The convergence of MD simulations is tested using varying measures for the intrinsically disordered amyloid-β peptide (Aβ). Markov state models show that 20–30 μs of MD is needed to reliably reproduce the thermodynamics and kinetics of Aβ.

Peroxisomes are ubiquitous membrane-bound organelles, and aberrant localisation of peroxisomal proteins contributes to the pathogenesis of several disorders. Many computational methods focus on assigning protein sequences to subcellular compartments, but there are no specific tools tailored for the sub-localisation (matrix vs. membrane) of peroxisome proteins. We present here In-Pero, a new method for predicting protein sub-peroxisomal cellular localisation. In-Pero combines standard machine learning approaches with recently proposed multi-dimensional deep-learning representations of the protein amino-acid sequence. It showed a classification accuracy above 0.9 in predicting peroxisomal matrix and membrane proteins. The method is trained and tested using a double cross-validation approach on a curated data set comprising 160 peroxisomal proteins with experimental evidence for sub-peroxisomal localisation. We further show that the proposed approach can be easily adapted (In-Mito) to the prediction of mitochondrial protein localisation obtaining performances for certain classes of proteins (matrix and inner-membrane) superior to existing tools.

The amlyoid-β peptide (Aβ) is closely linked to the development of Alzheimer’s disease. Molecular dynamics (MD) simulations have become an indispensable tool for studying the behavior of this peptide at the (sub)molecular level, thereby providing insight into the molecular basis of Alzheimer’s disease. General key aspects of MD simulations are the force field used for modeling the peptide or protein and its environment, which is important for accurate modeling of the system of interest, and the length of the simulations, which determines whether or not equilibrium is reached. In this study we address these points by analyzing 30-µs MD simulations acquired for Aβ40 using seven different force fields. We assess the convergence of these simulations based on the convergence of various structural properties and of NMR and fluorescence spectroscopic observables. Moreover, we calculate Markov state models for each of the seven MD simulations, which provide an unprecedented view of the thermodynamics and kinetics of the amyloid-β peptide. This further allows us to provide answers for pertinent questions, like: Which force fields are suitable for modeling Aβ? (a99SB-UCB and a99SB-ILDN/TIP4P-D); What does Aβ peptide really look like? (mostly extended and disordered) and; How long does it take MD simulations of Aβ to attain equilibrium? (20–30 µs). We believe the analyses presented in this study will provide a useful reference guide for important questions relating to the structure and dynamics of Aβin particular, and by extension other similar disordered peptides.

Peroxisomes are ubiquitous, oxidative subcellular organelles with important functions in cellular lipid metabolism and redox homeostasis. Loss of peroxisomal functions causes severe disorders with developmental and neurological abnormalities. Zebrafish are emerging as an attractive vertebrate model to study peroxisomal disorders as well as cellular lipid metabolism. Here, we combined bioinformatics analyses with molecular cell biology and reveal the first comprehensive inventory of Danio rerio peroxisomal proteins, which we systematically compared with those of human peroxisomes. Through bioinformatics analysis of all PTS1-carrying proteins, we demonstrate that D. rerio lacks two well-known mammalian peroxisomal proteins (BAAT and ZADH2/PTGR3), but possesses a putative peroxisomal malate synthase (Mlsl) and verified differences in the presence of purine degrading enzymes. Furthermore, we revealed novel candidate peroxisomal proteins in D. rerio, whose function and localisation is discussed. Our findings confirm the suitability of zebrafish as a vertebrate model for peroxisome research and open possibilities for the study of novel peroxisomal candidate proteins in zebrafish and humans.