Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Biochemistry
Title: Potential inhibitors of coronavirus 3-chymotrypsin-like protease (3CLpro): an in silico screening of alkaloids and terpenoids from African medicinal plants
Abstract: Abstract The novel coronavirus disease 2019 (COVID-19) caused by SARS-COV-2 has raised myriad of global concerns. There is currently no FDA approved antiviral strategy to alleviate the disease burden. The conserved 3-chymotrypsin-like protease (3CLpro), which controls coronavirus replication is a promising drug target for combating the coronavirus infection. This study screens some African plants derived alkaloids and terpenoids as potential inhibitors of coronavirus 3CLpro using in silico approach. Bioactive alkaloids (62) and terpenoids (100) of plants native to Africa were docked to the 3CLpro of the novel SARS-CoV-2. The top twenty alkaloids and terpenoids with high binding affinities to the SARS-CoV-2 3CLpro were further docked to the 3CLpro of SARS-CoV and MERS-CoV. The docking scores were compared with 3CLpro-referenced inhibitors (Lopinavir and Ritonavir). The top docked compounds were further subjected to ADEM/Tox and Lipinski filtering analyses for drug-likeness prediction analysis. This ligand-protein interaction study revealed that more than half of the top twenty alkaloids and terpenoids interacted favourably with the coronaviruses 3CLpro, and had binding affinities that surpassed that of lopinavir and ritonavir. Also, a highly defined hit-list of seven compounds (10-Hydroxyusambarensine, Cryptoquindoline, 6-Oxoisoiguesterin, 22-Hydroxyhopan-3-one, Cryptospirolepine, Isoiguesterin and 20-Epibryonolic acid) were identified. Furthermore, four non-toxic, druggable plant derived alkaloids (10-Hydroxyusambarensine, and Cryptoquindoline) and terpenoids (6-Oxoisoiguesterin and 22-Hydroxyhopan-3-one), that bind to the receptor-binding site and catalytic dyad of SARS-CoV-2 3CLpro were identified from the predictive ADME/tox and Lipinski filter analysis. However, further experimental analyses are required for developing these possible leads into natural anti-COVID-19 therapeutic agents for combating the pandemic. Communicated by Ramaswamy H. Sarma
Extracted statement — Bioactive compounds: 10-Hydroxyusambarensine, Cryptoquindoline, 6-Oxoisoiguesterin, 22-Hydroxyhopan-3-one, Cryptospirolepine, Isoiguesterin and 20-Epibryonolic acid
Research field: Bioinformatics
Title: Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment
Abstract: Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable for extending a pre-trained model to new workload conditions. Because brain dynamics are complex across different tasks, it is difficult to propose efficient human-designed features based on prior knowledge. Therefore, this paper proposes a concatenated structure of deep recurrent and 3D convolutional neural networks (R3DCNNs) to learn EEG features across different tasks without prior knowledge. First, this paper adds frequency and time dimensions to EEG topographic maps based on a Morlet wavelet transformation. Then, R3DCNN is proposed to simultaneously learn EEG features from the spatial, spectral, and temporal dimensions. The proposed model is validated based on the EEG signals collected from 20 subjects. This paper employs a binary classification of low and high mental workload across spatial n-back and arithmetic tasks. The results show that the R3DCNN achieves an average accuracy of 88.9%, which is a significant increase compared with that of the state-of-the-art methods. In addition, the visualization of the convolutional layers demonstrates that the deep neural network can extract detailed features. These results indicate that R3DCNN is capable of identifying the mental workload levels for cross-task conditions.
Extracted statement — Study cohort: 20 subjects
Research field: Bioinformatics
Title: Implementing LOINC – Current Status and Ongoing Work at a Medical University
Abstract: The Logical Observation Identifiers, Names and Codes (LOINC) is a common terminology used for standardizing laboratory terms. Within the consortium of the HiGHmed project, LOINC is one of the central terminologies used for health data sharing across all university sites. Therefore, linking the LOINC codes to the site-specific tests and measures is one crucial step to reach this goal. In this work we report our ongoing efforts in implementing LOINC to our laboratory information system and research infrastructure, as well as our challenges and the lessons learned. 407 local terms could be mapped to 376 LOINC codes of which 209 are already available to routine laboratory data. In our experience, mapping of local terms to LOINC is a widely manual and time consuming process for reasons of language and expert knowledge of local laboratory procedures.
Extracted statement — Data: 376 LOINC codes
Research field: Bioinformatics
Title: Deep learning based automatic diagnoses of attention deficit hyperactive disorder
Abstract: In this paper, we aim to develop a deep learning based automatic Attention Deficit Hyperactive Disorder (ADHD) diagnosis algorithm using resting state functional magnetic resonance imaging (rs-fMRI) scans. However, relative to millions of parameters in deep neural networks (DNN), the number of fMRI samples is still limited to learn discriminative features from the raw data. In light of this, we first encode our prior knowledge on 3D features voxel-wisely, including Regional Homogeneity (ReHo), fractional Amplitude of Low Frequency Fluctuations (fALFF) and Voxel-Mirrored Homotopic Connectivity (VMHC), and take these 3D images as the input to the DNN. Inspired by the way that radiologists examine brain images, we further investigate a novel 3D convolutional neural network (CNN) architecture to learn 3D local patterns which may boost the diagnosis accuracy. Investigation on the hold-out testing data of the ADHD-200 Global competition demonstrates that the proposed 3D CNN approach yields superior performances when compared to the reported classifiers in the literature, even with less training samples.
Extracted statement — Used models: 3D CNN
Research field: Bioinformatics
Title: Implementing LOINC – Current Status and Ongoing Work at a Medical University
Abstract: The Logical Observation Identifiers, Names and Codes (LOINC) is a common terminology used for standardizing laboratory terms. Within the consortium of the HiGHmed project, LOINC is one of the central terminologies used for health data sharing across all university sites. Therefore, linking the LOINC codes to the site-specific tests and measures is one crucial step to reach this goal. In this work we report our ongoing efforts in implementing LOINC to our laboratory information system and research infrastructure, as well as our challenges and the lessons learned. 407 local terms could be mapped to 376 LOINC codes of which 209 are already available to routine laboratory data. In our experience, mapping of local terms to LOINC is a widely manual and time consuming process for reasons of language and expert knowledge of local laboratory procedures.
Extracted statement — Data: 407 local terms
Research field: Bioinformatics
Title: LINNAEUS: A species name identification system for biomedical literature
Abstract: Abstract Background The task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles. Results In this paper we describe an open-source species name recognition and normalization software system, LINNAEUS, and evaluate its performance relative to several automatically generated biomedical corpora, as well as a novel corpus of full-text documents manually annotated for species mentions. LINNAEUS uses a dictionary-based approach (implemented as an efficient deterministic finite-state automaton) to identify species names and a set of heuristics to resolve ambiguous mentions. When compared against our manually annotated corpus, LINNAEUS performs with 94% recall and 97% precision at the mention level, and 98% recall and 90% precision at the document level. Our system successfully solves the problem of disambiguating uncertain species mentions, with 97% of all mentions in PubMed Central full-text documents resolved to unambiguous NCBI taxonomy identifiers. Conclusions LINNAEUS is an open source, stand-alone software system capable of recognizing and normalizing species name mentions with speed and accuracy, and can therefore be integrated into a range of bioinformatics and text-mining applications. The software and manually annotated corpus can be downloaded freely at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://linnaeus.sourceforge.net/" ext-link-type="uri">http://linnaeus.sourceforge.net/</jats:ext-link>.
Extracted statement — Results: 94% recall and 97% precision at the mention level
Research field: Bioinformatics
Title: LINNAEUS: A species name identification system for biomedical literature
Abstract: Abstract Background The task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles. Results In this paper we describe an open-source species name recognition and normalization software system, LINNAEUS, and evaluate its performance relative to several automatically generated biomedical corpora, as well as a novel corpus of full-text documents manually annotated for species mentions. LINNAEUS uses a dictionary-based approach (implemented as an efficient deterministic finite-state automaton) to identify species names and a set of heuristics to resolve ambiguous mentions. When compared against our manually annotated corpus, LINNAEUS performs with 94% recall and 97% precision at the mention level, and 98% recall and 90% precision at the document level. Our system successfully solves the problem of disambiguating uncertain species mentions, with 97% of all mentions in PubMed Central full-text documents resolved to unambiguous NCBI taxonomy identifiers. Conclusions LINNAEUS is an open source, stand-alone software system capable of recognizing and normalizing species name mentions with speed and accuracy, and can therefore be integrated into a range of bioinformatics and text-mining applications. The software and manually annotated corpus can be downloaded freely at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://linnaeus.sourceforge.net/" ext-link-type="uri">http://linnaeus.sourceforge.net/</jats:ext-link>.
Extracted statement — Results: 98% recall and 90% precision at the document level
Research field: Biophysics
Title: Differential Interaction of Antimicrobial Peptides with Lipid Structures Studied by Coarse-Grained Molecular Dynamics Simulations
Abstract: In this work; we investigated the differential interaction of amphiphilic antimicrobial peptides with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid structures by means of extensive molecular dynamics simulations. By using a coarse-grained (CG) model within the MARTINI force field; we simulated the peptide–lipid system from three different initial configurations: (a) peptides in water in the presence of a pre-equilibrated lipid bilayer; (b) peptides inside the hydrophobic core of the membrane; and (c) random configurations that allow self-assembled molecular structures. This last approach allowed us to sample the structural space of the systems and consider cooperative effects. The peptides used in our simulations are aurein 1.2 and maculatin 1.1; two well-known antimicrobial peptides from the Australian tree frogs; and molecules that present different membrane-perturbing behaviors. Our results showed differential behaviors for each type of peptide seen in a different organization that could guide a molecular interpretation of the experimental data. While both peptides are capable of forming membrane aggregates; the aurein 1.2 ones have a pore-like structure and exhibit a higher level of organization than those conformed by maculatin 1.1. Furthermore; maculatin 1.1 has a strong tendency to form clusters and induce curvature at low peptide–lipid ratios. The exploration of the possible lipid–peptide structures; as the one carried out here; could be a good tool for recognizing specific configurations that should be further studied with more sophisticated methodologies.
Extracted statement — Lead compound: Aurein 1.2
Research field: Biophysics
Title: Direct Visualization of Membrane Leakage Induced by the Antibiotic Peptides: Maculatin, Citropin, and Aurein
Abstract: Membrane lysis caused by antibiotic peptides is often rationalized by means of two different models: the so-called carpet model and the pore-forming model. We report here on the lytic activity of antibiotic peptides from Australian tree frogs, maculatin 1.1, citropin 1.1, and aurein 1.2, on POPC or POPC/POPG model membranes. Leakage experiments using fluorescence spectroscopy indicated that the peptide/lipid mol ratio necessary to induce 50% of probe leakage was smaller for maculatin compared with aurein or citropin, regardless of lipid membrane composition. To gain further insight into the lytic mechanism of these peptides we performed single vesicle experiments using confocal fluorescence microscopy. In these experiments, the time course of leakage for different molecular weight (water soluble) fluorescent markers incorporated inside of single giant unilamellar vesicles is observed after peptide exposure. We conclude that maculatin and its related peptides demonstrate a pore-forming mechanism (differential leakage of small fluorescent probe compared with high molecular weight markers). Conversely, citropin and aurein provoke a total membrane destabilization with vesicle burst without sequential probe leakage, an effect that can be assigned to a carpeting mechanism of lytic action. Additionally, to study the relevance of the proline residue on the membrane-action properties of maculatin, the same experimental approach was used for maculatin-Ala and maculatin-Gly (Pro-15 was replaced by Ala or Gly, respectively). Although a similar peptide/lipid mol ratio was necessary to induce 50% of leakage for POPC membranes, the lytic activity of maculatin-Ala and maculatin-Gly decreased in POPC/POPG (1:1 mol) membranes compared with that observed for the naturally occurring maculatin sequence. As observed for maculatin, the lytic action of Maculatin-Ala and maculatin-Gly is in keeping with the formation of pore-like structures at the membrane independently of lipid composition.
Extracted statement — Lead compound: Aurein 1.2
Research field: Biophysics
Title: Differential Stability of Aurein 1.2 Pores in Model Membranes of Two Probiotic Strains
Abstract: Aurein 1.2 is an antimicrobial peptide from the skin secretion of an Australian frog. In previous experimental work, we reported a differential action of aurein 1.2 on two probiotic strains Lactobacillus delbrueckii subsp. Bulgaricus (CIDCA331) and Lactobacillus delbrueckii subsp. Lactis (CIDCA133). The differences found were attributed to the bilayer compositions. Cell cultures and CIDCA331-derived liposomes showed higher susceptibility than the ones derived from the CIDCA133 strain, leading to content leakage and structural disruption. Here, we used Molecular Dynamics simulations to explore these systems at atomistic level. We hypothesize that if the antimicrobial peptides organized themselves to form a pore, it will be more stable in membranes that emulate the CIDCA331 strain than in those of the CIDCA133 strain. To test this hypothesis, we simulated pre-assembled aurein 1.2 pores embedded into bilayer models that emulate the two probiotic strains. It was found that the general behavior of the systems depends on the composition of the membrane rather than the pre-assemble system characteristics. Overall, it was observed that aurein 1.2 pores are more stable in the CIDCA331 model membranes. This fact coincides with the high susceptibility of this strain against antimicrobial peptide. In contrast, in the case of the CIDCA133 model membranes, peptides migrate to the water-lipid interphase, the pore shrinks and the transport of water through the pore is reduced. The tendency of glycolipids to make hydrogen bonds with peptides destabilize the pore structures. This feature is observed to a lesser extent in CIDCA 331 due to the presence of anionic lipids. Glycolipid transverse diffusion (flip-flop) between monolayers occurs in the pore surface region in all the cases considered. These findings expand our understanding of the antimicrobial peptide resistance properties of probiotic strains.
Extracted statement — Lead compound: Aurein 1.2
Research field: Biophysics
Title: Could Cardiolipin Protect Membranes against the Action of Certain Antimicrobial Peptides? Aurein 1.2, a Case Study
Abstract: The activity of a host of antimicrobial peptides has been examined against a range of lipid bilayers mimicking bacterial and eukaryotic membranes. Despite this, the molecular mechanisms and the nature of the physicochemical properties underlying the peptide–lipid interactions that lead to membrane disruption are yet to be fully elucidated. In this study, the interaction of the short antimicrobial peptide aurein 1.2 was examined in the presence of an anionic cardiolipin-containing lipid bilayer using molecular dynamics simulations. Aurein 1.2 is known to interact strongly with anionic lipid membranes. In the simulations, the binding of aurein 1.2 was associated with buckling of the lipid bilayer, the degree of which varied with the peptide concentration. The simulations suggest that the intrinsic properties of cardiolipin, especially the fact that it promotes negative membrane curvature, may help protect membranes against the action of peptides such as aurein 1.2 by counteracting the tendency of the peptide to induce positive curvature in target membranes.
Extracted statement — Lead compound: Aurein 1.2
Research field: Biophysics
Title: Direct Visualization of Membrane Leakage Induced by the Antibiotic Peptides: Maculatin, Citropin, and Aurein
Abstract: Membrane lysis caused by antibiotic peptides is often rationalized by means of two different models: the so-called carpet model and the pore-forming model. We report here on the lytic activity of antibiotic peptides from Australian tree frogs, maculatin 1.1, citropin 1.1, and aurein 1.2, on POPC or POPC/POPG model membranes. Leakage experiments using fluorescence spectroscopy indicated that the peptide/lipid mol ratio necessary to induce 50% of probe leakage was smaller for maculatin compared with aurein or citropin, regardless of lipid membrane composition. To gain further insight into the lytic mechanism of these peptides we performed single vesicle experiments using confocal fluorescence microscopy. In these experiments, the time course of leakage for different molecular weight (water soluble) fluorescent markers incorporated inside of single giant unilamellar vesicles is observed after peptide exposure. We conclude that maculatin and its related peptides demonstrate a pore-forming mechanism (differential leakage of small fluorescent probe compared with high molecular weight markers). Conversely, citropin and aurein provoke a total membrane destabilization with vesicle burst without sequential probe leakage, an effect that can be assigned to a carpeting mechanism of lytic action. Additionally, to study the relevance of the proline residue on the membrane-action properties of maculatin, the same experimental approach was used for maculatin-Ala and maculatin-Gly (Pro-15 was replaced by Ala or Gly, respectively). Although a similar peptide/lipid mol ratio was necessary to induce 50% of leakage for POPC membranes, the lytic activity of maculatin-Ala and maculatin-Gly decreased in POPC/POPG (1:1 mol) membranes compared with that observed for the naturally occurring maculatin sequence. As observed for maculatin, the lytic action of Maculatin-Ala and maculatin-Gly is in keeping with the formation of pore-like structures at the membrane independently of lipid composition.
Extracted statement — Lead compound: Citropin 1.1
Research field: Biophysics
Title: Differential Interaction of Antimicrobial Peptides with Lipid Structures Studied by Coarse-Grained Molecular Dynamics Simulations
Abstract: In this work; we investigated the differential interaction of amphiphilic antimicrobial peptides with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid structures by means of extensive molecular dynamics simulations. By using a coarse-grained (CG) model within the MARTINI force field; we simulated the peptide–lipid system from three different initial configurations: (a) peptides in water in the presence of a pre-equilibrated lipid bilayer; (b) peptides inside the hydrophobic core of the membrane; and (c) random configurations that allow self-assembled molecular structures. This last approach allowed us to sample the structural space of the systems and consider cooperative effects. The peptides used in our simulations are aurein 1.2 and maculatin 1.1; two well-known antimicrobial peptides from the Australian tree frogs; and molecules that present different membrane-perturbing behaviors. Our results showed differential behaviors for each type of peptide seen in a different organization that could guide a molecular interpretation of the experimental data. While both peptides are capable of forming membrane aggregates; the aurein 1.2 ones have a pore-like structure and exhibit a higher level of organization than those conformed by maculatin 1.1. Furthermore; maculatin 1.1 has a strong tendency to form clusters and induce curvature at low peptide–lipid ratios. The exploration of the possible lipid–peptide structures; as the one carried out here; could be a good tool for recognizing specific configurations that should be further studied with more sophisticated methodologies.
Extracted statement — Lead compound: Maculatin 1.1