Frustration in Biomolecules

Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.Comment: 97 pages, 30 figure

Structure-based function assignment for archaellum regulatory network proteins of Sulfolobus acidocaldarius

Der Ursprung des Lebens ist ein komplexes Thema, das weltweit wissenschaftliche Diskussionen befeuert. Im Allgemeinen ist es akzeptiert, dass die Endosymbiose eine zentrale Rolle bei der Eukaryogenese spielt. Für diese Arbeit wurde die inside-out Hypothese, welche sich auf die Asgard Archaeen stützt und ein frühes Endomembransystem voraussetzt, als Ausgangslage angenommen. Da Asgard Archaeen noch nicht kultiviert wurden, stellen die TACK Archaeen mit ihrem archaealen Modellorganismus Sulfolobus acidocaldarius einen der nächsten, verfügbaren Verwandten der Eukaryoten dar. Interessanterweise wurde das Protein ArnB des archaellum regulatory networks (Arn), welches strukturelle Homologien mit dem Sec23/24 Protein des COPII-systems aufweist, in S. acidocaldarius Exosomen gefunden. Entsprechend erweist sich ArnB als vielversprechendes Protein, um den prä-endosymbiotischen Transport in Archaeen zu untersuchen. Diese Arbeit umfasst eine Zusammenfassung der Forschungsergebnisse zu ArnB und seinen assoziierten Proteinen. Hervorzuheben ist dabei die Untersuchung der Interaktion von ArnB mit dem ZnF- und FHA-Domänen enthaltenden ArnA, welche eine sequenzielle, phosphorylierungsabhängige strukturelle Transition mit globalen Änderungen offenbart. Die Untersuchung des ArnAB-Komplexes ergab zum einen neuartigen sequenziellen Phosphorylierungsmechanismus und zeigte die Gegenwart des Sec23/24-Kernmotivs in allen Domänen des Lebens auf. In Zuge dieser Analysen, konnten dem ArnAB-Komplex bzw. dem Sec23/24-Kernmotiv zahlreiche potenzielle Funktionen zugeschrieben werden, die von der Beteiligung am Ubiquitin-Netzwerk bis zur Bildung von COPII-Vesikeln und Assemblierungsrollen reichen. Zudem beinhaltet diese Arbeit die Charakterisierung einer GPN-Loop-GTPase aus S. acidocaldarius, SaGPN, welche ebenfalls mit dem Arn-System assoziiert ist und potentiell mit dem ArnAB-Komplex interagiert. GPN-Loop-GTPasen stellen einen eigenen interessanten GTPase-Typ dar, der durch nukleotidunabhängige Homodimere gekennzeichnet ist. Durch die Kristallstrukturen die in dieser Arbeit gelöst wurden, lassen sich mechanistische Unklarheiten im Zusammenhang mit GPN-Loop-GTPasen lösen und ein Lock-Switch-Rock-Mechanismus für archaeale GPN-Loop-GTPasen ableiten. Zusammenfassend trägt diese Arbeit zum allgemeinen Verständnis der GPN-Loop- GTPasen bei und hebt das ArnAB-Sec23/24-Kernmotiv als eine der häufigsten und funktional vielfältigsten Protein-Faltungen in allen Bereichen des Lebens hervor

Using Structural Bioinformatics to Model and Design Membrane Proteins

Cells require membrane proteins for a wide spectrum of critical functions. Transmembrane proteins enable cells to communicate with its environment, catalysis, ion transport and scaffolding. The functional roles of membrane proteins are specified by their sequence composition and precise three dimensional folding. The exact mechanisms driving folding of membrane proteins is still not fully understood. Further, the association between membrane proteins occurs with pinpoint specificity. For example, there exists common sequence features within families of transmembrane receptors, yet there is little cross talk between families. Therefore, we ask how membrane proteins dial in their specificity and what factors are responsible for adoption of native structure. Advancements in membrane protein structure determination methods has been followed by a sharp increase in three dimensional structures. Structural bioinfomatics has been utilized effectively to study water soluble proteins. The field is now entering an era where structural bioinformatics can be applied to modeling membrane proteins without structure and engineering novel membrane proteins. The transmembrane domains of membrane proteins were first categorized structurally. From this analysis, we are able to describe the ways in which membrane proteins fold and associate. We further derived sequence profiles for the commonly occurring structural motifs, enabling us to investigate the role of amino acids within the bilayer. Utilizing these tools, a transmembrane structural model was constructed of principle cell surface receptors (integrins). The structural model enabled understanding of possible mechanisms used to signal and to propose a novel membrane protein packing motif. In addition, novel scoring functions for membrane proteins were developed and applied to modeling membrane proteins. We derived the first all-atom membrane statistical potential and introduced the usage of exposed volume. These potentials allowed modeling of complex interactions in membrane proteins, such as salt bridges. To understand the geometric preferences of salt bridges, we surveyed a structural database. We learned about large biases in salt bridge orientations that will be useful in modeling and design. Lastly, we combine these structural bioinformatic efforts, enabling us to model membrane proteins in ways which were previously inaccessible

UNDERSTANDING MOLECULAR INTERACTIONS BETWEEN PROTEINS AND CARBON NANOMATERIALS

Over the past few years there has been a rise in the number of nanomaterials engineered for a wide array of applications because of their unique properties. This rise in the development of engineered nanomaterials (ENMs) and its growing usage has also raised questions about its potential impact on the biological environment. Recent experimental studies suggest that these ENMs could be toxic due to the formation of a protein corona. Therefore, understanding the formation of a protein corona would provide some insights into the toxic behavior of ENMs. This requires understanding the interactions between proteins and ENMs. We employ molecular dynamics simulations to explore the factors and governing forces influencing interactions between carbon nanomaterials (CNMs) and proteins. We first study the interactions between bovine serum albumin (BSA) and a set of CNMs that are of varying shape and surface chemistry. These CNMs include a single walled carbon nanotube (SWCNT), a graphene nanoribbon (GNR) and a graphene oxide nanoribbon (GONR). Our results indicate that BSA interacted with all the three CNMs and its interaction strength follows the order GNR\u3eSWCNT~GONR. During this interaction, we found a strong correlation between the interaction energy and the number of heavy atoms of BSA near the CNM. However, there are no significant changes in the secondary structure content and all α helices are stable during this interaction in the timescales of our simulations. We have also not observed any one or two types of amino acids that are dominant during the interactions of BSA with the CNMs to identify the driving forces. In order to determine the role of various factors such as i) aromatic residues ii) arginine iii) water and also iv) neighboring residues of an aromatic residue or arginine in the interactions of proteins with CNMs, we have designed a set of tripeptide-CNM systems. We used an advanced sampling method, umbrella-sampling method in order to determine the free energy of interaction between the tripeptides and CNM, which will enable us to quantify the contributions of different factors to the interactions. Our initial results show that the influence of the neighboring residues (Val/Leu/Thr/Ser/Gly) in the free energy of interactions for the tripeptide-graphene system where the central residue is Phe is not significant. However, barriers appear during the interactions for the larger and polar side chains. We hypothesize this is due to conformational changes that the larger side chains need to make as the tripeptide associates with the CNMs

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction

Protein Structure

Since the dawn of recorded history, and probably even before, men and women have been grasping at the mechanisms by which they themselves exist. Only relatively recently, did this grasp yield anything of substance, and only within the last several decades did the proteins play a pivotal role in this existence. In this expose on the topic of protein structure some of the current issues in this scientific field are discussed. The aim is that a non-expert can gain some appreciation for the intricacies involved, and in the current state of affairs. The expert meanwhile, we hope, can gain a deeper understanding of the topic

Mass spectrometry-based studies of synthetic and natural macromolecules

Originally established as an analytical technique in the fields of physics and chemistry, mass spectrometry has recently become an essential tool in biological research. Advances in ionisation methods and novel types of instrumentation have led to the development of mass spectrometry for the analysis of a wide variety of biological samples. The work presented here describes the use of mass spectrometry to characterise a variety of synthetic and natural macromolecules. Transmissible spongiform encephalopathies (TSEs), also known as prion diseases, are a class of fatal, infectious neurodegenerative diseases that affect both humans and animals. Prion proteins are unprecedented infectious pathogens that cause a group of invariably fatal neurodegenerative diseases by means of an entirely novel mechanism. Ion mobility mass spectrometry (IM-MS) was used to probe the conformation of a variety of different prion proteins in the gas-phase. It was shown that IM-MS could distinguish between two recombinant structures representative of normal cellular prion protein, PrPC and the pathogenic scrapie form (PrPSc). The structure of the full-length prion protein was probed by means of IM-MS. A comparison of the estimated cross-sections of truncated prion protein constructs and full-length constructs suggested that the N-terminal flexible tail was associated with the core structure. Metal binding to two different prion protein constructs was investigated. It was observed that copper coordination to the N-terminal fragment could induce conformational changes in the octarepeat fragment. These changes were relatively small and could not be measured in the full-length prion protein. The data suggested that minor structural changes in the N-terminal could stimulate endocytosis via a minor, undetected, conformational change in the C-terminal domain. IM-MS was used as a high resolution separation technique to distinguish between mixtures of isobaric synthetic polymers. It was observed that the resolving power of IM-MS/MS was insufficient to resolve the higher molecular weight oligomers. In comparison, gel permeation chromatography (GPC)-nuclear magnetic resonance (NMR) spectroscopy (GPC-NMR) analysis of the same isobaric mixture could not separate the two components. It was observed that IM-MS was better than GPCNMR at separating isobaric poly(ethylene glycol) mixtures, especially when taking speed and sensitivity into account

Understanding the Structural and Functional Importance of Early Folding Residues in Protein Structures

Proteins adopt three-dimensional structures which serve as a starting point to understand protein function and their evolutionary ancestry. It is unclear how proteins fold in vivo and how this process can be recreated in silico in order to predict protein structure from sequence. Contact maps are a possibility to describe whether two residues are in spatial proximity and structures can be derived from this simplified representation. Coevolution or supervised machine learning techniques can compute contact maps from sequence: however, these approaches only predict sparse subsets of the actual contact map. It is shown that the composition of these subsets substantially influences the achievable reconstruction quality because most information in a contact map is redundant. No strategy was proposed which identifies unique contacts for which no redundant backup exists. The StructureDistiller algorithm quantifies the structural relevance of individual contacts and identifies crucial contacts in protein structures. It is demonstrated that using this information the reconstruction performance on a sparse subset of a contact map is increased by 0.4 A, which constitutes a substantial performance gain. The set of the most relevant contacts in a map is also more resilient to false positively predicted contacts: up to 6% of false positives are compensated before reconstruction quality matches a naive selection of contacts without any false positive contacts. This information is invaluable for the training to new structure prediction methods and provides insights into how robustness and information content of contact maps can be improved. In literature, the relevance of two types of residues for in vivo folding has been described. Early folding residues initiate the folding process, whereas highly stable residues prevent spontaneous unfolding events. The structural relevance score proposed by this thesis is employed to characterize both types of residues. Early folding residues form pivotal secondary structure elements, but their structural relevance is average. In contrast, highly stable residues exhibit significantly increased structural relevance. This implies that residues crucial for the folding process are not relevant for structural integrity and vice versa. The position of early folding residues is preserved over the course of evolution as demonstrated for two ancient regions shared by all aminoacyl-tRNA synthetases. One arrangement of folding initiation sites resembles an ancient and widely distributed structural packing motif and captures how reverberations of the earliest periods of life can still be observed in contemporary protein structures

Mass spectrometry-based studies of synthetic and natural macromolecules

Originally established as an analytical technique in the fields of physics and chemistry, mass spectrometry has recently become an essential tool in biological research. Advances in ionisation methods and novel types of instrumentation have led to the development of mass spectrometry for the analysis of a wide variety of biological samples. The work presented here describes the use of mass spectrometry to characterise a variety of synthetic and natural macromolecules. Transmissible spongiform encephalopathies (TSEs), also known as prion diseases, are a class of fatal, infectious neurodegenerative diseases that affect both humans and animals. Prion proteins are unprecedented infectious pathogens that cause a group of invariably fatal neurodegenerative diseases by means of an entirely novel mechanism. Ion mobility mass spectrometry (IM-MS) was used to probe the conformation of a variety of different prion proteins in the gas-phase. It was shown that IM-MS could distinguish between two recombinant structures representative of normal cellular prion protein, PrPC and the pathogenic scrapie form (PrPSc). The structure of the full-length prion protein was probed by means of IM-MS. A comparison of the estimated cross-sections of truncated prion protein constructs and full-length constructs suggested that the N-terminal flexible tail was associated with the core structure. Metal binding to two different prion protein constructs was investigated. It was observed that copper coordination to the N-terminal fragment could induce conformational changes in the octarepeat fragment. These changes were relatively small and could not be measured in the full-length prion protein. The data suggested that minor structural changes in the N-terminal could stimulate endocytosis via a minor, undetected, conformational change in the C-terminal domain. IM-MS was used as a high resolution separation technique to distinguish between mixtures of isobaric synthetic polymers. It was observed that the resolving power of IM-MS/MS was insufficient to resolve the higher molecular weight oligomers. In comparison, gel permeation chromatography (GPC)-nuclear magnetic resonance (NMR) spectroscopy (GPC-NMR) analysis of the same isobaric mixture could not separate the two components. It was observed that IM-MS was better than GPCNMR at separating isobaric poly(ethylene glycol) mixtures, especially when taking speed and sensitivity into account.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council (Great Britain) (EPSRC)AzkoNobelIntertek Measurement Science Group (IMSG)GBUnited Kingdo