Mutate and observe: utilizing deep neural networks to investigate the impact of mutations on translation initiation.

MOTIVATION: The primary regulatory step for protein synthesis is translation initiation, which makes it one of the fundamental steps in the central dogma of molecular biology. In recent years, a number of approaches relying on deep neural networks (DNNs) have demonstrated superb results for predicting translation initiation sites. These state-of-the art results indicate that DNNs are indeed capable of learning complex features that are relevant to the process of translation. Unfortunately, most of those research efforts that employ DNNs only provide shallow insights into the decision-making processes of the trained models and lack highly sought-after novel biologically relevant observations. RESULTS: By improving upon the state-of-the-art DNNs and large-scale human genomic datasets in the area of translation initiation, we propose an innovative computational methodology to get neural networks to explain what was learned from data. Our methodology, which relies on in silico point mutations, reveals that DNNs trained for translation initiation site detection correctly identify well-established biological signals relevant to translation, including (i) the importance of the Kozak sequence, (ii) the damaging consequences of ATG mutations in the 5'-untranslated region, (iii) the detrimental effect of premature stop codons in the coding region, and (iv) the relative insignificance of cytosine mutations for translation. Furthermore, we delve deeper into the Beta-globin gene and investigate various mutations that lead to the Beta thalassemia disorder. Finally, we conclude our work by laying out a number of novel observations regarding mutations and translation initiation. AVAILABILITY AND IMPLEMENTATION: For data, models, and code, visit github.com/utkuozbulak/mutate-and-observe.

Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit.

While transcriptome- and proteome-wide technologies to assess processes in protein biogenesis are now widely available, we still lack global approaches to assay post-ribosomal biogenesis events, in particular those occurring in the eukaryotic secretory system. We here develop a method, SECRiFY, to simultaneously assess the secretability of >105 protein fragments by two yeast species, S. cerevisiae and P. pastoris, using custom fragment libraries, surface display and a sequencing-based readout. Screening human proteome fragments with a median size of 50-100 amino acids, we generate datasets that enable datamining into protein features underlying secretability, revealing a striking role for intrinsic disorder and chain flexibility. The SECRiFY methodology generates sufficient amounts of annotated data for advanced machine learning methods to deduce secretability patterns. The finding that secretability is indeed a learnable feature of protein sequences provides a solid base for application-focused studies.

ToxDL: deep learning using primary structure and domain embeddings for assessing protein toxicity.

MOTIVATION: Genetically engineering food crops involves introducing proteins from other species into crop plant species or modifying already existing proteins with gene editing techniques. In addition, newly synthesized proteins can be used as therapeutic protein drugs against diseases. For both research and safety regulation purposes, being able to assess the potential toxicity of newly introduced/synthesized proteins is of high importance. RESULTS: In this study, we present ToxDL, a deep learning-based approach for in silico prediction of protein toxicity from sequence alone. ToxDL consists of (i) a module encompassing a convolutional neural network that has been designed to handle variable-length input sequences, (ii) a domain2vec module for generating protein domain embeddings and (iii) an output module that classifies proteins as toxic or non-toxic, using the outputs of the two aforementioned modules. Independent test results obtained for animal proteins and cross-species transferability results obtained for bacteria proteins indicate that ToxDL outperforms traditional homology-based approaches and state-of-the-art machine-learning techniques. Furthermore, through visualizations based on saliency maps, we are able to verify that the proposed network learns known toxic motifs. Moreover, the saliency maps allow for directed in silico modification of a sequence, thus making it possible to alter its predicted protein toxicity. AVAILABILITY AND IMPLEMENTATION: ToxDL is freely available at http://www.csbio.sjtu.edu.cn/bioinf/ToxDL/. The source code can be found at https://github.com/xypan1232/ToxDL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Electrochromic Photodetectors: Toward Smarter Glasses and Nano Reflective Displays via an Electrolytic Mechanism.

Electrochromic devices, serving as smart glasses, have not yet been intelligent enough to regulate lighting conditions independent of external photosensing devices. On the other hand, their bulky sandwich structures have been suffering setbacks utilized for reflective displays in an effort to compete with mature emissive displays. The key to resolve both problems lies in incorporating the photosensing function into electrochromic devices while simplifying their configuration via replacing ionic electrolytes. However, so far it has not yet been achieved because of the essential operating difference between the optoelectronic devices and the ionic devices. Herein, a concept of a smarter and thinner device: "electrochromic photodetector" is proposed to solve such problems. It is all-solid-state and electrolyte-free and operates with a simple thin metal-semiconductor-metal structure via an electrolytic mechanism. As a proof of concept, a configuration of the electrochromic photodetector is presented in this work based on a tungsten trioxide (WO3) thin film deposited on Au electrodes via facile, low-cost solution processes. The electrochromic photodetector switches between its photosensing and electrochromic functions via voltage modulation within 5 V, which is the result of the semiconductor-metal transition. The transition mechanism is further analyzed to be the voltage-triggered reversible oxygen/water vapor adsorption/intercalation from ambient air.

SpliceRover: interpretable convolutional neural networks for improved splice site prediction.

Motivation: During the last decade, improvements in high-throughput sequencing have generated a wealth of genomic data. Functionally interpreting these sequences and finding the biological signals that are hallmarks of gene function and regulation is currently mostly done using automated genome annotation platforms, which mainly rely on integrated machine learning frameworks to identify different functional sites of interest, including splice sites. Splicing is an essential step in the gene regulation process, and the correct identification of splice sites is a major cornerstone in a genome annotation system. Results: In this paper, we present SpliceRover, a predictive deep learning approach that outperforms the state-of-the-art in splice site prediction. SpliceRover uses convolutional neural networks (CNNs), which have been shown to obtain cutting edge performance on a wide variety of prediction tasks. We adapted this approach to deal with genomic sequence inputs, and show it consistently outperforms already existing approaches, with relative improvements in prediction effectiveness of up to 80.9% when measured in terms of false discovery rate. However, a major criticism of CNNs concerns their 'black box' nature, as mechanisms to obtain insight into their reasoning processes are limited. To facilitate interpretability of the SpliceRover models, we introduce an approach to visualize the biologically relevant information learnt. We show that our visualization approach is able to recover features known to be important for splice site prediction (binding motifs around the splice site, presence of polypyrimidine tracts and branch points), as well as reveal new features (e.g. several types of exclusion patterns near splice sites). Availability and implementation: SpliceRover is available as a web service. The prediction tool and instructions can be found at http://bioit2.irc.ugent.be/splicerover/. Supplementary information: Supplementary data are available at Bioinformatics online.