Proteins biophysical an biochemistry features make identity of each protein unique and these units change shape, function and structure protein for special purpose.
Proteins are usually known as consisting of multiple structural units. These units include domains, motifs, and folds. With this vision that there are about 100,000 different proteins expressed in eukaryotic living organisms, there are many fewer different domains, structural motifs and folds.
A structural domain is an element of the protein’s overall structure that is self-stabilizing and often folds independently of the rest of the protein chain. Many domains are not unique to the protein products of one gene or one gene family but instead appear in a variety of proteins. Domains often are named and singled out because they figure prominently in the biological function of the protein they belong to; for example, the “calcium-binding domain of calmodulin”. Because they are independently stable, domains can be “swapped” by genetic engineering between one protein and another to make chimera proteins.
Structural and sequence motif
The structural and sequence motifs refer to short segments of protein three-dimensional structure or amino acid sequence that were found in a large number of different proteins.
The supersecondary structure refers to a specific combination of secondary structure elements, such as ?-?-? units or a helix-turn-helix motif. Some of them may be also referred to as structural motifs.
A protein fold refers to the general protein architecture, like a helix bundle, ?-barrel, Rossman fold or different “folds” provided in the Structural Classification of Proteins database. A related concept is protein topology that refers to the arrangement of contacts within the protein.
A superdomain consists of two or more nominally unrelated structural domains that are inherited as a single unit and occur in different proteins. An example is provided by the protein tyrosine phosphatase domain and C2 domain pair in PTEN, several tensin proteins, auxilin and proteins in plants and fungi. The PTP-C2 superdomain evidently came into existence prior to the divergence of fungi, plants and animals is therefore likely to be about 1.5 billion years old.
Once translated by a ribosome, each polypeptide folds into its characteristic three-dimensional structure from a random coil. Since the fold is maintained by a network of interactions between amino acids in the polypeptide, the native state of the protein chain is determined by the amino acid sequence (Anfinsen’s dogma).
Protein structure determination
Around 90% of the protein structures available in the Protein Data Bank have been determined by X-ray crystallography. This method allows one to measure the three-dimensional (3-D) density distribution of electrons in the protein, in the crystallized state, and thereby infer the 3-D coordinates of all the atoms to be determined to a certain resolution. Roughly 9% of the known protein structures have been obtained by nuclear magnetic resonance techniques. The secondary structure composition can be determined via circular dichroism. Vibrational spectroscopy can also be used to characterize the conformation of peptides, polypeptides, and proteins. Two-dimensional infrared spectroscopy has become a valuable method to investigate the structures of flexible peptides and proteins that cannot be studied with other methods. Cryo-electron microscopy has recently become a means of determining protein structures to high resolution, less than 5 ångströms or 0.5 nanometer, and is anticipated to increase in power as a tool for high resolution work in the next decade. This technique is still a valuable resource for researchers working with very large protein complexes such as virus coat proteins and amyloid fibers. A more qualitative picture of protein structure is often obtained by proteolysis, which is also useful to screen for more crystallizable protein samples. Novel implementations of this approach, including fast parallel proteolysis (FASTpp), can probe the structured fraction and its stability without the need for purification.
Protein Sequence Analysis: Ensembles
Proteins are often thought of as relatively stable structures that have a set tertiary structure and experience conformational changes as a result of being modified by other proteins or as part of enzymatic activity. However proteins have varying degrees of stability and some of the less stable variants are intrinsically disordered proteins. These proteins exist and function in a relatively ‘disordered’ state lacking a stable tertiary structure. As a result, they are difficult to describe in a standard protein structure model that was designed for proteins with a fixed tertiary structure. Conformational ensembles have been devised as a way to provide a more accurate and ‘dynamic’ representation of the conformational state of intrinsically disordered proteins. Conformational ensembles function by attempting to represent the various conformations of intrinsically disordered proteins within an ensemble file (the type found at the Protein Ensemble Database).
Protein ensemble files are a representation of a protein that can be considered to have a flexible structure. Creating these files requires determining which of the various theoretically possible protein conformations actually exist. One approach is to apply computational algorithms to the protein data in order to try to determine the most likely set of conformations for an ensemble file.
There are multiple methods for preparing data for the Protein Ensemble Database that fall into two general methodologies – pool and molecular dynamics (MD) approaches (diagrammed in the figure). The pool based approach uses the protein’s amino acid sequence to create a massive pool of random conformations. This pool is then subjected to more computational processing that creates a set of theoretical parameters for each conformation based on the structure. Conformational subsets from this pool whose average theoretical parameters closely match known experimental data for this protein are selected.
The molecular dynamics approach takes multiple random conformations at a time and subjects all of them to experimental data. Here the experimental data is serving as limitations to be placed on the conformations (e.g. known distances between atoms). Only conformations that manage to remain within the limits set by the experimental data are accepted. This approach often applies large amounts of experimental data to the conformations which is a very computationally demanding task.
(adapted from image in “Computational approaches for inferring the functions of intrinsically disordered proteins”)
Protein structures can be grouped based on their similarity or a common evolutionary origin. The Structural Classification of Proteins database and CATH database provide two different structural classifications of proteins. Shared structure between proteins is considered evidence of evolutionary relatedness between proteins and is used group proteins together into protein superfamilies.
Computational prediction of protein structure
The generation of a protein sequence is much easier than the determination of a protein structure. However, the structure of a protein gives much more insight in the function of the protein than its sequence. Therefore, a number of methods for the computational prediction of protein structure from its sequence have been developed. Ab initio prediction methods use just the sequence of the protein. Threading and homology modeling methods can build a 3-D model for a protein of unknown structure from experimental structures of evolutionarily-related proteins, called a protein family.