scholarly journals Biochemistry of fluoroprolines: the prospect of making fluorine a bioelement

2021 ◽  
Vol 17 ◽  
pp. 439-460
Author(s):  
Vladimir Kubyshkin ◽  
Rebecca Davis ◽  
Nediljko Budisa
Keyword(s):  
Amino Acids ◽  
Protein Expression ◽  
Physicochemical Properties ◽  
Protein Production ◽  
Living Cells ◽  
Evolutionary Methods ◽  
Hypothetical Scenario ◽  
Synthetic Cells ◽  
A Cell ◽  
Heterocyclic Structure

Due to the heterocyclic structure and distinct conformational profile, proline is unique in the repertoire of the 20 amino acids coded into proteins. Here, we summarize the biochemical work on the replacement of proline with (4R)- and (4S)-fluoroproline as well as 4,4-difluoroproline in proteins done mainly in the last two decades. We first recapitulate the complex position and biochemical fate of proline in the biochemistry of a cell, discuss the physicochemical properties of fluoroprolines, and overview the attempts to use these amino acids as proline replacements in studies of protein production and folding. Fluorinated proline replacements are able to elevate the protein expression speed and yields and improve the thermodynamic and kinetic folding profiles of individual proteins. In this context, fluoroprolines can be viewed as useful tools in the biotechnological toolbox. As a prospect, we envision that proteome-wide proline-to-fluoroproline substitutions could be possible. We suggest a hypothetical scenario for the use of laboratory evolutionary methods with fluoroprolines as a suitable vehicle to introduce fluorine into living cells. This approach may enable creation of synthetic cells endowed with artificial biodiversity, containing fluorine as a bioelement.

Recognition of Myeloma by MAGE-A3 Specific Cytotoxic T Lymphocytes Induced by Treatment with Azacitidine and MGCD0103

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 3674-3674
Author(s):  
Amberly Moreno-Bost ◽  
Susann Szmania ◽  
Katie Stone ◽  
Jumei Shi ◽  
Tarun K. Garg ◽  
...  
Keyword(s):  
Protein Expression ◽  
Cell Line ◽  
Protein Production ◽  
Sequential Treatment ◽  
Rna Expression ◽  
Specific Lysis ◽  
Demethylating Agents ◽  
Specific Ctls ◽  
A Cell

Abstract Demethylating agents and histone deacetylase inhibitors (HDACi) are epigenetic modulators that can induce re-expression of tumor suppressor and cell cycle proteins that have been silenced through aberrant hypermethylation associated with tumoral transformation. Azacitidine (Aza) is a cytosine analogue that primarily affects RNA during transcription. However, DNA is also a target for demethylation during replication thereby increasing gene re-expression. Treatment with HDACi, such as MGCD0103 (MGC), can synergize with demethylating agents to boost the epigenetic effects of either drug used alone. Our gene expression profiling data shows that the cancer-testis antigen MAGE-A3 is expressed in 31% of myeloma patients at diagnosis and the frequency of expression is increased at relapse to 49% (n=51 paired samples, p<0.001, unpublished data). However, expression of MAGE-A3 is often heterogeneous. We hypothesized that the combination of Aza and MGC could induce MAGE-A3 expression, thus facilitating killing of myeloma cells by MAGE-A3 specific CTLs isolated from a HLA-A68 positive patient post MAGE-A3 protein vaccination (J Immunother2007; 30:847). The MAGE-A3 negative myeloma cell line LP1 was first transfected with HLA-A68. MAGE-A3 protein production was optimized by dose finding and time course experiments using Aza alone or Aza and MGC sequentially. Induction of MAGE-A3 RNA expression was assessed by real time PCR and protein expression by Western blotting. 51Cr-release assays were used to measure killing of Aza/MGC treated cell lines by MAGE-A3 specific CTLs. MAGE-A3 RNA expression was detected in LP1-A68 treated with 500nM Aza for 3 days and expression was enhanced by sequential treatment with 1mM MGC for 1 day when compared to Aza treatment alone. However, protein expression was low. In an effort to optimize protein production, we increased the time of treatment with 500nM Aza to 5 days and with 500nM MGC to 2 days. After this sequential treatment, protein was clearly expressed (Figure 1) and LP1-A68 cells were killed by MAGE-A3 specific CTLs (specific lysis: 70% ± 9% at E:T ratio of 5:1), whilst untreated controls only showed background killing (specific lysis: 12% ± 5%) (Figure 2). Repeat experiments are in progress to verify these results. 500nM Aza in vitro is comparable to a clinically achievable in vivo dose of 12.5mg/m2 (Leukemia2008; 22:965). 500nM MGC is comparable to a 280mg/m2in vivo dose (Blood2006; 108: 1954). Additional titration experiments with MGC will be tested to achieve clinically relevant concentrations in vivo that can induce MAGE-A3 expression. In conclusion, epigenetic modulation by Aza and MGC can enhance MAGE-A3 expression and result in increased killing by MAGE-A3 specific CTLs. Hypomethylating agents and HDACi may be useful to sensitize tumor cells to immune effectors. Figure 1. Treatment with 50nM Aza and/or sequential MGC at 500 nM induces de nono expression of MAGE-A3 protein in the myeloma a cell line transfectant LP1 A68. Figure 1. Treatment with 50nM Aza and/or sequential MGC at 500 nM induces de nono expression of MAGE-A3 protein in the myeloma a cell line transfectant LP1 A68. Figure 2. Lysis of LP-1 A68 Aza/MGC treated targets by MAGE-A3 specific CTL effective. Figure 2. Lysis of LP-1 A68 Aza/MGC treated targets by MAGE-A3 specific CTL effective.

scholarly journals Simplified Methodology for a Modular and Genetically Expanded Protein Synthesis in Cell-Free Systems

2019 ◽  
Author(s):  
Yonatan Chemla ◽  
Eden Ozer ◽  
Michael Shaferman ◽  
Ben Zaad ◽  
Rambabu Dandela ◽  
...  
Keyword(s):  
Amino Acids ◽  
Genetic Code ◽  
Unnatural Amino Acids ◽  
High Efficiency ◽  
Living Cells ◽  
Trna Synthetase ◽  
Complex Nature ◽  
Reporter Protein ◽  
A Cell ◽  
Genetic Code Expansion

ABSTRACTGenetic code expansion, which enables the site-specific incorporation of unnatural amino acids into proteins, has emerged as a new and powerful tool for protein engineering. Currently, it is mainly utilized inside living cells for a myriad of applications. However, utilization of this technology in a cell-free, reconstituted platform has several advantages over living systems. The common limitations to the employment of these systems are the laborious and complex nature of its preparation and utilization. Herein, we describe a simplified method for the preparation of this system from Escherichia coli cells, which is specifically adapted for the expression of the components needed for cell-free genetic code expansion. In addition, we propose and demonstrate a modular approach to its utilization. By this approach, it is possible to prepare and store different extracts, harboring various translational components, and mix and match them as needed for more than four years retaining its high efficiency. We demonstrate this with the simultaneous incorporation of two different unnatural amino acids into a reporter protein. Finally, we demonstrate the advantage of cell-free systems over living cells for the incorporation of δ-thio-boc-lysine into ubiquitin by using the methanosarcina mazei wild-type pyrrolysyl tRNACUA and tRNA-synthetase pair, which can not be achieved in a living cell.

scholarly journals Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2

2021 ◽  
Author(s):  
Bo Wang ◽  
Eric R. Gamazon
Keyword(s):  
Neural Networks ◽  
Amino Acids ◽  
Protein Expression ◽  
Physicochemical Properties ◽  
Binding Affinity ◽  
Convolutional Neural Networks ◽  
Strong Dependence ◽  
Independent Study ◽  
Solvent Accessible Surface Area ◽  
Good Prediction

ABSTRACTBiochemical phenotypes are major indexes for protein structure and function characterization. They are determined, at least in part, by the intrinsic physicochemical properties of amino acids and may be reflected in the protein three-dimensional structure. Modeling mutational effects on biochemical phenotypes is a critical step for understanding protein function and disease mechanism as well as enabling drug discovery. Deep Mutational Scanning (DMS) experiments have been performed on SARS-CoV-2’s spike receptor binding domain and the human ACE2 zinc-binding peptidase domain – both central players in viral infection and evolution and antibody evasion - quantifying how mutations impact binding affinity and protein expression. Here, we modeled biochemical phenotypes from massively parallel assays, using convolutional neural networks trained on protein sequence mutations in the virus and human host. We found that neural networks are significantly predictive of binding affinity, protein expression, and antibody escape, learning complex interactions and higher-order features that are difficult to capture with conventional methods from structural biology. Integrating the intrinsic physicochemical properties of amino acids, including hydrophobicity, solvent-accessible surface area, and long-range non-bonded energy per atom, significantly improved prediction (empirical p<0.01) though there was such a strong dependence on the sequence data alone to yield reasonably good prediction. We observed concordance of the DMS data and our neural network predictions with an independent study on intermolecular interactions from molecular dynamics (multiple 500 ns or 1 μs all-atom) simulations of the spike protein-ACE2 interface, with critical implications for the use of deep learning to dissect molecular mechanisms. The mutation- or genetically-determined component of a biochemical phenotype estimated from the neural networks has improved causal inference properties relative to the original phenotype and can facilitate crucial insights into disease pathophysiology and therapeutic design.

scholarly journals A User’s Guide to Cell-Free Protein Synthesis

2019 ◽  
Vol 2 (1) ◽  
pp. 24 ◽  
Author(s):  
Nicole E. Gregorio ◽  
Max Z. Levine ◽  
Javin P. Oza
Keyword(s):  
Protein Synthesis ◽  
Protein Expression ◽  
Living Cells ◽  
Development Education ◽  
Free Protein ◽  
Therapeutic Development ◽  
A Cell ◽  
New Applications ◽  
Cell Free Protein Synthesis

Cell-free protein synthesis (CFPS) is a platform technology that provides new opportunities for protein expression, metabolic engineering, therapeutic development, education, and more. The advantages of CFPS over in vivo protein expression include its open system, the elimination of reliance on living cells, and the ability to focus all system energy on production of the protein of interest. Over the last 60 years, the CFPS platform has grown and diversified greatly, and it continues to evolve today. Both new applications and new types of extracts based on a variety of organisms are current areas of development. However, new users interested in CFPS may find it challenging to implement a cell-free platform in their laboratory due to the technical and functional considerations involved in choosing and executing a platform that best suits their needs. Here we hope to reduce this barrier to implementing CFPS by clarifying the similarities and differences amongst cell-free platforms, highlighting the various applications that have been accomplished in each of them, and detailing the main methodological and instrumental requirement for their preparation. Additionally, this review will help to contextualize the landscape of work that has been done using CFPS and showcase the diversity of applications that it enables.

scholarly journals Use of Amino Acids as Inducers for High-Level Protein Expression in the Single-Protein Production System

2010 ◽  
Vol 76 (21) ◽  
pp. 7371-7371
Author(s):  
S. Thangminlal Vaiphei ◽  
Lili Mao ◽  
Tsutomu Shimazu ◽  
Jung-Ho Park ◽  
Masayori Inouye
Keyword(s):  
Amino Acids ◽  
Protein Expression ◽  
Production System ◽  
Protein Production ◽  
Single Protein ◽  
High Level

scholarly journals How Do Cells Adapt? Stories Told in Landscapes

2020 ◽  
Vol 11 (1) ◽  
pp. 155-182 ◽  
Author(s):  
Luca Agozzino ◽  
Gábor Balázsi ◽  
Jin Wang ◽  
Ken A. Dill
Keyword(s):  
Protein Folding ◽  
Protein Expression ◽  
Protein Production ◽  
Molecular Motor ◽  
Cell Populations ◽  
Protein Diffusion ◽  
Gene Effects ◽  
Testing Hypotheses ◽  
Changing Environments ◽  
A Cell

Cells adapt to changing environments. Perturb a cell and it returns to a point of homeostasis. Perturb a population and it evolves toward a fitness peak. We review quantitative models of the forces of adaptation and their visualizations on landscapes. While some adaptations result from single mutations or few-gene effects, others are more cooperative, more delocalized in the genome, and more universal and physical. For example, homeostasis and evolution depend on protein folding and aggregation, energy and protein production, protein diffusion, molecular motor speeds and efficiencies, and protein expression levels. Models provide a way to learn about the fitness of cells and cell populations by making and testing hypotheses.

A Study on Host Tropism Determinants of Influenza Virus Using Machine Learning

2020 ◽  
Vol 15 (2) ◽  
pp. 121-134 ◽  
Author(s):  
Eunmi Kwon ◽  
Myeongji Cho ◽  
Hayeon Kim ◽  
Hyeon S. Son
Keyword(s):  
Machine Learning ◽  
Amino Acids ◽  
Influenza Virus ◽  
Random Forest ◽  
Physicochemical Properties ◽  
Protein Sequences ◽  
Influenza Viruses ◽  
Host Tropism ◽  
Post Hoc ◽  
Ha Protein

Background: The host tropism determinants of influenza virus, which cause changes in the host range and increase the likelihood of interaction with specific hosts, are critical for understanding the infection and propagation of the virus in diverse host species. Methods: Six types of protein sequences of influenza viral strains isolated from three classes of hosts (avian, human, and swine) were obtained. Random forest, naïve Bayes classification, and knearest neighbor algorithms were used for host classification. The Java language was used for sequence analysis programming and identifying host-specific position markers. Results: A machine learning technique was explored to derive the physicochemical properties of amino acids used in host classification and prediction. HA protein was found to play the most important role in determining host tropism of the influenza virus, and the random forest method yielded the highest accuracy in host prediction. Conserved amino acids that exhibited host-specific differences were also selected and verified, and they were found to be useful position markers for host classification. Finally, ANOVA analysis and post-hoc testing revealed that the physicochemical properties of amino acids, comprising protein sequences combined with position markers, differed significantly among hosts. Conclusion: The host tropism determinants and position markers described in this study can be used in related research to classify, identify, and predict the hosts of influenza viruses that are currently susceptible or likely to be infected in the future.

scholarly journals Amino Acids as the Potential Co-Former for Co-Crystal Development: A Review

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3279
Author(s):  
Ilma Nugrahani ◽  
Maria Anabella Jessica
Keyword(s):  
Amino Acids ◽  
Physicochemical Properties ◽  
Deep Eutectic Solvent ◽  
Strong Interactions ◽  
Green Method ◽  
Pharmaceutical Ingredients ◽  
Gastrointestinal Fluid ◽  
Non Covalent Interactions ◽  
Natural Deep Eutectic Solvent ◽  
Covalent Interactions

Co-crystals are one of the most popular ways to modify the physicochemical properties of active pharmaceutical ingredients (API) without changing pharmacological activity through non-covalent interactions with one or more co-formers. A “green method” has recently prompted many researchers to develop solvent-free techniques or minimize solvents for arranging the eco-friendlier process of co-crystallization. Researchers have also been looking for less-risk co-formers that produce the desired API’s physicochemical properties. This review purposed to collect the report studies of amino acids as the safe co-former and explored their advantages. Structurally, amino acids are promising co-former candidates as they have functional groups that can form hydrogen bonds and increase stability through zwitterionic moieties, which support strong interactions. The co-crystals and deep eutectic solvent yielded from this natural compound have been proven to improve pharmaceutical performance. For example, l-glutamine could reduce the side effects of mesalamine through an acid-base stabilizing effect in the gastrointestinal fluid. In addition, some amino acids, especially l-proline, enhances API’s solubility and absorption in its natural deep eutectic solvent and co-crystals systems. Moreover, some ionic co-crystals of amino acids have also been designed to increase chiral resolution. Therefore, amino acids are safe potential co-formers, which are suitable for improving the physicochemical properties of API and prospective to be developed further in the dosage formula and solid-state syntheses.

scholarly journals Mitigation of deleterious phenotypes in chloroplast-engineered plants accumulating high levels of foreign proteins

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jennifer A. Schmidt ◽  
Lubna V. Richter ◽  
Lisa A. Condoluci ◽  
Beth A. Ahner
Keyword(s):  
Protein Expression ◽  
Protein Production ◽  
Biomass Accumulation ◽  
Cellulase Production ◽  
Protein Yield ◽  
Total Soluble Protein ◽  
Foreign Protein ◽  
Active Components ◽  
Dwarf Phenotype ◽  
Mutant Phenotypes

Abstract Background The global demand for functional proteins is extensive, diverse, and constantly increasing. Medicine, agriculture, and industrial manufacturing all rely on high-quality proteins as major active components or process additives. Historically, these demands have been met by microbial bioreactors that are expensive to operate and maintain, prone to contamination, and relatively inflexible to changing market demands. Well-established crop cultivation techniques coupled with new advancements in genetic engineering may offer a cheaper and more versatile protein production platform. Chloroplast-engineered plants, like tobacco, have the potential to produce large quantities of high-value proteins, but often result in engineered plants with mutant phenotypes. This technology needs to be fine-tuned for commercial applications to maximize target protein yield while maintaining robust plant growth. Results Here, we show that a previously developed Nicotiana tabacum line, TetC-cel6A, can produce an industrial cellulase at levels of up to 28% of total soluble protein (TSP) with a slight dwarf phenotype but no loss in biomass. In seedlings, the dwarf phenotype is recovered by exogenous application of gibberellic acid. We also demonstrate that accumulating foreign protein represents an added burden to the plants’ metabolism that can make them more sensitive to limiting growth conditions such as low nitrogen. The biomass of nitrogen-limited TetC-cel6A plants was found to be as much as 40% lower than wildtype (WT) tobacco, although heterologous cellulase production was not greatly reduced compared to well-fertilized TetC-cel6A plants. Furthermore, cultivation at elevated carbon dioxide (1600 ppm CO2) restored biomass accumulation in TetC-cel6A plants to that of WT, while also increasing total heterologous protein yield (mg Cel6A plant−1) by 50–70%. Conclusions The work reported here demonstrates that well-fertilized tobacco plants have a substantial degree of flexibility in protein metabolism and can accommodate considerable levels of some recombinant proteins without exhibiting deleterious mutant phenotypes. Furthermore, we show that the alterations to protein expression triggered by growth at elevated CO2 can help rebalance endogenous protein expression and/or increase foreign protein production in chloroplast-engineered tobacco.

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