Progressive changes in the molecular environment of neurons and Neurodegeneration has its implication in psychological functioning. Among the neurodegenerative diseases "Alzheimer's disease" produces an impairment of cognitive abilities that is gradual in onset but relentless in progression. Impairment of short-term memory usually is the first clinical feature, whereas retrieval of distant memories is preserved relatively well into the course of the disease. As the condition progresses, additional cognitive abilities are impaired, among them the ability to calculate, exercise visuospatial skills, and use common objects and tools (ideomotor apraxia). The level of arousal or alertness of the patient is not affected until the condition is very advanced, nor is there motor weakness, although muscular contractures are an almost universal feature of advanced stages of the disease. Death, most often from a complication of immobility such as pneumonia or pulmonary embolism,usually ensues within 6 to 12 years of onset. The diagnosis of AD is based on careful clinical assessment of the patient and appropriate laboratory tests to exclude other disorders that may mimic AD; at present, no direct ante mortem confirmatory test exists. Alzhiemers are of different types ( Snell 1974):
Early-onset Alzheimer's
Late-onset Alzheimer's
Familial Alzheimer's disease (FAD)
Early-onset Alzheimer's:
This is a rare form of Alzheimer's disease in which people are diagnosed with the disease before age 65.Less than 10% of all Alzheimer's disease patients have this type. Because they experience premature aging, people with Down syndrome are particularly at risk for a form of early onset Alzheimer's disease. Adults with Down syndrome are often in their mid- to late 40s or early. When symptoms first appear, younger people who develop Alzheimer's disease have more of the brain abnormalities that are associated with it. Early-onset Alzheimer's appears to be linked with a genetic defect on chromosome 14, to which late-onset Alzheimer's is not linked. Mutations of three genes, namely presenilin1, presenilin2, and amyloid precursor protein, are associated with Early Onset Alzheimer's disease. These genes in isolation do not cause Alzheimer's, however, mutations of these genes, can cause the disease.
Changes in the brains of younger people affected by Alzheimer's disease are microscopic, involving twisting of nerve cells "known as neurofibrillary tangles" and formation of structures called plaques by a sticky protein called beta amyloid. These plaques and tangles tend to damage healthy brain cells leading to shrinking and atrophy.
A condition called myoclonus which causes muscle twitching and spasms is much more common in people with early onset than those who develop the disease later in life. These will all combine to make it very difficult for someone in the younger age group to continue to work or even take part in normal family life.
Individuals with early-onset Alzheimer's disease will exhibit many of the same symptoms as those whose disease appears later in life. Memory loss, confusion, personality changes and difficulties performing simple tasks are all very common symptoms and as the disease progresses emotional and social withdrawal is the norm. Anyone who has this combination of symptoms should see a physician as soon as possible. Alzheimer's diagnosis usually comes as a result of ruling out all other possibilities. The only way to biologically diagnose it is to examine brain tissue under a microscope, which is typically done only after death. (www.alzhiemerssociety.org)
Late-onset Alzheimer's:
This is the most common type of the disease affecting about 90% of all those with Alzheimer's. It affects people over the age of 65 with around 50% of all people over the age of 85 suffering from it. And the likelihood of developing late-onset Alzheimer's doubles every five years after the age of 65. Late-onset Alzheimer's disease may not be hereditary. It is also known as "sporadic Alzheimer's" because it can affect any elderly person with no other common link other than the fact that they are all over 65.Late onset Alzheimer's causes memory loss, confusion and difficulties in carrying out even the simplest tasks. Eventually a person will need constant care as they will be unable to look after themselves. On average people live roughly eight to ten years after diagnosis. Sometimes with sporadic Alzheimer's, because it affects people so late in life, another disease associated with old age could also be the cause of death. There is no cure and the jury is still out as to why some people get it and others don't. It is indiscriminate of race, color, creed and lifestyle. In fact the only thing sufferers have in common appears to be old age. Unfortunately finding genes for incredibly complex conditions like sporadic Alzheimer's is a complicated business as there appears to be no link between who gets it and who doesn't. So far researchers haven't come across one single common factor to determine the eventual development of late-onset Alzheimer's. What they have done, however, is identify a gene which may be a risk factor. Apo lipoprotein E (ApoE) is interesting in that it has both a negative and positive side in the development of Alzheimer's. The e4 type of the gene is found to carry a higher risk of Alzheimer's while the e2 type is believed to offer protection against it. Having this gene doesn't necessarily mean that a person will get Alzheimer's - what it does mean is that it may increase their risk. Environmental factors, lifestyle and toxins can all play a part in weakening genes and making a person more susceptible to an illness. Sporadic Alzheimer's is a very difficult and complex disease for researchers because there is no real rhyme or reason to it. Until they can come up with an identifying factor other than age, there will be no cure. (Elbertyn 1984)
Familial Alzheimer's disease (FAD):
This is a form of Alzheimer's disease that is known to be entirely inherited. In affected families, members of at least two generations have had Alzheimer's disease. FAD is extremely rare, accounting for less than 1% of all cases of Alzheimer's disease. It has a much earlier onset (often in the 40s) and can be clearly seen to run in families. In some extremely rare cases people in their 30s have been known to develop it. Histological, familial AD is practically indistinguishable from other forms of the disease. Deposits of amyloid can be seen in sections brain tissue (visible as an apple-green yellow birefringence under polarized light). This amyloid protein forms plaques and neurofibrillary tangles that progress through the memory centers of the brain. Very rarely the plaque may be unique, or uncharacteristic of AD; this can happen when there is a mutation in one of the genes that creates a functional, but malformed, protein instead of the ineffective gene products that usually result from mutations. This type is genetically inherited due to a fault on chromosomes 1, 14 or 21.When this happens roughly 50% of the offspring of these sufferers will carry the genetic fault and all of them will go on to develop Alzheimer's. Mutations in different genes the amyloid precursor protein (APP) gene and the presenilin 1 and 2 (PSEN1 and PSEN2) genes have been discovered in families with early-onset familial Alzheimer's disease. Taken together, these mutations only account for about 20-50% of familial Alzheimer's, indicating that other genes remain to be found in this disorder. The APP gene encodes the beta-amyloid protein which accumulates abnormally in the brain in Alzheimer's disease. The protein products of the PSEN1 and PSEN2 genes interact with proteins are involved in signalling processes within and between cells
Treatment of Alzheimer's Disease:
A major approach to the treatment of AD has involved attempts to augment the cholinergic function of the brain (Johnston, 1992). An early approach was the use of precursors of acetylcholine synthesis, such as choline chloride and phosphatidyl choline (lecithin).Although these supplements generally are well tolerated, randomized trials have failed to demonstrate any clinically significant efficacy.
A somewhat more successful strategy has been the use of inhibitors of acetyl cholinesterase (AChE), the catabolic enzyme for acetylcholine. Physostigmine, a rapidly acting, reversible AChE inhibitor, produces improved responses in animal models of learning, and some studies have demonstrated mild transitory improvement in memory following physostigmine treatment in patients with AD. The use of physostigmine has been limited because of its short half-life and tendency to produce symptoms of systemic cholinergic excess at therapeutic doses. Four inhibitors of AChE currently are approved by the FDA for treatment of Alzheimer's disease: tacrine (1,2,3,4-tetrahydro-9-aminoacridine; COGNEX), donepzil (ARICEPT), Rivastigmine (EXCELON), and Galantamine (RAZADYNE) (Mayeux and Sano, 1999).
Tacrine is a potent centrally acting inhibitor of AChE (Freeman and Dawson, 1991). Studies of oral tacrine in combination with lecithin have confirmed that there is indeed an effect of tacrine on some measures of memory performance, but the magnitude of improvement observed with the combination of lecithin and tacrine is modest at best (Chatellier and Lacomblez, 1990). The side effects of tacrine often are significant and dose-limiting; abdominal cramping, anorexia, nausea, vomiting, and diarrhoea are observed in up to one-third of patients receiving therapeutic doses, and elevations of serum transaminases are observed in up to 50% of those treated. Because of significant side effects, tacrine are not used widely clinically.
Donepezil is a selective inhibitor of AChE in the CNS with little effect on AChE in peripheral tissues. It produces modest improvements in cognitive scores in Alzheimer's disease patients (Rogers and Friedhoff, 1998) and has a long half-life, allowing once-daily dosing.
Rivastigmine and Galantamine are dosed twice daily and produce a similar degree of cognitive improvement. Adverse effects associated with Donepzil, Rivastigmine, and Galantamine are similar in character but generally less frequent and less severe than those observed with tacrine; they include nausea, diarrhoea, vomiting, and insomnia. Donepzil, Rivastigmine, and Galantamine are not associated with the hepato-toxicity that limits the use of tacrine.
An alternative strategy for the treatment of AD is the use of the NMDA glutamate-receptor antagonist Memantine. Memantine produces a use-dependent blockade of NMDA receptors. In patients with moderate to severe AD, use of memantine is associated with a reduced rate of clinical deterioration (Reisberg et al., 2003). Whether this is due to a true disease modifying effect, possibly reduced excitotoxicity, or is a symptomatic effect of the drug is unclear. Adverse effects of memantine usually are mild and reversible and may include headache or dizziness.
At present Dipeptidylpeptidase-9(DPP-9) enzyme are in current investigation for the treatment of AD.
OVERVIEW OF ACETYL CHOLINE ESTERASE:
Acetyl choline esterase is an enzyme involved in lysis of acetyl group and choline group in acetyl choline (CH3-CH2-(CO)2-CH2-CH2-N-(CH3)3. Acetylcholine (Ach) is a neurotransmitter in both the peripheral nervous system(PNS) and central nervous system (CNS) is one of many neurotransmitters in the autonomic nervous system(ANS),and the only neurotransmitter used in the motor division of the somatic nervous system. (Sensory neurons use glutamate and various peptides at their synapses.)
Synthesis and degradation:
Acetylcholine is synthesized in certain neurons by the enzyme choline acetyl transferase from the compounds choline and acetyl-CoA.
The enzyme acetyl cholinesterase converts acetylcholine into the inactive metabolites choline and acetate. This enzyme is abundant in the synaptic cleft, and its role in rapidly clearing free acetylcholine from the synapse is essential for proper muscle function. Certain neurotoxins work by inhibiting acetyl cholinesterase, thus leading to excess acetylcholine at the neuromuscular junction, thus causing paralysis of the muscles needed for breathing and stopping the beating of the heart.
B)
File:Acetylcholine.svg File:ACh-stick.png
Fig: Acetyl choline structure A) normal view B) stick model
Acetylcholine is also the principal neurotransmitter in all autonomic ganglia and the Post Synaptic Parasympathetic neuron and causes the release of the acetyl choline from the synaptic vesicles thereby innervate the neuromuscular junction and in pre synaptic Sympathetic nervous system it causes the release of acetyl choline and causes the innervation of the post synaptic nerve fibres and causes the release of nor adrenaline.
On release of acetylcholine from the receptor site they causes the contraction of the muscle fibres. If this enzyme has been degraded by acetyl choline esterases then depletion of Ach leads to various disease and disorders like Myasthenia gravis,Alzhiemer's disease, and Glaucoma. The most prevalant in western and some of the asian races is alzhiemers disease.
The thorough study on molecular basis of AchE and pathological neuronal degeneration paves the way for treating AD.
MOLECULAR BASIS OF ACETYLCHOLINE ESTERASE AND ITS ACTIVE SITE:
AchE exists in two general classes of molecular forms, simple homomeric oligomers of catalytic subunits (i.e. monomers, dimers, and tetramers) and heteromeric associations of catalytic subunits with structural subunits. The homomeric forms are found as soluble species in the cell, presumably destined for export, or associated with outer membrane of the cell through either an intrinsic hydrophobic amino acids sequence or an attached glycophospholipid. One heterologous form, largely found in neuronal synapses, is a tetramer of catalytic subunits disulfide-linked to a 20,000-dalton lipid linked sub unit. Similar to the glycophospholipid-attached form, it is found in the outer surface of the cell membrane. The other consists of tetramers of catalytic subunits, di sulphide linked to each of three strands of collagen-like structural subunit. This molecular species, whose molecular mass approaches 106 Daltons, is associated with the basal lamina of junctional areas of skeletal muscle.
File:PBB Protein ACHE image.jpg
Fig: 3-dimensional structural image (ribbon-like) of Acetylcholine esterase
The 3-dimensional structure of acetyl choline esterase shows the active centre to be nearly centerosymmetric to each subunit and reside at the base of a narrow gorge about 20Ȧ in depth (Sussman et al., 1995). At the base of the gorge lie the residues of the catalytic triad: serine 203, histidine 447, and glutamate 334.
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fig: Active site gorge of AchE with serine 203, histidine 447 & glutamate 334 (source: Goodman & gillman,the pharmacological basis of therapeutics 11th edition)
The catalytic mechanism resembles that of other hydrolases, where the serine hydroxyl group is rendered highly nucleophilic through a charge- relay system involving the carboxyl from glutamate, the Imidazole on the histidine, and the hydroxyl of the serine. During the enzymatic attack of the, an ester with trigonal geometry, a tetrahedral intermediate between enzyme and substrate is formed that collapse to an acyl enzyme conjugate with the concomitant release of choline. The acetyl enzyme is very labile to hydrolysis, which results in the formation of acetate and active enzyme. AchE is one of the most efficient enzymes known and has the capacity to hydrolyse 6 x 105 Ach molecules per molecule of enzyme per minute; this yields a turnover time of 150 microseconds.
Fig. Schematic representation of the binding sites of AChE based upon biochemical studies performed prior to determination of the 3D structure. ES -esteratic site; AS-anionic substrate binding site; ACS-aromatic cation binding site; PAS-peripheral anionic binding site.
In the diagram, the hatched areas represent putative hydrophobic binding regions. ACh is shown spanning the esteratic and anionic sites of the catalytic center. Imidazole and hydroxyl side chains of His and Ser are shown within the esteratic site. Within the anionic site (COO−) n represents 6-9 putative negative charges.
PATHOPHYSIOLOGY:
AD is characterized by marked atrophy of the cerebral cortex and loss of cortical and sub cortical neurons. The pathological hallmarks of AD are senile plaques, which are spherical accumulations of the protein b-amyloid accompanied by degenerating neuronal processes, and neurofibrillary tangles, composed of paired helical filaments and other proteins (Arnold et al., 1991; Braak and Braak, 1994). Although small numbers of senile plaques and neurofibrillary tangles can be observed in intellectually normal individuals, they are far more abundant in patients with AD, and the abundance of tangles is roughly proportional to the severity of cognitive impairment. In advanced AD, senile plaques and neurofibrillary tangles are numerous and most abundant in the hippocampus and associative regions of the cortex, whereas areas such as the visual and motor cortices are relatively spared. This corresponds to the clinical features of marked impairment of memory and abstract reasoning, with preservation of vision and movement. The factors underlying the selective vulnerability of particular cortical neurons to the pathology of the effect is unknown
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Fig: Diagram showing the peptides showing the γ-seretase and β-secretase complex
Neurochemistry:
The neurochemical disturbances that arise in AD have been studied intensively (Johnston, 1992). Direct analysis of neurotransmitter content in the cerebral cortex shows a reduction of many transmitter substances that parallels neuronal loss; there is a striking and disproportionate deficiency of acetylcholine. The anatomical basis of the cholinergic deficit is the atrophy and degeneration of subcortical cholinergic neurons, particularly those in the forebrain (nucleus basalis of Meynert), that provide cholinergic innervations to the whole cerebral cortex. The selective deficiency of acetylcholine as well as the observation that central cholinergic antagonists such as atropine can induce a confusional state that bears some resemblance to the dementia of AD, has given rise to the "cholinergic hypothesis," which proposes that a deficiency of acetylcholine is critical in the genesis of the symptoms of AD (Perry, 1986). Although the conceptualization of AD as a "cholinergic deficiency syndrome" in parallel with the "dopaminergic deficiency syndrome" of PD provides a useful framework, it is important to note that the deficit in AD is far more complex, involving multiple neurotransmitter systems, including serotonin, glutamate, and neuropeptides, and that in AD there is destruction of not only cholinergic neurons but also the cortical and hippocampal targets that receive input.
Role of β-Amyloid. The presence of aggregates of β--amyloid is a constant feature of AD. Until recently, it was not clear whether the amyloid protein was causally linked to the disease process or merely a by-product of neuronal death. The application of molecular genetics has shed some light on this question.
β-amyloid from affected brains and found to be a short polypeptide of 42 to 43 amino acids.
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Fig: Neuronal pathways and signalling of parasympathetic nerve fibres involved in amyloid plaques accumulation and formation.
This information led to cloning of amyloid precursor protein (APP), a much larger protein of more than 700 amino acids, which is expressed widely by neurons throughout the brain in normal individuals as well as in those with AD. The function of APP is unknown, although the structural features of the protein suggest that it may serve as a cell surface receptor for an as-yet-unidentified ligand. The production of β-amyloid from APP appears to result from abnormal proteolytic cleavage of APP by the b-site APP-cleaving enzyme BACE. This may be an important target of future therapies (Vassar et al., 1999).
Analysis of APP gene structure in pedigrees exhibiting autosomal dominant inheritance of AD has shown that in some families, mutations of the β-amyloid-forming region of APP are present, whereas in others, mutations of proteins involved in the processing of APP are implicated (Selkoe, 2002).
These results suggest that it is possible for abnormalities in APP or its processing to cause AD. The vast majority of cases of AD, however, are not familial, and structural abnormality of APP or related proteins has not been observed consistently in these sporadic cases of AD.As noted earlier, common alleles of the Apo E protein have been found to influence the probability of developing AD. Many investigators believe that modifying the metabolism of APP might alter the course of AD in both familial and sporadic cases,but no Clinically practical strategies have been developed.
MOLECULAR DOCKING:
Molecular docking screens large databases of small molecules by orienting them in the binding site of protein. Top ranked molecules may be tested for binding affinity in vitro, and may become lead compounds, the starting point for drug development and optimization.
The conformational and orientational sampling of the putative drug in the protein site is crude. The model of the protein site itself is even cruder: often, the receptor is kept completely rigid. The scoring function used to rank good ligands above poor compounds is problematic too: the net energy of binding is a small difference of large values with large uncertainties, and the calculation of desolvating the ligand when it binds to the protein is complex to calculate.
Molecular docking is divided into two types:
There are two main types of docking (molecular docking) in practice: 1.Small molecule - protein (called "ligand - protein docking") and
2. Protein - protein docking.
There is also small molecule - DNA or RNA docking done by some researchers. Protein - protein docking involves two protein molecules simulated by the computer/computer program to bind/interact with one another. However, in this case, the interactions are basically rigid compared to the ligand - protein docking. It might be able to see that by simulating certain protein - protein interactions in a specific biological reaction.
Docking techniques, designed to find the correct conformation of a ligand and its receptor. The accurate prediction of the binding mode of a ligand within a protein receptor is of great importance in modern structure-base drug design, where docking is often used in virtual screening methods to reduce large virtual libraries of compounds to a meaningful subset, which includes molecules with high binding affinities for the target receptor. The process of binding a small molecule to its protein target is not simple; several entropic and enthalpic factors influence the interactions between them. The mobility of both ligand and receptor, the effect of the protein environment on the charge distribution over the ligand, and their interactions with the surrounding water molecules, further complicate the quantitative description of the process. The three dimensional structure of both ligand and protein are usually necessary for the application of docking techniques. While the manifold of conformational structures of small molecules may be relatively easy to predict, the lowest energy conformation obtained may not correspond to that of the bound ligand. The structures of proteins, on the other hand, present a bigger challenge. Although experimental techniques involving X-ray and NMR analysis are now routinely used, inherent difficulties in the preparation of samples and data collection and interpretation mean we are still far from a complete automated and high-throughput process. Many proteins targeted for drug design do not have an experimentally determined structure and, therefore, docking studies cannot be performed directly. In some cases, computational techniques can be used to predict the 3-D structure of a protein provided the structure of a closely related protein homologue is known. Homology modelling or sequence threading techniques may be used to generate models of protein structures which, although not as good as experimentally determined structures, can be used as docking target . The docking algorithms specifically designed for modelled structures..
Library of chemical compounds
X-ray, NMR , Homology model, etc.,
Target structureEnrichment
Enriched library
Set of targets Receptor structure preparation
(e.g. addition of flexibility)
Docking
Successfully docked compounds
Leads
Complexes validation
Lead optimization Accurate free energy calculations
Drug candidates
Experimental Testing
Drug
Fig: Drug Design Process. Schematic representation of the protocol commonly followed during a drug design process, when the structure of the protein target is known or can be modelled. Steps within brackets are not always performed.
structure of a protein provided the structure of a closely related protein homologue is known. Homology modelling or sequence threading techniques may be used to generate models of protein structures which, although not as good as experimentally determined structures, can be used as docking target. The docking algorithms specifically designed for modelled structures. Molecular docking can be divided into two separate problems.
The search algorithm should create an optimum number of configurations that include the experimentally determined binding modes. These configurations are evaluated using scoring functions to distinguish the experimental binding modes from all other modes explored through the searching algorithm. A rigorous searching algorithm would go through all possible binding modes between the two molecules. However, this is impractical due to the size of the search space. Consider a simple system comprised of a ligand with four rotatable bonds and six rigid-body alignment parameters and a cubic active site measuring 103 Ȧ. The translational and rotational properties add up to six degrees of freedom. If the angles are considered in 10 degree increments and translational parameters on a 0.5 Ȧ ,grid there are approximately 4x108 rigid body degrees of freedom to sample, corresponding to 6 x1014 configurations to be searched. This would require approximately 2,000,000 years of computational time at a rate of 10 configurations per second. As a consequence only a small amount of the total conformational space can be sampled, and so a balance must be reached between the computational expense and the amount of the search space examined
Common searching algorithms:
Molecular dynamics
Monte Carlo methods
Genetic algorithms
Fragment-based methods
Point complementary methods
Distance geometry methods
Tabu searches
Systematic searches
Molecular dynamics: (MD)
MD involves the calculation of solutions to Newton's equations of motions. To find the global minimum energy of a docked complex is difficult since traversing the rugged hyper surface of a biological problem is problematic. (Kaapro et al)
Monte Carlo methods (MCM):
The Monte Carlo simulation method occupies a special place in the history of molecular modelling, as it was the technique used to perform the 1st computer simulation of a molecular system. The expression Monte Carlo simulation seems to be extremely general and many algorithms are called by that whenever they contain a stochastic process or some kind of random sampling. For those interested, in molecular docking the expression Monte Carlo usually means importance sampling or Metropolis method. The Metropolis method, which is actually a Markov chain Monte Carlo method, generates random moves to the system and then accepts or rejects the move based on a Boltzmann probability. The Monte Carlo methods play an important role in molecular docking but the variety of different kinds of algorithms is too large be considered. Programs using MC methods include Auto Dock, Pro Dock, ICM, MCDOCK, Dock Vision, QXP and Affinity.
Genetic algorithms (GA):
Genetic algorithms and evolutionary programming are quite suitable for solving docking problems because of their usefulness in solving complex optimization problems. The essential idea of genetic algorithms is the evolution of a population of possible solutions via genetic operators (mutation, crossovers, and migrations) to a final population, optimizing a predentness function. The process of applying genetic algorithms starts with encoding the variables (i.e., the degrees of freedom) into a "genetic code", e.g. binary strings. Then a random initial population of solutions is created. Genetic operators are then applied to this population leading to a new population. This new population is then scored and ranked, and using "the survival of the fittest", their probabilities of getting to the next iteration round depends on their score.
Fragment-based methods:
Fragment based methods can be described as dividing the ligand into separate portions or fragments, docking the fragments, and finally linking these fragments together. These methods require subjective decisions on the importance of the various functional groups in the ligand, because a good choice of base fragment is essential for these methods. A poor choice can significantly act the quality of the results. The base fragment must contain the predominant interactions with the receptor. Early algorithms required manual selection of base fragment, but this has been automated in newer implementations. Some well known programs using fragment based methods are FlexX and DOCK
Point complementary methods:
Point complementary methods are based on evaluating the shape and/or chemical complementarity between interacting molecules.
Distance geometry methods:
Many types of structural information can be expressed as intra- or intermolecular distances. The distance geometry formalism allows these distances to be assembled and three-dimensional structures consistent with them to be calculated.
Tabu searches:
Tabu searches are based on stochastic processes, in which new states are randomly generated from an initial state (referred to as the current solution). These new solutions are then scored and ranked in ascending order. The best new solution is then chosen as the new current solution and the same process is then repeated again. To avoid loops and ensure diversity of the current solution, a Tabu list is used. This list acts as a memory. It contains information about previous current solutions and a new solution is rejected if it reminds a previous solution too much. An example of docking algorithm using Tabu search is PRO LEADS.
Systematic searches:
These methods systematically go through all possible conformations and represent the brute force solution to the docking problem. All molecules are usually assumed to be rigid and interaction energy is evaluated from a force field model. Some constraints and restraints can be used to reduce the dimensionality of the problem.
Common scoring functions:
Force-field methods
Empirical free energy scoring functions
Knowledge-based potential of mean force
Force-field methods:
Geometry of a molecule can be approximated effectively by taking all the interacting forces into account. Bonded interactions are described by spring forces and non-bonded interactions are usually approximated by potentials resembling van-der Waals interaction. The desired parameters are determined by experimental observations. Geometry is further optimized by binding the energy minimum. Total energy is represented by a set of potential energy functions. In addition to these functions, a set of parameters is also needed to compute the total energy. It is worthwhile to notice, that force field parameters have no meaning unless they are considered together with the potential energy functions. Thus a comparison between force field models is very difficult. In addition to these two parts, information about atom types and atom charges are also required. We also usually need - a set of rules to type atoms, generate parameters not explicitly defined and to assign functional forms and parameters. These methods together form a force field.
- Potential energy functions
- Parameters for function terms
- List of atoms and atom charges
- Rules for atom-typing, parameter generation and
functional form assigning.
Force fields are usually employed to generate accurate predictions to complex problems by interpolating and extrapolating from relatively simple experimental set of molecules. There are generally two approaches to force fields. They are either very accurate with small set of molecules and compounds. They also may be more general, in which case the accuracy is often compromised.
Classical force field models:
Examples of classical force field models include AMBER, CHARMM and CVFF.
AMBER (Assisted Model Building with Energy Refinenement):
AMBER refers to two things: it may mean a set of molecular mechanic force fields used for the simulation of bio molecules, or it may also mean a package of molecular simulation programs. AMBER's set of parameters is experimentally derived. AMBER force fields are probably the most widespread ones. AMBER is designed especially for biological macromolecules.
CHARMM (Chemistry at Harvard Macromolecular Mechanics):
CHARMM is a program for macromolecular dynamics. Performing MD using algorithms for time-stepping, long range force calculation and periodic images, it can be used for energy minimization, normal modes and crystal optimizations. There are several potential energy functions parameterized for protein, lipid and nucleic acid simulations. CHARMM also incorporates free energy methods for chemical and conformational free energy calculations.
CVFF (Consistent Valence Force Field)
CVFF has parameters that are acquired by fitting crystal and gas structures to small organic molecules. CVFF is designed mainly for organic materials, and it is commonly used to predict structures and compute binding energies.
Second generation force field models:
Second generation force fields examples include CFF and COMPASS.
CFF (Consistent Force Field)
CFF is a bit more complex compared to AMBER. The potential energy functions in CFF are expanded in order to avoid problems concerning the complexity of potential energy surfaces. CFF also uses quantum calculations to determine the parameters for energy functions. This approach gives a great advantage over classical models, since parameters can be determined much more accurately. Other advantages include the possibility to cover larger number of compounds into the force field model, and the fact that all parameters are determined the same way (which makes the model more consistent).
COMPASS (Condensed-phase Optimized Molecular Potentials for Atomistic Simulation Studies):
COMPASS is another ab initio (from the beginning) force field model. Like CFF, it also has parameters defined by quantum mechanical calculations and validated by empirical data.
Generalized force field models:
Generalized force fields are not as accurate as the ones presented above; They can be applied to systems that are not covered by more accurate force field models. Generalized force field models are based on atomic parameters and rules to determine the explicit form of parameters. Examples include ESFF and UFF.
ESFF (Extensible Systematic Force Field):
ESFF covers all elements up to Rn. ESFF can be used for both the organic and inorganic systems.
UFF (Universal Force Field):
UFF covers the whole periodic table. However, it is not very accurate, and thus its main application is systems that are not covered by other force fields.
CURRENTLY USED SOFTWARES FOR DOCKING PROGRAMS:
#Auto Dock
#DOCK
#FlexX
#Gold
#Glide
#Slide
DOCK:
DOCK is one of the oldest and best known ligand-protein docking programs. The initial version used rigid ligands; flexibility was later incorporated via incremental construction of the ligand in the binding pocket. DOCK is a fragment-based method using shape and chemical complementary methods for creating possible orientations for the ligand. These orientations can be scored using three different scoring functions; however none of them contain explicit hydrogen-bonding terms, salvation/desolvation terms, or hydrophobicity terms thus limiting serious use. DOCK seems to handle well a polar binding site and is useful for fast docking, but it is not the most accurate software available.
FlexX:
FlexX is another fragment based method using flexible ligands and rigid proteins. It uses MIMUMBA torsion angle database for the creation of conformers. The MIMUMBA is an interaction geometry database used to exactly describe intermolecular interaction patterns. For scoring, the Boehm function (with minor adaptions necessary for docking) is applied. FlexX is introduced here to pronounce the importance of scoring functions. Although FlexX and DOCK both are fragment based methods, they produce quite different results. On the contrary to DOCK which performs well with a polar binding sites, FlexX shows totally opposite behaviour. It has a bit lower hit rate than DOCK but provides better estimates of Root Mean Square Distance for compounds with correctly predicted binding mode. There is an extension of FlexX called FlexE with flexible receptors which has shown to produce better results with significantly lower running times.
Gold:
Gold has won a lot of new users during the last few years because of its good results in impartial tests. It has a good hit rate overall, however it somewhat suffers when dealing with hydrophobic binding pockets. Gold uses genetic algorithm to provide docking of flexible ligand and a protein with flexible hydroxyl groups. Otherwise the protein is considered to be rigid. This makes it a good choice when the binding pocket contains amino acids that form hydrogen bonds with the ligand. Gold uses a scoring function that is based on favourable conformations found in Cambridge Structural Database and on empirical results on weak chemical interactions. The development of GOLD is currently focused on improving the computational algorithm and adding a support for parallel processing.
This dissertation utilizes Auto Dock version 4.2 for the in-silico investigation of AchE Inhibition
AUTO DOCK:
Auto Dock uses Monte Carlo simulated annealing and Lamarckian genetic algorithm (LGA) to create a set of possible conformations. LGA is used as a global optimizer and energy minimization as a local search method. Possible orientations are evaluated with AMBER force field model in conjunction with free energy scoring functions and a large set of protein-ligand complexes with known protein-ligand constants.
Auto Dock is an automated procedure for predicting the interaction of ligands with bio macromolecular targets. The motivation for this work arises from problems in the design of bioactive compounds, and in particular the field of computer-aided drug design. Progress in bio molecular x-ray crystallography continues to provide important protein and nucleic acid structures. These structures could be targets for bioactive agents in the control of animal and plant diseases, or simply key to the understanding of fundamental aspects of biology. The precise interaction of such agents or candidate molecules with their targets is important in the development process. Our goal has been to provide a computational tool to assist researchers in the determination of bio molecular complexes.
In any docking scheme, two conflicting requirements must be balanced: "The desire for a robust and accurate procedure and the desire to keep the computational demands at a reasonable level". The ideal procedure would find the global minimum in the interaction energy between the substrate and the target protein, exploring all available degrees of freedom (DOF) for the system. However, it must also run on a laboratory workstation within an amount of time comparable to other computations that a structural researcher may undertake, such as a crystallographic refinement. In order to meet these demands a number of docking techniques simplify the docking procedure. Auto Dock combines two methods to achieve these goals: rapid grid-based energy evaluation and efficient search of torsional freedom.
Versions of auto dock tools:
Auto Dock 3.0
Auto Dock 4.0
Auto Dock 4.2
Getting Started with Auto Dock:
Auto dock and Auto dock Tools, the graphical user interface for Auto Dock are available on the WWW at: http://autodock.scripps.edu/
Auto Dock calculations are performed in several steps:
1) Preparation of coordinate files using AutoDockTools,
2) Pre calculation of atomic affinities using Auto Grid,
3) Docking of ligands using Auto Dock, and
4) Analysis of results using AutoDockTools.
Step 1: Coordinate File Preparation.
AutoDock4.2 is parameterized to use a model of the protein and ligand that includes polar hydrogen atoms, but not hydrogen atoms bonded to carbon atoms. An extended PDB format, termed PDBQT, is used for coordinate files, which includes atomic partial charges and atom types. The current Auto Dock force field uses several atom types for the most common atoms, including separate types for aliphatic and aromatic carbon atoms, and separate types for polar atoms that form hydrogen bonds and those that do not. PDBQT files also include information on the torsional degrees of freedom. In cases where specific side chains in the protein are treated as flexible, a separate PDBQT file is also created for the side chain coordinates. In most cases, AutoDockTools will be used for creating PDBQT files from traditional PDB files.
Step2: Auto Grid Calculation.
Rapid energy evaluation is achieved by pre-calculating atomic affinity potentials for each atom type in the ligand molecule being docked. In the Auto Grid procedure the protein is embedded in a three-dimensional grid and a probe atom is placed at each grid point. The energy of interaction of this single atom with the protein is assigned to the grid point. Auto Grid affinity grids are calculated for each type of atom in the ligand, typically carbon, oxygen, nitrogen and hydrogen, as well as grids of electrostatic and desolvation potentials. Then, during the Auto Dock calculation, the energetics of a particular ligand configuration is evaluated using the values from the grids.
Step 3: Docking using Auto Dock.
Docking is carried out using one of several search methods. The most efficient method is a Lamarckian genetic algorithm (LGA), but traditional genetic algorithms and simulated annealing are also available. For typical systems, Auto Dock is run several times to give several docked conformations, and analysis of the predicted energy and the consistency of results is combined to identify the best solution.
Step 4: Analysis using AutoDockTools.
Auto Dock Tools includes a number of methods for analyzing the results of docking simulations, including tools for clustering results by conformational similarity, visualizing conformations, visualizing interactions between ligands and proteins, and visualizing the affinity potentials created by Auto Grid.
Auto Dock 4.2 includes several enhancements over the methods available in Auto Dock 3.0:
Side chain Flexibility: Auto Dock 4.2 allows incorporation of limited side chain flexibility into the receptor. This is achieved by separating the receptor into two files, and treating the rigid portion with the Auto Grid energy evaluation and treating the flexible portion with the same methods as the flexible ligand.
Force Field: The Auto Dock 4.2 force field is designed to estimate the free energy of binding of ligands to receptors. It includes an updated charge-based desolvation term, improvements in the directionality of hydrogen bonds, and several improved models of the unbound state.
Expanded Atom Types: Parameters have been generated for an expanded set of atom types including halogens and common metal ions.
Desolvation Model : The desolvation model is now parameterized for all supported atom types instead of just carbon. Because of this, the constant function in Auto Grid is no longer used, since desolvation of polar atoms is treated explicitly. The new model requires calculation of a new map in Auto Grid which holds the charge-based desolvation information.
Unbound State: Several models are available for estimating the energetics of the unbound state, including an extended model and a model where the unbound state is assumed to be identical with the protein-bound state.
Auto Dock 4.0, there are several changes in Auto Dock 4.2:
Default Unbound State: The default model for the unbound state has been changed from "extended" to "bound=unbound". This is in response to persistent problems sterically-crowded ligands. The "extended" unbound state model is available in Auto Dock 4.2 through use of the "unbound extended" keyword.
Backwards Compatibility: We have made every attempt to ensure that docking parameter files generated for use in Auto Dock 4.0 should be correctly run by Auto Dock 4.2.
Theory background:
Overview of the Free Energy Function:
Auto Dock 4.2 uses a semi empirical free energy force field to evaluate conformations during docking simulations. The force field was parameterized using a large number of protein-inhibitor complexes for which both structure and inhibition constants, and Ki, are known
Fig: The interaction of ligand and protein in bound and unbound receptor state
The force field evaluates binding in two steps. The ligand and protein start in an unbound conformation. In the first step, the intramolecular energetics are estimated for the transition from these unbound states to the conformation of the ligand and protein in the bound state. The second step then evaluates the intermolecular energetics of combining the ligand and protein in their bound conformation. The force field includes six pair-wise evaluations (V) and an estimate of the conformational entropy lost upon binding (ΔS conf):
where L refers to the "ligand" and P refers to the "protein" in a ligand-protein docking calculation. Each of the pair-wise energetic terms includes evaluations for dispersion/repulsion, hydrogen bonding, electrostatics, and desolvation:
The weighting constants 'W' have been optimized to calibrate the empirical free energy based on a set of experimentally-determined binding constants. The first term is a typical 6/12 potential for dispersion/repulsion interactions. The parameters are based on the Amber force field. The second term is a directional H-bond term based on a 10/12 potential. The parameters C and D are assigned to give a maximal well depth of 5kcal/mol at 1.9Å for hydrogen bonds with oxygen and nitrogen, and a well depth of 1 kcal/mol at 2.5Å for hydrogen bonds with sulphur. The function E (t) provides directionality based on the angle't' from ideal H-bonding geometry. The third term is a screened Coulomb potential for electrostatics. The final term is a desolvation potential based on the volume of atoms (V) that surround a given atom and shelter it from solvent, weighted by a salvation parameter (S) and exponential term with distance-weighting factor σ = 3.5Å.
By default, Auto Grid and Auto Dock use a standard set of parameters and weights for the force field. The parameter file keyword may be used, however, to use custom parameter files.
Fig: Viewing Grids in AutoDockTools
In the fig the protein is shown on the left in white bonds, and the grid box is shown on the right side. The blue contours surround areas in the box that are most favorable for binding of carbon atoms, and the red contours show areas that favor oxygen atoms.A ligand is shown inside the box at upper right.
Fig: Graph of Auto Dock Potentials:
In the graphical figure examples of the four contributions to the Auto Dock force field are shown. The dispersion/repulsion potential is for interaction between two carbon atoms. The hydrogen bond potential, which extends down to a minimum of about 2 kcal/mol, is shown for an oxygen-hydrogen interaction. The electrostatic potential is shown for interaction of two oppositely charged atoms with a full atomic charge. The desolvation potential is shown for a carbon atom, with approximately 10 atoms displacing water at each distance.
Auto Dock has applications in:
X-ray crystallography
Structure-based drug design
Lead optimization
Virtual screening (HTS)
Combinatorial library design
Protein-protein docking
Chemical mechanism studies
