Design and synthesis of farnesyl diphosphate analogues for modulating the chemistry of aristolochene synthase

A variety of farnesyl pyrophosphate (FPP) analogues were prepared by both published protocols and novel methodology, which was developed by modification and improvement of the traditional Suzuki-Miyaura coupling. These compounds were incubated with aristolochene synthase in order to probe its mechanism of action and resulted in many interesting results. The synthesis 12,13-difluoro farnesyl pyrophosphate was achieved in 13 steps using Suzuki-Miyaura chemistry. It proved to be a potent inhibitor of aristolochene synthase (AS), which revealed that the initial cyclisation to germacryl cation occurs in a concerted fashion. Analogue 2-fluoro FPP was synthesized and upon incubation with aristolochene synthase was converted to a single pentane extractable product according to GC/MS analysis. On the basis of NMR-analyses and GC-MS experiments this product was identified as 2-fluorogermacrene A. This work suggests that after an initial concerted cyclisation of FPP to germacryl cation, deprotonation leads to the formation of germacrene A and provides compelling evidence that germacrene A is indeed an on-pathway product of catalysis by aristolochene synthase. Analogue 6-fluoro FPP was prepared using the Weiler's chain extension method in 10 steps, and has been postulated to be give 6-fluoro germacrene A as product, which is consistent with published results on Epi-aristolochene synthase. 10-Fluoro FPP was made with sulfonylation-alkylation-desulfonylation methodology and it was found not to act as substrates of the AS. This result is fully consistent with the conclusions drawn from the results with 12,13-difluoro FPP. FPP analogues with one fluoro substituent at position C14 and CI5 were made using methodology employing the Horner-Emmons Wittig condensation as a key step. These two compounds were tested with AS and both gave one major extractable terpene product according to GC/MS analysis. These two products are postulated as two different compounds—14-fluoro aristolochene and 15-fluoro germacrene A respectively, because of the destabilizing effect of the p-substituted fluorine atom on the carbocation in their vicinity. Analogues of farnesyl pyrophosphate containing phenyl substituents in place of methyl groups have been prepared in syntheses that feature use of Suzuki-Miyaura reactions as key steps. These analogues were found not to act as substrates of the aristolochene synthase. However, they were potent competitive inhibitors of AS, which indicate that the active sites of terpene synthases are sufficiently flexible to accommodate even substrate analogues with large substituents suggesting a potential way for the generation of non-natural terpenoids.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Design and synthesis of farnesyl diphosphate analogues for modulating the chemistry of aristolochene synthase

A variety of farnesyl pyrophosphate (FPP) analogues were prepared by both published protocols and novel methodology, which was developed by modification and improvement of the traditional Suzuki-Miyaura coupling. These compounds were incubated with aristolochene synthase in order to probe its mechanism of action and resulted in many interesting results. The synthesis 12,13-difluoro farnesyl pyrophosphate was achieved in 13 steps using Suzuki-Miyaura chemistry. It proved to be a potent inhibitor of aristolochene synthase (AS), which revealed that the initial cyclisation to germacryl cation occurs in a concerted fashion. Analogue 2-fluoro FPP was synthesized and upon incubation with aristolochene synthase was converted to a single pentane extractable product according to GC/MS analysis. On the basis of NMR-analyses and GC-MS experiments this product was identified as 2-fluorogermacrene A. This work suggests that after an initial concerted cyclisation of FPP to germacryl cation, deprotonation leads to the formation of germacrene A and provides compelling evidence that germacrene A is indeed an on-pathway product of catalysis by aristolochene synthase. Analogue 6-fluoro FPP was prepared using the Weiler's chain extension method in 10 steps, and has been postulated to be give 6-fluoro germacrene A as product, which is consistent with published results on Epi-aristolochene synthase. 10-Fluoro FPP was made with sulfonylation-alkylation-desulfonylation methodology and it was found not to act as substrates of the AS. This result is fully consistent with the conclusions drawn from the results with 12,13-difluoro FPP. FPP analogues with one fluoro substituent at position C14 and CI5 were made using methodology employing the Horner-Emmons Wittig condensation as a key step. These two compounds were tested with AS and both gave one major extractable terpene product according to GC/MS analysis. These two products are postulated as two different compounds—14-fluoro aristolochene and 15-fluoro germacrene A respectively, because of the destabilizing effect of the p-substituted fluorine atom on the carbocation in their vicinity. Analogues of farnesyl pyrophosphate containing phenyl substituents in place of methyl groups have been prepared in syntheses that feature use of Suzuki-Miyaura reactions as key steps. These analogues were found not to act as substrates of the aristolochene synthase. However, they were potent competitive inhibitors of AS, which indicate that the active sites of terpene synthases are sufficiently flexible to accommodate even substrate analogues with large substituents suggesting a potential way for the generation of non-natural terpenoids

Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech

Modelling prosody variation is critical for synthesizing natural and expressive speech in end-to-end text-to-speech (TTS) systems. In this paper, a cross-utterance conditional VAE (CUC-VAE) is proposed to estimate a posterior probability distribution of the latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features obtained from both past and future sentences. At inference time, instead of the standard Gaussian distribution used by VAE, CUC-VAE allows sampling from an utterance-specific prior distribution conditioned on cross-utterance information, which allows the prosody features generated by the TTS system to be related to the context and is more similar to how humans naturally produce prosody. The performance of CUC-VAE is evaluated via a qualitative listening test for naturalness, intelligibility and quantitative measurements, including word error rates and the standard deviation of prosody attributes. Experimental results on LJ-Speech and LibriTTS data show that the proposed CUC-VAE TTS system improves naturalness and prosody diversity with clear margins

Cross-Utterance Conditioned VAE for Speech Generation

Speech synthesis systems powered by neural networks hold promise for multimedia production, but frequently face issues with producing expressive speech and seamless editing. In response, we present the Cross-Utterance Conditioned Variational Autoencoder speech synthesis (CUC-VAE S2) framework to enhance prosody and ensure natural speech generation. This framework leverages the powerful representational capabilities of pre-trained language models and the re-expression abilities of variational autoencoders (VAEs). The core component of the CUC-VAE S2 framework is the cross-utterance CVAE, which extracts acoustic, speaker, and textual features from surrounding sentences to generate context-sensitive prosodic features, more accurately emulating human prosody generation. We further propose two practical algorithms tailored for distinct speech synthesis applications: CUC-VAE TTS for text-to-speech and CUC-VAE SE for speech editing. The CUC-VAE TTS is a direct application of the framework, designed to generate audio with contextual prosody derived from surrounding texts. On the other hand, the CUC-VAE SE algorithm leverages real mel spectrogram sampling conditioned on contextual information, producing audio that closely mirrors real sound and thereby facilitating flexible speech editing based on text such as deletion, insertion, and replacement. Experimental results on the LibriTTS datasets demonstrate that our proposed models significantly enhance speech synthesis and editing, producing more natural and expressive speech.Comment: 13 pages

Structural bias in T4 RNA ligase-mediated 3′-adapter ligation

T4 RNA ligases are commonly used to attach adapters to RNAs, but large differences in ligation efficiency make detection and quantitation problematic. We developed a ligation selection strategy using random RNAs in combination with high-throughput sequencing to gain insight into the differences in efficiency of ligating pre-adenylated DNA adapters to RNA 3′-ends. After analyzing biases in RNA sequence, secondary structure and RNA-adapter cofold structure, we conclude that T4 RNA ligases do not show significant primary sequence preference in RNA substrates, but are biased against structural features within RNAs and adapters. Specifically, RNAs with less than three unstructured nucleotides at the 3′-end and RNAs that are predicted to cofold with an adapter in unfavorable structures are likely to be poorly ligated. The effect of RNA-adapter cofold structures on ligation is supported by experiments where the ligation efficiency of specific miRNAs was changed by designing adapters to alter cofold structure. In addition, we show that using adapters with randomized regions results in higher ligation efficiency and reduced ligation bias. We propose that using randomized adapters may improve RNA representation in experiments that include a 3′-adapter ligation step

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions