Diabetes Clinical Syndrome Characterized By Hyperglycemia Deficiency Insulin Biology Essay


Diabetes is a clinical syndrome characterized by hyperglycemia due to absolute or relative deficiency of insulin. Lack of insulin affects the metabolism of carbohydrates, proteins, fat and causes significance disturbance of water and electrolyte homeostasis. This results primarily in elevated fasting and post prandial (PP) blood glucose levels. If this imbalance in homeostasis does not return to normal, and continues for a longer period, it leads to hyperglycemia (high blood glucose concentration). Excess glucose in blood is harmful too. Sugar accumulation result in increased workload on kidney and increase sugar level in urine. The sugar enters the urine in solution form, draining water from the cell. When the renal threshold for glucose re-absorption exceeds, glucose spills over into urine (polyurea), which in turn, results in dehydration, induced thirst (polydipsia)., which in due course turns into a syndrome called Diabetes Mellitus (DM).

Diabetes is a state in which homeostasis of carbohydrate and lipid metabolism is improperly regulated by insulin. It is a chronic disorder characterized by hyperglycaemias and caused due to insulin deficiency and insulin resistance. Hyperglycaemia occurs because of uncontrolled hepatic glucose output and reduced uptake of glucose by skeletal muscle with reduced glycogen synthesis. Insulin deficiency causes wasting through increased break down and reduced synthesis of protein. Diabetic keto acidosis is an acute emergency, that develops in the absence of insulin, because of accelerated fat breakdown to acetyl CoA, in the absence of aerobic carbohydrate metabolism which is converted to acetoacetate and -hydroxyl butyrate (Rang and Dale, 2007).

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Diabetes mellitus involve not only a deficiency of insulin but also an excess of certain other hormones such as growth hormones, glucocorticoids, and glucagons. Thus not only pancreas is involved in glucose homeostasis but also the anterior pituitary gland and adrenal gland (Cantrill and Wood, 2003).

A balance is preserved between the entry of glucose into the circulation from the liver, supplemented by intestinal absorption after meals, and glucose uptake by peripheral tissue, particularly skeletal muscle. When intestinal glucose absorption declines between meals, hepatic glucose output is increased in response to insulin level and increased levels of the counter-regulation hormones, glucagons and adrenaline. The liver produces glucose by gluconeogenesis and glycogen breakdown. The main substrates for gluconeogenesis are glycerol and amino acids. After meals blood insulin levels rise. Insulin is anabolic hormones with profound effects on metabolism of carbohydrate, fat and protein. Insulin into the portal circulation, with a brisk increase in response to a rise in blood glucose. Insulin lowers blood glucose by suppressing hepatic glucose production and stimulating glucose uptake in skeletal muscle and fat, mediated by the glucose transporters, GLUT 4. Insulin stimulates lipogenesis and supresses lipolysis. The release of intermediate metabolites including amino acids 3-carbon intermediate in oxidation and free fatty acids (FFA) is controlled by insulin, so preventing fat catabolism. In absence of insulin these processes are reversed and favour gluconeogenesis in liver from glucogen, glycerol, amino acids and other 3-arbon precursor (Frier and Fisher, 2006).


Types of diabetes

Type 1 diabetes previously known as insulin dependent diabetes mellitus (IDDM) or juvenile-onset diabetes, which accounts for only 5-10% of the total cases of diabetes. Type 1 diabetes can affect children, young or adults below 40 years of age but was traditionally termed "juvenile diabetes" because it represents a majority of the diabetes cases in children. Type 1 diabetes mellitus is characterized by loss of the insulin-producing beta cells of the islets of Langerhans in the pancreas, leading to a deficiency of insulin. This type of diabetes can be further classified as immune-mediated or idiopathic. The majority of type 1 diabetes is of the immune-mediated variety, where beta cell loss is a T-cell mediated autoimmune attack There is no known preventive measure which can be taken against type 1 diabetes.

Type 2 diabetes previously known as non-insulin dependent diabetes mellitus (NIDDM), type II diabetes or adult-onset diabetes, accounts for almost 90-95% of the diabetes cases. This is related to overweight. Type 2 diabetes is seen mostly in people over 40 years of age. Hereditary and the lifestyle are the major causes of type 2 diabetes. In this case, the body still can produce insulin, but it is not enough for the body functions or the insulin does not work properly. This condition is called insulin resistance.  Type 2 diabetes builds up excess glucose in the blood, thereby causing serious health problems like heart disease, failure of kidneys and eye discomforts.

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Type 3 diabetes refers to multiple other specific cause of elevated blood glucose, non-pancreatic diseases, drug therapy... etc.

Type 4 diabetes refers to Gestational diabetes mellitus (GDM), which is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. Gestational diabetes occurs in about 2% of pregnancies usually appears in second or third trimester at the time when pregnancy associated insulin antagonistic hormone peak. No specific cause has been identified, but it is believed that the hormones produced during pregnancy reduce a woman's sensitivity to insulin, resulting in high blood sugar levels. After delivery glucose tolerance usually reverts to normal. Babies born to mothers with gestational diabetes are at increased risk of complications, primarily growth abnormalities and chemical imbalances such as low blood sugar. Women with gestational diabetes are at increased risk of developing type 2 diabetes mellitus after pregnancy, while their offspring are prone to developing childhood obesity, with type 2 diabetes later in life. GDM generally causes only mild, asymptomatic hyperglycemia rigorous treatment often with insulin is required to protect against fetal morbidity and mortility. Most patients are treated only with diet modification and moderate exercise but some take anti-diabetic drugs, including insulin (American Diabetes Association 2004).

Diabetes Mellitus-world wide scenario

Diabetes is one of the worlds leading chronic disease and has serious social and economic consideration. Billions of dollars are spent every year by healthcare system for treating diabetes and its complication. According to WHO, at least 171 million people worldwide have Diabetes. This figure is likely to more than double by 2030. WHO predicts that developing countries will bear the brunt of this epidemic in the 21st century, with 80% of all new cases of diabetes expected to appear in 2020. among developing countries, the highest increase in prevalence in China followed by India. However greatest increase in number will be seen in India , where the number of diabetic patient will rise from 19 million in 1995 to 57 million in 2025, heading the list of countries with greatest number of diabetes. India is thus designated to become the "diabetes capital of world". The countries with the largest number of diabetic people will be India, China and USA by 2030

Diabetes is one of the major causes of premature death worldwide. Every ten second a person dies from diabetes related causes mainly from cardiovascular diseases. In 2007, diabetes caused 3.5 million deaths globally.

Table: Top ten countries for their estimated number of adults with diabetes (millions)


Year 1995

Year 2025










Russian Federation





















Other countries






Diabetes in India: Current Status

Diabetes mellitus is epidemic in India is a result of societal influence and changing lifestyles. Diabetes has been known in India for centuries as 'a disease of rich man', now spread among all masses (Gupta and Misra, 2007). The studies in Indian population showed that major risk factor for high prevalence of type 2 diabetes mellitus are, genetic predisposition, insulin resistance, obesity, central obesity (greater abdominal adiposity), urbanization with change in diet habit like fast food culture and sedentary life style. Rapid urbanisation and industrialisation have produced advancement on the social and economic front in developing countries such as India which have resulted in dramatic lifestyle changes leading to lifestyle related diseases (Gupta, 2008). According to the International Diabetes Federation (IDF) Diabetes Atlas (2006) was released at 19th World Diabetes Congress , there were an estimated 40.9 million persons with diabetes in India and this number is predicted to rise to almost 70 million people by 2025. The so called "Asian Indian Phenotype" refer to certain unique clinical and biochemical abnormalities in Indian which include increased insulin resistance, greater abdominal adiposity. It is estimated that every fifth person with diabetes will be an Indian.

There are several studies from various parts of India which reveal a rising trend in the prevalence of type 2 diabetes in the urban areas. A National Urban Survey in 2000 observed that the prevalence of diabetes in urban India in adults was 12.1 per cent per cent. Recent data has illustrated the impact of socio-economic transition occurring in rural India. The transition has occurred in the last 15 years and the prevalence has risen from 2.4 per cent to 6.4 per cent.


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Hyperglycaemia or high blood sugar is a condition in which an excessive amount of glucose circulates in the blood plasma. This is generally a blood glucose level of 10+ mmol/L (180 mg/dl), but symptoms and effects may not start to become noticeable until later numbers like 15-20+ mmol/L (270-360 mg/dl). The origin of the term is Greek: hyper-, meaning excessive; -glyc-, meaning sweet; and -aemia, meaning "of the blood". Hyperglycaemia is a very common biochemical abnormality. It is major casual link between diabetes and diabetic complication. Glucose is the driving force in microvascular pathology and therefore diabetes is leading cause of blindness, end stage renal failure (ESRF) and variety of debilitating neuropathies. Hyperglycaemia in type 2 diabetes results from an undefined genetic defect, the expression of which is modified by environmental factor. The metabolic disturbances in diabetes are associated with a number of complications including cardiovascular diseases, nephropathy, and neuropathy, retinopathy leading to blindness and embryopathy or congenital malformations. Maternal diabetes is associated with a high incidence of congenital malformations and foetal abortions. Heart and kidney anomalies, along with central nervous system defects are frequent manifestations of a maternal diabetic environment.

Diabetes related complications can be broadly classified as

Microvascular complication - affecting the retina (diabetic retinopathy), kidney (diabetic nephropathy) and the peripheral nerves (diabetic neuropathy).

Macrovascular complication - which affects the heart (cardiovascular disease), brain (cerebrovascular disease) and the peripheral arteries( peripheral vascular disease)

The most specific complications of diabetes are microvascular complication, of which retinopathy is considered as the hallmark of diabetes. Diabetic retinopathy is leading cause of new onset blindness in adults. In pathogenesis of retinopathy, hyperglycaemia increases retinal blood flow and metabolism and has direct effect on retinal endothelial cell. The resulting uncontrolled blood flow increase production of vasoactive substance and endothelial cell proliferation resulting in capillary closure.

Diabetic nephropathy is now among the most causes of end stage renal failure (ESRF). Kidney diseases in diabetes are clinically characterized by increasing rates of urinary albumin excretion. In diabetic nephropathy, the first changes are thickening of glomerular basement membrane and accumulation of matrix material in the mesangium.

Diabetic neuropathy is a relative early and common complication. Like retinopathy, it occurs secondary to metabolic disturbance and prevalence is related to the duration of diabetes and degree of metabolic control. Oxidative stress may play an important role in the pathogenesis of diabetic neuropathy, a condition characterized by pain and numbness of the extremities (Arumo et al., 2006). These are because of glucose auto-oxidation and formation of ROS (superoxide and hydroxyl radicals) and a reduction in reactive oxygen species scavenger such as glutathione, catalase, and superoxide dismutase or both. Much of the effects of oxidative stress may be mediated indirectly via a reduction in nerve blood flow.


The major goal in treating diabetes is to minimize any elevation of blood sugar (glucose) without causing abnormally low levels of blood sugar. Type 1 diabetes is treated with insulin, exercise, and a diabetic diet. Type 2 diabetes is treated first with weight reduction, a diabetic diet and exercise. When these measures fail to control the elevated blood sugars, oral medications are used. If oral medications are still insufficient, treatment with insulin is considered.


In the past, the insulin was being derived from animal sources, particularly cows and pigs. Not only was there a problem with enough supply of insulin to meet the demand, but beef and pork insulin also had specific problems. Originating from animals, these types of insulin caused immune reactions in some people. Patients would become intolerant or resistant to animal insulin. With the acceleration of scientific research in the latter half of the twentieth century, beef and pork insulin were replaced by human insulin. In 1977, the gene for human insulin was cloned, and through modern recombinant DNA technology, manufactured human insulin was made available. Human insulin is now widely used.

Rapid-acting insulin, such as insulin lispro (Eli Lilly), insulin aspart (Novo Nordisk), or insulin glulisine (Sanofi-Aventis), begin to work about 5 minutes after injection, peak in about 1 h, and continue to work for 2 to 4 h.

Regular or Short-acting insulin (human) usually reaches the bloodstream within 30 minutes after injection, peaks anywhere from 2 to 3 h after injection, and is effective for approximately 3 to 6 h.

Intermediate-acting insulin (human) generally reaches the bloodstream about 2 to 4 h after injection, peaks 4 to 12 h later and is effective for about 12 to 18 h.

Long-acting insulin (ultra lente) reaches the bloodstream 6 to 10 h after injection and is usually effective for 20 to 24 h. There are also two long-acting insulin analogues, glargine and detemir. They both tend to lower glucose levels fairly evenly over a 24 h period with less of a peak of action than ultra lente.

Premixed insulin can be helpful for people who have trouble drawing up insulin out of two bottles and reading the correct directions and dosages. It is also useful for those who have poor eyesight or dexterity and is a convenience for people whose diabetes has been stabilized on this combination.

Although there has been a dramatic decrease in the incidence of resistance and allergic reactions to insulin with the use of recombinant human insulin or highly purified preparations of the hormone, these reactions still occur as a result of reactions to the small amounts of aggregated or denatured insulin in all preparations or because of sensitivity to one of the components added to insulin in its formulation (protamine, Zn2+, phenol, etc.). The most frequent allergic manifestations are IgE-mediated local cutaneous reactions, although on rare occasions patients may develop life-threatening systemic responses or insulin resistance owing to IgG antibodies.

The most common adverse reaction to insulin is hypoglycaemia. This may result from an inappropriately large dose, from a mismatch between the time of peak delivery of insulin and food intake, or from superimposition of additional factors that increase sensitivity to insulin (e.g., adrenal or pituitary insufficiency) or that increase insulin-independent glucose uptake (e.g., exercise).


Medications that increase the insulin output by the pancreas - sulfonylureas and meglitinides

Sulfonylureas primarily lower blood glucose levels by increasing the release of insulin from the pancreas. It also may further increase insulin levels by reducing hepatic clearance of the hormone (Davis and Granner, 2001). Sulfonylureas bind to the SUR1 subunits and block the ATP-sensitive K+ channel. The drugs thus resemble physiological secretagogues (e.g., glucose, leucine), which also lower the conductance of this channel. Reduced K+ conductance causes membrane depolarization and influx of Ca2+ through voltage-sensitive Ca2+ channels. Older generations of these drugs include chlorpropamide and tolbutamide, while newer drugs include glyburide), glipizide, and glimepiride. These drugs are effective in rapidly lowering blood sugar but run the risk of causing hypoglycemia (abnormally low and dangerous levels of blood sugar), including coma. Other side effects of sulfonylureas include nausea and vomiting, cholestatic jaundice, agranulocytosis, aplastic and hemolytic anemias, generalized hypersensitivity reactions, and dermatological reactions.

Meglitinides - repaglinide and nateglinide also work on the pancreas to promote insulin secretion. Unlike sulfonylureas that bind to receptors on the insulin producing cells, meglitinides work through a separate potassium based channel on the cell surface and nateglinide has major therapeutic effect by reducing postprandial glycaemic elevations in type 2 DM patients. Since meglitinides increases insulin levels, it has the risk of causing abnormally low blood sugars. Blood sugars that remain severely low can result in sweating, tremors, confusion, and may lead to coma and seizure. In addition, the use of meglitinides has been associated with headaches, muscle and joint aches, along with sinus infections in some individuals. This drug should not be used in pregnancy or by nursing mothers (Rang and Dale, 2007).

Medications that decrease the amount of glucose produced by the liver-

Biguanides - Metformin and phenformin are the drugs of this class. They increase glucose uptake and utilisation in skeletal muscle (thereby reducing insulin resistance) and reduce hepatic glucose production (gluconeogenesis). It does not increase insulin levels, when used alone; it does not usually cause hypoglycaemia, even in large doses. Metformin has no significant effects on the secretion of glucagon, cortisol, growth hormone, or somatostatin. The commonest unwanted effects of metformin are dose-related gastrointestinal disturbances (e.g. anorexia, diarrhoea, nausea), which are usually but not always transient. Lactic acidosis is a rare but potentially fatal toxic effect, and metformin should not be given to patients with renal or hepatic disease, hypoxic pulmonary disease, heart failure or shock. Such patients are predisposed to lactic acidosis because of reduced drug elimination or reduced tissue oxygenation. It should also be avoided in other situations that predispose to lactic acidosis, and is contraindicated in pregnancy.

Medications that increase the sensitivity of cells to insulin-

Thiazolidinediones - The class of drugs known as thiazolidinediones (rosiglitazone and pioglitazone) lowers blood glucose by improving target cell response to insulin (that is, increasing the sensitivity of the cells to insulin). The thiazolidinediones tend to increase high-density lipoprotein (HDL) cholesterol but have variable effects on triglycerides and low-density lipoprotein (LDL) cholesterol. Thiazolidinediones increase glucose transport into muscle and adipose tissue by enhancing the synthesis and translocation of specific forms of the glucose transporters. The thiazolidinediones also can activate genes that regulate fatty acid metabolism in peripheral tissue. The most important contraindications to these medications include any type of liver disease and heart failure and also contraindicated in pregnant or breast-feeding women and in children.

Medications that decrease the absorption of carbohydrates from the intestine

Alpha-glucosidase inhibitors are oral anti-diabetic drug used for diabetes mellitus type 2 that work by preventing the digestion of carbohydrates (such as starch). Carbohydrates are normally converted into simple sugars (monosaccharides) which can be absorbed through the intestine. Hence, alpha-glucosidase inhibitors reduce the impact of carbohydrates on blood sugar. Examples of alpha-glucosidase inhibitors include: acarbose, miglitol , voglibose etc. Even though the drugs have a similar mechanism of action, there are subtle differences between acarbose and miglitol. Acarbose is an oligosaccharide, whereas miglitol resembles a monosaccharide. Miglitol is fairly well-absorbed by the body, as opposed to acarbose. Moreover, acarbose inhibits pancreatic alpha-amylase in addition to alpha-glucosidase. Since acarbose works in the intestine, its effects are additive to diabetic medications that work at other sites, such as sulfonylureas. Acarbose is currently used alone or in combination with a sulfonylurea. Acarbose has significant gastrointestinal side effects (abdominal pain, diarrhoea and gas are common).

Inhibition of amylase and glucosidase enzymes involved in digestion of carbohydrates can significantly decrease the post prandial increase of blood glucose after a mixed carbohydrate diet and therefore can be an important strategy in management of post prandial blood glucose level in type 2 diabetes patient (Ali et al., 2006).

Significance of α amylase and α glucosidases activity inhibition in DM

Only monosaccharides, such as glucose and fructose, can be transported out of the intestinal lumen into the bloodstream after hydrolysis of glucosidic bonds in digestible food, containing starch. Complex starches, oligosaccharides, and disaccharides must be broken down into individual monosaccharides before being in duodenum and upper jejunum. This digestition is facilated by enteric enzymes, including α-amylase and α-glucosidases that are attached to brush border of the intestinal cells (Ortiz-Andrade et al., 2007). The enzyme is found in saliva and pancreatic secretions, where it serves an obvious role in polysaccharide digestion.  More surprisingly, α -amylase is also found in blood, sweat and tears, possibly for anti-bacterial activity. The elevated levels of the enzyme are associated with liver and pancreatic disorders, as well as other diseases.

α-Amylase (α -1,4-glucan 4-glucanohydrolase) is an enzyme that degrades starch, first to oligosaccharides and then in turn to maltose plus glucose, by hydrolyzing α-1,4-glucan bonds. In digestion, the role of α -amylase is primarily the first reaction of this process, generating oligosaccharides that are then hydrolyzed by other enzymes.

Starch--------> Oligosaccharides-------- Maltose + Glucose

In vitro, α -amylase is also able to hydrolyze the α -1, 4 linkages in glycogen, but has no activity on the α -1, 6 linkages responsible for the more highly branched structure of glycogen. These branched structures also reduce the activity of α -amylase toward glycogen by limiting the accessibility of the target α -1, 4-glucan bonds.


Fig name: Starch molecule (α 1-6, α1-4 linkages)

The main nutrition energy of human diet is supplied by carbohydrates. Dietary carbohydrate are broken down to monosaccharides by hydrolytic enzyme α-glucosidases, and absorbed into the intestinal brush border membrane, so intestinal α-glucosidases are physiologically important enzyme in the digestive process of dietary carbohydrates. The intestinal α-glucosidases are divided into four enzymes: maltase, glucoamylase, sucrase and isomaltase (Toda et al., 2001). Among them, maltase is the major enzyme which is responsible for digestion and absorption of dietary starch, wheras sucrase can only hydrolyze sucrose. (Toda et al, 2000)

Hence one of the therapeutic approach for reducing Post Prandial (PP) blood glucose levels in patient with diabetes mellitus is to prevent absorption of carbohydrate after food uptake. Inhibition of these enzymes (α -amylase and α-glucosidases) reduced the high post prandial (PP) blood glucose peaks in diabetes (Conforti et al., 2005). Since the discovery of acarbose that is the 1st member of α-glucosidases inhibitor approved for the treatment of type 2 diabetes. Acarbose and miglitol are competitive inhibitor of α glucosidases and reduces absorption of starch and disaccharides (Davis and Granner, 2001).

Aldose reductase

Under normoglycemia most of the cellular glucose is phosphorylated into glucose 6 phosphate by hexokinase. Non Phosphorylated glucose enters into alternative route of glucose metabolism called polyol pathway. Aldose reductase is a rate limiting enzyme in the polyol pathway which converts glucose to sorbitol. Sorbitol is converted to fructose by sorbitol dehydrogenase. Aldose reductase is located in the eye (cornea, lens, and retina) kidney and myelin sheath.

Under hyperglycemia because of saturation of hexokinase with glucose there is increased flux of glucose through polyol pathway. This leads to overflow of products of polyol pathway and dpletion in reduced nicotinamide adenine dinucleotide phosphate (NADPH) and oxidized form of nicotinamide adenine dinucleotide (NAD+) The acceleration of polyol pathway elicit metabolic imbalance in insulin independent uptake of glucose. This provokes the tissue damage in target organs of diabetic complication such as ocular lens, retina, peripheral nerve and renal glomerulus.


Flavonoids are low molecular weight substances, which are a group of natural products which exhibits various biological and pharmacological activities like antibacterial, antiviral, antioxidant, anti inflammatory, antiallergic hepatoprotective, antithrombotic, antiviral and antimutagenic effects and inhibition of several enzymes. A wide range of different biological properties including antibacterial, antithrombotic, vasodilatory, anti-inflammatory and anticarcinogenic are exhibited by flavonoids have been reviewed (Knekt et al., 2002). It has been reported that flavonoids inhibit xanthine oxidase, glucosidase, α amylase and have superoxide scavaging activities. Therefore, flavonoids could be a promising remedy for diabetes by decreasing regulating the glycaemia control in the body (Sharma et al., 2008).

Flavonoids can prevent cell death by scavenging the ROS, maintaining the GSH levels and inhibiting Ca2+ influx (Aruma et al 2007). Flavonols are essential dietary factors contributing to the maintenance of the capillary permeability and it has been accepted as dietary supplement in protecting the body against chronic diseases such as cancer, cardiovascular diseases and diabetes mellitus. The potent antioxidant activity may contribute to the actions in the body. Flavonoids can exert their antioxidant activity by various mechanisms such as quenching free radicals, by chelating metal ions, by inhibiting enzymatic systems responsible for the generation of free radicals, etc. (Lukacinova et al., 2008).

Flavonoids are products of plant metabolism and they have varied phenolic structures. They are effective against the ROS due to their free radical scavenging properties and metal chelating property. Thus it may protect the tissues against radicals and lipid peroxidation. Under certain conditions they may behave as pro-oxidants also.

It is estimated that about 2% of all carbon photosynthesized by plants is converted into flavonoids. Most tannins are flavonoid derivatives. Thus flavonoids constitute the largest group of naturally occurring phenols, which are ubiquitous in green plants. Flavonoids aglycones occur in a variety of structural forms. All contain fifteen carbon atoms in their basic nucleus and these are arranged in a C6-C3-C6 configuration. The flavonoid varients are all related by a common biosynthetic pathway which incorporated precursors from both shikimic acid and acetate malonate pathways. Flavonoids commonly occur as flavonoid O -glycosidase, where its hydroxyl group is bonded to a sugar molecule. Sugars may also be C-linked to flavonoids as they are directly attached to the benzene nucleus. Optically active flavonoid includes flavanones, dihydroflavonols, catechins, pterocarpans, caretenoids, and some biflavonoids. Flavonoids are the characteristic constituents of the green plants. They occur mostly in all parts of the plants, especially in angiosperms. The content of flavonoids depends upon the plant organ investigated and the qualitative and quantitative study of the flavonoids profiles can be done (Males et al., 2006).

Flavonoids aglycones are broadly classified into:

Flavones - Chrysin, apigenin, luteolin, tricin, baicalein,acacetin, scutellarein, hispidulin, chrysoeriol, diosmetin, tricetin.

Flavonols - kaempferol, quercetin, myricetin, galangin, fisetin, kaempferide, robinetin, herbacetin, rhamnetin, isorhamnetin, quercetagetin, gossypetin.

Anthocyanidins - apigenidin, luteolinidin, pelargonidin, cyaniding, peonidin, delphinidin, petunidin, malvidin.

Isoflavones - diadzein, formonetin, genistein, babtigenin, biochanin, orobol, tectorigenin.

Flavonons - naringenin, hesperetin, pinocembrin, liquiritigenin, sakuranetin, eriodictyol.

Dihydroflavonols - pinobaksin, aromadendron, fustin, taxifolin.

Biflavonoids - agasthisflavone, cupressuflavone, amentoflavone, ginkgetin, sciadopitysin, robustaflavone, hinokiflavone, ochnaflavone.

Chalcones - isoliquiritigenin, chalconaringenin, butein, okanin.

Aurones - sulphuretin, auresidin, martimetin, leptosidin.

AutoDock is an automated procedure for predicting the interaction of ligands with biomacromolecular 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 biomolecular 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 biomolecular 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.

AutoDock combines two methods to achieve these goals: rapid grid-based energy evaluation and efficient search of torsional freedom.

AutoDock calculations are performed in several steps: 1) preparation of coordinate files using AutoDockTools, 2) precalculation of atomic affinities using AutoGrid, 3) docking of ligands using AutoDock, 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 AutoDock 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 sidechains in the protein are treated as flexible, a separate PDBQT file is also created for the sidechain coordinates. In most cases, AutoDockTools will be used for creating PDBQT files from traditional PDB files.

Step2-AutoGrid Calculation. Rapid energy evaluation is achieved by precalculating atomic affinity potentials for each atom type in the ligand molecule being docked. In the AutoGrid 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. AutoGrid 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 AutoDock calculation, the energetics of a particular ligand configuration is evaluated using the values from the grids.

Step 3-Docking using AutoDock. 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, AutoDock 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. AutoDockTools 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 AutoGrid.

Mechanism of docking

To perform a docking screen, the first requirement is a structure of the protein of interest. Usually the structure has been determined using a biophysical technique such as x-ray crystallography, or less often, NMR spectroscopy. This protein structure and a database of potential ligands serve as inputs to a docking program. The success of a docking program depends on two components: the search algorithm and the scoring function

Search algorithm

The search space in theory consists of all possible orientations and conformations of the protein paired with the ligand. However in practice with current computational resources, it is impossible to exhaustively explore the search space-this would involve enumerating all possible distortions of each molecule (molecules are dynamic and exist in an ensemble of conformational states) and all possible rotational and translational orientations of the ligand relative to the protein at a given level of granularity. Most docking programs in use account for a flexible ligand, and several attempt to model a flexible protein receptor. Each "snapshot" of the pair is referred to as a pose.

A variety of conformational search strategies have been applied to the ligand and to the receptor. These include:

systematic or stochastic torsional searches about rotatable bonds

molecular dynamics simulations

genetic algorithms to evolve new low energy conformations

Ligand flexibility

Conformations of the ligand may be generated in the absence of the receptor and subsequently docked or conformations may be generated on-the-fly in the presence of the receptor binding cavity. Force field energy evaluation are most often used to select energetically reasonable conformations, but knowledge-based methods have also been used.

Receptor flexibility

Computational capacity has increased dramatically over the last decade making possible the use of more sophisticated and computationally intensive methods in computer-assisted drug design. However, dealing with receptor flexibility in docking methodologies is still a thorny issue. The main reason behind this difficulty is the large number of degrees of freedom that have to be considered in this kind of calculations. However, neglecting it, leads to poor docking results in terms of binding pose prediction.

Multiple static structures experimentally determined for the same protein in different conformations are often used to emulate receptor flexibility. Alternatively rotamer libraries of amino acid side chains that surround the binding cavity may be searched to generate alternate but energetically reasonable protein conformations.

Scoring function

The scoring function takes a pose as input and returns a number indicating the likelihood that the pose represents a favorable binding interaction.

Most scoring functions are physics-based molecular mechanics force fields that estimate the energy of the pose; a low (negative) energy indicates a stable system and thus a likely binding interaction. An alternative approach is to derive a statistical potential for interactions from a large database of protein-ligand complexes, such as the Protein Data Bank, and evaluate the fit of the pose according to this inferred potential.

There are a large number of structures from X-ray crystallography for complexes between proteins and high affinity ligands, but comparatively fewer for low affinity ligands as the later complexes tend to be less stable and therefore more difficult to crystallize. Scoring functions trained with this data can dock high affinity ligands correctly, but they will also give plausible docked conformations for ligands that do not bind. This gives a large number of false positive hits, i.e., ligands predicted to bind to the protein that actually don't when placed together in a test tube.

One way to reduce the number of false positives is to recalculate the energy of the top scoring poses using (potentially) more accurate but computationally more intensive techniques such as Generalized Born or Poisson-Boltzmann methods.

There are three general classes of scoring functions:

Force field - affinities are estimated by summing the strength of intermolecular van der Waals and electrostatic interactions between all atoms of the two molecules in the complex. The intramolecular energies (also referred to as strain energy) of the two binding partners are also frequently included. Finally since the binding normally takes place in the presence of water, the desolvation energies of the ligand and of the protein are sometimes taken into account using implicit solvation methods such as GBSA or PBSA.

Empirical - based on counting the number of various types of interactions between the two binding partners. Counting may be based on the number of ligand and receptor atoms in contact with each other or by calculating the change in solvent accessible surface area (ΔSASA) in the complex compared to the uncomplexed ligand and protein. The coefficients of the scoring function are usually fit using multiple linear regression methods. These interactions terms of the function may include for example:

hydrophobic - hydrophobic contacts (favorable),

hydrophobic - hydrophilic contacts (unfavorable),

hydrophilic - hydrophilic contacts (no contribution to affinity except for the following special cases):

number of hydrogen bonds (favorable electrostatic contribution to affinity, especially if shielded from solvent, if solvent exposed no contribution),

number of hydrogen bond "mismatches" or other types of electrostatic repulsion (very unfavorable and rarely seen in stable complexes),

number of rotatable bonds immobilized in complex formation (unfavorable entropic contribution).

Knowledge-based - based on statistical observations of intermolecular close contacts in large 3D databases (such as the Cambridge Structural Database or Protein Data Bank) which are used to derive "potentials of mean force". This method is founded on the assumption that close intermolecular interactions between certain types of atoms or functional groups that occur more frequently than one would expect by a random distribution are likely to be energetically favorable and therefore contribute favorably to binding affinity.

Finally hybrid scoring functions have also been developed in which the components from two or more of the above scoring functions are combined into one function.

Applications of docking:

Docking is most commonly used in the field of drug design - most drugs are small organic molecules, and docking may be applied to:

hit identification - docking combined with a scoring function can be used to quickly screen large databases of potential drugs in silico to identify molecules that are likely to bind to protein target of interest.

lead optimization - docking can be used to predict in where and in which relative orientation a ligand binds to a protein (also referred to as the binding mode or pose). This information may in turn be used to design more potent and selective analogs.

Bioremediation - Protein ligand docking can also be used to predict pollutants that can be degraded by enzymes