1,066 research outputs found
Is EC class predictable from reaction mechanism?
No full textWe thank the Scottish Universities Life Sciences Alliance (SULSA) and the Scottish Overseas Research Student Awards Scheme of the Scottish Funding Council (SFC) for financial support.Background: We investigate the relationships between the EC (Enzyme Commission) class, the associated chemical reaction, and the reaction mechanism by building predictive models using Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbours (kNN). We consider two ways of encoding the reaction mechanism in descriptors, and also three approaches that encode only the overall chemical reaction. Both cross-validation and also an external test set are used. Results: The three descriptor sets encoding overall chemical transformation perform better than the two descriptions of mechanism. SVM and RF models perform comparably well; kNN is less successful. Oxidoreductases and hydrolases are relatively well predicted by all types of descriptor; isomerases are well predicted by overall reaction descriptors but not by mechanistic ones. Conclusions: Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. Oxidoreductases, hydrolases, and to some extent isomerases and ligases, have clear chemical signatures, making them easier to predict than transferases and lyases. We find evidence that isomerases as a class are notably mechanistically diverse and that their one shared property, of substrate and product being isomers, can arise in various unrelated ways. The performance of the different machine learning algorithms is in line with many cheminformatics applications, with SVM and RF being roughly equally effective. kNN is less successful, given the role that non-local information plays in successful classification. We note also that, despite a lack of clarity in the literature, EC number prediction is not a single problem; the challenge of predicting protein function from available sequence data is quite different from assigning an EC classification from a cheminformatics representation of a reaction.Peer reviewe
Quantitative and evolutionary global analysis of enzyme reaction mechanisms
No full textThe most widely used classification system describing enzyme-catalysed reactions
is the Enzyme Commission (EC) number. Understanding enzyme
function is important for both fundamental scientific and pharmaceutical
reasons. The EC classification is essentially unrelated to the reaction mechanism.
In this work we address two important questions related to enzyme
function diversity. First, to investigate the relationship between the reaction
mechanisms as described in the MACiE (Mechanism, Annotation,
and Classification in Enzymes) database and the main top-level class of the
EC classification. Second, how well these enzymes biocatalysis are adapted
in nature.
In this thesis, we have retrieved 335 enzyme reactions from the MACiE
database. We consider two ways of encoding the reaction mechanism in
descriptors, and three approaches that encode only the overall chemical
reaction.
To proceed through my work, we first develop a basic model to cluster
the enzymatic reactions. Global study of enzyme reaction mechanism
may provide important insights for better understanding of the diversity of
chemical reactions of enzymes. Clustering analysis in such research is very
common practice. Clustering algorithms suffer from various issues, such as
requiring determination of the input parameters and stopping criteria, and
very often a need to specify the number of clusters in advance.
Using several well known metrics, we tried to optimize the clustering
outputs for each of the algorithms, with equivocal results that suggested the
existence of between two and over a hundred clusters. This motivated us to
design and implement our algorithm, PFClust (Parameter-Free Clustering),
where no prior information is required to determine the number of cluster. The analysis highlights the structure of the enzyme overall and mechanistic
reaction. This suggests that mechanistic similarity can influence approaches
for function prediction and automatic annotation of newly discovered protein
and gene sequences.
We then develop and evaluate the method for enzyme function prediction
using machine learning methods. Our results suggest that pairs of similar
enzyme reactions tend to proceed by different mechanisms. The machine
learning method needs only chemoinformatics descriptors as an input and
is applicable for regression analysis.
The last phase of this work is to test the evolution of chemical mechanisms
mapped onto ancestral enzymes. This domain occurrence and abundance
in modern proteins has showed that the / architecture is probably
the oldest fold design. These observations have important implications for
the origins of biochemistry and for exploring structure-function relationships.
Over half of the known mechanisms are introduced before architectural
diversification over the evolutionary time. The other halves of the mechanisms
are invented gradually over the evolutionary timeline just after organismal
diversification. Moreover, many common mechanisms includes fundamental
building blocks of enzyme chemistry were found to be associated
with the ancestral fold
From sequence to enzyme mechanism using multi-label machine learning
No full textBackground: In this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes. Hence we can predict not only whether a putative enzyme in a newly sequenced organism has the potential to perform a certain reaction, but how the reaction is performed, using which cofactors and with susceptibility to which drugs or inhibitors, details with important consequences for drug and enzyme design. Work that predicts enzyme catalytic activity based on 3D protein structure features limits the prediction of mechanism to proteins already having either a solved structure or a close relative suitable for homology modelling. Results: In this study, we evaluate whether sequence identity, InterPro or Catalytic Site Atlas sequence signatures provide enough information for bulk prediction of enzyme mechanism. By splitting MACiE (Mechanism, Annotation and Classification in Enzymes database) mechanism labels to a finer granularity, which includes the role of the protein chain in the overall enzyme complex, the method can predict at 96% accuracy (and 96% micro-averaged precision, 99.9% macro-averaged recall) the MACiE mechanism definitions of 248 proteins available in the MACiE, EzCatDb (Database of Enzyme Catalytic Mechanisms) and SFLD (Structure Function Linkage Database) databases using an off-theshelf K-Nearest Neighbours multi-label algorithm. Conclusion: We find that InterPro signatures are critical for accurate prediction of enzyme mechanism. We also find that incorporating Catalytic Site Atlas attributes does not seem to provide additional accuracy. The software code (ml2db), data and results are available online at http://sourceforge.net/projects/ml2db/ and as supplementary files.Peer reviewe
Recommended from our members
Framework for Computational Modelling of Cellular Diffusion Systems
No full textMany biological processes are regulated by the presence and movement of cellular Ca²⁺ ions. The concentration of Ca²⁺ in a cellular environment is regulated by IP₃ sensitive channels that lie on the surface of a cell’s endoplasmic reticulum. Little is known about the macroscopic effects of intracellular Ca²⁺ activity, so these processes are of key interest to the field of experimental biophysics. Experiments that study macroscopic processes that result from intracellular Ca²⁺ action are difficult to conduct in a lab, so a computational simulation that accurately simulates ion diffusion within a multicellular system is a key tool for studying the large scale effects of intracellular Ca²⁺ fluctuations.
The product of this thesis is a computational framework for intracellular ion diffusion that will be used as the basis for modeling of multicellular systems in the future. This model generates a spatial boundary from a cell image and overlays a grid comprised of rectangular boxes suitable for discretized diffusion calculations on the cell space. Simulations of particle movement are performed by calculating the particle flux through the boundaries of each box in the grid using Fick’s laws of diffusion. An adaptive gridding method has been developed to increase the accuracy of the representation of cellular structures within the grid while greatly increasing calculation efficiency. Efficiency differences between simulations using the adaptive and non-adaptive gridding techniques have been analyzed.Keywords: Diffusion Modeling, Simulation, Cell DiffusionKeywords: Diffusion Modeling, Simulation, Cell Diffusio
Phi Kappa Phi Charter Members
No full textDean Weems, Dewey McCain, H.D. Bunch, Ben Hilbun, Ida Mohn, Fred Mitchell, Frank Kern, Charles Miller, Glover Moore, John Bettersworth, Roscoe Saville, Clyde Sheeley, Spencer Murray, M.W. Myers, Lyell C. Behr, James Hill, Bo Stafford, Harold Flinch, WIlliam Raney, Joe Edmond, Erwin Price, William Knight, Ben Barrentine, Ross Hutchens, Gene Overcash, WIlliam Giles, Harry Simrall, August Raspet, H.H. Bennett, and Harold Snellgrovehttps://scholarsjunction.msstate.edu/ua-photo-collection/8591/thumbnail.jp
Informatics, machine learning and computational medicinal chemistry
No full text This article reviews the use of informatics and computational chemistry methods in medicinal chemistry, with special consideration of how computational techniques can be adapted and extended to obtain more and higher-quality information. Special consideration is given to the computation of protein–ligand binding affinities, to the prediction of off-target bioactivities, bioactivity spectra and computational toxicology, and also to calculating absorption-, distribution-, metabolism- and excretion-relevant properties, such as solubility. </jats:p
Prize the doubt : the life and work of Francis William Newman.
No full textAvailable from British Library Document Supply Centre- DSC:DX180157 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo
The Symphonies of John Kinsella
No full textSéamas de Barra
The Symphonies of John Kinsella
ABSTRACT
This thesis offers the first comprehensive analytical and critical study of the symphonies of John Kinsella (b. 1932), one of the leading figures in contemporary Irish music. This cycle of ten works represents the most substantial contribution to the genre by an Irish composer, and Kinsella’s varied handling to the form is examined and discussed in relation both to historical and contemporary developments. While his understanding of musical structure and the manner in which he shapes musical time are deeply indebted to the work of Jean Sibelius, Kinsella’s compositional idiom is derived from a personal adaptation of serialism in which the technique of the note-row is manipulated to readmit the forces of tonal attraction. The result of these twin influences is an arrestingly individual approach to composition, the development of which is traced across the cycle as each of the symphonies in turn is subjected to extensive analysis. Because he chose to pursue an independent path in the 1980s, Kinsella seemed a somewhat isolated figure to his contemporaries. Retrospectively, his work can be seen as instinctively in tune with broader developments, however, as both serialism (understood as a way of thinking rather than as a style) and the music of Sibelius have emerged as two of the dominant influences on current musical thinking
Network Capital and Social Trust: Pre-Conditions for ‘Good’ Diversity?
No full textThis paper unpicks the assumption that because social networks underpin social capital, they directly create it – more of one inevitably making more of the other. If it were that simple, the sheer quantity of networks criss-crossing a defined urban space would be a proxy measure for the local stock of social capital. Of course the interrelationships are more complex. Two kinds of complication stand out. The first is specific: networks have both quantitative and qualitative dimensions, but the two elements have no necessary bearing on each other. The shape and extent of a network says nothing about the content of the links between its nodes. Certainly the line we draw between any two of them indicates contact and potential connection, but what kind of contact, how often, how trusting, in what circumstances, to what end…? Reliable answers to these questions need more than surface maps or bird’s eye accounts of who goes where, who speaks to whom. The second complication is a general, not to say universal, difficulty. We are stuck with the fact that sociological concepts - networks, social capital and trust included - are ‘only’ abstractions. They are ways of thinking about the apparent chaos of people behaving all over the place – here, to make it worse, in multi-cultural urban environments - but none of them is visible to be measured, weighed or quantified. This does not make the concepts ‘untrue’, and it should not stop them being useful. My hope is that we can find a nuanced perspective which will at least make the complications intelligible. At best, a multi-layered model will account for diversity in the nature of trust; and for variations in the way social capital is hoarded or distributed within and across ethnic boundaries. It would be contribution enough if we were able to specify the conditions which cause social capital, as Puttnam formulates it, to be exclusionary or inclusionary in its effect.Network capital, Social trust, ‘Good’ diversity
Enzyme function and its evolution
No full textWith rapid increases over recent years in the determination of protein sequence and structure, alongside knowledge of thousands of enzyme functions and hundreds of chemical mechanisms, it is now possible to combine breadth and depth in our understanding of enzyme evolution. Phylogenetics continues to move forward, though determining correct evolutionary family trees is not trivial. Protein function prediction has spawned a variety of promising methods that offer the prospect of identifying enzymes across the whole range of chemical functions and over numerous species. This knowledge is essential to understand antibiotic resistance, as well as in protein re-engineering and de novo enzyme design.Peer reviewe
- …
