Domain Structure Prediction
Domain Structure Prediction - Related Videos
16:41
A Protocol for Computer-Based Protein Structure and Function Prediction
0 Views •
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.
13:42
RNA Secondary Structure Prediction Using High-throughput SHAPE
0 Views •
Cited by 16 •
2013
High-throughput selective 2' hydroxyl acylation analyzed by primer extension (SHAPE) utilizes a novel chemical probing technology, reverse transcription, capillary electrophoresis and secondary structure prediction software to determine the structures of RNAs from several hundred to several thousand nucleotides at single nucleotide resolution.
02:26
Conservation of Protein Domains Over Different Proteins
0 Views •
2020
Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms. A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
02:27
Predicting Molecular Geometry
0 Views •
2020
VSEPR Theory for Determination of Electron Pair Geometries The following procedure uses VSEPR theory to determine the electron pair geometries and the molecular structures: Write the Lewis structure of the molecule or polyatomic ion. Count the number of electron groups (lone pairs and bonds) around the central atom. A single, double, or triple bond counts as one region of electron density. Identify the electron-pair geometry based on the number of electron groups: linear, trigonal planar,...
11:31
Crystal Structure of the N-terminal Domain of Ryanodine Receptor from Plutella xylostella
0 Views •
Cited by 1 •
2018
In this article, we describe the protocols of protein expression, purification, crystallization and structure determination of the N-terminal domain of ryanodine receptor from diamondback moth (Plutella xylostella).
FAQs
These signals provide complementary evidence about whether parts of an amino acid sequence resemble recurring protein units. Evolutionary conservation highlights residues or regions preserved across related sequences, while sequence profiles summarize patterns shared by a family. Motifs add recognizable local features, and homology to known domains supplies comparative support for estimating boundaries, folds, and possible interactions.
Boundary estimates help separate recurring units within a larger protein sequence, making subsequent structural and functional interpretation more focused. They can influence how researchers model a protein, assign annotations, and relate particular sequence regions to molecular activity. Clear boundaries are especially useful when investigating mutations or interactions that may affect one domain without involving the entire protein.
Comparative approaches draw on similarity or homology to previously characterized domains, whereas machine-learning models estimate structural or functional features from patterns learned across sequence data. Both can use sequence-derived evidence to predict boundaries, folds, or interactions, but they represent different computational strategies. Their results can guide interpretation when direct experimental structural information is unavailable.
The available evolutionary conservation, quality of sequence profiles, presence of informative motifs, and degree of homology to known domains all shape the prediction. Stronger recognizable patterns can support clearer assignments, while limited similarity or sparse comparative evidence may make interpretation more uncertain. Researchers therefore examine multiple sequence signals rather than relying on one feature alone.
A practical workflow begins with an amino acid sequence and computational analysis of conservation, profiles, motifs, or homology to known domains. The resulting estimates identify candidate boundaries, folds, and interactions, which researchers then use for annotation or structure modeling. Predictions can subsequently help prioritize particular regions or targets for laboratory validation when experimental data are limited.
Predicted domains can provide hypotheses about how sequence regions contribute to biological activity, how a protein may be organized structurally, and which regions could participate in interactions. These results support protein annotation and structure modeling, but they also identify questions for experimental testing. The main outcome is a focused interpretation of sequence evidence rather than a replacement for validation.
In biology, domain-level predictions support studies of enzyme activity, signaling pathways, and protein evolution by connecting sequence regions with possible roles. They can also help investigators examine disease-associated mutations and prioritize proteins or regions for laboratory validation. In applied research, this prioritization may contribute to target selection for drug-discovery efforts when experimental information is incomplete.