Novel visualization methods for protein data

Visualization of high-dimensional data has always been a challenging task. Here we discuss and propose variants of non-linear data projection methods (Generative Topographic Mapping (GTM) and GTM with simultaneous feature saliency (GTM-FS)) that are adapted to be effective on very high-dimensional data. The adaptations use log space values at certain steps of the Expectation Maximization (EM) algorithm and during the visualization process. We have tested the proposed algorithms by visualizing electrostatic potential data for Major Histocompatibility Complex (MHC) class-I proteins. The experiments show that the variation in the original version of GTM and GTM-FS worked successfully with data of more than 2000 dimensions and we compare the results with other linear/nonlinear projection methods: Principal Component Analysis (PCA), Neuroscale (NSC) and Gaussian Process Latent Variable Model (GPLVM).

A comparative molecular similarity indices (CoMSIA) study of peptide binding to the HLA-A3 superfamily

Epitope identification is the basis of modern vaccine design. The present paper studied the supermotif of the HLA-A3 superfamily, using comparative molecular similarity indices analysis (CoMSIA). Four alleles with high phenotype frequencies were used: A*1101, A*0301, A*3101 and A*6801. Five physicochemical properties—steric bulk, electrostatic potential, local hydro-phobicity, hydrogen-bond donor and acceptor abilities—were considered and ‘all fields’ models were produced for each of the alleles. The models have a moderate level of predictivity and there is a good correlation between the data. A revised HLA-A3 supermotif was defined based on the comparison of favoured and disfavoured properties for each position of the MHC bound peptide. The present study demonstrated that CoMSIA is an effective tool for studying peptide–MHC interactions.

Visualisation of bioinformatics datasets

Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset.

Receptor activity-modifying proteins 2 and 3 generate adrenomedullin receptor subtypes with distinct molecular properties

Adrenomedullin (AM) is a peptide hormone with numerous effects in the vascular systems. AM signals through the AM1 and AM2 receptors formed by the obligate heterodimerization of a G protein-coupled receptor, the calcitonin receptor-like receptor (CLR), and receptor activity-modifying proteins (RAMP) 2 and 3, respectively. These different CLR-RAMP interactions yield discrete receptor pharmacology and physiological effects. The effective design of therapeutics that target the individual AM receptors is dependent on understanding the molecular details of the effects of RAMPs on CLR. To understand the role of RAMPs 2 and 3 on the activation and conformation of the CLR subunit of AM receptors we mutated 68 individual amino acids in the juxtamembrane region of CLR, a key region for activation of AM receptors and determined the effects on cAMP signalling. Sixteen CLR mutations had differential effects between the AM1 and AM2 receptors. Accompanying this, independent molecular modelling of the full-length AM-bound AM1 and AM2 receptors predicted differences in the binding pocket, and differences in the electrostatic potential of the two AM receptors. Druggability analysis indicated unique features that could be used to develop selective small molecule ligands for each receptor. The interaction of RAMP2 or RAMP3 with CLR induces conformational variation in the juxtamembrane region, yielding distinct binding pockets, probably via an allosteric mechanism. These subtype-specific differences have implications for the design of therapeutics aimed at specific AM receptors and for understanding the mechanisms by which accessory proteins affect G protein-coupled receptor function.

Hydrophobicity and surface electrostatic charge of conidia of the mycoparasite Coniothyrium minitans

The effect of increasing culture age on cell surface hydrophobicity (CSH) and cell surface electrostatic charge (measured as zeta potential) of conidia from five isolates of Coniothyrium minitans representing three different morphological types was examined. Conidial CSH of three isolates (A2 960/1, CH1 and CH2) decreased with culture age, whereas CSH of two others (B 1300/2 and IMI 134523) remained high for the whole 42 day experimental period. In contrast, cell surface electrostatic charge decreased uniformly in conidia of all five isolates for the first 34 d and then rose slightly at 42 d. The variation in cell surface electrostatic charge (spectrum width) of the sampled conidia decreased with age for all five isolates. In all five isolates cell surface electrostatic charge of conidia became increasingly negative as the pH of the buffer used to suspend conidia was increased from pH 3.0 to 9.0. No relationship between colony morphology of C. minitans and conidial CSH and cell surface electrostatic charge was found.

A study of the properties and applications of electrostatic charged particle oscillators

This investigation originated from work by Dr. A.H. McIlraith of the National Physical Laboratory who, in 1966, described a new type of charged particle oscillator. This makes use of two equal cylindrical electrodes to constrain the particles in such a way that they follow extremely long oscillatory paths between the electrodes under the influence of an electrostatic field alone. The object of this work has been to study the principle of the oscillator in detail and to investigate its properties and applications. Any device which is capable of creating long electron trajectories has potential application in the field of ultra high vacuum technology. It was therefore considered that a critical review of the problems associated with the production and measurement of ultra high vacuum was relevant in the initial stages of the work. The oscillator has been applied with a considerable degree of success as a high energy electrostatic ion source. This offers several advantages over existing ion sources. It can be operated at much lower pressures without the need of a magnetic field. The oscillator principle has also been applied as a thermionic ionization gauge and has been compared with other ionization gauges to pressures as low as 5 x 10- 11 torr.. This new gauge exhibited a number of advantages over most of the existing gauges. Finally the oscillator has been used in an evaporation ion pump and has exhibited fairly high pumping speeds for argon gas relative to those for nitrogen. This investigation supports the original work of Dr. A.H. McIlraith and shows that his proposed oscillator has considerable potential in the fields of vacuum technology and electron physics.