Towards a generic prototyping approach for therapeutically-relevant peptides and proteins in a cell-free translation system

Advances in peptide and protein therapeutics increased the need for rapid and cost-effective polypeptide prototyping. While in vitro translation systems are well suited for fast and multiplexed polypeptide prototyping, they suffer from misfolding, aggregation and disulfide-bond scrambling of the translated products. Here we propose that efficient folding of in vitro produced disulfide-rich peptides and proteins can be achieved if performed in an aggregation-free and thermodynamically controlled folding environment. To this end, we modify an E. coli-based in vitro translation system to allow co-translational capture of translated products by affinity matrix. This process reduces protein aggregation and enables productive oxidative folding and recycling of misfolded states under thermodynamic control. In this study we show that the developed approach is likely to be generally applicable for prototyping of a wide variety of disulfide-constrained peptides, macrocyclic peptides with non-native bonds and antibody fragments in amounts sufficient for interaction analysis and biological activity assessment.

Is Protein Folding a Thermodynamically Unfavorable, Active, Energy-Dependent Process?

The prevailing current view of protein folding is the thermodynamic hypothesis, under which the native folded conformation of a protein corresponds to the global minimum of Gibbs free energy G. We question this concept and show that the empirical evidence behind the thermodynamic hypothesis of folding is far from strong. Furthermore, physical theory-based approaches to the prediction of protein folds and their folding pathways so far have invariably failed except for some very small proteins, despite decades of intensive theory development and the enormous increase of computer power. The recent spectacular successes in protein structure prediction owe to evolutionary modeling of amino acid sequence substitutions enhanced by deep learning methods, but even these breakthroughs provide no information on the protein folding mechanisms and pathways. We discuss an alternative view of protein folding, under which the native state of most proteins does not occupy the global free energy minimum, but rather, a local minimum on a fluctuating free energy landscape. We further argue that ΔG of folding is likely to be positive for the majority of proteins, which therefore fold into their native conformations only through interactions with the energy-dependent molecular machinery of living cells, in particular, the translation system and chaperones. Accordingly, protein folding should be modeled as it occurs in vivo, that is, as a non-equilibrium, active, energy-dependent process.

Machine Translation System Using Deep Learning for English to Urdu

Machine translation is an ongoing field of research from the last decades. The main aim of machine translation is to remove the language barrier. Earlier research in this field started with the direct word-to-word replacement of source language by the target language. Later on, with the advancement in computer and communication technology, there was a paradigm shift to data-driven models like statistical and neural machine translation approaches. In this paper, we have used a neural network-based deep learning technique for English to Urdu languages. Parallel corpus sizes of around 30923 sentences are used. The corpus contains sentences from English-Urdu parallel corpus, news, and sentences which are frequently used in day-to-day life. The corpus contains 542810 English tokens and 540924 Urdu tokens, and the proposed system is trained and tested using 70 : 30 criteria. In order to evaluate the efficiency of the proposed system, several automatic evaluation metrics are used, and the model output is also compared with the output from Google Translator. The proposed model has an average BLEU score of 45.83.

Anuvaad - A Hindi-Sanskrit-Hindi Bilingual Machine Translation System using Rule-based Approach

Machine Translation is best alternative to traditional manual translation. The corpus of Sanskrit literature includes a rich tradition of philosophical and religious texts as well as poetry, music, drama, scientific, technical and other texts. Due to the modernization of tradition and languages, Sanskrit is not on everyone's lips. Translation makes it convenient for users to understand the unknown text. This paper presents a language Machine Translation System from Hindi to Sanskrit and Sanskrit to Hindi using a rule-based technique. We developed a machine translation tool 'anuvaad' which translates Sanskrit prose text into Hindi & vice versa. We also developed bi-lingual corpora to deal with Sanskrit and Hindi grammar rules and text applied rule based method to perform the translation. The experimental results on different 110 examples show that the proposed anuvaad tool achieves overall 93% accuracy for both types of translations. The objective of our work is to ensure confidentiality and multilingual support, which can be tedious and time consuming in case of manual translation.

Deep Learning Based TTS-STT Model with Transliteration for Indic Languages

This paper introduces a new text-to-speech presentation from end-to-end (E2E-TTS) using toolkit called ESPnet-TTS, which is an open source extension. ESPnet speech processing tools kit. Various models come under ESPnet TTS TacoTron 2, Transformer TTS, and Fast Speech. This also provides recipes recommended by the Kaldi speech recognition tool kit (ASR). Recipes based on the composition combined with the ESPnet ASR recipe, which provides high performance. This toolkit also provides pre-trained models and samples of all recipes for users to use as a base .It works on TTS-STT and translation features for various indicator languages, with a strong focus on English, Marathi and Hindi. This paper also shows that neural sequence-to-sequence models find the state of the art or near the effects of the art state on existing databases. We also analyze some of the key design challenges that contribute to the development of a multilingual business translation system, which includes processing bilingual business data sets and evaluating multiple translation methods. The test result can be obtained using tokens and these test results show that our models can achieve modern performance compared to the latest LJ Speech tool kit data. Terms of Reference — Open source, end-to-end, text-to-speech

Stable m7G Cap-Distal 5'UTR Hairpin Structure Mediates Distinct 40S and 60S Binding Dynamics

The 5' untranslated region (UTR) of diverse mRNAs contains secondary structures that can influence protein synthesis by modulating the initiation step of translation. Studies support the ability of these structures to inhibit 40S subunit recruitment and scanning, but the dynamics of ribosomal subunit interactions with mRNA remain poorly understood. Here, we developed a reconstituted Saccharomyces cerevisiae cell-free translation system with fluorescently labeled ribosomal subunits. We applied this extract and single-molecule fluorescence microscopy to monitor, in real time, individual 40S and 60S interactions with mRNAs containing 5' UTR hairpin structures with varying thermostability. In comparison to mRNAs containing no or weak 5' UTR hairpins (ΔG >= -5.4 kcal/mol), mRNAs with stable hairpins (ΔG <= -16.5 kcal/mol) showed reduced numbers of 60S recruitment to mRNA, consistent with the expectation of reduced translation efficiency for such mRNAs. Interestingly, such mRNAs showed increased numbers of 40S recruitment events to individual mRNAs but with shortened duration on mRNA. Correlation analysis showed that these unstable 40S binding events were nonproductive for 60S recruitment. Furthermore, although the mRNA sequence is long enough to accommodate multiple 40S, individual mRNAs are predominantly observed to engage with a single 40S at a time, indicating the sequestering of mRNA 5' end by initiating 40S. Altogether, these observations suggest that stable cap-distal hairpins in 5' UTR reduce initiation and translation efficiency by destabilizing 40S-mRNA interactions and promoting 40S dissociation from mRNA. The premature 40S dissociation frees mRNA 5'-end accessibility for new initiation events, but the increased rate of 40S recruitment is insufficient to compensate for the reduction of initiation efficiency due to premature 40S dissociation. This study provides the first single-molecule kinetic characterization of 40S/60S interactions with mRNA during cap-dependent initiation and the modulation of such interactions by cap-distal 5' UTR hairpin structures.