Protein Structure Prediction
Protein Structure 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.
13:42
RNA Secondary Structure Prediction Using High-throughput SHAPE
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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.
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...
22:00
Crystallizing Membrane Proteins for Structure Determination using Lipidic Mesophases
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Cited by 48 •
2010
Herein is described the procedure implemented in the Caffrey Membrane Structural and Functional Biology Group to set up manually crystallization trials of membrane proteins in lipidic mesophases.
02:39
Protein and Protein Structure
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2026
Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.A protein's shape is critical to its function. For example, an enzyme can...
FAQs
Sequence patterns, evolutionary relationships, and physical and chemical interactions provide the main clues for modeling a stable fold. Related sequences can reveal conserved features, while interaction-based analysis helps estimate how amino acid properties shape the structure. Together, these signals support predictions of arrangements that are biologically plausible rather than relying on sequence length or composition alone.
Predictions can be interpreted across several structural levels. Secondary structure describes local arrangements within a polypeptide, tertiary structure captures its overall three-dimensional organization, and quaternary structure concerns associations among multiple protein components. Distinguishing these levels helps researchers connect local sequence-derived features with the larger organization required for activity or molecular interaction.
A single sequence may support multiple conformations, meaning the protein can adopt more than one three-dimensional arrangement. Cellular conditions and interactions with other molecules may also influence which form predominates. Consequently, a computational model should be treated as a testable representation of possible structure, not automatically as the only biologically relevant state.
Researchers begin with an amino acid sequence and analyze sequence patterns, evolutionary relationships, and relevant physical or chemical interactions. They then examine the resulting model for structural features that could explain activity, binding, or mutation effects. The prediction can guide experiments by identifying regions or relationships that warrant direct testing.
A model can suggest how a protein’s shape supports enzyme activity, where molecular contacts may occur, or which structural regions could be affected by a disease-associated mutation. These interpretations generate focused biological hypotheses and help organize follow-up studies. The same structural information can also support early reasoning about potential drug interactions.
Experimental validation is important when conclusions depend on a model’s precise conformation or on behavior inside cells. Predictions may not capture alternative states or changes caused by cellular interactions, so researchers should compare structural interpretations with experimental evidence whenever possible. This validation helps distinguish a useful hypothesis from a model that does not represent the relevant biological context.