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Tackling Achilles' Heel in Synthetic Biology: Pairing Intracellular Synthesis of Noncanonical Amino Acids with Genetic-Code Expansion to Foster Biotechnological Applications.
Biava, HD
Chembiochem : a European journal of chemical biology. 2020;(9):1265-1273
Abstract
For the last two decades, synthetic biologists have been able to unlock and expand the genetic code, generating proteins with unique properties through the incorporation of noncanonical amino acids (ncAAs). These evolved biomaterials have shown great potential for applications in industrial biocatalysis, therapeutics, bioremediation, bioconjugation, and other areas. Our ability to continue developing such technologies depends on having relatively easy access to ncAAs. However, the synthesis of enantiomerically pure ncAAs in practical quantitates for large-scale processes remains a challenge. Biocatalytic ncAA production has emerged as an excellent alternative to traditional organic synthesis in terms of cost, enantioselectivity, and sustainability. Moreover, biocatalytic synthesis offers the opportunity of coupling the intracellular generation of ncAAs with genetic-code expansion to overcome the limitations of an external supply of amino acid. In this minireview, we examine some of the most relevant achievements of this approach and its implications for improving technological applications derived from synthetic biology.
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Antioxidant Proteins' Identification Based on Support Vector Machine.
Xu, Y, Wen, Y, Han, G
Combinatorial chemistry & high throughput screening. 2020;(4):319-325
Abstract
BACKGROUND Evidence have increasingly indicated that for human disease, cell metabolism are deeply associated with proteins. Structural mutations and dysregulations of these proteins contribute to the development of the complex disease. Free radicals are unstable molecules that seek for electrons from the surrounding atoms for stability. Once a free radical binds to an atom in the body, a chain reaction occurs, which causes damage to cells and DNA. An antioxidant protein is a substance that protects cells from free radical damage. Accurate identification of antioxidant proteins is important for understanding their role in delaying aging and preventing and treating related diseases. Therefore, computational methods to identify antioxidant proteins have become an effective prior-pinpointing approach to experimental verification. METHODS In this study, support vector machines was used to identify antioxidant proteins, using amino acid compositions and 9-gap dipeptide compositions as feature extraction, and feature reduction by Principal Component Analysis. RESULTS The prediction accuracy Acc of this experiment reached 98.38%, the recall rate Sn of the positive sample was found to be 99.27%, the recall rate Sp of the negative sample reached 97.54%, and the MCC value was 0.9678. To evaluate our proposed method, the predictive performance of 20 antioxidant proteins from the National Center for Biotechnology Information(NCBI) was studied. As a result, 20 antioxidant proteins were correctly predicted by our method. Experimental results demonstrate that the performance of our method is better than the state-of-the-art methods for identification of antioxidant proteins. CONCLUSION We collected experimental protein data from Uniport, including 253 antioxidant proteins and 1552 non-antioxidant proteins. The optimal feature extraction used in this paper is composed of amino acid composition and 9-gap dipeptide. The protein is identified by support vector machine, and the model evaluation index is obtained based on 5-fold cross-validation. Compared with the existing classification model, it is further explained that the SVM recognition model constructed in this paper is helpful for the recognition of antioxidized proteins.
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Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting.
Mahmud, SMH, Chen, W, Meng, H, Jahan, H, Liu, Y, Hasan, SMM
Analytical biochemistry. 2020;:113507
Abstract
Accurate identification of drug-target interaction (DTI) is a crucial and challenging task in the drug discovery process, having enormous benefit to the patients and pharmaceutical company. The traditional wet-lab experiments of DTI is expensive, time-consuming, and labor-intensive. Therefore, many computational techniques have been established for this purpose; although a huge number of interactions are still undiscovered. Here, we present pdti-EssB, a new computational model for identification of DTI using protein sequence and drug molecular structure. More specifically, each drug molecule is transformed as the molecular substructure fingerprint. For a protein sequence, different descriptors are utilized to represent its evolutionary, sequence, and structural information. Besides, our proposed method uses data balancing techniques to handle the imbalance problem and applies a novel feature eliminator to extract the best optimal features for accurate prediction. In this paper, four classes of DTI benchmark datasets are used to construct a predictive model with XGBoost. Here, the auROC is utilized as an evaluation metric to compare the performance of pdti-EssB method with recent methods, applying five-fold cross-validation. Finally, the experimental results indicate that our proposed method is able to outperform other approaches in predicting DTI, and introduces new drug-target interaction samples based on prediction probability scores. pdti-EssB webserver is available online at http://pdtiessb-uestc.com/.
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Effect of Apabetalone Added to Standard Therapy on Major Adverse Cardiovascular Events in Patients With Recent Acute Coronary Syndrome and Type 2 Diabetes: A Randomized Clinical Trial.
Ray, KK, Nicholls, SJ, Buhr, KA, Ginsberg, HN, Johansson, JO, Kalantar-Zadeh, K, Kulikowski, E, Toth, PP, Wong, N, Sweeney, M, et al
JAMA. 2020;(16):1565-1573
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Abstract
IMPORTANCE Bromodomain and extraterminal proteins are epigenetic regulators of gene transcription. Apabetalone is a selective bromodomain and extraterminal protein inhibitor targeting bromodomain 2 and is hypothesized to have potentially favorable effects on pathways related to atherothrombosis. Pooled phase 2 data suggest favorable effects on clinical outcomes. OBJECTIVE To test whether apabetalone significantly reduces major adverse cardiovascular events. DESIGN, SETTING, AND PARTICIPANTS A randomized, double-blind, placebo-controlled trial, conducted at 190 sites in 13 countries. Patients with an acute coronary syndrome in the preceding 7 to 90 days, type 2 diabetes, and low high-density lipoprotein cholesterol levels were eligible for enrollment, which started November 11, 2015, and ended July 4, 2018, with end of follow-up on July 3, 2019. INTERVENTIONS Patients were randomized (1:1) to receive apabetalone, 100 mg orally twice daily (n = 1215), or matching placebo (n = 1210) in addition to standard care. MAIN OUTCOMES AND MEASURES The primary outcome was a composite of time to the first occurrence of cardiovascular death, nonfatal myocardial infarction, or stroke. RESULTS Among 2425 patients who were randomized (mean age, 62 years; 618 women [25.6%]), 2320 (95.7%) had full ascertainment of the primary outcome. During a median follow-up of 26.5 months, 274 primary end points occurred: 125 (10.3%) in apabetalone-treated patients and 149 (12.4%) in placebo-treated patients (hazard ratio, 0.82 [95% CI, 0.65-1.04]; P = .11). More patients allocated to apabetalone than placebo discontinued study drug (114 [9.4%] vs 69 [5.7%]) for reasons including elevations of liver enzyme levels (35 [2.9%] vs 11 [0.9%]). CONCLUSIONS AND RELEVANCE Among patients with recent acute coronary syndrome, type 2 diabetes, and low high-density lipoprotein cholesterol levels, the selective bromodomain and extraterminal protein inhibitor apabetalone added to standard therapy did not significantly reduce the risk of major adverse cardiovascular events. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02586155.
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Individualised physical exercise training and enhanced protein intake in older citizens during municipality-based rehabilitation: protocol for a randomised controlled trial.
Teljigovic, S, Søgaard, K, Sandal, LF, Dalager, T, Nielsen, NO, Sjøgaard, G, Holm, L
BMJ open. 2020;(11):e041605
Abstract
INTRODUCTION Successful rehabilitation of the growing number of older citizens receiving healthcare services can lead to preservation of functional independence and improvement in quality of life. Adequate intake of dietary protein and physical training are key factors in counteracting the age-related decline in strength performance and physical function. However, during rehabilitation, many older people/persons have insufficient protein intake, and difficulties in performing exercise training with sufficient intensity and volume. The primary aim of this trial is to investigate if individualised physical exercise training programmes combined with increased protein intake (IPET+P) can improve measures on all International Classification of Functioning, Disability and Health levels, such as strength, gait speed and health-related quality of life, when compared with care as usual in municipality-based rehabilitation alone (usual care, UC) or care as usual in combination with increased protein intake (UC+P). Further, the trial investigates whether UC+P will potentiate more significant improvements in outcome measures than UC. METHODS AND ANALYSIS The trial is a three-armed multicentre, block-randomised controlled trial consisting of a 12-week intervention period with a 1-year follow-up. Citizens above 65 years referred to rehabilitation in the municipality without restricting comorbidities are eligible. Participants are randomised to either a UC group, a UC group with protein supplementation receiving 27.5 g protein/day (UC+P), or an IPET+P supplementation of 27.5 g protein/day. The Short Musculoskeletal Function Assessment questionnaire is the primary outcome. ETHICS AND DISSEMINATION Approvals from The Ethics Committee in Region Zealand, Denmark (SJ-758), and the General Data Protection Regulation at the University of Southern Denmark, Odense (10.330) have been obtained. TRIAL REGISTRATION NUMBER NCT04091308.
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Proteomic analysis of follicular fluid during human ovulation.
Zakerkish, F, Brännström, M, Carlsohn, E, Sihlbom, C, van der Post, S, Thoroddsen, A
Acta obstetricia et gynecologica Scandinavica. 2020;(7):917-924
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Abstract
INTRODUCTION Human ovulation is a biologically complex process that involves several biochemical factors, promoting follicular rupture and release of a fertilizable oocyte. Proteins which are present in follicular fluid at high concentrations during ovulation are likely to be active participants in the biochemical pathways of ovulation. The aim of the study was to identify, by use of a modern proteomic technique, proteins of human follicular fluid which are differentially regulated during ovulation of the natural menstrual cycle. MATERIAL AND METHODS This prospective experimental study over 3 years included women planned for laparoscopic sterilization. During surgery, retrieval of the dominant follicle was performed either at the preovulatory stage or during ovulation. Four women of preovulatory phase and four women of ovulatory phase met the predetermined criteria of hormone levels for respective phases, and samples of these were finally included out of the 15 women operated. Follicular fluid was aspirated from the excised follicle and subjected to mass spectrometry with the isobaric tags for relative and absolute quantification (iTRAQ) technology for isobaric tagging of peptides. This enables simultaneous identification and quantification of proteins. The protein profiles of the follicular fluid of the preovulatory phase and the ovulatory phase were analyzed, and proteins that were present were identified. RESULTS A total of 502 proteins were identified, several of which previously have not been identified in human follicular fluid. Of the 115 proteins that were found in all samples, 20 proteins were at higher levels during ovulation. These were inflammatory-related proteins, coagulation factors, proteins in lipid metabolism, complement factors and antioxidants. Five proteins were present in lower levels during ovulation, with three being enzymes and the other two proteins of lipid metabolism and iron transport. CONCLUSIONS Twenty-five follicular fluid proteins, with differential regulation during ovulation, were identified in human follicular fluid of the natural menstrual cycle. These proteins may have essential roles in the ovulatory cascade.
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Amalgamation of 3D structure and sequence information for protein-protein interaction prediction.
Jha, K, Saha, S
Scientific reports. 2020;(1):19171
Abstract
Protein is the primary building block of living organisms. It interacts with other proteins and is then involved in various biological processes. Protein-protein interactions (PPIs) help in predicting and hence help in understanding the functionality of the proteins, causes and growth of diseases, and designing new drugs. However, there is a vast gap between the available protein sequences and the identification of protein-protein interactions. To bridge this gap, researchers proposed several computational methods to reveal the interactions between proteins. These methods merely depend on sequence-based information of proteins. With the advancement of technology, different types of information related to proteins are available such as 3D structure information. Nowadays, deep learning techniques are adopted successfully in various domains, including bioinformatics. So, current work focuses on the utilization of different modalities, such as 3D structures and sequence-based information of proteins, and deep learning algorithms to predict PPIs. The proposed approach is divided into several phases. We first get several illustrations of proteins using their 3D coordinates information, and three attributes, such as hydropathy index, isoelectric point, and charge of amino acids. Amino acids are the building blocks of proteins. A pre-trained ResNet50 model, a subclass of a convolutional neural network, is utilized to extract features from these representations of proteins. Autocovariance and conjoint triad are two widely used sequence-based methods to encode proteins, which are used here as another modality of protein sequences. A stacked autoencoder is utilized to get the compact form of sequence-based information. Finally, the features obtained from different modalities are concatenated in pairs and fed into the classifier to predict labels for protein pairs. We have experimented on the human PPIs dataset and Saccharomyces cerevisiae PPIs dataset and compared our results with the state-of-the-art deep-learning-based classifiers. The results achieved by the proposed method are superior to those obtained by the existing methods. Extensive experimentations on different datasets indicate that our approach to learning and combining features from two different modalities is useful in PPI prediction.
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Advances in the discovery of novel biomarkers for cancer: spotlight on protein N-glycosylation.
Zhang, W, Yang, Z, Gao, X, Wu, Q
Biomarkers in medicine. 2020;(11):1031-1045
Abstract
Progress on glycosylation and tumor markers has not been extensively reported. Glycosylation plays an important part in post-translational modification. Previous research on glycosylation-modified biomarkers has lagged behind due to insufficient understanding of glycosylation-related regulations. However, some new methods and ideas illustrated in recent research may provide new inspirations in the field. This article aims to review current advances in revealing relationship between tumors and abnormal N-glycosylation and discuss leading-edge applications of N-glycosylation in developing novel tumor biomarkers.
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Stark fluorescence spectroscopy on peridinin-chlorophyll-protein complex of dinoflagellate, Amphidinium carterae.
Ara, AM, Shakil Bin Kashem, M, van Grondelle, R, Wahadoszamen, M
Photosynthesis research. 2020;(3):233-239
Abstract
Because of their peculiar but intriguing photophysical properties, peridinin-chlorophyll-protein complexes (PCPs), the peripheral light-harvesting antenna complexes of photosynthetic dinoflagellates have been unique targets of multidimensional theoretical and experimental investigations over the last few decades. The major light-harvesting chlorophyll a (Chl a) pigments of PCP are hypothesized to be spectroscopically heterogeneous. To study the spectral heterogeneity in terms of electrostatic parameters, we, in this study, implemented Stark fluorescence spectroscopy on PCP isolated from the dinoflagellate Amphidinium carterae. The comprehensive theoretical modeling of the Stark fluorescence spectrum with the help of the conventional Liptay formalism revealed the simultaneous presence of three emission bands in the fluorescence spectrum of PCP recorded upon excitation of peridinin. The three emission bands are found to possess different sets of electrostatic parameters with essentially increasing magnitude of charge-transfer character from the blue to redder ones. The different magnitudes of electrostatic parameters give good support to the earlier proposition that the spectral heterogeneity in PCP results from emissive Chl a clusters anchored at a different sites and domains within the protein network.
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Whole-body protein kinetics in critically ill patients during 50 or 100% energy provision by enteral nutrition: A randomized cross-over study.
Sundström Rehal, M, Liebau, F, Wernerman, J, Rooyackers, O
PloS one. 2020;(10):e0240045
Abstract
BACKGROUND Enteral nutrition (EN) is a ubiquitous intervention in ICU patients but there is uncertainty regarding the optimal dose, timing and importance for patient-centered outcomes during critical illness. Our research group has previously found an improved protein balance during normocaloric versus hypocaloric parenteral nutrition in neurosurgical ICU patients. We now wanted to investigate if this could be demonstrated in a general ICU population with established enteral feeding, including patients on renal replacement therapy. METHODS Patients with EN >80% of energy target as determined by indirect calorimetry were randomized to or 50% or 100% of current EN rate. After 24 hours, whole-body protein kinetics were determined by enteral and parenteral stable isotope tracer infusions. Treatment allocation was then switched, and tracer investigations repeated 24 hours later in a crossover design with patients serving as their own controls. RESULTS Six patients completed the full protocol. During feeding with 100% EN all patients received >1.2 g/kg/day of protein. Mean whole-body protein balance increased from -6.07 to 2.93 µmol phenylalanine/kg/h during 100% EN as compared to 50% (p = 0.044). The oxidation rate of phenylalanine was unaltered (p = 0.78). CONCLUSIONS It is possible to assess whole-body protein turnover using a stable isotope technique in critically ill patients during enteral feeding and renal replacement therapy. Our results also suggest a better whole-body protein balance during full dose as compared to half dose EN. As the sample size was smaller than anticipated, this finding should be confirmed in larger studies.