Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.

Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.

In vitro toxic effects of puff adder (Bitis arietans) venom, and their neutralization by antivenom.

This study investigated the in vitro toxic effects of Bitis arietans venom and the ability of antivenom produced by the South African Institute of Medical Research (SAIMR) to neutralize these effects. The venom (50 µg/mL) reduced nerve-mediated twitches of the chick biventer muscle to 19% ± 2% of initial magnitude (n = 4) within 2 h. This inhibitory effect of the venom was significantly attenuated by prior incubation of tissues with SAIMR antivenom (0.864 µg/µL; 67% ± 4%; P < 0.05; n = 3-5, unpaired t-test). Addition of antivenom at t50 failed to prevent further inhibition or reverse the inhibition of twitches and responses to agonists. The myotoxic action of the venom (50 µg/mL) was evidenced by a decrease in direct twitches (30% ± 6% of the initial twitch magnitude) and increase in baseline tension (by 0.7 ± 0.3 g within 3 h) of the chick biventer. Antivenom failed to block these effects. Antivenom however prevented the venom induced cytotoxic effects on L6 skeletal muscle cells. Venom induced a marginal but significant reduction in plasma clotting times at concentrations above 7.8 µg/100 µL of plasma, indicating poor procoagulant effects. In addition, the results of western immunoblotting indicate strong immunoreactivity with venom proteins, thus warranting further detailed studies on the neutralization of the effects of individual venom toxins by antivenom.