A representation and deep learning model for annotating ubiquitylation sentences stating E3 ligase - substrate interaction

Background Ubiquitylation is an important post-translational modification of proteins that not only plays a central role in cellular coding, but is also closely associated with the development of a variety of diseases. The specific selection of substrate by ligase E3 is the key in ubiquitylation. As various high-throughput analytical techniques continue to be applied to the study of ubiquitylation, a large amount of ubiquitylation site data, and records of E3-substrate interactions continue to be generated. Biomedical literature is an important vehicle for information on E3-substrate interactions in ubiquitylation and related new discoveries, as well as an important channel for researchers to obtain such up to date data. The continuous explosion of ubiquitylation related literature poses a great challenge to researchers in acquiring and analyzing the information. Therefore, automatic annotation of these E3-substrate interaction sentences from the available literature is urgently needed. Results In this research, we proposed a model based on representation and attention mechanism based deep learning methods, to automatic annotate E3-substrate interaction sentences in biomedical literature. Focusing on the sentences with E3 protein inside, we applied several natural language processing methods and a Long Short-Term Memory (LSTM)-based deep learning classifier to train the model. Experimental results had proved the effectiveness of our proposed model. And also, the proposed attention mechanism deep learning method outperforms other statistical machine learning methods. We also created a manual corpus of E3-substrate interaction sentences, in which the E3 proteins and substrate proteins are also labeled, in order to construct our model. The corpus and model proposed by our research are definitely able to be very useful and valuable resource for advancement of ubiquitylation-related research. Conclusion Having the entire manual corpus of E3-substrate interaction sentences readily available in electronic form will greatly facilitate subsequent text mining and machine learning analyses. Automatic annotating ubiquitylation sentences stating E3 ligase-substrate interaction is significantly benefited from semantic representation and deep learning. The model enables rapid information accessing and can assist in further screening of key ubiquitylation ligase substrates for in-depth studies.

A predictive model for the ichnological suitability of the Jezero crater, Mars: searching for fossilized traces of life-substrate interactions in the 2020 Rover Mission Landing Site

Ichnofossils, the fossilized products of life-substrate interactions, are among the most abundant biosignatures on Earth and therefore they may provide scientific evidence of potential life that may have existed on Mars. Ichnofossils offer unique advantages in the search for extraterrestrial life, including the fact that they are resilient to processes that obliterate other evidence for past life, such as body fossils, as well as chemical and isotopic biosignatures. The goal of this paper is evaluating the suitability of the Mars 2020 Landing Site for ichnofossils. To this goal, we apply palaeontological predictive modelling, a technique used to forecast the location of fossil sites in uninvestigated areas on Earth. Accordingly, a geographic information system (GIS) of the landing site is developed. Each layer of the GIS maps the suitability for one or more ichnofossil types (bioturbation, bioerosion, biostratification structures) based on an assessment of a single attribute (suitability factor) of the Martian environment. Suitability criteria have been selected among the environmental attributes that control ichnofossil abundance and preservation in 18 reference sites on Earth. The goal of this research is delivered through three predictive maps showing which areas of the Mars 2020 Landing Site are more likely to preserve potential ichnofossils. On the basis of these maps, an ichnological strategy for the Perseverance rover is identified, indicating (1) 10 sites on Mars with high suitability for bioturbation, bioerosion and biostratification ichnofossils, (2) the ichnofossil types, if any, that are more likely to be present at each site, (3) the most efficient observation strategy for detecting eventual ichnofossils. The predictive maps and the ichnological strategy can be easily integrated in the existing plans for the exploration of the Jezero crater, realizing benefits in life-search efficiency and cost-reduction.

Direct observation of Hsp90-induced compaction in a protein chain

The chaperone Hsp90 is well known to undergo important conformational changes, which depend on nucleotide, co-chaperones, substrate interactions and post-translational modifications. Conversely, how the conformations of its unstable and disordered substrates are affected by Hsp90 is difficult to address experimentally, yet central to its function. Here, using optical tweezers and luciferase and glucocorticoid receptor substrates, we find that Hsp90 promotes local contractions in unfolded chains that drive their global compaction down to dimensions of folded states. This compaction has a gradual nature while showing small steps, is stimulated by ATP, and performs mechanical work against counteracting forces that expand the chain dimensions. The Hsp90 interactions suppress the formation of larger-scale folded, misfolded and aggregated structures. The observations support a model in which Hsp90 alters client conformations directly by promoting local intra-chain interactions while suppressing distant ones. We conjecture that chain compaction may be central to how Hsp90 protects unstable kinases and receptor clients, regulates their activity, and how Hsp90 cooperates with Hsp70.