Protein Function Prediction
Protein Function Prediction - Related Videos
16:41
A Protocol for Computer-Based Protein Structure and Function Prediction
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Cited by 79 •
2011
Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.
01:56
Structural Protein Function
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2020
Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin. Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity. In bones and teeth, it mineralizes to form...
01:58
Mechanical Protein Functions
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2020
Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force.
10:12
Characterization and Functional Prediction of Bacteria in Ovarian Tissues
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Cited by 1 •
2021
Immunohistochemistry staining and 16S ribosomal RNA gene (16S rRNA gene) sequencing were performed in order to discover and distinguish bacteria in cancerous and noncancerous ovarian tissues in situ. The compositional and functional differences of the bacteria were predicted by using BugBase and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt).
10:21
Protein Target Prediction and Validation of Small Molecule Compound
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2024
The experiment used here shows a method of molecular docking combined with cellular thermal shift assay to predict and validate the interaction between small molecules and protein targets.
FAQs
A sequence that resembles a characterized protein can provide functional evidence, especially when the similarity reflects shared evolutionary history. Conserved regions may indicate domains or motifs that remain important for molecular activity. Comparing an unknown sequence with annotated homologs therefore helps transfer plausible functional information, while also identifying proteins that require additional structural or experimental support.
Domains are recurring structural or functional units, whereas motifs are shorter conserved patterns that may be linked to particular activities. Structural modeling adds information about how a protein folds and presents these features in three dimensions. Considering sequence patterns together with structure can refine predictions beyond overall similarity and help distinguish proteins with related but different functional roles.
A protein’s likely role depends not only on its sequence or structure but also on where it occurs and which cellular processes surround it. Cellular context can help interpret predicted interactions, molecular activity, or localization. Machine-learning methods can combine such evidence with annotated sequence and structural data, producing estimates that support broader biological interpretation.
A typical workflow begins by comparing the sequence with characterized homologs, then examining conserved domains and functional motifs. Researchers can add structural modeling, evolutionary evidence, and cellular context before using machine-learning predictions where appropriate. The resulting functional estimates are reviewed as candidates for laboratory validation, rather than treated as a substitute for experimental evidence.
Predicted functions provide annotations for proteins whose roles have not yet been established experimentally. These annotations help organize newly sequenced genomes and make large-scale genomic or proteomic datasets biologically interpretable. By assigning plausible activities, interactions, or localizations, prediction methods also help researchers prioritize which proteins or pathways deserve more focused investigation.
Functional predictions can clarify pathways associated with health and disease and identify proteins that may warrant laboratory study. They can also help prioritize targets for drug discovery or reveal useful activities for biotechnology. Because the predictions are evidence-based estimates, experimental validation remains important before assigning a confirmed role or using a protein in a practical application.