amyloidogenic chemically similar residues abundant in many amyloids), AmyloidMutants surprisingly predicts a greatly reduced capacity of the glutamine mutant to form amyloid. We confirm this finding by conducting mutagenesis experiments.National Institutes of Health (U.S.) (grant 1R01GM081871)National Institutes of Health (U.S.) (grant GM25874

All-Atom Modeling of Protein Folding and Aggregation

Theoretical investigations of biorelevant processes in the life-science research require highly optimized simulation methods. Therefore, massively parallel Monte Carlo algorithms, namely MTM, were successfully developed and applied to the field of reversible protein folding allowing the thermodynamic characterization of proteins on an atomistic level. Further, the formation process of trans-membrane pores in the TatA system could be elucidated and the structure of the complex could be predicted

Capturing Atomic Interactions with a Graphical Framework in Computational Protein Design

A protein's amino acid sequence determines both its chemical and its physical structures, and together these two structures determine its function. Protein designers seek new amino acid sequences with chemical and physical structures capable of performing some function. The vast size of sequence space frustrates efforts to find useful sequences. Protein designers model proteins on computers and search through amino acid sequence space computationally. They represent the three-dimensional structures for the sequences they examine, specifying the location of each atom, and evaluate the stability of these structures. Good structures are tightly packed but are free of collisions. Designers seek a sequence with a stable structure that meets the geometric and chemical requirements to function as desired; they frame their search as an optimization problem. In this dissertation, I present a graphical model of the central optimization problem in protein design, the side-chain-placement problem. This model allows the formulation of a dynamic programming solution, thus connecting side-chain placement with the class of NP-complete problems for which certain instances admit polynomial time solutions. Moreover, the graphical model suggests a natural data structure for storing the energies used in design. With this data structure, I have created an extensible framework for the representation of energies during side-chain-placement optimization and have incorporated this framework into the Rosetta molecular modeling program. I present one extension that incorporates a new degree of structural variability into the optimization process. I present another extension that includes a non-pairwise decomposable energy function, the first of its kind in protein design, laying the ground-work to capture aspects of protein stability that could not previously be incorporated into the optimization of side-chain placement

Rigidity Analysis for Modeling Protein Motion

Protein structure and motion plays an essential role in nearly all forms of life. Understanding both protein folding and protein conformational change can bring deeper insight to many biochemical processes and even into some devastating diseases thought to be the result of protein misfolding. Experimental methods are currently unable to capture detailed, large-scale motions. Traditional computational approaches (e.g., molecular dynamics and Monte Carlo simulations) are too expensive to simulate time periods long enough for anything but small peptide fragments. This research aims to model such molecular movement using a motion framework originally developed for robotic applications called the Probabilistic Roadmap Method. The Probabilistic Roadmap Method builds a graph, or roadmap, to model the connectivity of the movable object?s valid motion space. We previously applied this methodology to study protein folding and obtained promising results for several small proteins. Here, we extend our existing protein folding framework to handle larger proteins and to study a broader range of motion problems. We present a methodology for incrementally constructing roadmaps until they satisfy a set of evaluation criteria. We show the generality of this scheme by providing evaluation criteria for two types of motion problems: protein folding and protein transitions. Incremental Map Generation eliminates the burden of selecting a sampling density which in practice is highly sensitive to the protein under study and difficult to select. We also generalize the roadmap construction process to be biased towards multiple conformations of interest thereby allowing it to model transitions, i.e., motions between multiple known conformations, instead of just folding to a single known conformation. We provide evidence that this generalized motion framework models large-scale conformational change more realistically than competing methods. We use rigidity theory to increase the efficiency of roadmap construction by introducing a new sampling scheme and new distance metrics. It is only with these rigidity-based techniques that we were able to detect subtle folding differences between a set of structurally similar proteins. We also use it to study several problems related to protein motion including distinguishing secondary structure formation order, modeling hydrogen exchange, and folding core identification. We compare our results to both experimental data and other computational methods

Prediksi Stabilitas Mucroporin sebagai Kandidat Obat Berbasis Peptida melalui Simulasi Dinamika Molekular

Beberapa peptida yang terkandung dalam racun kalajengking (Lychas mucronatus) menunjukkan beragam aktivitas biologis dengan spesifisitas tinggi terhadap target. Peptida ini memiliki efek potensial terhadap mikroba dan menunjukkan potensi untuk memodulasi berbagai mekanisme biologis yang terlibat dalam imunitas, saraf, kardiovaskular, dan penyakit neoplastik. Keragaman struktural dan fungsional yang penting dari peptida tersebut membuktikan bahwa peptida dari racun kalajengking dapat digunakan dalam pengembangan obat spesifik baru. Melalui penelitian ini akan dilakukan identifikasi, evaluasi, dan eksplorasi terhadap stabilitas peptida Mucroporin yang diproduksi dari racun kalajengking dengan menggunakan simulasi dinamika molekular. Sekuens molekul peptida Mucroporin dimodelkan dengan menggunakan server PEPstrMOD. Konformasi terbaik hasil pemodelan dipilih untuk diamati stabilitasnya dengan menggunakan software Gromacs 2016.3. Trajektori yang terbentuk kemudian dianalisis berdasarkan visulasiasi dengan menggunakan software VMD 1.9.4 serta dilakukan analisis grafik RMSD dan RMSF. Hasil analisis trajektori dari simulasi dinamika molekular membuktikan bahwa molekul peptida Mucroporin-S2 memiliki stabilitas yang paling baik. Dengan demikian, molekul peptida tersebut diprediksi dapat dipilih sebagai kandidat obat berbasis peptida

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Mathematical models of cellular signaling and supramolecular self-assembly

Synthetic biologists endeavor to predict how the increasing complexity of multi-step signaling cascades impacts the fidelity of molecular signaling, whereby cellular state information is often transmitted with proteins diffusing by a pseudo-one-dimensional stochastic process. We address this problem by using a one-dimensional drift-diffusion model to derive an approximate lower bound on the degree of facilitation needed to achieve single-bit informational efficiency in signaling cascades as a function of their length. We find that a universal curve of the Shannon-Hartley form describes the information transmitted by a signaling chain of arbitrary length and depends upon only a small number of physically measurable parameters. This enables our model to be used in conjunction with experimental measurements to aid in the selective design of biomolecular systems. Another important concept in the cellular world is molecular self-assembly. As manipulating the self-assembly of supramolecular and nanoscale constructs at the single-molecule level increasingly becomes the norm, new theoretical scaffolds must be erected to replace the classical thermodynamic and kinetics-based models. The models we propose use state probabilities as its fundamental objects and directly model the transition probabilities between the initial and final states of a trajectory. We leverage these probabilities in the context of molecular self-assembly to compute the overall likelihood that a specified experimental condition leads to a desired structural outcome. We also investigated a larger complex self-assembly system, the heterotypic interactions between amyloid-beta and fatty acids by an independent ensemble kinetic simulation using an underlying differential equation-based system which was validated by biophysical experiments

Biological and structure characterisation of eukaryotic prefoldin

Prefoldin is a hexameric protein complex ubiquitously expressed and found to influence the conformation of amyloidogenic peptides. Relatively high degrees of sequence identity and conservation across evolutionary lineages are observed, however differences in binding abilities have been noted between the homologs. This thesis describes work examining the structure of eukaryotic prefoldin and its biological activities with respect to interaction with amyloid β. The structure and biological activities of prefoldin’s individual subunits are also explored. Although many studies have investigated the structure of prokaryotic prefoldin, there is limited information available for eukaryotic prefoldin. Two-dimensional ¹H-¹H and ¹H-¹³C nuclear magnetic resonance (NMR) spectroscopy was utilised to probe the structure of both α and β human prefoldin subunits. The data revealed the highly alpha helical secondary structure of the subunits, which was further verified through far-UV circular dichroism. Further thermal aggregation assays utilising this technique have demonstrated the stability of the prefoldin subunits. The biological effect of prefoldin on the amyloid fibril formation of the Alzheimer’s disease related amyloid β peptide was investigated using a combination of dye-binding assays and cytotoxicity assays. The presence and absence of fibrils was confirmed by transmission electron microscopy. In terms of fibril formation, prefoldin and its subunits prevented in vitro conversion of the amyloid β peptide to amyloid fibrils. In some cases, total inhibition of fibril formation occurred and a 3-(4,5- dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was conducted on the resultant products. The product was incubated with healthy PC-12 cells and induced cellular death, therefore establishing the cytotoxicity of the resultant oligomeric amyloid β form. Previous investigations into the binding capabilities of prokaryotic prefoldin identified the distal tips as an important structural aspect, interacting with the amyloidogenic peptide. The binding interface of prefoldin subunits 5 and 6 with amyloid β was probed using chemical cross-linking (CXL) experiments. Traditional methods to identify cross-linked peptides are challenging and the results are often ambiguous. In this study, CXL products were analysed by liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) to investigate the utility of IM in enhancing the CXL analytical workflow. The orthogonal separation of ion mobility enabled the identification of the cross-linked amino acids. The distal end of prefoldin subunit 5 was found to interact with the Nterminus of the amyloid peptide, whereas prefoldin subunit 6 was identified to interact with the peptide in the middle of its sequence. Ion mobility-mass spectrometry (IM-MS) analysis of the eukaryotic prefoldin complex identified the collisional cross section of the intact hexamer. Solution disruption experiments of the intact complex revealed the disengaging sub-complexes, and information on the intersubunit contacts and relative interfacial strengths were obtained. A capillary temperature controller (CTC) was developed to observe the thermal dissociation of the complex using nano-electrospray IM-MS. The combination of these results confirmed a structural aspect common to both mammalian prefoldin and prokaryotic prefoldin, despite the primary sequence differences. The biological assays revealed the ability of prefoldin to prevent the aggregation and amyloid fibril formation of amyloid β, and low resolution MS techniques were able to postulate the arrangement of the subunits and the possible interface interactions of the hexameric complex with the amyloidogenic peptide. This thesis has therefore provided an in-depth investigation of the structural characteristics of eukaryotic prefoldin and its chaperoning capability, therefore implicating a potential role for prefoldin in modulating protein misfolding and aggregation.Thesis (Ph.D.) -- University of Adelaide, School of Physical Sciences, 201

Navigating the Extremes of Biological Datasets for Reliable Structural Inference and Design

Structural biologists currently confront serious challenges in the effective interpretation of experimental data due to two contradictory situations: a severe lack of structural data for certain classes of proteins, and an incredible abundance of data for other classes. The challenge with small data sets is how to extract sufficient information to draw meaningful conclusions, while the challenge with large data sets is how to curate, categorize, and search the data to allow for its meaningful interpretation and application to scientific problems. Here, we develop computational strategies to address both sparse and abundant data sets. In the category of sparse data sets, we focus our attention on the problem of transmembrane (TM) protein structure determination. As X-ray crystallography and NMR data is notoriously difficult to obtain for TM proteins, we develop a novel algorithm which uses low-resolution data from protein cross-linking or scanning mutagenesis studies to produce models of TM helix oligomers and show that our method produces models with an accuracy on par with X-ray crystallography or NMR for a test set of known TM proteins. Turning to instances of data abundance, we examine how to mine the vast stores of protein structural data in the Protein Data Bank (PDB) to aid in the design of proteins with novel binding properties. We show how the identification of an anion binding motif in an antibody structure allowed us to develop a phosphate binding module that can be used to produce novel antibodies to phosphorylated peptides - creating antibodies to 7 novel phospho-peptides to illustrate the utility of our approach. We then describe a general strategy for designing binders to a target protein epitope based upon recapitulating protein interaction geometries which are over-represented in the PDB. We follow this by using data describing the transition probabilities of amino acids to develop a novel set of degenerate codons to create more efficient gene libraries. We conclude by describing a novel, real-time, all-atom structural search engine, giving researchers the ability to quickly search known protein structures for a motif of interest and providing a new interactive paradigm of protein design