156 research outputs found
A Computational Methodology to Screen Activities of Enzyme Variants
We present a fast computational method to efficiently screen enzyme activity.
In the presented method, the effect of mutations on the barrier height of an
enzyme-catalysed reaction can be computed within 24 hours on roughly 10
processors. The methodology is based on the PM6 and MOZYME methods as
implemented in MOPAC2009, and is tested on the first step of the amide
hydrolysis reaction catalyzed by Candida Antarctica lipase B (CalB) enzyme. The
barrier heights are estimated using adiabatic mapping and are shown to give
barrier heights to within 3kcal/mol of B3LYP/6-31G(d)//RHF/3-21G results for a
small model system. Relatively strict convergence criteria
(0.5kcal/(mol{\AA})), long NDDO cutoff distances within the MOZYME method
(15{\AA}) and single point evaluations using conventional PM6 are needed for
reliable results. The generation of mutant structure and subsequent setup of
the semiempirical calculations are automated so that the effect on barrier
heights can be estimated for hundreds of mutants in a matter of weeks using
high performance computing
Online failure prediction in UNIX systems
This thesis investigates the possibility of enhancing an existing performance
monitoring system for UNIX servers, by adding the capability of predicting upcoming
failures, using generic UNIX operating system performance metrics like used server
memory, CPU utilization, I/O traffic etc. as input data for machine learning and pattern
recognition. In this thesis we survey possible research methods based on input data
they process, and propose a novel approach for symptom based failure predicting. In
order to make a generic solution that can be used on any UNIX computer, we have
only used open source software. We evaluate the classifiers Naive Bayes and Logistic
Regression with input data in both standard and vectorized format. Furthermore we
use the search algorithm Forward stepwise selection to find an optimal generic set of
variables (features) that improves the quality of the classification. Our empirical
testing demonstrates that our proposed method is capable of predicting symptoms with
high overall accuracy, but the uncertain quality of the monitored performance data
used as input makes it difficult to ascertain if the symptoms are actually failures.
Applying the search algorithm for feature selection and vectorizing the input data set
we improved the time for classification with an order of magnitude. In our opinion the
proposed technique for online failure prediction will benefit to applications concerning
performance monitoring and contribute to the research field of online failure
prediction with new insight
Sea level reconstruction: Exploration of methods for combining altimetry with other data to beyond the 20-year altimetric record
60-year Nordic and arctic sea level reconstruction based on a reprocessed two decade altimetric sea level record and tide gauges
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