Mm Madalina Drugan

Straightforward ladder sequencing of peptides using a Lys-N metalloendopeptidase

We introduce a method for sequencing peptides by mass spectrometry using a metalloendopeptidase that cleaves proteins at the amino side of lysine (Lys-N). When analyzed by electron transfer dissociation (ETD)–based mass spectrometric sequencing, Lys-N–digested peptides that contain a single lysine residue produce spectra dominated by c-type fragment ions, providing simple ladders for sequence determination. This method should be a valuable strategy for de novo sequencing and the analysis of post-translational modifications.

Strong Cation Exchange-based Fractionation of Lys-N-generated Peptides Facilitates the Targeted Analysis of Post-translational Modifications

In proteomics multi-dimensional fractionation techniques are widely used to reduce the complexity of peptide mixtures subjected to mass spectrometric analysis. Here, we describe the sequential use of strong cation exchange and reversed phase liquid chromatography in the separation of peptides generated by a relatively little explored metallo-endopeptidase with Lys-N cleavage specificity. When such proteolytic peptides are subjected to low-pH strong cation exchange we obtain fractionation profiles in which peptides from different functional categories are well separated. The four categories we distinguish and are able to separate to near completion are (I) acetylated N-terminal peptides; (II) singly phosphorylated peptides containing a single basic (Lys) residue; (III) peptides containing a single basic (Lys) residue; and (IV) peptides containing more than one basic residue. Analyzing these peptides by LC-MS/MS using an ion trap with both collision as well as electron transfer-induced dissociation provides unique optimal targeted strategies for proteome analysis. The acetylated peptides in category I can be identified confidently by both CID and ETcaD, whereby the ETcaD spectra are dominated by sequence informative Z-ion series. For the phosphorylated peptides in category II and the “normal” single Lys containing peptides in category III ETcaD provides unique straightforward sequence ladders of c′-ions, from which the exact location of possible phosphorylation sites can be easily determined. The later fractions, category IV, require analysis by both ETcaD and CID, where it is shown that electron transfer dissociation performs relatively well for these multiple basic residues containing peptides, as is expected. We argue that the well resolved separation of functional categories of peptides observed is characteristic for Lys-N-generated peptides. Overall, the combination of Lys-N proteolysis, low-pH strong cation exchange, and reversed phase separation, with CID and ETD induced fragmentation, adds a new very powerful method to the toolbox of proteomic analyses.

Stochastic Pareto local search: Pareto neighbourhood exploration and perturbation strategies

Pareto local search (PLS) methods are local search algorithms for multi-objective combinatorial optimization problems based on the Pareto dominance criterion. PLS explores the Pareto neighbourhood of a set of non-dominated solutions until it reaches a local optimal Pareto front. In this paper, we discuss and analyse three different Pareto neighbourhood exploration strategies: best, first, and neutral improvement. Furthermore, we introduce a deactivation mechanism that restarts PLS from an archive of solutions rather than from a single solution in order to avoid the exploration of already explored regions. To escape from a local optimal solution set we apply stochastic perturbation strategies, leading to stochastic Pareto local search algorithms (SPLS). We consider two perturbation strategies: mutation and path-guided mutation. While the former is unbiased, the latter is biased towards preserving common substructures between 2 solutions. We apply SPLS on a set of large, correlated bi-objective quadratic assignment problems (bQAPs) and observe that SPLS significantly outperforms multi-start PLS. We investigate the reason of this performance gain by studying the fitness landscape structure of the bQAPs using random walks. The best performing method uses the stochastic perturbation algorithms, the first improvement Pareto neigborhood exploration and the deactivation technique.

Scalarized multi-objective reinforcement learning: Novel design techniques

In multi-objective problems, it is key to find compromising solutions that balance different objectives. The linear scalarization function is often utilized to translate the multi-objective nature of a problem into a standard, single-objective problem. Generally, it is noted that such as linear combination can only find solutions in convex areas of the Pareto front, therefore making the method inapplicable in situations where the shape of the front is not known beforehand, as is often the case. We propose a non-linear scalarization function, called the Chebyshev scalarization function, as a basis for action selection strategies in multi-objective reinforcement learning. The Chebyshev scalarization method overcomes the flaws of the linear scalarization function as it can (i) discover Pareto optimal solutions regardless of the shape of the front, i.e. convex as well as non-convex , (ii) obtain a better spread amongst the set of Pareto optimal solutions and (iii) is not particularly dependent on the actual weights used.

StatQuant: a post-quantification analysis toolbox for improving quantitative mass spectrometry

MOTIVATION Mass spectrometric protein quantitation has emerged as a high-throughput tool to yield large amounts of data on peptide and protein abundances. Currently, differential abundance data can be calculated from peptide intensity ratios by several automated quantitation software packages available. There is, however, still a great need for additional processing to validate and refine the quantitation results. Here, we present a software tool, termed StatQuant, that offers a set of statistical tools to process, filter, compare and represent data from several quantitative proteomics software packages such as MSQuant. StatQuant offers the researcher post-processing methods to achieve improved confidence on the obtained protein ratios. AVAILABILITY StatQuant can be downloaded from: (https://gforge.nbic.nl/projects/statquant/) (binary and source code).

Designing multi-objective multi-armed bandits algorithms: A study

We propose an algorithmic framework for multi-objective multi-armed bandits with multiple rewards. Different partial order relationships from multi-objective optimization can be considered for a set of reward vectors, such as scalarization functions and Pareto search. A scalarization function transforms the multi-objective environment into a single objective environment and are a popular choice in multi-objective reinforcement learning. Scalarization techniques can be straightforwardly implemented into the current multi-armed bandit framework, but the efficiency of these algorithms depends very much on their type, linear or non-linear (e.g. Chebyshev), and their parameters. Using Pareto dominance order relationship allows to explore the multi-objective environment directly, however this can result in large sets of Pareto optimal solutions. In this paper we propose and evaluate the performance of multi-objective MABs using three regret metric criteria. The standard UCB1 is extended to scalarized multi-objective UCB1 and we propose a Pareto UCB1 algorithm. Both algorithms are proven to have a logarithmic upper bound for their expected regret. We also introduce a variant of the scalarized multi-objective UCB1 that removes online inefficient scalarizations in order to improve the algorithms efficiency. These algorithms are experimentally compared on multi-objective Bernoulli distributions, Pareto UCB1 being the algorithm with the best empirical performance.

Hypervolume-Based Multi-Objective Reinforcement Learning

Indicator-based evolutionary algorithms are amongst the best performing methods for solving multi-objective optimization (MOO) problems. In reinforcement learning (RL), introducing a quality indicator in an algorithm’s decision logic was not attempted before. In this paper, we propose a novel on-line multi-objective reinforcement learning (MORL) algorithm that uses the hypervolume indicator as an action selection strategy. We call this algorithm the hypervolume-based MORL algorithm or HB-MORL and conduct an empirical study of the performance of the algorithm using multiple quality assessment metrics from multi-objective optimization. We compare the hypervolume-based learning algorithm on different environments to two multi-objective algorithms that rely on scalarization techniques, such as the linear scalarization and the weighted Chebyshev function. We conclude that HB-MORL significantly outperforms the linear scalarization method and performs similarly to the Chebyshev algorithm without requiring any user-specified emphasis on particular objectives.

Feature selection for Bayesian network classifiers using the MDL-FS score

When constructing a Bayesian network classifier from data, the more or less redundant features included in a dataset may bias the classifier and as a consequence may result in a relatively poor classification accuracy. In this paper, we study the problem of selecting appropriate subsets of features for such classifiers. To this end, we propose a new definition of the concept of redundancy in noisy data. For comparing alternative classifiers, we use the Minimum Description Length for Feature Selection (MDL-FS) function that we introduced before. Our function differs from the well-known MDL function in that it captures a classifiers conditional log-likelihood. We show that the MDL-FS function serves to identify redundancy at different levels and is able to eliminate redundant features from different types of classifier. We support our theoretical findings by comparing the feature-selection behaviours of the various functions in a practical setting. Our results indicate that the MDL-FS function is more suited to the task of feature selection than MDL as it often yields classifiers of equal or better performance with significantly fewer attributes.

Path-guided mutation for stochastic pareto local search algorithms

Stochastic Pareto local search (SPLS) methods are local search algorithms for multi-objective combinatorial optimization problems that restart local search from points generated using a stochastic process. Examples of such stochastic processes are Brownian motion (or random processes), and the ones resulting from the use of mutation and recombination operators. We propose a path-guided mutation operator for SPLS where an individual solution is mutated in the direction of the path to another individual solution in order to restart a PLS.We study the exploration of the landscape of the bi-objective Quadratic assignment problem (bQAP) using SPLSs that restart the PLSs from: i) uniform randomly generated solutions, ii) solutions generated from best-so-far local optimal solutions with uniform random mutation and iii) with path-guided mutation. Experiments on a bQAP with a large number of facilities and high correlation between the flow matrices show that using mutation, and especially path-guided mutation, is beneficial for performance of SPLS. The performance of SPLSs is partially explained using their dynamical behavior like the probability of escaping the local optima and the speed of enhancing the Pareto front.

Model-based multi-objective reinforcement learning

This paper describes a novel multi-objective reinforcement learning algorithm. The proposed algorithm first learns a model of the multi-objective sequential decision making problem, after which this learned model is used by a multi-objective dynamic programming method to compute Pareto optimal policies. The advantage of this model-based multi-objective reinforcement learning method is that once an accurate model has been estimated from the experiences of an agent in some environment, the dynamic programming method will compute all Pareto optimal policies. Therefore it is important that the agent explores the environment in an intelligent way by using a good exploration strategy. In this paper we have supplied the agent with two different exploration strategies and compare their effectiveness in estimating accurate models within a reasonable amount of time. The experimental results show that our method with the best exploration strategy is able to quickly learn all Pareto optimal policies for the Deep Sea Treasure problem.