masstodon: A Tool for Assigning Peaks and Modeling Electron Transfer Reactions in Top-Down Mass Spectrometry

Top-down mass spectrometry methods are becoming continuously more popular in the effort to describe the proteome. They rely on the fragmentation of intact protein ions inside the mass spectrometer. Among the existing fragmentation methods, electron transfer dissociation is known for its precision and wide coverage of different cleavage sites. However, several side reactions can occur under electron transfer dissociation (ETD) conditions, including nondissociative electron transfer and proton transfer reaction. Evaluating their extent can provide more insight into reaction kinetics as well as instrument operation. Furthermore, preferential formation of certain reaction products can reveal important structural information. To the best of our knowledge, there are currently no tools capable of tracing and analyzing the products of these reactions in a systematic way. In this Article, we present in detail masstodon: a computer program for assigning peaks and interpreting mass spectra. Besides being a general purpose tool, masstodon also offers the possibility to trace the products of reactions occurring under ETD conditions and provides insights into the parameters driving them. It is available free of charge under the GNU AGPL V3 public license

Estimation of Rates of Reactions Triggered by Electron Transfer in Top-Down Mass Spectrometry

Electron transfer dissociation (ETD) is a versatile technique used in mass spectrometry for the high-throughput characterization of proteins. It consists of several concurrent reactions triggered by the transfer of an electron from its anion source to sample cations. Transferring an electron causes peptide backbone cleavage while leaving labile post-translational modifications intact. The obtained fragmentation spectra provide valuable information for sequence and structure analyses. In this study, we propose a formal mathematical model of the ETD fragmentation process in the form of a system of stochastic differential equations describing its joint dynamics. Parameters of the model correspond to the rates of occurring reactions. Their estimates for various experimental settings give insight into the dynamics of the ETD process. We estimate the model parameters from the relative quantities of fragmentation products in a given mass spectrum by solving a nonlinear optimization problem. The cost function penalizes for the differences between the analytically derived average number of reaction products and their experimental counterparts. The presented method proves highly robust to noise in silico. Moreover, the model can explain a considerable amount of experimental results for a wide range of instrumentation settings. The implementation of the presented workflow, code-named ETDetective, is freely available under the two-clause BSD license

An adaptive parallel tempering algorithm

Parallel tempering is a generic Markov chainMonteCarlo samplingmethod which allows good mixing with multimodal target distributions, where conventionalMetropolis- Hastings algorithms often fail. The mixing properties of the sampler depend strongly on the choice of tuning parameters, such as the temperature schedule and the proposal distribution used for local exploration. We propose an adaptive algorithm with fixed number of temperatures which tunes both the temperature schedule and the parameters of the random-walk Metropolis kernel automatically. We prove the convergence of the adaptation and a strong law of large numbers for the algorithm under general conditions. We also prove as a side result the geometric ergodicity of the parallel tempering algorithm. We illustrate the performance of our method with examples. Our empirical findings indicate that the algorithm can cope well with different kinds of scenarios without prior tuning. Supplementary materials including the proofs and the Matlab implementation are available online. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America

Asymptotics of Monte Carlo maximum likelihood estimators

We describe Monte Carlo approximation to the maximum likelihood estimator in models with intractable norming constants and explanatory variables. We consider both sources of randomness (due to the initial sample and to Monte Carlo simulations) and prove asymptotical normality of the estimator

A strategy for Bayesian inference for computationally expensive models with application to the estimation of stem cell properties

Bayesian inference is considered for statistical models that depend on the evaluation of a computationally expensive computer code or simulator. For such situations, the number of evaluations of the likelihood function, and hence of the unnormalized posterior probability density function, is determined by the available computational resource and may be extremely limited. We present a new example of such a simulator that describes the properties of human embryonic stem cells using data from optical trapping experiments. This application is used to motivate a novel strategy for Bayesian inference which exploits a Gaussian process approximation of the simulator and allows computationally efficient Markov chain Monte Carlo inference. The advantages of this strategy over previous methodology are that it is less reliant on the determination of tuning parameters and allows the application of model diagnostic procedures that require no additional evaluations of the simulator. We show the advantages of our method on synthetic examples and demonstrate its application on stem cell experiments