7,253 research outputs found
The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases
One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs
Knowledge-based energy functions for computational studies of proteins
This chapter discusses theoretical framework and methods for developing
knowledge-based potential functions essential for protein structure prediction,
protein-protein interaction, and protein sequence design. We discuss in some
details about the Miyazawa-Jernigan contact statistical potential,
distance-dependent statistical potentials, as well as geometric statistical
potentials. We also describe a geometric model for developing both linear and
non-linear potential functions by optimization. Applications of knowledge-based
potential functions in protein-decoy discrimination, in protein-protein
interactions, and in protein design are then described. Several issues of
knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe
Quantification of the morphological characteristics of hESC colonies
The maintenance of the pluripotent state in human embryonic stem cells
(hESCs) is critical for further application in regenerative medicine, drug
testing and studies of fundamental biology. Currently, the selection of the
best quality cells and colonies for propagation is typically performed by eye,
in terms of the displayed morphological features, such as prominent/abundant
nucleoli and a colony with a tightly packed appearance and a well-defined edge.
Using image analysis and computational tools, we precisely quantify these
properties using phase-contrast images of hESC colonies of different sizes (0.1
-- 1.1) during days 2, 3 and 4 after plating. Our analyses
reveal noticeable differences in their structure influenced directly by the
colony area . Large colonies () have cells with
smaller nuclei and a short intercellular distance when compared with small
colonies (). The gaps between the cells, which are
present in small and medium sized colonies with ,
disappear in large colonies () due to the proliferation
of the cells in the bulk. This increases the colony density and the number of
nearest neighbours.
We also detect the self-organisation of cells in the colonies where newly
divided (smallest) cells cluster together in patches, separated from larger
cells at the final stages of the cell cycle. This might influence directly
cell-to-cell interactions and the community effects within the colonies since
the segregation induced by size differences allows the interchange of
neighbours as the cells proliferate and the colony grows. Our findings are
relevant to efforts to determine the quality of hESC colonies and establish
colony characteristics database
Machine Learning of Molecular Electronic Properties in Chemical Compound Space
The combination of modern scientific computing with electronic structure
theory can lead to an unprecedented amount of data amenable to intelligent data
analysis for the identification of meaningful, novel, and predictive
structure-property relationships. Such relationships enable high-throughput
screening for relevant properties in an exponentially growing pool of virtual
compounds that are synthetically accessible. Here, we present a machine
learning (ML) model, trained on a data base of \textit{ab initio} calculation
results for thousands of organic molecules, that simultaneously predicts
multiple electronic ground- and excited-state properties. The properties
include atomization energy, polarizability, frontier orbital eigenvalues,
ionization potential, electron affinity, and excitation energies. The ML model
is based on a deep multi-task artificial neural network, exploiting underlying
correlations between various molecular properties. The input is identical to
\emph{ab initio} methods, \emph{i.e.} nuclear charges and Cartesian coordinates
of all atoms. For small organic molecules the accuracy of such a "Quantum
Machine" is similar, and sometimes superior, to modern quantum-chemical
methods---at negligible computational cost
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