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Biological function is based on molecular interactions, and these are a consequence of macromolecular structures. Since initial structure determinations in the 50s, both in the protein and in the nucleic acid worlds, the increase in the knowledge of how macromolecular structures are built has been continuous.

At present, protein data bank PDB 1 holds more than , entries, including more than , proteins, 2, nucleic acids, alone or forming complexes, and approximately 20, small molecules complexed to macromolecules. Molecular recognition rules as defined by such structural knowledge powers the understanding of basic biological phenomena, like enzyme mechanisms and regulation, transport across membranes, the building of large structures like ribosomes, or viral capsids, or how DNA is read and transcription is controlled.

The study and prediction of protein—protein interaction networks is one of the growing fields in modern systems biology. On a more practical note, protein three-dimensional 3D structures are the basis for structure-based drug design. The simple visual analysis of 3D structures of protein or nucleic acids, as obtained from the experiments, has driven large number of successful studies in biochemistry.

However, despite their enormous utility, structures stored at the PDB provide only a partial view of 3D structure. Both protein and nucleic acids are flexible entities, and dynamics can play a key role in their functionality. Proteins undergo significant conformational changes while performing their function.


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As a rule, any complex made by any protein implies some structural rearrangement. This can be easily checked just by comparing a series of PDB entries that just differ in a small ligand bound to a given protein. Figure 1 shows a superimposition of experimental acetylcholinesterase structures. There are no changes in the overall fold, just small rearrangements in the structure; however, these differences are large enough to fool ligand-docking algorithms. Larger conformational changes are also present in the known protein structures. Allosteric regulation is entirely based on the possibility of a given protein to coexist in two or more conformations of comparable stability.

Binding to ligands allosteric regulators , or simply protein concentration, or crowding, may switch stabilities among conformations and trigger the shape transition. Additionally, some features of protein function can be understood only when dynamic properties are taken into account. For instance, diffusion of small substrates through heme-dependent enzyme molecules requires the transient appearance of channels in the protein structure. Figure 1 Structure variability within a protein family. Structures of acetylcholinesterase 1acg, 1ax9, 1dx6, 1hbj, 1qon, 1vot, and 2ace crystallized with different active-site ligands.

In the case of nucleic acids, conformational changes are even more complex. Standard B-DNA has a relatively simple structure in comparison with protein or complex RNAs; however, it is an extremely plastic molecule that undergoes large conformational changes to adapt to its interaction partners. Binding of transcription factors to DNA, for example, is not only dependent on DNA sequence recognition, but also a direct consequence of the ability of the DNA molecule to adapt to the protein surface. The traditional approach to understand conformation influence on macromolecular function is to cumulate experimental structures covering the conformational space.

This has led to the generation of crystal structures for macromolecules in several environments, or macromolecules complexed with different molecules, and contributes to the enormous redundancy seen in the PDB. Examples of this approach are the 87 structures for CK2 homologues Figure 2A , where a common fold is maintained, and different degrees of conformational variation are clearly visible, especially in loop regions. A single experiment could generate conformational ensembles as those taken from nuclear magnetic resonance experiments Figure 2B.

In the latter case, the source of the variability found is rather a consequence of the lack of experimental data in some specific regions of the structure. Indeed, the study of PDB as source for molecular flexibility has been exploited in some extent. Theoretical techniques appear as the most convenient way to obtain a picture of macromolecular dynamic properties.

Ensembles can be analyzed to derive thermodynamic properties of the system, like entropy or free energy. Figure 2 Experimental ensembles. A Superimposition of experimental structures of protein kinases. Molecular dynamics MD simulation, first developed in the late 70s, 32 , 33 has advanced from simulating several hundreds of atoms to systems with biological relevance, including entire proteins in solution with explicit solvent representations, membrane embedded proteins, or large macromolecular complexes like nucleosomes 34 , 35 or ribosomes. This remarkable improvement is in large part a consequence of the use of high performance computing HPC , and the simplicity of the basic MD algorithm Figure 3.

An initial model of the system is obtained from either experimental structures or comparative modeling data. The simulated system could be represented at different levels of detail. Atomistic representation is the one that leads to the best reproduction of the actual systems.

However, coarse-grained representations are becoming very popular when large systems or long simulations are required see Orozco et al 38 for a review of such strategies. Solvent representation is a key issue in system definition. Several approaches have been assayed 39 — 47 but, again, the most effective is the simplest one, the explicit representation of solvent molecules, although at the expense of increasing the size of the simulated systems.

Explicit solvent is able to recover most of the solvation effects of real solvent including those from entropic origin like the hydrophobic effect. Once the system is built, forces acting on every atom are obtained by deriving equations, the force-fields, where potential energy is deduced from the molecular structure. The simplicity of the force-field representation of molecular features: Force-fields currently used in atomistic molecular simulations differ in the way they are parameterized.

Parameters are not necessarily interchangeable, and not all force-fields allow to represent all molecule types, but simulations conducted using modern force-fields are normally equivalent. As integration of movement is done numerically, to avoid instability, a time step shorter than the fastest movements in the molecule should be used. This ranks normally between 1 and 2 fs for atomistic simulations, and is the major bottleneck of the simulation procedure. Microsecond-long simulations, barely scratching the time scales of biological processes, require iterating over this calculation cycle 10 9 times.

This is one of the strengths of coarse-grained strategies. As a more simplified representation of the system is used, much larger time steps are possible, and therefore the effective length of the simulations is dramatically extended. Of course, this can be obtained at the expense of the accuracy of the simulation ensemble. Algorithmic advances, that include fine-tuning of energy calculations, parallelization, or the use of graphical processing units GPUs , have largely improved the performance of MD simulations.

Figure 3 Molecular dynamics basic algorithm. The simulation output, the trajectory, is an ordered list of 3N atom coordinates for each simulation time or snapshot. E pot , potential energy; t , simulation time; dt , iteration time; For each spatial coordinate of the N simulated atoms i: The present generation of computers takes benefit of parallelism and accelerators to speed-up the process.

When a large number of computer cores can be used simultaneously, MPI can greatly reduce the computation time. To benefit the locality of interactions, the general strategy is to distribute the system to simulate among processors. This strategy is called spatial decomposition. Only a small fragment of the system has to be simulated in each processor. The most efficient division is not based in the list of particles, but in their position in space. Each processor deals with a region of space irrespective of which particles are present there.

Communication between processors is also reduced, as only those simulating neighboring regions have to share information see Larsson et al 60 for a review. As stated, the use of accelerators, mainly GPU, has become a major breakthrough in simulation codes. Originally designed to handle computer graphics, GPUs have evolved into general-purpose, fully programmable, high-performance processors and represent a major technical improvement to perform atomistic MD.

Remarkably, while simulations have been the most popular use of HPC in life sciences, the increasing power and sophistication of GPUs is leading to a greater use of personal workstations with a comparable performance. Pure computational brute force, just making longer simulations, is not enough to extend the conformational sampling in biomolecular systems. The complex shape of the free energy landscape makes most of the simulations explore just a small region around the energy minimum closest to the initial conformation. With the availability of the present HPC systems, an obvious strategy is to perform a series of parallel simulations with several starting conformations.

Although this could be efficient, it requires a specific knowledge of the system to simulate, and cannot be applied as a general strategy. This approach is particularly useful when several crystal structures are available for instance in the case of allosterically regulated enzymes.

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A second problem that appears when collections of parallel simulations are calculated, is the generation of a usable ensemble out of the trajectories obtained. Recently the Markov state model MSM theory has been used to this end. The analysis of such a matrix would allow reconstruction of the global behavior of the system. Since the transition rates converge more rapidly than the population of the involved states, this approach has the advantage that the collection of simulations is not required to be especially long. This approach has been used mainly in the study of folding processes, 28 , 66 but also in the kinetic characterization of the formation of ligand—protein complexes.

The obtained ensemble with a single simulation is limited to those states that are accessible at the simulation temperature. Simulations at high temperatures were common in the origins of MD, but they lead to unrealistic trajectories, and hence should be combined to room temperature runs. This approach, called simulated annealing, has been largely replaced by replica exchange methods. The most common variation is simulation temperature. The sampling ability of the simulation increases with temperature. Higher temperature simulations can surmount energy barriers and explore new regions of the ensemble.

Periodically, energies of the different simulations are compared and structures are swapped according to its energy rank. The resulting simulation has sampled a larger conformational space, due to high temperature simulations, and retains the ability to represent the low-temperature states of the system.

The main difference with the simulated annealing approach is that a realistic ensemble of the system is obtained and thermodynamic information can be derived from simulations.

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The idea has been extended to other simulation strategies. Most remarkably, replicas based on differences in the Hamiltonian replica exchange, including alchemical free energy calculations 78 or constant-pH simulations, 79 — 83 are becoming popular. Preparation for simulation implies the following of a series of operations that are far from being just routine. First, the initial structure comes from the experiment. Expected issues include nonstructured or missing regions or residues, nonstandard ligands, or even structures bearing errors in the interpretation of experimental data.

When a single system is simulated, all the effort in the preparation of the system is worth, as it assures the quality of the simulation result. Such setup is usually done manually, with a considerable human effort. A standard procedure to set up a system implies a number of well-known procedures: An expert modeler normally carries out these procedures using a set of helper programs. Such an expert has the necessary knowledge to surmount specific problems that may arise.

For instance, the workflow used in the MoDEL project 84 was programmed to run automatically, but a nonnegligible fraction of over 1, proteins prepared failed at some point of the process. With this scenario, for newcomers to MD simulation, even a single system setup could represent an unaffordable problem. Even worse, nonexpert users tend to blindly use default procedures leading easily to artifactual trajectories, which are hard to distinguish from the correct ones.

This strongly contributes to the lack of popularity of biomolecular simulations among the bioinformatics or the biochemical community. MD simulations have been restricted to those research groups bearing the necessary expertise. Solving this issue requires an automatic setup of the simulation system. We would be looking for a clever black box for the nonexperts, but also for a robust software suite that can account for a large set of unrelated protein structures. All major MD codes 56 — 59 come with a set of accompanying programs, which perform most steps of the preparation.

Additionally, a number of initiatives, combining those tools with a user-friendly interface, have come into the scene to address this problem. Most of these tools provide a friendly environment to prepare systems for simulation without the need of a deep knowledge of the underlying operations, thus facilitating the access to the field for the newcomers. Unfortunately, due to the lack of a standard for the representation of molecular simulation data, most helper applications are restricted to a single MD package, and data is not easily interchangeable.

Besides, although most use some kind of embedded scripting language, automation of procedures is not a straightforward task. Lessons learned in the preparation of the MoDEL database, by our group, leaded to the generation of a new set of tools, MDMoby and MDWeb 91 that try to cover both aspects of the problem.

On one hand, MDMoby provides a full set of web services, covering all setup, simulation, and analysis operations. The modular nature of such collection of web services allows incorporating them as a tool kit to the design of complex setup protocols and to run them programmatically. In turn, MDWeb, a web-based interface, provides a user-friendly bench where user can check for the quality of the input structure, tailor their own setup protocols, or use a collection of predefined ones. Most regulation phenomena in proteins are explained within conformational transitions.

The concept of allostery that translates conformational dynamics in functional implications has been analyzed since the early times of protein biochemistry. In any case, there is a general agreement that conformational shifts involved in allosteric transitions are simple in terms of collective movements.

However, the ability of free atomistic simulation algorithms to follow a complete transition path is limited. Most of the traditional reports of simulations in this field use simplified frameworks, like discrete MD 96 or Go-Models, 97 or even popular nonsimulation equivalents like elastic network models, 98 — and seek to find the transition path between known experimental structures.

With full atom representations, it is usual to trick the algorithm by using targeted, — or supervised MD where the simulation is artificially driven to the desired conformation. In this case, the analysis of the path could give insight into the energetics and details of the allosteric transition. For those cases where allosteric regulation is known to occur, but one of the ends is unknown, long simulations alone , or with enhanced conformational sampling are required. Figure 4 shows an example where only 50 ns of simulation allowed for a conformational shift in Bacillus stearothermophilus lactate dehydrogenase PDB code: This enzyme is known to exist in two states: Fructose-1,6-bisphosphate is a known allosteric regulator that allows the tetrameric state to be formed.

In this case, no computational bias was introduced; however, protein setup was done mimicking experimental conditions where the conformational shift is known to occur. The use of experimental restrains but not necessarily the target structure is being exploited to guide the simulation.

The power of molecular simulations to uncover allosteric regulations is not in any doubt; however, there is still a long way until it could be routinely applied to all cases. Figure 4 R-T transition on Bacillus stearothermophilus lactate dehydrogenase after 50 ns simulation in explicit solvent. Simulation was initiated from a dimeric model of the protein 1ldn , and allowed to evolve without restrains.

Conformational shift is indicated as R-T shift in the main figure and with an arrow in the inset. One of the most practical application of the concept of molecular recognition are docking strategies, either small molecule or protein docking. To understand how a ligand, typically a substrate or a regulator, binds to its macromolecular counterpart is a key issue in the understanding of function itself, and it is the basis of structurally driven drug design. The recognition process is by nature dynamic. Although this is a generally well-accepted idea, docking algorithms are far from considering dynamic effects as a routine.

Most docking or virtual screening codes work on rigid structures as obtained from the PDB. Figure 5 shows a traditional cross-docking experiment where a collection of acetylcholinesterase ligands are docked back in the same set of receptor structures. Protein structures correspond to the ones shown in Figure 1. In this experiment, all receptor structures correspond to the same protein, but crystallized with a different ligand; and all ligands are known to bind the receptor in the same place and pose.

In these conditions, the experiment just measures the impact of small receptor rearrangements caused by ligand binding, on the docking efficiency. The usual result, as the one shown in Figure 5 , is that even though the protein does not change, a different PDB structure implies poorer docking results.

Even docking of a ligand back on its original PDB structure diagonal results in Figure 5 tends to fail due to the usual overcompression of structures derived from X-ray crystallography. This problem is especially relevant in protein—protein docking where considerable differences are found between bound and unbound structures. For ligand-docking methods, ligand flexibility could be largely recovered by using conformer families. Most of them use algorithms to select from a limited alternative set of protein conformations, either precomputed or simulated. Figure 5 Cross-docking experiment with selected acetylcholinesterase structures from PDB.

Performance of all possible combinations of rigid docking experiments done in standard conditions, using a series of seven acetylcholinesterase ligands extracted from left column PDB entries onto the same empty protein structures upper row. The use of simulations for the improvement of virtual screening or docking processes has a clear advantage.

However, due to the speed requirements of docking, most methods based on traditional atomistic simulations are too slow to be considered, when used in a real scenario. Coarse-grained methods or any sort of accelerated MD could be a way to take benefit of simulation in a near future. Structure prediction has been one of the most ancient problems addressed in structural bioinformatics. MD, including the longest simulations performed, has been extensively used for ab initio protein structure prediction, — aiming to simulate protein folding from scratch, although this is not the preferred strategy to obtain theoretical model of protein structure.

Biomolecular simulation and modelling: status, progress and prospects

Instead, template-based modeling is the most efficient technique. Irrespective of the modeling algorithm, the end result is a model bearing the new amino acid sequence and a structure averaging the used templates. In most cases, the last step of the prediction procedure implies relaxation of the structure using normally molecular mechanics.

In others, restrained simulations are used throughout all the process. Although this point is reasonable as a concept, MD simulations require systems to be close to their equilibrium native conformation. Otherwise, significant and difficult to detect artifacts may occur.

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Critical assessment of protein structure prediction contests, where prediction algorithms face problems with known but nonpublic 3D structures, provide an excellent dataset to test this issue. Applying different MD approaches to the refinement of such predictions has led to a number of conclusions. The most naive approach, a single simulation starting from the predicted conformation, tends to deviate significantly from the desired structure.

Instead, results clearly indicate that deviation from the original structure is directly correlated with the loss of quality of the model. A second conclusion is that the ensemble of structures taken from the simulations is a closer representation of the target structure, thus indicating that the native and original structures both lie within the conformational space of the simulation. MD simulations have already more than 40 years of history. However, it was not until the recent years that MD has achieved time scales that begin to be compatible with biological processes.

At present, when routine simulations are approaching the microsecond scale, conformational changes, or ligand binding can be effectively simulated. The improvement of the computational equipment, especially the use of GPUs, and the improvements made in the optimization of MD algorithms, including coarse-grained ones, allow us to move from the analysis of single structures, the basis of the molecular modeling as we know it, to the analysis of conformational ensembles. Conformational ensembles are a much better representation of real macromolecules, as they account for flexibility and dynamic properties including all thermodynamic information and ease the match with experimental results.

Although the shift in concept is clear, and the technology is coming along, there is still a long way until biomolecular simulations, the generation of conformational ensembles, would become a routine. Tools exist that make the setup of a macromolecular system much easier, and even allow the nonexperts to enter the simulation world. However, lack of representation standards, much less optimized analysis tools, and even the difficulties in simply storing and transmitting the huge amount of trajectory data that is generated are still issues that remain to be solved.

In any case, MD is already a valuable tool in helping to understand biology. The protein data bank. Acta Crystallogr D Biol Cryst. The database of macromolecular motions: J Mol Graph Model. Gerstein M, Krebs W. A database of macromolecular motions. Conformational changes associated with protein-protein interactions.

Proteins can interact with many types of molecules, including with other proteins , with lipids , with carboyhydrates , and with DNA. It has been estimated that average-sized bacteria contain about 2 million proteins per cell e. Smaller bacteria, such as Mycoplasma or spirochetes contain fewer molecules, on the order of 50, to 1 million. By contrast, eukaryotic cells are larger and thus contain much more protein. For instance, yeast cells have been estimated to contain about 50 million proteins and human cells on the order of 1 to 3 billion.

For instance, of the 20, or so proteins encoded by the human genome, only 6, are detected in lymphoblastoid cells. Eukaryotes, bacteria, archaea and viruses have on average , , and 42 proteins respectively coded in their genomes. Proteins are assembled from amino acids using information encoded in genes.

Each protein has its own unique amino acid sequence that is specified by the nucleotide sequence of the gene encoding this protein. The genetic code is a set of three-nucleotide sets called codons and each three-nucleotide combination designates an amino acid, for example AUG adenine - uracil - guanine is the code for methionine. Because DNA contains four nucleotides, the total number of possible codons is 64; hence, there is some redundancy in the genetic code, with some amino acids specified by more than one codon.

Most organisms then process the pre-mRNA also known as a primary transcript using various forms of Post-transcriptional modification to form the mature mRNA, which is then used as a template for protein synthesis by the ribosome. In prokaryotes the mRNA may either be used as soon as it is produced, or be bound by a ribosome after having moved away from the nucleoid.

In contrast, eukaryotes make mRNA in the cell nucleus and then translocate it across the nuclear membrane into the cytoplasm , where protein synthesis then takes place. The rate of protein synthesis is higher in prokaryotes than eukaryotes and can reach up to 20 amino acids per second. The process of synthesizing a protein from an mRNA template is known as translation. The mRNA is loaded onto the ribosome and is read three nucleotides at a time by matching each codon to its base pairing anticodon located on a transfer RNA molecule, which carries the amino acid corresponding to the codon it recognizes.

The growing polypeptide is often termed the nascent chain. Proteins are always biosynthesized from N-terminus to C-terminus. The size of a synthesized protein can be measured by the number of amino acids it contains and by its total molecular mass , which is normally reported in units of daltons synonymous with atomic mass units , or the derivative unit kilodalton kDa. The average size of a protein increases from Archaea to Bacteria to Eukaryote , , residues and 31, 34, 49 kDa respecitvely due to a bigger number of protein domains constituting proteins in higher organisms.

Short proteins can also be synthesized chemically by a family of methods known as peptide synthesis , which rely on organic synthesis techniques such as chemical ligation to produce peptides in high yield. Chemical synthesis is inefficient for polypeptides longer than about amino acids, and the synthesized proteins may not readily assume their native tertiary structure.

Most chemical synthesis methods proceed from C-terminus to N-terminus, opposite the biological reaction. The shape into which a protein naturally folds is known as its native conformation. Proteins are not entirely rigid molecules. In addition to these levels of structure, proteins may shift between several related structures while they perform their functions. In the context of these functional rearrangements, these tertiary or quaternary structures are usually referred to as " conformations ", and transitions between them are called conformational changes.

Such changes are often induced by the binding of a substrate molecule to an enzyme's active site , or the physical region of the protein that participates in chemical catalysis. In solution proteins also undergo variation in structure through thermal vibration and the collision with other molecules. Proteins can be informally divided into three main classes, which correlate with typical tertiary structures: Almost all globular proteins are soluble and many are enzymes. Fibrous proteins are often structural, such as collagen , the major component of connective tissue, or keratin , the protein component of hair and nails.

Membrane proteins often serve as receptors or provide channels for polar or charged molecules to pass through the cell membrane. A special case of intramolecular hydrogen bonds within proteins, poorly shielded from water attack and hence promoting their own dehydration , are called dehydrons.

Many proteins are composed of several protein domains , i.

Domains usually also have specific functions, such as enzymatic activities e. Short amino acid sequences within proteins often act as recognition sites for other proteins. Proteins are the chief actors within the cell, said to be carrying out the duties specified by the information encoded in genes.

The chief characteristic of proteins that also allows their diverse set of functions is their ability to bind other molecules specifically and tightly. The region of the protein responsible for binding another molecule is known as the binding site and is often a depression or "pocket" on the molecular surface.

This binding ability is mediated by the tertiary structure of the protein, which defines the binding site pocket, and by the chemical properties of the surrounding amino acids' side chains. Extremely minor chemical changes such as the addition of a single methyl group to a binding partner can sometimes suffice to nearly eliminate binding; for example, the aminoacyl tRNA synthetase specific to the amino acid valine discriminates against the very similar side chain of the amino acid isoleucine. Proteins can bind to other proteins as well as to small-molecule substrates.

When proteins bind specifically to other copies of the same molecule, they can oligomerize to form fibrils; this process occurs often in structural proteins that consist of globular monomers that self-associate to form rigid fibers. Protein—protein interactions also regulate enzymatic activity, control progression through the cell cycle , and allow the assembly of large protein complexes that carry out many closely related reactions with a common biological function.

Proteins can also bind to, or even be integrated into, cell membranes. The ability of binding partners to induce conformational changes in proteins allows the construction of enormously complex signaling networks. The best-known role of proteins in the cell is as enzymes , which catalyse chemical reactions.

Enzymes are usually highly specific and accelerate only one or a few chemical reactions. Some enzymes act on other proteins to add or remove chemical groups in a process known as posttranslational modification. About 4, reactions are known to be catalysed by enzymes.

The molecules bound and acted upon by enzymes are called substrates. Although enzymes can consist of hundreds of amino acids, it is usually only a small fraction of the residues that come in contact with the substrate, and an even smaller fraction—three to four residues on average—that are directly involved in catalysis. Dirigent proteins are members of a class of proteins that dictate the stereochemistry of a compound synthesized by other enzymes. Many proteins are involved in the process of cell signaling and signal transduction. Some proteins, such as insulin , are extracellular proteins that transmit a signal from the cell in which they were synthesized to other cells in distant tissues.

Others are membrane proteins that act as receptors whose main function is to bind a signaling molecule and induce a biochemical response in the cell. Many receptors have a binding site exposed on the cell surface and an effector domain within the cell, which may have enzymatic activity or may undergo a conformational change detected by other proteins within the cell. Antibodies are protein components of an adaptive immune system whose main function is to bind antigens , or foreign substances in the body, and target them for destruction. Antibodies can be secreted into the extracellular environment or anchored in the membranes of specialized B cells known as plasma cells.

Whereas enzymes are limited in their binding affinity for their substrates by the necessity of conducting their reaction, antibodies have no such constraints. An antibody's binding affinity to its target is extraordinarily high. Many ligand transport proteins bind particular small biomolecules and transport them to other locations in the body of a multicellular organism. These proteins must have a high binding affinity when their ligand is present in high concentrations, but must also release the ligand when it is present at low concentrations in the target tissues.

The canonical example of a ligand-binding protein is haemoglobin , which transports oxygen from the lungs to other organs and tissues in all vertebrates and has close homologs in every biological kingdom. Lectins typically play a role in biological recognition phenomena involving cells and proteins.

Transmembrane proteins can also serve as ligand transport proteins that alter the permeability of the cell membrane to small molecules and ions. The membrane alone has a hydrophobic core through which polar or charged molecules cannot diffuse.


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Membrane proteins contain internal channels that allow such molecules to enter and exit the cell. Many ion channel proteins are specialized to select for only a particular ion; for example, potassium and sodium channels often discriminate for only one of the two ions. Structural proteins confer stiffness and rigidity to otherwise-fluid biological components. Most structural proteins are fibrous proteins ; for example, collagen and elastin are critical components of connective tissue such as cartilage , and keratin is found in hard or filamentous structures such as hair , nails , feathers , hooves , and some animal shells.

Other proteins that serve structural functions are motor proteins such as myosin , kinesin , and dynein , which are capable of generating mechanical forces. These proteins are crucial for cellular motility of single celled organisms and the sperm of many multicellular organisms which reproduce sexually. They also generate the forces exerted by contracting muscles [42] and play essential roles in intracellular transport.

The activities and structures of proteins may be examined in vitro , in vivo , and in silico. In vitro studies of purified proteins in controlled environments are useful for learning how a protein carries out its function: By contrast, in vivo experiments can provide information about the physiological role of a protein in the context of a cell or even a whole organism. In silico studies use computational methods to study proteins. To perform in vitro analysis, a protein must be purified away from other cellular components. This process usually begins with cell lysis , in which a cell's membrane is disrupted and its internal contents released into a solution known as a crude lysate.

The resulting mixture can be purified using ultracentrifugation , which fractionates the various cellular components into fractions containing soluble proteins; membrane lipids and proteins; cellular organelles , and nucleic acids.

Large scale simulation of protein mechanics and function

Precipitation by a method known as salting out can concentrate the proteins from this lysate. Various types of chromatography are then used to isolate the protein or proteins of interest based on properties such as molecular weight, net charge and binding affinity. Additionally, proteins can be isolated according to their charge using electrofocusing.

For natural proteins, a series of purification steps may be necessary to obtain protein sufficiently pure for laboratory applications. To simplify this process, genetic engineering is often used to add chemical features to proteins that make them easier to purify without affecting their structure or activity. Here, a "tag" consisting of a specific amino acid sequence, often a series of histidine residues a " His-tag " , is attached to one terminus of the protein. As a result, when the lysate is passed over a chromatography column containing nickel , the histidine residues ligate the nickel and attach to the column while the untagged components of the lysate pass unimpeded.

A number of different tags have been developed to help researchers purify specific proteins from complex mixtures. The study of proteins in vivo is often concerned with the synthesis and localization of the protein within the cell. Although many intracellular proteins are synthesized in the cytoplasm and membrane-bound or secreted proteins in the endoplasmic reticulum , the specifics of how proteins are targeted to specific organelles or cellular structures is often unclear.

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A useful technique for assessing cellular localization uses genetic engineering to express in a cell a fusion protein or chimera consisting of the natural protein of interest linked to a " reporter " such as green fluorescent protein GFP. Other methods for elucidating the cellular location of proteins requires the use of known compartmental markers for regions such as the ER, the Golgi, lysosomes or vacuoles, mitochondria, chloroplasts, plasma membrane, etc.

With the use of fluorescently tagged versions of these markers or of antibodies to known markers, it becomes much simpler to identify the localization of a protein of interest. For example, indirect immunofluorescence will allow for fluorescence colocalization and demonstration of location. Fluorescent dyes are used to label cellular compartments for a similar purpose. Other possibilities exist, as well. For example, immunohistochemistry usually utilizes an antibody to one or more proteins of interest that are conjugated to enzymes yielding either luminescent or chromogenic signals that can be compared between samples, allowing for localization information.

Another applicable technique is cofractionation in sucrose or other material gradients using isopycnic centrifugation. Finally, the gold-standard method of cellular localization is immunoelectron microscopy. This technique also uses an antibody to the protein of interest, along with classical electron microscopy techniques. The sample is prepared for normal electron microscopic examination, and then treated with an antibody to the protein of interest that is conjugated to an extremely electro-dense material, usually gold.

Chapter 23 – Protein Chemistry: Part 1 of 10

This allows for the localization of both ultrastructural details as well as the protein of interest. Through another genetic engineering application known as site-directed mutagenesis , researchers can alter the protein sequence and hence its structure, cellular localization, and susceptibility to regulation. This technique even allows the incorporation of unnatural amino acids into proteins, using modified tRNAs, [51] and may allow the rational design of new proteins with novel properties. The total complement of proteins present at a time in a cell or cell type is known as its proteome , and the study of such large-scale data sets defines the field of proteomics , named by analogy to the related field of genomics.

Key experimental techniques in proteomics include 2D electrophoresis , [53] which allows the separation of a large number of proteins, mass spectrometry , [54] which allows rapid high-throughput identification of proteins and sequencing of peptides most often after in-gel digestion , protein microarrays , which allow the detection of the relative levels of a large number of proteins present in a cell, and two-hybrid screening , which allows the systematic exploration of protein—protein interactions.

A vast array of computational methods have been developed to analyze the structure, function, and evolution of proteins. The development of such tools has been driven by the large amount of genomic and proteomic data available for a variety of organisms, including the human genome. It is simply impossible to study all proteins experimentally, hence only a few are subjected to laboratory experiments while computational tools are used to extrapolate to similar proteins.

Such homologous proteins can be efficiently identified in distantly related organisms by sequence alignment. Genome and gene sequences can be searched by a variety of tools for certain properties. Sequence profiling tools can find restriction enzyme sites, open reading frames in nucleotide sequences, and predict secondary structures.

Phylogenetic trees can be constructed and evolutionary hypotheses developed using special software like ClustalW regarding the ancestry of modern organisms and the genes they express. The field of bioinformatics is now indispensable for the analysis of genes and proteins. Discovering the tertiary structure of a protein, or the quaternary structure of its complexes, can provide important clues about how the protein performs its function.

Common experimental methods of structure determination include X-ray crystallography and NMR spectroscopy , both of which can produce information at atomic resolution. However, NMR experiments are able to provide information from which a subset of distances between pairs of atoms can be estimated, and the final possible conformations for a protein are determined by solving a distance geometry problem. Dual polarisation interferometry is a quantitative analytical method for measuring the overall protein conformation and conformational changes due to interactions or other stimulus.

Cryoelectron microscopy is used to produce lower-resolution structural information about very large protein complexes, including assembled viruses ; [58] a variant known as electron crystallography can also produce high-resolution information in some cases, especially for two-dimensional crystals of membrane proteins. Many more gene sequences are known than protein structures. Further, the set of solved structures is biased toward proteins that can be easily subjected to the conditions required in X-ray crystallography , one of the major structure determination methods.

In particular, globular proteins are comparatively easy to crystallize in preparation for X-ray crystallography. Membrane proteins, by contrast, are difficult to crystallize and are underrepresented in the PDB.