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1.
Brief Bioinform ; 2022 May 10.
Artigo em Inglês | MEDLINE | ID: mdl-35536545

RESUMO

The three-dimensional (3D) chromosomal structure plays an essential role in all DNA-templated processes, including gene transcription, DNA replication and other cellular processes. Although developing chromosome conformation capture (3C) methods, such as Hi-C, which can generate chromosomal contact data characterized genome-wide chromosomal structural properties, understanding 3D genomic nature-based on Hi-C data remains lacking. Here, we propose a persistent spectral simplicial complex (PerSpectSC) model to describe Hi-C data for the first time. Specifically, a filtration process is introduced to generate a series of nested simplicial complexes at different scales. For each of these simplicial complexes, its spectral information can be calculated from the corresponding Hodge Laplacian matrix. PerSpectSC model describes the persistence and variation of the spectral information of the nested simplicial complexes during the filtration process. Different from all previous models, our PerSpectSC-based features provide a quantitative global-scale characterization of chromosome structures and topology. Our descriptors can successfully classify cell types and also cellular differentiation stages for all the 24 types of chromosomes simultaneously. In particular, persistent minimum best characterizes cell types and Dim (1) persistent multiplicity best characterizes cellular differentiation. These results demonstrate the great potential of our PerSpectSC-based models in polymeric data analysis.

2.
Neuroimage ; 255: 119166, 2022 Jul 15.
Artigo em Inglês | MEDLINE | ID: mdl-35398282

RESUMO

Magnetic Resonance Imaging (MRI) technology has been increasingly used in neuroscience studies. Reproducibility of statistically significant findings generated by MRI-based studies, especially association studies (phenotype vs. MRI metric) and task-induced brain activation, has been recently heavily debated. However, most currently available reproducibility measures depend on thresholds for the test statistics and cannot be use to evaluate overall study reproducibility. It is also crucial to elucidate the relationship between overall study reproducibility and sample size in an experimental design. In this study, we proposed a model-based reproducibility index to quantify reproducibility which could be used in large-scale high-throughput MRI-based studies including both association studies and task-induced brain activation. We performed the model-based reproducibility assessments for a few association studies and task-induced brain activation by using several recent large sMRI/fMRI databases. For large sample size association studies between brain structure/function features and some basic physiological phenotypes (i.e. Sex, BMI), we demonstrated that the model-based reproducibility of these studies is more than 0.99. For MID task activation, similar results could be observed. Furthermore, we proposed a model-based analytical tool to evaluate minimal sample size for the purpose of achieving a desirable model-based reproducibility. Additionally, we evaluated the model-based reproducibility of gray matter volume (GMV) changes for UK Biobank (UKB) vs. Parkinson Progression Marker Initiative (PPMI) and UK Biobank (UKB) vs. Human Connectome Project (HCP). We demonstrated that both sample size and study-specific experimental factors play important roles in the model-based reproducibility assessments for different experiments. In summary, a systematic assessment of reproducibility is fundamental and important in the current large-scale high-throughput MRI-based studies.

3.
Bioinformatics ; 2022 Mar 07.
Artigo em Inglês | MEDLINE | ID: mdl-35253843

RESUMO

MOTIVATION: The identification of binding hotspots in protein-RNA interactions is crucial for understanding their potential recognition mechanisms and drug design. The experimental methods have many limitations, since they are usually time-consuming and labor-intensive. Thus, developing an effective and efficient theoretical method is urgently needed. RESULTS: Here we present SREPRHot, a method to predict hotspots, defined as the residues whose mutation to alanine generate a binding free energy change ≥ 2.0 kcal/mol, while others use a cutoff of 1.0 kcal/mol to obtain balanced datasets. To deal with the dataset imbalance, Synthetic Minority Over-sampling Technique (SMOTE) is utilized to generate minority samples to achieve a dataset balance. Additionally, besides conventional features, we use two types of new features, residue interface propensity previously developed by us, and topological features obtained using node-weighted networks, and propose an effective Random Grouping feature selection strategy combined with a two-step method to determine an optimal feature set. Finally, a stacking ensemble classifier is adopted to build our model. The results show SREPRHot achieves a good performance with SEN, MCC and AUC of 0.900, 0.557 and 0.829 on the independent testing dataset. The comparison study indicates SREPRHot shows a promising performance. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/ChunhuaLiLab/SREPRHot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

4.
Front Med (Lausanne) ; 9: 815355, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35223913

RESUMO

Human P-glycoprotein (P-gp) is a kind of ATP-binding cassette (ABC) transporters. Once human P-gp is overexpressed in tumor cells, which can lead to tumor multidrug resistance (MDR). However, the present experimental methods are difficult to obtain the large-scale conformational transition process of human P-gp. In this work, we explored the allosteric pathway of human P-gp from the inward-facing (IF) to the outward-facing (OF) state in the substrate transport process with the two-state anisotropic network model (tANM). These results suggest that the allosteric transitions proceed in a coupled way. The conformational changes of nucleotide-binding domains (NBDs) finally make the transmembrane domains (TMDs) to the OF state via the role of the allosteric propagation of the intracellular helices IH1 and IH2. Additionally, this allosteric pathway is advantageous in energy compared with other methods. This study reveals the conformational transition of P-gp, which contributes to an understanding of the allosteric mechanism of ABC exporters.

5.
Nano Lett ; 22(4): 1688-1693, 2022 Feb 23.
Artigo em Inglês | MEDLINE | ID: mdl-35148114

RESUMO

The diode effect means that carriers can only flow in one direction but not the other. While diode effects for electron charge, spin, or photon have been widely discussed, it remains a question whether a chiral phonon diode can be realized, which utilizes the chiral degree of freedom of lattice vibrations. In this work, we reveal an intrinsic connection between the chiralities of a crystal structure and its phonon excitations, which naturally leads to the chiral phonon diode effect in chiral crystals. At a certain frequency, phonons with a definite chirality can propagate only in one direction but not the opposite. We demonstrate the idea in concrete materials including bulk Te and α-quartz (SiO2). Our work discovers the fundamental physics of chirality coupling between different levels of a system, and the predicted effect will provide a new route to control phonon transport and design information devices.

6.
Mol Psychiatry ; 27(2): 967-975, 2022 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-34650205

RESUMO

OBJECTIVE: To investigate the relation between parental age, and behavioral, cognitive and brain differences in the children. METHOD: Data with children aged 9-11 of 8709 mothers with parental age 15-45 years were analyzed from the Adolescent Brain Cognitive Development (ABCD) study. A general linear model was used to test the associations of the parental age with brain structure, and behavioral and cognitive problems scores. RESULTS: Behavioral and cognitive problems were greater in the children of the younger mothers, and were associated with lower volumes of cortical regions in the children. There was a linear correlation between the behavioral and cognitive problems scores, and the lower brain volumes (r > 0.6), which was evident when parental age was included as a stratification factor. The regions with lower volume included the anterior cingulate cortex, medial and lateral orbitofrontal cortex and amygdala, parahippocampal gyrus and hippocampus, and temporal lobe (FDR corrected p < 0.01). The lower cortical volumes and areas in the children significantly mediated the association between the parental age and the behavioral and cognitive problems in the children (all p < 10-4). The effects were large, such as the 71.4% higher depressive problems score, and 27.5% higher rule-breaking score, in the children of mothers aged 15-19 than the mothers aged 34-35. CONCLUSIONS: Lower parental age is associated with behavioral problems and reduced cognitive performance in the children, and these differences are related to lower volumes and areas of some cortical regions which mediate the effects in the children. The findings are relevant to psychiatric understanding and assessment.

7.
Proteins ; 90(2): 589-600, 2022 02.
Artigo em Inglês | MEDLINE | ID: mdl-34599611

RESUMO

Transactive response DNA binding protein 43 (TDP-43), an alternative-splicing regulator, can specifically bind long UG-rich RNAs, associated with a range of neurodegenerative diseases. Upon binding RNA, TDP-43 undergoes a large conformational change with two RNA recognition motifs (RRMs) connected by a long linker rearranged, strengthening the binding affinity of TDP-43 with RNA. We extend the equally weighted multiscale elastic network model (ewmENM), including its Gaussian network model (ewmGNM) and Anisotropic network model (ewmANM), with the multiscale effect of interactions considered, to the characterization of the dynamics of binding interactions of TDP-43 and RNA. The results reveal upon RNA binding a loss of flexibility occurs to TDP-43's loop3 segments rich in positively charged residues and C-terminal of high flexibility, suggesting their anchoring RNA, induced fit and conformational adjustment roles in recognizing RNA. Additionally, based on movement coupling analyses, it is found that RNA binding strengthens the interactions among intra-RRM ß-sheets and between RRMs partially through the linker's mediating role, which stabilizes RNA binding interface, facilitating RNA binding efficiency. In addition, utilizing our proposed thermodynamic cycle method combined with ewmGNM, we identify the key residues for RNA binding whose perturbations induce a large change in binding free energy. We identify not only the residues important for specific binding, but also the ones critical for the conformational rearrangement between RRMs. Furthermore, molecular dynamics simulations are also performed to validate and further interpret the ENM-based results. The study demonstrates a useful avenue to utilize ewmENM to investigate the protein-RNA interaction dynamics characteristics.


Assuntos
Proteínas de Ligação a DNA/metabolismo , DNA/metabolismo , Humanos , Ligação Proteica
8.
Hum Brain Mapp ; 43(5): 1598-1610, 2022 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-34904766

RESUMO

Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic cells and atrophy in subcortical regions. However, the impact of these pathological changes on large-scale dynamic integration and segregation of the cortex are not well understood. In this study, we investigated the effect of subcortical dysfunction on cortical dynamics and cognition in PD. Spatiotemporal dynamics of the phase interactions of resting-state blood-oxygen-level-dependent signals in 159 PD patients and 152 normal control (NC) individuals were estimated. The relationships between subcortical atrophy, subcortical-cortical fiber connectivity impairment, cortical synchronization/metastability, and cognitive performance were then assessed. We found that cortical synchronization and metastability in PD patients were significantly decreased. To examine whether this is an effect of dopamine depletion, we investigated 45 PD patients both ON and OFF dopamine replacement therapy, and found that cortical synchronization and metastability are significantly increased in the ON state. The extent of cortical synchronization and metastability in the OFF state reflected cognitive performance and mediates the difference in cognitive performance between the PD and NC groups. Furthermore, both the thalamic volume and thalamocortical fiber connectivity had positive relationships with cortical synchronization and metastability in the dopaminergic OFF state, and mediate the difference in cortical synchronization between the PD and NC groups. In addition, thalamic volume also reflected cognitive performance, and cortical synchronization/metastability mediated the relationship between thalamic volume and cognitive performance in PD patients. Together, these results highlight that subcortical dysfunction and reduced dopamine levels are responsible for decreased cortical synchronization and metastability, further affecting cognitive performance in PD. This might lead to biomarkers being identified that can predict if a patient is at risk of developing dementia.


Assuntos
Doença de Parkinson , Atrofia , Cognição , Sincronização Cortical , Dopamina , Humanos , Testes Neuropsicológicos , Doença de Parkinson/complicações , Doença de Parkinson/diagnóstico por imagem , Doença de Parkinson/patologia
9.
Neuroimage ; 243: 118513, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34450262

RESUMO

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.


Assuntos
Mapeamento Encefálico/métodos , Encéfalo/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Rede Nervosa/diagnóstico por imagem , Big Data , Humanos , Modelos Estatísticos , Análise de Regressão
10.
J Phys Chem B ; 125(28): 7651-7661, 2021 07 22.
Artigo em Inglês | MEDLINE | ID: mdl-34242030

RESUMO

Aminoacyl-tRNA synthetases (aaRSs), a family of ubiquitous and essential enzymes, can bind target tRNAs and catalyze the aminoacylation reaction in genetic code translation. In this work, we explore the dynamic properties and allosteric communication of human mitochondrial phenylalanyl-tRNA synthetase (hmPheRS) in free and bound states to understand the mechanisms of its tRNAPhe recognition and allostery using molecular dynamics simulations combined with the torsional mutual information-based network model. Our results reveal that hmPheRS's residue mobility and inter-residue motional coupling are significantly enhanced by tRNAPhe binding, and there occurs a strong allosteric communication which is critical for the aminoacylation reaction, suggesting the vital role of tRNAPhe binding in the enzyme's function. The identified signaling pathways mainly make the connections between the anticodon binding domain (ABD) and catalytic domain (CAD), as well as within the CAD composed of many functional fragments and active sites, revealing the co-regulation role of them to act coordinately and achieve hmPheRS's aminoacylation function. Besides, several key residues along the communication pathways are identified to be involved in mediating the coordinated coupling between anticodon recognition at the ABD and activation process at the CAD, showing their pivotal role in the allosteric network, which are well consistent with the experimental observation. This study sheds light on the allosteric communication mechanism in hmPheRS and can provide important information for the structure-based drug design targeting aaRSs.


Assuntos
Aminoacil-tRNA Sintetases , Fenilalanina-tRNA Ligase , Aminoacil-tRNA Sintetases/genética , Aminoacil-tRNA Sintetases/metabolismo , Anticódon/genética , Domínio Catalítico , Humanos , Mitocôndrias/metabolismo , Fenilalanina-tRNA Ligase/metabolismo
11.
Nat Commun ; 12(1): 3769, 2021 06 18.
Artigo em Inglês | MEDLINE | ID: mdl-34145259

RESUMO

Children's behavioral problems have been associated with their family environments. Here, we investigate whether specific features of brain structures could relate to this link. Using structural magnetic resonance imaging of 8756 children aged 9-11 from the Adolescent Brain Cognitive Developmental study, we show that high family conflict and low parental monitoring scores are associated with children's behavioral problems, as well as with smaller cortical areas of the orbitofrontal cortex, anterior cingulate cortex, and middle temporal gyrus. A longitudinal analysis indicates that psychiatric problems scores are associated with increased family conflict and decreased parental monitoring 1 year later, and mediate associations between the reduced cortical areas and family conflict, and parental monitoring scores. These results emphasize the relationships between the brain structure of children, their family environments, and their behavioral problems.


Assuntos
Conflito Familiar/psicologia , Giro do Cíngulo/fisiologia , Relações Pais-Filho , Córtex Pré-Frontal/fisiologia , Comportamento Problema/psicologia , Lobo Temporal/fisiologia , Criança , Cognição/fisiologia , Meio Ambiente , Saúde da Família , Feminino , Humanos , Estudos Longitudinais , Imageamento por Ressonância Magnética , Masculino
12.
Front Hum Neurosci ; 15: 657857, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34025376

RESUMO

Several previous studies have reported atypicality in resting-state functional connectivity (FC) in autism spectrum disorder (ASD), yet the relatively small effect sizes prevent us from using these characteristics for diagnostic purposes. Here, canonical correlation analysis (CCA) and hierarchical clustering were used to partition the high-functioning ASD group (i.e., the ASD discovery group) into subgroups. A support vector machine (SVM) model was trained through the 10-fold strategy to predict Autism Diagnostic Observation Schedule (ADOS) scores within the ASD discovery group (r = 0.30, P < 0.001, n = 260), which was further validated in an independent sample (i.e., the ASD validation group) (r = 0.35, P = 0.031, n = 29). The neuroimage-based partition derived two subgroups representing severe versus mild autistic patients. We identified FCs that show graded changes in strength from ASD-severe, through ASD-mild, to controls, while the same pattern cannot be observed in partitions based on ADOS score. We also identified FCs that are specific for ASD-mild, similar to a partition based on ADOS score. The current study provided multiple pieces of evidence with replication to show that resting-state functional magnetic resonance imaging (rsfMRI) FCs could serve as neural biomarkers in partitioning high-functioning autistic individuals based on their symptom severity and showing advantages over traditional partition based on ADOS score. Our results also indicate a compensatory role for a frontocortical network in patients with mild ASD, indicating potential targets for future clinical treatments.

13.
Neuroimage ; 237: 118188, 2021 08 15.
Artigo em Inglês | MEDLINE | ID: mdl-34020018

RESUMO

Age-related changes in the brain are associated with a decline in functional flexibility. Intrinsic functional flexibility is evident in the brain's dynamic ability to switch between alternative spatiotemporal states during resting state. However, the relationship between brain connectivity states, associated psychological functions during resting state, and the changes in normal aging remain poorly understood. In this study, we analyzed resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP; N = 812) and the UK Biobank (UKB; N = 6,716). Using signed community clustering to identify distinct states of dynamic functional connectivity, and text-mining of a large existing literature for functional annotation of each state, our findings from the HCP dataset indicated that the resting brain spontaneously transitions between three functionally specialized states: sensory, somatomotor, and internal mentation networks. The occurrence, transition-rate, and persistence-time parameters for each state were correlated with behavioural scores using canonical correlation analysis. We estimated the same brain states and parameters in the UKB dataset, subdivided into three distinct age ranges: 50-55, 56-67, and 68-78 years. We found that the internal mentation network was more frequently expressed in people aged 71 and older, whereas people younger than 55 more frequently expressed sensory and somatomotor networks. Furthermore, analysis of the functional entropy - a measure of uncertainty of functional connectivity - also supported this finding across the three age ranges. Our study demonstrates that dynamic functional connectivity analysis can expose the time-varying patterns of transition between functionally specialized brain states, which are strongly tied to increasing age.


Assuntos
Envelhecimento/fisiologia , Encéfalo/fisiologia , Conectoma , Rede de Modo Padrão/fisiologia , Processos Mentais/fisiologia , Rede Nervosa/fisiologia , Adulto , Idoso , Atenção/fisiologia , Encéfalo/diagnóstico por imagem , Conjuntos de Dados como Assunto , Rede de Modo Padrão/diagnóstico por imagem , Feminino , Humanos , Imageamento por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Atividade Motora/fisiologia , Rede Nervosa/diagnóstico por imagem , Percepção/fisiologia , Teoria da Mente/fisiologia , Adulto Jovem
14.
Front Psychiatry ; 12: 627996, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34040552

RESUMO

Brain age prediction from brain MRI scans not only helps improve brain ageing modelling generally, but also provides benchmarks for predictive analysis methods. Brain-age delta, which is the difference between a subject's predicted age and true age, has become a meaningful biomarker for the health of the brain. Here, we report the details of our brain age prediction models and results in the Predictive Analysis Challenge 2019. The aim of the challenge was to use T1-weighted brain MRIs to predict a subject's age in multicentre datasets. We apply a lightweight deep convolutional neural network architecture, Simple Fully Convolutional Neural Network (SFCN), and combined several techniques including data augmentation, transfer learning, model ensemble, and bias correction for brain age prediction. The model achieved first place in both of the two objectives in the PAC 2019 brain age prediction challenge: Mean absolute error (MAE) = 2.90 years without bias removal (Second Place = 3.09 yrs; Third Place = 3.33 yrs), and MAE = 2.95 years with bias removal, leading by a large margin (Second Place = 3.80 yrs; Third Place = 3.92 yrs).

15.
Med Image Anal ; 71: 102050, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33905882

RESUMO

Neuroimaging allows for the non-invasive study of the brain in rich detail. Data-driven discovery of patterns of population variability in the brain has the potential to be extremely valuable for early disease diagnosis and understanding the brain. The resulting patterns can be used as imaging-derived phenotypes (IDPs), and may complement existing expert-curated IDPs. However, population datasets, comprising many different structural and functional imaging modalities from thousands of subjects, provide a computational challenge not previously addressed. Here, for the first time, a multimodal independent component analysis approach is presented that is scalable for data fusion of voxel-level neuroimaging data in the full UK Biobank (UKB) dataset, that will soon reach 100,000 imaged subjects. This new computational approach can estimate modes of population variability that enhance the ability to predict thousands of phenotypic and behavioural variables using data from UKB and the Human Connectome Project. A high-dimensional decomposition achieved improved predictive power compared with widely-used analysis strategies, single-modality decompositions and existing IDPs. In UKB data (14,503 subjects with 47 different data modalities), many interpretable associations with non-imaging phenotypes were identified, including multimodal spatial maps related to fluid intelligence, handedness and disease, in some cases where IDP-based approaches failed.


Assuntos
Encéfalo , Conectoma , Encéfalo/diagnóstico por imagem , Humanos , Imageamento por Ressonância Magnética , Neuroimagem , Fenótipo
16.
NPJ Schizophr ; 7(1): 18, 2021 Mar 03.
Artigo em Inglês | MEDLINE | ID: mdl-33658499

RESUMO

Schizophrenia is a neurocognitive illness of synaptic and brain network-level dysconnectivity that often reaches a persistent chronic stage in many patients. Subtle language deficits are a core feature even in the early stages of schizophrenia. However, the primacy of language network dysconnectivity and language-related genetic variants in the observed phenotype in early stages of illness remains unclear. This study used two independent schizophrenia dataset consisting of 138 and 53 drug-naïve first-episode schizophrenia (FES) patients, and 112 and 56 healthy controls, respectively. A brain-wide voxel-level functional connectivity analysis was conducted to investigate functional dysconnectivity and its relationship with illness duration. We also explored the association between critical language-related genetic (such as FOXP2) mutations and the altered functional connectivity in patients. We found elevated functional connectivity involving Broca's area, thalamus and temporal cortex that were replicated in two FES datasets. In particular, Broca's area - anterior cingulate cortex dysconnectivity was more pronounced for patients with shorter illness duration, while thalamic dysconnectivity was predominant in those with longer illness duration. Polygenic risk scores obtained from FOXP2-related genes were strongly associated with functional dysconnectivity identified in patients with shorter illness duration. Our results highlight the criticality of language network dysconnectivity, involving the Broca's area in early stages of schizophrenia, and the role of language-related genes in this aberration, providing both imaging and genetic evidence for the association between schizophrenia and the determinants of language.

17.
BMC Bioinformatics ; 22(1): 133, 2021 Mar 19.
Artigo em Inglês | MEDLINE | ID: mdl-33740884

RESUMO

BACKGROUND: Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA-protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA-protein interactions. RESULTS: In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA-protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA-protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA-protein networks of Mus musculus successfully. CONCLUSIONS: In general, our proposed method EDLMFC improved the accuracy of ncRNA-protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC .


Assuntos
Biologia Computacional , Aprendizado Profundo , Animais , Camundongos , Redes Neurais de Computação , RNA não Traduzido , Software
18.
J Mol Biol ; 433(10): 166944, 2021 05 14.
Artigo em Inglês | MEDLINE | ID: mdl-33741411

RESUMO

Genome-wide protein-protein interaction (PPI) determination remains a significant unsolved problem in structural biology. The difficulty is twofold since high-throughput experiments (HTEs) have often a relatively high false-positive rate in assigning PPIs, and PPI quaternary structures are more difficult to solve than tertiary structures using traditional structural biology techniques. We proposed a uniform pipeline, Threpp, to address both problems. Starting from a pair of monomer sequences, Threpp first threads both sequences through a complex structure library, where the alignment score is combined with HTE data using a naïve Bayesian classifier model to predict the likelihood of two chains to interact with each other. Next, quaternary complex structures of the identified PPIs are constructed by reassembling monomeric alignments with dimeric threading frameworks through interface-specific structural alignments. The pipeline was applied to the Escherichia coli genome and created 35,125 confident PPIs which is 4.5-fold higher than HTE alone. Graphic analyses of the PPI networks show a scale-free cluster size distribution, consistent with previous studies, which was found critical to the robustness of genome evolution and the centrality of functionally important proteins that are essential to E. coli survival. Furthermore, complex structure models were constructed for all predicted E. coli PPIs based on the quaternary threading alignments, where 6771 of them were found to have a high confidence score that corresponds to the correct fold of the complexes with a TM-score >0.5, and 39 showed a close consistency with the later released experimental structures with an average TM-score = 0.73. These results demonstrated the significant usefulness of threading-based homologous modeling in both genome-wide PPI network detection and complex structural construction.


Assuntos
Proteínas de Escherichia coli/genética , Escherichia coli/genética , Proteínas de Choque Térmico HSP70/genética , Fosfotransferases/genética , Proteoma/genética , Fatores de Transcrição/genética , Teorema de Bayes , Análise por Conglomerados , Escherichia coli/metabolismo , Proteínas de Escherichia coli/química , Proteínas de Escherichia coli/metabolismo , Regulação Bacteriana da Expressão Gênica , Genoma Bacteriano , Proteínas de Choque Térmico HSP70/química , Proteínas de Choque Térmico HSP70/metabolismo , Fosfotransferases/química , Fosfotransferases/metabolismo , Dobramento de Proteína , Mapeamento de Interação de Proteínas , Mapas de Interação de Proteínas/genética , Estrutura Quaternária de Proteína , Proteoma/química , Proteoma/metabolismo , Transdução de Sinais , Fatores de Transcrição/química , Fatores de Transcrição/metabolismo
19.
J Phys Chem B ; 125(13): 3353-3363, 2021 04 08.
Artigo em Inglês | MEDLINE | ID: mdl-33780247

RESUMO

Understanding generic mechanisms of functions shared by the secretory phospholipase A2 (sPLA2) family involved in the lipid metabolism and cell signaling and the molecular basis of function specificity for family members is an intriguing but challenging problem for biologists. Here, we explore the issue through extensive analyses using a combination of structure-based methods and bioinformatics tools on130 sPLA2 family members. The principal component analysis of the structure ensemble reveals that the enzyme has an open-close motion which helps widen the substrate binding channel, facilitating its binding to phospholipid. Performing elastic network model and sequence analyses found that the residues critical for family functions, such as cysteine and catalytic residues, are highly conserved and undergo minimal movements, which is evolutionarily essential as their perturbation would impact the function, while the four residue regions involved in the association with the calcium ion/membrane are lowly conserved and of high mobility and large variations in low-to-intermediate frequency modes, which reflects the specificity of members. The analyses from perturbation response scanning also reveal that the above four regions with high sensitivity to an external perturbation are member-specific, suggesting their different roles in allosteric modulation, while the minimal sensitive residues are the shared characteristics across family members, which play an important role in maintaining structural stability as the folding core. This study is helpful for understanding how sequences, structures, and dynamics of sPLA2 family members evolve to ensure their common and specific functions and can provide a guide for accurate design of proteins with finely tuned activities.


Assuntos
Fosfolipases A2 Secretórias , Biologia Computacional , Fosfolipases A2 Secretórias/genética , Fosfolipases A2 Secretórias/metabolismo , Fosfolipídeos , Transdução de Sinais
20.
J Mol Recognit ; 34(6): e2887, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-33442949

RESUMO

Protein-RNA interactions play essential roles in a wide variety of biological processes. Recognition of RNA-binding residues on proteins has been a challenging problem. Most of methods utilize the position-specific scoring matrix (PSSM). It has been found that considering the evolutionary information of sequence neighboring residues can improve the prediction. In this work, we introduce a novel method SNB-PSSM (spatial neighbor-based PSSM) combined with the structure window scheme where the evolutionary information of spatially neighboring residues is considered. The results show our method consistently outperforms the standard and smoothed PSSM methods. Tested on multiple datasets, this approach shows an encouraging performance compared with RNABindRPlus, BindN+, PPRInt, xypan, Predict_RBP, SpaPF, PRNA, and KYG, although is inferior to RNAProSite, RBscore, and aaRNA. In addition, since our method is not sensitive to protein structure changes, it can be applied well on binding site predictions of modeled structures. Thus, the result also suggests the evolution of binding sites is spatially cooperative. The proposed method as an effective tool of considering evolutionary information can be widely used for the nucleic acid-/protein-binding site prediction and functional motif finding.


Assuntos
Sítios de Ligação/fisiologia , Ligação Proteica/fisiologia , Proteínas de Ligação a RNA/metabolismo , RNA/metabolismo , Algoritmos , Biologia Computacional/métodos , Bases de Dados de Proteínas , Matrizes de Pontuação de Posição Específica
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