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1.
The Influences of Palindromes in mRNA on Protein Folding Rates.
Li, R, Li, H, Yang, S, Feng, X
Protein and peptide letters. 2020;(4):303-312
Abstract
BACKGROUND It is currently believed that protein folding rates are influenced by protein structure, environment and temperature, amino acid sequence and so on. We have been working for long to determine whether and in what ways mRNA affects the protein folding rate. A large number of palindromes aroused our attention in our previous research. Whether these palindromes do have important influences on protein folding rates and what's the mechanism? Very few related studies are focused on these problems. OBJECTIVE In this article, our motivation is to find out if palindromes have important influences on protein folding rates and what's the mechanism. METHODS In this article, the parameters of the palindromes were defined and calculated, the linear regression analysis between the values of each parameter and the experimental protein folding rates were done. Furthermore, to compare the results of different kinds of proteins, proteins were classified into the two-state proteins and the multi-state proteins. For the two kinds of proteins, the above linear regression analysis were performed respectively. RESULTS Protein folding rates were negatively correlated to the palindrome frequencies for all proteins. An extremely significant negative linear correlation appeared in the relationship between palindrome densities and protein folding rates. And the repeatedly used bases by different palindromes simultaneously have an important effect on the relationship between palindrome density and protein folding rate. CONCLUSION The palindromes have important influences on protein folding rates, and the repeatedly used bases in different palindromes simultaneously play a key role in influencing the protein folding rates.
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Metabolic modeling for predicting VFA production from protein-rich substrates by mixed-culture fermentation.
Regueira, A, Lema, JM, Carballa, M, Mauricio-Iglesias, M
Biotechnology and bioengineering. 2020;(1):73-84
Abstract
Proteinaceous organic wastes are suitable substrates to produce high added-value products in anaerobic mixed-culture fermentations. In these processes, the stoichiometry of the biotransformation depends highly on operational conditions such as pH or feeding characteristics and there are still no tools that allow the process to be directed toward those products of interest. Indeed, the lack of product selectivity strongly limits the potential industrial development of these bioprocesses. In this work, we developed a mathematical metabolic model for the production of volatile fatty acids from protein-rich wastes. In particular, the effect of pH on the product yields is analyzed and, for the first time, the observed changes are mechanistically explained. The model reproduces experimental results at both neutral and acidic pH and it is also capable of predicting the tendencies in product yields observed with a pH drop. It also offers mechanistic insights into the interaction among the different amino acids (AAs) of a particular protein and how an AA might yield different products depending on the relative abundance of other AAs. Particular emphasis is placed on the utility of this mathematical model as a process design tool and different examples are given on how to use the model for this purpose.
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3.
Recent Advances in the Development of Protein- and Peptide-Based Subunit Vaccines against Tuberculosis.
Bellini, C, Horváti, K
Cells. 2020;(12)
Abstract
The World Health Organization (WHO) herald of the "End TB Strategy" has defined goals and targets for tuberculosis prevention, care, and control to end the global tuberculosis endemic. The emergence of drug resistance and the relative dreadful consequences in treatment outcome has led to increased awareness on immunization against Mycobacterium tuberculosis (Mtb). However, the proven limited efficacy of Bacillus Calmette-Guérin (BCG), the only licensed vaccine against Mtb, has highlighted the need for alternative vaccines. In this review, we seek to give an overview of Mtb infection and failure of BCG to control it. Afterward, we focus on the protein- and peptide-based subunit vaccine subtype, examining the advantages and drawbacks of using this design approach. Finally, we explore the features of subunit vaccine candidates currently in pre-clinical and clinical evaluation, including the antigen repertoire, the exploited adjuvanted delivery systems, as well as the spawned immune response.
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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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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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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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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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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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10.
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.