Neural proteins protein residues. Medical search. Frequent questions

Although protein structures have been solved by experiments at an increasing rate, a flood of new sequences have been determined even more rapidly due to the advance of sequencing technologies[ 6 , 7 ]. (biomedcentral.com)
Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences. (ku.dk)
It uses evolutionarily related sequences, multiple sequence alignment (MSA), and a representation of amino acid residue pairs to refine this graph. (bigthink.com)
The method adopts amino acid composition (AAC), conjoint triad (CT), and auto covariance (AC) to extract global and local features of protein sequences, and leverages self-attention to enhance DNN feature extraction to more effectively accomplish the prediction of PPIs. (biomedcentral.com)
20 ] proposed auto covariance (AC) to extract information from protein sequences and used support vector machine model to predict PPIs in the Saccharomyces cerevisiae dataset with 88.09% accuracy. (biomedcentral.com)
21 ] proposed local descriptors (LD) to represent protein sequences and successfully predicted potential PPIs on Saccharomyces cerevisiae (core subset) dataset by implementing K-neighbor model. (biomedcentral.com)
Correlations between amino-acid residues can be observed in sets of aligned protein sequences, and the analysis of their statistical and evolutionary significance and distribution has been thoroughly investigated. (strath.ac.uk)
In this paper, we present a model based on such covariations in protein sequences in which the pairs of residues that have mutual influence combine to produce a system analogous to a Hopfield neural network. (strath.ac.uk)
Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. (bvsalud.org)
ADpred is a deep learning model to predict acidic transcription activation domains (ADs) within protein sequences. (fredhutch.org)
For protein sequences longer than 1500 amino-acids, please consider chopping it into smaller parts (domains or structural groups) or download adpred tool and use a local installation of psipred. (fredhutch.org)

Home Bioinformatics 'DeepRank' - A Deep Learning Framework for Data Mining Protein-Protein Interfaces Using. (cbirt.net)
Computational protein structure prediction remains one of the most challenging problems in structural bioinformatics. (biomedcentral.com)
His research spans a wide range, from the quantum chemistry of small molecules and the spectroscopic properties of proteins, to the application of state-of-the-art statistical and computer science methodology to problems in bioinformatics, drug design and sustainable chemistry. (nottingham.ac.uk)
Huang X, Zheng W, Pearce R, Zhang Y, Huang X, Zheng W, Pearce R, Zhang Y, SSIPe: accurately estimating protein-protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function, Bioinformatics;36(8):2429-2437. (lu.se)
Jemimah S, Sekijima M, Gromiha M, ProAffiMuSeq: sequence-based method to predict the binding free energy change of protein-protein complexes upon mutation using functional classification, Bioinformatics;36(6):1725-1730. (lu.se)

The tertiary structure of proteins provides crucial information for understanding molecular mechanisms of biological functions. (biomedcentral.com)
AlphaFold has proven to be more accurate than its competitors in predicting the three-dimensional structure of proteins from a list of ingredients. (vpchothuegoldenking.com)
Participants must blindly predict the structure of proteins that have only recently - or in some cases not yet - been experimentally determined, and wait for their predictions to be compared to experimental data. (rockingrobots.com)

Background: Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation.Results: We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. (elsevierpure.com)
The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. (ku.dk)
Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed. (ku.dk)
Case studies are presented to validate the method, yielding protein designs specifically focused on structural materials, but also exploring the applicability in the design of soluble, antimicrobial biomaterials. (aip.org)
Those structural motifs are well known to play a key role in stabilizing protein structure and likely to be important in the protein folding process. (nih.gov)
The MICS program is developed based on a systematic study of the NMR chemical shift (and amino acid sequence) patterns observed for each type of structural motif by using a database of proteins of known structure and known NMR chemical shifts. (nih.gov)
Disease-linked missense mutations and multiplication of the SNCA gene encoding α-synuclein have been reported in familial forms of α-synucleinopathies, indicating that structural changes and overexpression of α-synuclein protein are involved in the development of α-synucleinopathies ( Wong and Krainc, 2017 ). (elifesciences.org)
For example, a protein can become an antibody that binds to foreign particles to protect, an enzyme that carries out chemical reactions, or a structural component that supports cells. (bigthink.com)
ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residue-residue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. (biomedcentral.com)
TBM method predicts the structure of query protein by modifying the structural framework of its homologous protein with known structure in accordance with template-query alignment. (biomedcentral.com)
This model suggests that an explanation for observed characters of proteins such as the diminution of function by substitutions distant from the active site, the existence of protein folds (superfolds) that can perform several functions based on one architecture, and structural and functional resilience to destabilizing substitutions might derive from their inherent network-like structure. (strath.ac.uk)
His calculations on protein circular dichroism spectroscopy, a key technique in structural biology, are the most accurate to be published. (nottingham.ac.uk)
Choosing the right templates in the immunoglobulin superfamily (IgSF) is challenging because its members share low sequence identity and display a wide range of alternative binding sites despite structural homology.ResultsWe present a new approach to predict protein interfaces. (edu.in)

In GeomNet, the co-evolutionary features extracted from MSA that search from the sequence databases are sent to an improved residual neural network to predict the inter-residue geometric constraints. (sciencegate.app)
In EmaNet, the 1D and 2D features are extracted from the folded model and sent to the deep residual neural network to estimate the inter-residue distance deviation and per-residue lDDT of the model, which will be fed back to GeomNet as dynamic features to correct the geometries prediction and progressively improve model accuracy. (sciencegate.app)

3D structures of protein complexes give essential data to decipher biological processes at the molecular scale. (cbirt.net)
Rafts may serve to cluster protein complexes, such as those involved in signal transduction, thereby facilitating signaling. (nih.gov)
Vertebrate L3MBTL, a member of the Polycomb group of proteins, which function as transcriptional repressors in large protein complexes. (embl.de)
S487, 478 variants in 56 protein complexes. (lu.se)
S4191, 4191 single variants in 265 protein complexes, M1707, 1707 multiple variants in 120 protein complexes. (lu.se)

Why choose BioLegend recombinant proteins? (biolegend.com)

The scientists present in the study, DeepRank, a generic, configurable deep learning system for data mining PPIs by the utilization of 3D convolutional neural networks (CNNs). (cbirt.net)
This article formulates IDR prediction as a sequence labeling problem and employs a new machine learning method called Deep Convolutional Neural Fields (DeepCNF) to solve it. (nsf.gov)
J. Singh, K. Paliwal, J. Singh, Y. Zhou, "RNA Backbone Torsion and Pseudotorsion Angle Prediction using Dilated Convolutional Neural Networks. (sparks-lab.org)

We used this analysis to develop a neural network-based method that predicts flexible-rigid residues from amino acid sequence. (rostlab.org)

Deciphering protein–protein interactions. (crossref.org)
Effective encoding of residue contact information is crucial for protein structure prediction since it has a unique role to capture long-range residue interactions compared to other commonly used scoring terms. (biomedcentral.com)
Among various structure-based terms, residue-residue contact potentials[ 21 - 23 ] are unique in that they capture long-range interactions in a protein structure[ 24 ]. (biomedcentral.com)
Within a protein, pairs of amino acids can interact in numerous ways, and these particular interactions determine the final shape of the protein. (bigthink.com)
This graph is important for understanding the physical interactions within proteins, as well as their evolutionary history. (bigthink.com)
Protein-protein interactions (PPIs) dominate intracellular molecules to perform a series of tasks such as transcriptional regulation, information transduction, and drug signalling. (biomedcentral.com)
22 ] utilized four categories of protein sequence information (AC, CT, LD, MAC) to encode proteins as feature vectors focusing on dimensionality reduction and proposed a new hierarchical PCA-EELM (principal component analysis-integrated extreme learning machine) model to predict protein interactions. (biomedcentral.com)
Another aspect of Hirst's research focuses on the study of protein-ligand interactions, using techniques including QSAR, machine learning, neural networks, docking, molecular dynamics (MD) simulations and quantum chemistry. (nottingham.ac.uk)
Rodrigues C, Pires D, Ascher D, mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions, Nucleic Acids Res;49(W1):W417-W424. (lu.se)
Li M, Simonetti F, Goncearenco A, Panchenko A, MutaBind estimates and interprets the effects of sequence variants on protein-protein interactions, Nucleic Acids Res. (lu.se)
Rodrigues C, Myung Y, Pires D, Ascher D, mCSM-PPI2: predicting the effects of mutations on protein-protein interactions, Nucleic Acids Res. (lu.se)

Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. (elsevierpure.com)
This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods.Conclusions: We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. (elsevierpure.com)
NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. (ku.dk)
Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. (biomedcentral.com)
As a result, the final protein will have an incredibly many variants of structures - 3 to the hundredth power . (vpchothuegoldenking.com)
But any neural network needs input data, forwhich it can rely on, in which case the scientists uploaded information on the structures of approximately 170,000 proteins. (vpchothuegoldenking.com)
The ability to predict protein structures accurately enables a better understanding of what they do and how they work. (rockingrobots.com)
There are currently over 200 million proteins in the main database and only a fraction of their 3D structures have been mapped out. (rockingrobots.com)
The system was trained on publicly available data consisting of ~170,000 protein structures from the protein data bank, using a relatively modest amount of compute by modern machine learning standards - approximately 128 TPUv3-cores (roughly equivalent to ~100-200 GPUs) run over a few weeks. (rockingrobots.com)

Protein intrinsically disordered regions (IDRs) play an important role in many biological processes. (nsf.gov)

Wnt-3a belongs to Wnt family of proteins that consist of structurally related genes encoding highly conserved cysteine rich secreted glycoproteins. (biolegend.com)
Wnt-3a is a highly conserved lipid-modified, secreted hydrophobic glycoprotein with conserved 24 cysteine residues that is essential for cell signaling. (biolegend.com)
Cysteine-Selective Modification of Peptides and Proteins via Desulfurative C-C Bond Formation CHEMISTRY-A EUROPEAN JOURNAL. (nottingham.ac.uk)
The CCHHC-type zinc finger contains five absolutely conserved cysteine and histidine residues (rather than the more usual four) with the sequence C-P-x-P-G-C-x-G-x-G-H-x(7)-H-R-x(4)-C. The second histidine has been shown to coordinate Zn(II) along with the three cysteines residues. (embl.de)

In this paper, AAC, CT and AC methods are used to encode the sequence, and SDNN-PPI method is proposed to predict PPIs based on self-attention deep learning neural network. (biomedcentral.com)

We propose a novel protein single-model QA method which is built on a new representation that converts raw atom information into a series of carbon-alpha (Cα) atoms with side-chain information, defined by their dihedral angles and bond lengths to the prior residue. (sciencegate.app)

From the selected features, neural networks were trained to identify catalytic residues. (elsevierpure.com)
The trained neural networks were further validated using a smaller database which contains 11 new proteins not present in the training database. (nih.gov)
Various other neurodegenerative disease-related proteins, including amyloid-β, tau and TDP-43, can also propagate through neural networks in a similar manner. (elifesciences.org)
Profoundly regulated protein-protein interaction networks coordinate most cellular processes, going from DNA replications to viral invasion and immune defense. (cbirt.net)
Unlike other machine learning methodologies, deep neural networks hold the guarantee of learning from a large set of data without arriving at a performance level rapidly, which is computationally tractable by reaping hardware accelerators (like GPUs, TPUs) and parallel file system technologies. (cbirt.net)
The scientists have trained 3D deep convolutional networks (CNNs) on 3D grids addressing protein-protein interfaces to assess the quality of docking models (DOVE). (cbirt.net)
Protein is an important part of everyone's lifehuman, but despite the fact that we live in the 21st century, when neural networks paint pictures, and 3D printers are full-fledged organs, scientists have not yet had the opportunity to fully study the protein. (vpchothuegoldenking.com)
Until recently, the protein folding mechanism remained unknown, until the team of DeepMind, the Google division that creates neural networks, decided to use artificial intelligence to solve this problem. (vpchothuegoldenking.com)
Strokach A, Lu T, Kim P, ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations, J Mol Biol;433(11):166810. (lu.se)

To solve this problem, it is necessary to determine the amino acid sequence of the protein and analyze the connections between the members of this sequence. (vpchothuegoldenking.com)

Suppressor of cytokine signaling (SOCS)-3 and protein-tyrosine phosphatase 1B (PTP-1B) are two endogenous inhibitors of tyrosine kinase signaling pathways and suppress both insulin and leptin signaling via different molecular mechanisms. (diabetesjournals.org)
The 334 amino acid recombinant protein has a predicted molecular mass of approximately 37.4 kD. (biolegend.com)
Molecular modeling of these enzymes has revealed an amphipathic alpha helix within the C-terminal domains of these proteins. (nih.gov)
Disclaimer note: The observed molecular weight of the protein may vary from the listed predicted molecular weight due to post translational modifications, post translation cleavages, relative charges, and other experimental factors. (novusbio.com)
Molecular cloning of cDNA indicated that the AML1-MTG8-binding protein (MTGR1) is highly related to MTG8 and similar to Drosophila Nervy. (embl-heidelberg.de)

ADpred uses a convolutional deep neural network to predict the probability of peptides having potential AD function. (fredhutch.org)

The huge measure of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the chance to train deep learning models to aid the predictions of their biological relevance. (cbirt.net)
Accurate prediction of protein structure is fundamentally important to understand biological function of proteins. (biomedcentral.com)
Serine/arginine-rich proteins (SR proteins), a group of proteins that harbor an arginine/serine (RS) domain either at their N' or C' terminus, play crucial roles in a plethora of biological processes such as cell cycle and signaling, developmental pathways, DNA replication and repair, transcription and mRNA splicing. (biologists.com)

We found that overall the residue contact pattern can distinguish protein folds best when contacts are defined for residue pairs whose Cβ atoms are at 7.0 Å or closer to each other. (biomedcentral.com)
Lower fold recognition accuracy was observed when inaccurate threading alignments were used to identify common residue contacts between protein pairs. (biomedcentral.com)
In other words, AlphaFold learned that particular amino acid configurations-say, distances between pairs, angles between chemical bonds-signaled that the protein would likely take a particular shape. (bigthink.com)
These convertase enzymes are expressed exclusively in neural and endocrine cells, and cleave prohormones at pairs of basic amino acids. (nih.gov)

The chemical shifts, together with the PDB-extracted amino acid preference of the helix capping and beta-turn motifs, are then used as input data for training an artificial neural network algorithm, which outputs the statistical probability of finding each motif at any given position in the protein. (nih.gov)
The MICS output assigns a normalized probability for each residue to participate in any of the specific motifs, or to be part of a regular element of secondary structure. (nih.gov)
During the prediction, MICS will generate a single output file ' predMICS.tab ' to store the the normalized probability of finding each motif at any given position in the protein, respectively. (nih.gov)

After that, the algorithm builds the final three-dimensional structure of the protein from the resulting connections. (vpchothuegoldenking.com)
The algorithm was presented at the recent CASP conference, where AlphaFold2 took first place, gaining 92.4 out of 100 possible points (based on the correctness of located amino acid residues in the protein chain). (vpchothuegoldenking.com)

The output 411 residues contain 70 of the annotated 111 catalytic residues. (elsevierpure.com)

Of note, phosphorylation of SR proteins mediated by Serine/arginine protein kinases (SRPKs) drive their functionality 1 . (biologists.com)

CASP is able to verify the accuracy of these predictions by comparing them to the actual shape of proteins, which it obtains through the unpublished results of lab experiments. (bigthink.com)
Traditional protein QA methods suffer from searching databases or comparing with other models for making predictions, which usually fail. (sciencegate.app)
The most accurate protein interface predictions rely on finding homologous proteins with known interfaces since most interfaces are conserved within the same protein family. (edu.in)
By iterating this process, the system develops strong predictions of the underlying physical structure of the protein. (rockingrobots.com)
DeepMind is collaborating with others to learn more about AlphaFold's potential, and the AlphaFold team is looking into how protein structure predictions could contribute to understanding of certain diseases with a few specialist groups. (rockingrobots.com)

Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. (elsevierpure.com)
AbstractMotivationMolecular-level classification of protein-protein interfaces can greatly assist in functional characterization and rational drug design. (edu.in)

We report a flexible language-model-based deep learning strategy, applied here to solve complex forward and inverse problems in protein modeling, based on an attention neural network that integrates transformer and graph convolutional architectures in a causal multi-headed graph mechanism, to realize a generative pretrained model. (aip.org)
A folded protein can be thought of as a 'spatial graph,' where residues are the nodes and edges connect the residues in close proximity. (bigthink.com)
For the latest version of AlphaFold, used at CASP14, we created an attention-based neural network system, trained end-to-end, that attempts to interpret the structure of this graph, while reasoning over the implicit graph that it's building. (bigthink.com)
Q. Yuan, J. Chen, H. Zhao, Y. Zhou, and Y. Yang, "Structure-aware protein-protein interaction site prediction using deep graph convolutional network. (sparks-lab.org)
The idea is to consider the sequence of amino acids as a graph: its vertices are amino acid residues, and the edges are the connections between them. (vpchothuegoldenking.com)

Misfolding and aggregation of normally soluble proteins are common pathological features of many neurodegenerative diseases, including Alzheimer's, Parkinson's, Creutzfeldt-Jacob and Huntington's diseases ( Ross and Poirier, 2004 ). (elifesciences.org)
Of the various chaperones known to be associated with neurodegenerative disease, the small secretory chaperone known as proSAAS (named after four residues in the amino terminal region) has many attractive properties. (au.dk)

However, we have found that not all peptides with potential AD function are in the proper protein context to work as transcription activators. (fredhutch.org)

DeepMind developed a system that's able to predict "protein folding" in a fraction of the time of human experiments, and with unprecedented accuracy. (bigthink.com)
Now, DeepMind has developed new deep learning architectures for CASP14, drawing inspiration from the fields of biology, physics, and machine learning, as well as the work of many scientists in the protein folding field over the past half-century. (rockingrobots.com)

AbstractComputational methods that produce accurate protein structure models from limited experimental data, e.g. from nuclear magnetic resonance (NMR) spectroscopy, hold great potential for biomedical research. (sciencegate.app)

Firstly, VPA induced cell growth inhibition and apoptotic activity, as demonstrated by sulforhodamine B protein assay, annexin V assay and by Western blot analysis for Bcl2 and Bax expression levels, in all three cell lines. (iiarjournals.org)
The SRB assay is based on the ability of SRB dye to bind protein basic amino acid residues. (iiarjournals.org)

Zinc finger (Znf) domains are relatively small protein motifs which contain multiple finger-like protrusions that make tandem contacts with their target molecule. (embl.de)
This entry represents a predicted zinc finger with eight potential zinc ligand binding residues. (embl.de)
Animal myelin transcription factor 1 (MyT1), or neural zinc finger 2 (NZF2), a transcription factor that contains seven copies of the CCHHC-type zinc finger. (embl.de)

AlphaFold was able to predict protein shapes by "training" itself on vast amounts of data on known amino acid strings and their corresponding protein shapes. (bigthink.com)

To seek the most effective definition of residue contacts for template-based protein structure prediction, we evaluated 45 different contact definitions, varying bases of contacts and distance cutoffs, in terms of their ability to identify proteins of the same fold. (biomedcentral.com)
In the case of threading, alignment accuracy strongly influences the fraction of common contacts identified among proteins of the same fold, which eventually affects the fold recognition accuracy. (biomedcentral.com)
The largest deterioration of the fold recognition was observed for β-class proteins when the threading methods were used because the average alignment accuracy was worst for this fold class. (biomedcentral.com)
When results of fold recognition were examined for individual proteins, we found that the effective contact definition depends on the fold of the proteins. (biomedcentral.com)
Residue contacts defined by Cβ−Cβ distance of 7.0 Å work best overall among tested to identify proteins of the same fold. (biomedcentral.com)
We also found that effective contact definitions differ from fold to fold, suggesting that using different residue contact definition specific for each template will lead to improvement of the performance of threading. (biomedcentral.com)
In 1994, a group of scientists created a competition to solve one of the most perplexing problems in biology: how do proteins fold themselves into 3D shapes, which then carry out fundamental processes within living organisms? (bigthink.com)
Professor John Moult, Co-Founder and Chair of CASP, University of Maryland said: "We have been stuck on this one problem - how do proteins fold up - for nearly 50 years. (rockingrobots.com)
A major challenge is the astronomical number of ways a protein could theoretically fold before settling into its final 3D structure. (rockingrobots.com)

The model is applied to predict the secondary structure content (per-residue level and overall content), protein solubility, and sequencing tasks. (aip.org)

AlphaFold's performance in the 2018 contest was impressive, but not reliable enough to consider the problem of "protein folding" solved. (bigthink.com)
The American Physiological Society (2018) Retraction: Acute exercise suppresses hypothalamic PTP1B protein level and improves insulin and leptin signaling in obese rats. (anonymityblaize.com)

A proper encoding of residue contact information is crucial for structure prediction because in principle, a full distance map or a residue contact map has sufficient information for reconstructing the tertiary structure of a protein[ 25 ]. (biomedcentral.com)
Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary structure prediction. (biomedcentral.com)
We propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. (biomedcentral.com)

The possible reason to choose RNF12 among other candidates could be based on its role in stem cell biology and neural development (the host lab's previous work 10 ). (biologists.com)

In this study, the anticancer properties of VPA on neural crest-derived human tumor cell lines G361 melanoma, U87MG glioblastoma and SKNMC Askin tumor cells were investigated. (iiarjournals.org)
The effectiveness of HDAC inhibitors, expecially VPA, in neuroblastoma cells, prompted us to investigate the anticancer activity of VPA in other neural crest-derived malignancies, such as glioblastoma, melanoma and Askin tumor. (iiarjournals.org)
Like CNTF, it promotes the survival of embryonic motor neurons, and could increase the proliferation of neural precursor cells in the presence of EGF and FGF-2. (peprotech.com)
Proteins are organic macromolecules made up of amino acids, which are essential components of cells and sustain life activities. (biomedcentral.com)
Zika virus NS4A and NS4B proteins deregulate akt-mTOR signaling in human fetal neural stem cells to inhibit neurogenesis and induce autophagy. (cdc.gov)
Beneficial effect on dendrites in neural cells was detectable already before any aggregates or fibrils were detectable. (lu.se)

We typically look for ADpred scores of ≥0.8 over ≥10-15 continuous residues as indicating a high propensity for AD function. (fredhutch.org)

The system uses both global and local information (i.e., features from the entire protein such as secondary structure composition, protein length, and fraction of surface residues, and features from a local window of sequence-consecutive residues). (rostlab.org)

These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. (elsevierpure.com)

J. Singh, T. Litfin, K. Paliwal, J. Singh, A. K. Hanumanthappa, and Y. Zhou, "SPOT-1D-Single: Improving the single-sequence-based prediction of protein secondary structure, back-bone angles, solvent accessibility and half-sphere exposures using a large training set and ensembled deep learning. (sparks-lab.org)

Next, based on the similarity of residue level conservation scores derived from the evolutionary profiles, a query protein is hierarchically clustered with all available template proteins in its superfamily with known interface definitions. (edu.in)

Here, we investigate whether model quality assessment can be introduced into structure prediction to form a closed-loop feedback, and iteratively improve the accuracy of de novo protein structure prediction. (sciencegate.app)
Results: In this study, we propose a de novo protein structure prediction method called RocketX. (sciencegate.app)
S. Liang, Z. Li, J. Zhan, and Y. Zhou, "De novo protein design by an energy function based on series expansion in distance and orientation dependence. (sparks-lab.org)

B-values derived from experimental data are widely used to measure residue flexibility. (rostlab.org)
Our experiment points out new directions for QA problem and our method could be widely used for protein structure prediction problem. (sciencegate.app)

Protein structure is fundamentally important to understand protein functions. (biomedcentral.com)
Many of the greatest challenges facing society, like developing treatments for diseases or finding enzymes that break down industrial waste, are fundamentally tied to proteins and the role they play. (rockingrobots.com)

Signaling is initiated when the Wnt ligand binds to the Frizzled receptor on the cell membrane and the LDL receptor-associated protein 5/6 (LRP5/6) co-receptor. (frontiersin.org)
α-Synuclein is a natively unfolded protein of 140 amino acid residues, normally found in both soluble and membrane-associated fractions and localized in synaptic termini. (elifesciences.org)
In chick embryos, neural tube-derived signals are required for this conversion, as the interposition of a membrane between neural tube and somites results in a failure of the dermatome to lose its epithelial arrangement. (biologists.com)

Intracellular accumulation of beta-catenin increases translocation of the protein into the nucleus, where it binds to TCF/LEF transcription factors to promote expression of Wnt target genes. (biolegend.com)
The transcription initiation TFIID complex is composed of TATA binding protein (TBP) and a number of TBP-associated factors (TAFs). (embl-heidelberg.de)

Three mammalian SRPKs - SRPK1, SRPK2, and SRPK3 - relay information between environmental cues and gene expression by regulating SR protein phosphorylation 1,2 . (biologists.com)

Post-translational modifications (PTMs) of proteins, including phosphorylation, acetylation, ubiquitination, and SUMOylation, can regulate the function of proteins, determine the active state and subcellular location of proteins, and dynamically interact with other proteins related to carcinogenesis and progression ( 17 - 20 ). (frontiersin.org)

Proteins associated with the Wnt/β-catenin pathway have been identified as SUMOylated substrates, and evidences suggested that the initiation and progression of cancers depended on the function of the SUMOylation ( 23 ). (frontiersin.org)
This model may also provide a basis for mapping the relationship between structure, function and evolutionary history of a protein family, and thus be a powerful tool for rational engineering. (strath.ac.uk)

Scientists formulate a deep learning framework, DeepRank, for the data mining of 3D protein-protein interfaces. (cbirt.net)
ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. (biomedcentral.com)
These results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins. (biomedcentral.com)
Inspired by the success of non-linear models in TBM methods, we would like to study if we can improve TBM methods' model accuracy using more advanced neural network architecture such as deep residual network which has proven very successful in protein residue-residue contacts prediction. (biomedcentral.com)
In this paper, we present a new method, called ThreaderAI, which uses a deep residual neural network to perform template-query alignment. (biomedcentral.com)
Motivation: The successful application of deep learning has promoted progress in protein model quality assessment. (sciencegate.app)

It can also correctly predict the protein interaction of cell and tumor information contained in one-core network and crossover network.The SDNN-PPI proposed in this paper not only explores the mechanism of protein-protein interaction, but also provides new ideas for drug design and disease prevention. (biomedcentral.com)

This discovery will allow the creation of new medicinaldrugs against diseases, because with the help of the structure, scientists will know how the protein works, how it folds and interacts with other elements so that it can be used painlessly in medicines. (vpchothuegoldenking.com)

Anatomy1

Organisms1

Chemicals and Drugs15

Analytical, Diagnostic and Therapeutic Techniques and Equipment12

Phenomena and Processes23

Disciplines and Occupations1

Information Science6

Health Care2

Sequences11

Bioinformatics5

Structure of proteins3

Structural13

Inter-residue2

Complexes5

Recombinant proteins1

Convolutional neural3

Predicts1

Interactions11

Structures9

Intrinsically disordered regions1

Cysteine4

Learning neural network1

Dihedral angles1

Networks9

Amino acid seq1

Molecular5

Network to predict1

Biological3

Pairs4

Probability3

Algorithm2

Catalytic1

Serine1

Predictions5

Functional2

Graph5

Neurodegenerative2

Peptides1

DeepMind2

Accurate protein1

Assay2

Zinc finger3

Predict protein1

Fold9

Applied to predict1

20182

Tertiary structure3

Biology1

Cells6

Propensity1

Fraction1

Nodes1

Secondary1

Evolutionary profiles1

Novo protein3

Widely2

Fundamentally2

Membrane3

Transcription2

Mammalian1

Phosphorylation1

Function2

Deep6

Tumor1

Folds1