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Post: #1

The worldâ„¢s most advanced super computer doesnâ„¢t require a single semiconductor chip.
The human brain consists of organic molecules that combines to form a highly sophisticated network able to calculate, perceive, manipulate, self-repair, think and feel. Digital computers can certainly perform calculations much faster and more precisely than humans, but even simple organisms are superior to computers in the other five domains. Computer designers may never be able to make machines having all the facilities of natural brain,but we can exploit some special properties of biological molecular-particularly proteins-to build computer components that are faster ,smaller and more powerful than any electronic devices .
Devices fabricated from biological molecules promise compact size and faster data storage. They lead themselves to use in parallel processing computers,3Dmemories and neural networks.
As the trend towards miniaturization continues, the cost of manufacturing a chip increases considerably. On the other hand ,the use of biological molecules as the active components in a computer circuitry may offer an alternative approach that is more economical.

Molecular electronics is an emerging field that lies at the interface of chemical physics, electrical engineering and solid state science. It involves encoding, manipulation and retrieval of information at the macromolecular level in contrast to current techniques which are fast approaching their practical limits.
Molecular electronics provides new methodologies for high speed signal processing, holographic associate memories and 3D optical memories. Molecular devices are reliable and competitive with semiconductor devices when monomolecular state assignment averaging can be implemented. Biomolecular electronics offers significant promise in addressing some of the inherent limitations of semiconductor architecture.

Is a computer based on the dynamics of bio molecular activities rather than on electronic switching. By exploiting some special properties of biological molecules, particularly proteins, components that are smaller, faster and more powerful than any electronic device can be made to function.
Since 1960â„¢s the computer industry has been compelled to make the individual components on semiconductor chips smaller and smaller inorder to manufacture large memories and more powerful processors economically. These chips consists of array of switches, usually of the kind known as logical gates that flip between two states-0 or 1 in response to electric current passing through them. If the trends toward miniaturization continues, the size of single logic gate will approach the size of molecules in the year 2030.
A serious roadblock to miniaturization is the increase in cost of manufacturing a chip. At some point the search for even smaller electronic devices may be limited by economics rather than physics. So the use of biological molecules as the active components in computer circuitry may offer an alternative approach that is more economical.


Molecules can potentially serve as computers switches because their atoms are mobile and change position in a predictable way. If we can direct the atomic motion and thereby constantly generate two discrete states in a molecule, we can use each state to represent either 0 or 1.This results in reduction of size, that is, a biomolecular computer in principle is one-fifth of the size of the present day semiconductor computer. This theoretically makes it thousand times modern computers.
Researchers have introduced parallel processing architecture which allows multiple rows of data to be manipulated simultaneously. In order to expand memory capacities, they are devising hardware that stores data in 3D instead of usual ways. So scientists have built nueral networks that mimic the leasing by association capabilities of the brain. The ability of creating proteins to change their properties in response to light should simplify the hardware required for its implementations.
Although no computer components made from proteins are in the market yet, ongoing international research efforts are making enticing headway. Several molecules are under consideration for the use in computers. Bacteriorhodopsin has generated the most interest.
Bacteriorhodopsin is a light harvesting protein in the purple membrane of a micro organism called Halobacterium halobium .Bacterior-hodopsin , the bacterial protein , is the basic unit of protein memory and is the key protein in Halobacterial photosynthesis .It functions like a light “driven photo pump. Under exposure to light it transports photons from the halobacterial cell to another medium, changes its mode of operation from photosynthesis to respiration, and converts light energy to chemical energy. The response of this molecule to light energy can be utilised to frame prutein memories.
Bacteriorhodopsin grows in salt marshes ,where temperature can exceed 150 degree F for the extended time period and the salt concentration is approximately six times that of sea water. Survival in such an environment implies that this protein can resist thermal and photochemical damages. Upon absorption of light it generates a chemical and osmotic potential that serves as energy source. It has the ability to form thin films that exhibit excellent optical characteristics and offer long term stability .
Soviet scientists were the first to recognize and develop the potential of the Bacteriorhodopsin sea for computing. Many aspect of this ambitious project are still considered military secrets.

At first interests were on the protein called rhodopsin ,but later were focused on Bacteriorhodopsin because of the greater stability and better optical properties It can be prepared in large quantities also. The application under study for computer processors and the memories on which they operate exploit the photocycle of Bacteriorhopdopsin.

Bacteriorhodopsin comprises a light absorbing component known as CHROMOPHORE , that absorbs light energy and triggers a series of complex internal structural changes to alter the proteinâ„¢s optical and electrical characteristics. This phenomenon is known as photocycle.
The sequence of structural changes induced by light as in figure allows for the storage of data in memory. Green light Changes the initial resting state known as Br to the intermediate K.Next K relaxes, forming M and then O. If the O intermediate is exposed to red light, a so called branching section occurs.

O converts to the P state and quickly relaxes to the Q state-a form that remains stable indefinitely. Blue light will however convert Q back to bR .Any two long lasting states can be assigned the binary value 0 or 1,making it possible to store information as a series of bacteriorhodopsin molecules in one state or another.
The intermediates absorb light in different regions of the spectrum. As a consequence, we can read the data by shining laser beams on molecules and noting the wavelengths that donâ„¢t pass through the detector. Since we can alter the structure of bacteriorhodops in with one laser and another laser, we have the needed basis for writing and then reading from memory.
Most devices under study make use of resting state and one intermediate. One state is designated as 0 and other as 1.Switching between the states are controlled by means of laser beams. Most of the early memory devices based on bacteriorhodopsin could operate only at extremely cold temperatures of liquid nitrogen, at which the light induced switching between the initial bR structure and intermediate known as the K could be controlled. These devices were very fast compared with semiconductor switches. But the need for low temperatures precluded general application.

Today most bacteriorhodopsin devices functions at or near room temperature, a condition under which another intermediate M is stable. Although most bR based memory devices incorporate bR “ M switch, other structures may actually prove more useful in protein - based computer systems.

Over the past two decades VLSI circuit technology has developed rapidly. Unfortunately in complex VLSI systems these increases cause serious interconnection problems in chip area, power consumption and noise. One promising candidate for breaking through these difficulties is the biomolecular computer. The model is based on the specificity of enzymes in their choice of reactants and substrates. They carry information by their presence or absence in solution. At the specified destination, enzyme based biosensors selectively detect the released substrates which automatically triggers a specific biomolecular switch in solution.
The foundation of any computing system is its logic. To support the systematic design of biomolecule computing systems, an algebraic system called set valued logic (SLV),special class of multivalued logic is used. In the SLV concept we use a large number of enzymes and their substrates in our system and the varieties of substrate molecules represent SLV logic states.

Electronic VLSI systems have very effective execution and fast interactive capabilities. Though a biomolecular computer has low data rates, their advantage in natural and massive parallelism. They offer a new parallel processing architecture and bioprocessor executes operations in a data driven manner that makes it possible to exploit the maximum parallelism of a given algorithm.
Logic value 0 logic value 1------------------logic value r-1

Substrate 0 substrate 1---------------------substrate r-1


Let L be the set of all the substrate that can be transmitted simultaneously in solution. This simultaneous transmission is interpreted algebraically as logic value multiplexing. An enzyme based biosensors can exactly discriminate the molecular information.
In this the concept of MLV to design biodevice networks for interconnection free computation is discussed.The use of more than two levels of logic can reduce the complexity of intergrated circuit interconnection. Practical MLV use continous electrical variables such as voltage,current and change to convey information.


Certain intermediates produced after bacteriorhodopsin initially exposed to light will change to unusual structures when they absorb energy from second laser beam, in a process known as sequential 1-photon architecture. In the photocycle above, a branching section occurs from 0 intermediate to form P and Q. These are generated by two consecutive pulses of laser light-first green and then red. Although P is fairly shortlived, it relaxes to form Q which is stable for extended periods. Because of its extended stability, the Q state has greater significance in search for long term, high density memory.
The intermediate PandQ formedin the sequential 1- photon, are particularly useful for parallel processing. For writing data in parallel our approach incorporates another information-these dimensional data storage. A cube of bacteriorhodops in is surrounded by two arrays of laser beams placed 90 degree from each other. One array of laser, all set to green and called pagging beams, activates the photocycle of proteins in any selected square plane or page within the cube. After a few milliseconds, when the number of 0 intermediates reaches near maximum, the other laser array of red beams is fired.

This second array is programmed to illuminate only the region of activated square where data bits are to be written, switching the molecules to the P structure. T he P intermediate then relaxes. Since the laser array can activate molecules in various places throughout the chosen illuminated page, multiple data locations, known as addressed can be written in parallel.
The system for reading stored memory during processing or during the contraction of result relies on the selective absorption of red light by the 0 intermediate. To read multiple bits of data in parallel ,we start just as we do in the writing process First the green paging beam fire at the square of the protein to be read , starting the normal photocycIe of molecules in bR state. After two milli seconds, the entire laser assay is turned on at a very low intensity of red light .Molecules that are in the binary 1 state do not absorbe these, red molecules that started out in the binary 0 state (bR) do absorbe the beams .The detector reads 0â„¢s and lâ„¢sin terms of the binary code .The process is complete in approximately 10 ms, a rate of 10 megabytes per second for each page of memory

In addition to facilitating parallel processing, 3D cubes of bactrioshodcpsin provides much more space that two dimensional optical memories. Three dimensional optical memories can theoratically approach storage densities of one trillion bits per cubic centimeters .A 300 folds improvement in storage capacity over 2-D devices should be possible .So a major impact of bioelectronics on computer hardware will be in the area of volumatric memory.
Speed is also an important benefit. of volumatric memories. The complication of 3-D storage within the use of parallel architectures enhances the speed of such memories , just a parallel processing in the human brain overcomes relatively slow nueral processor and allows the brain, to be a thinking machine with fast reflexces and rapid decision making capability .If we illuminate a square measuring 1,024 bits by 1,024 bits within a larger tube of protein, we can write 105 KB into memory in a 10 mS cycle. .So it means an overall write speed of 10 million characters per second comparable to slow semiconductor memory.

Associative memories operate rather differently from the memories that dominate current computer architectures .This type of architecture takes a set of data often in the form of an image and scans the entire memory bank until it finds a data set that matches it .Since human brain operates in a nueral associative mode , many computer scientists believe large - capacity associative memories will be required if we are to achieve artificial intelligence.
As associative memory device that relies in the holographic properties of thin films of bacterioshodopsin, holograms allows multiple image to be stored in the same segment of memory, permitting large data sets to be analysed simultaneously. Associative memories have significant potential for applications in optical computer architectures optically coupled nueral network computers etc.

The hyhrid computer we envision would be highly flexible by taking advantage of particular combinations of the memory card described above, large pools of data carry out complex scientific simulations or serve as a unique plate form for investigation of artfical intelligence With above a tetra byte of memory in cubes of bacteriorhodopsin , this machine would handle large data bases with alacrity. Associative memory processing coupled with volumetric memory would make databases searches. Many orders of magnitude faster than is currently possible. Since this hybrid computer can be designed to function as a nueral associative computer capable of learning and analysing data like a human brain, the importance of hybrid computers to studies in artificial intelligence cannot be under estimated.


2. http://www.protein
Post: #2
Protein Secondary Structure Prediction


Protein: from the Greek word PROTEUO which means "to be first (in rank or influence)"

Why are proteins important to us:

Proteins make up about 15% of the mass of the average person and maintain the structural integrity of the cell.
Enzyme – acts as a biological catalyst
Storage and transport – Haemoglobin
Hormones – Insulin

Introduction to proteins

These values can be calculated?

Ramachandran Plot: Founded by G.N.Ramachandran.

Green region indicates the
stericially permitted φ & Ψ values except Gly and Pro.

Yellow circles represent the conformational angles of several secondary structures..α-helix, parallel & anti parallel β-sheet
Secondary Structure

8 different categories (DSSP):
H:  - helix
G: 310 – helix
I:  - helix (extremely rare)
E:  - strand
B:  - bridge
T: - turn
S: bend
L: the rest
Three secondary structure states
Prediction methods are normally trained and assessed for only 3 states (residues):
H (helix), E (strands) and L (coil)
There are many published 8-to-3 states reduction methods
Standard reduction methods are defined by programs DSSP (Dictionary of SS of Proteins), STRIDE, and DEFINE
Improvement of predictive accuracy of different SSP (Secondary Structure Prediction) programs depends on the choice of the reduction method
For more information about this article,please follow the link:
Post: #3
Prediction of protein conformations from protein sequences

Protein Conformations

Predict protein 3D structure from (amino acid) sequence
Sequence ® secondary structure ® 3D structure ® function

Protein 3D Structure Detection

X-ray Crys


Protein Structure

Protein 3D structure → biological function
Lock & key model of enzyme function (docking)
Folding problem
protein sequence Û 3D structure
Structure prediction and alignment
Protein design, drug design, etc …
The “holy grail” of bioinformatics

The Prediction Problem

Can we predict the final 3D protein structure knowing only its amino acid sequence?

Studied for 4 Decades
Primary Motivation for Bioinformatics
Based on this 1-to-1 Mapping of Sequence to Structure
Still very much an OPEN PROBLEM

Predicting Protein Structure

Find best fit of sequence to 3D structure
Comparative (homology) modeling (同源建模法)
Construct 3D model from alignment to protein sequences with known structure
Threading (fold recognition) (折叠识别法)
Pick best fit to sequences of known 2D / 3D structures (folds)
Ab initio / de novo methods (从头预测法)
Attempt to calculate 3D structure “from scratch”
Molecular dynamics
Energy minimization
Lattice models
Post: #4

S.G. Srikantia
University of Mysore

It has long been recognised that proteins differ in their ability to promote growth. The primary function of dietary protein is to supply nitrogen and aminoacids - both essential and non essential in amounts and proportions needed for the synthesis of tissue protein. The aminoacid content and profile is thus a critical determinant of protein quality and most methods which measure protein quality are directly or indirectly related to the efficacy with which they can satisfy aminoacid requirements.
All aminoacids have to be present simultaneously in adequate quantities and proper proportions at the site, for protein synthesis. A deficit in any one essential aminoacid (EAA) would limit protein synthesis proportionate to the extent of deficit (1). From this has developed the concept of the “most limiting aminoacid” and applied to judge dietary protein quality. If the composition of an “ideal” protein is known, which is completely utilised and has neither a deficit or excess of any EAA, the value of any given protein can be arrived at by calculating the extent of deficit of each EAA, in relation to that present in the ideal protein. The EAA in greatest deficit would become the limiting aminoacid and determine the protein value.
2. Use of standards
Egg protein has been used in the past as a standard, but this is not ideal. Although at levels of intake below requirement it is almost totally utilised, it is not so at higher levels. Egg protein has several EAA in excess, and at high intake levels, it can stand dilution with proteins of inferior quality without a compromise in its own quality. Recognising this, an expert committee of the FAO in 1957, proposed that an aminoacid scoring pattern in which the amounts of EAA in one gram of protein were about double that of the estimated adult human requirements should serve as an appropriate standard (2). It soon became apparent that the FAO pattern was not satisfacory. An FAO/WHO Committee which met in 1965 replaced the FAO pattern with that of egg/milk (3). A later FAO/WHO Committee in 1973 suggested the use of a new aminoacid pattern as the basic for computing aminoacid scores, instead of using a naturally occuring protein. The pattern was based upon current knowledge of human aminoacid requirements - largely that of adults.
The use of an aminoacid scoring system, though rational, has limitations. It does not take into account several factors which influence its practical application. Data on the aminoacid content after acid hydrolysis of a raw protein do not provide information on the availability of the aminoacid for absorption. Cooking and processing can adversely affect aminoacid availability, particularly of lysine and sulphur aminoacid, while severe heat treatment can lower availability of all aminoacids because of a decrease in digestibility of protein (5). Data on aminoacid composition do not provide information on the rate of release of aminoacids during digestion in the intestinal tract, the relative amounts of various EAA in the pool from which aminoacids are actively absorbed and of aminoacid imbalances which can influence utilisation of EAA (6).
Apart from these considerations, not all of the ingested protein is absorbed after degradation to aminoacids but some is absorbed in dipeptide form. The profile of dipeptides formed and the ratio of aminoacids to dipeptides in the intestinal lumen may have some significance with respect to protein quality.
The use of a reference protein or aminoacid scoring system for expressing the quality of other proteins has these limitations. In addition, the validity of the formulation and use of a reference protein or score depends upon the accuracy of knowledge of human aminoacid requirements. Requirements for adults and children are believed to be different and yet, so far a single standard has been used. This has been justified on the ground that a pattern which satisfies the needs of children will certainly satisfy those of an adult.
Recommended Dietary Allowances (RDA) based on requirement data should not be used to formulate practical diets, but used as guides to assess the probability of inadequate intake by population groups and to help countries plan their food supplies. The validity of the argument that an aminoacid score satisfactory for a child would meet the needs of an adult is not in question, but it may be debated whether there is not a need to formulate two separate patterns, since the use of the recommended pattern is most likely to overestimate RDA of approximately one half of a country's population with its implications for food supply.
3.Biological value
Among methods used to determine protein quality, N balance is one. From data on N balance, Net Protein Utilisation (NPU) which is the proportion of ingested protein retained can be calculated. By measuring True Digestibility (TD), the Biological Value (BV) can be arrived at - which is the proportion of the absorbed nitrogen retained. Biological Value and NPU may be considered as good measures of protein quality, since presumably, the retained N is used for protein synthesis. In experimental animals NPU can be directly estimated by carcass analysis and values are therefore likely to be more accurate than when BV and NPU are derived from N balance data, as it is done in human studies. The inaccuracies inherent in N balance studies are known, no matter how carefully conducted. NPU and BV thus measure the same parameter (N retained, except that BV is calculated from N absorbed and NPU from N ingested).
The concept of BV has the merit that it can be used to assess requirements of protein derived from foods with known quality differences, because BV is directly related to the efficiency of protein utilisation. It however has some serious limitations. It ignores the importance of factors which influence digestion of the protein and interaction of protein with other dietary factors before absorption. On theoretical grounds, the requirement of a protein which has a BV of 100, would be half that of another whose BV is only 50. The application of BV data for human protein requirements, is however, not this straightforward, because of methodological considerations. Conventionally, BV and NPU are determined using a single level of protein; more importantly, they are measured when the protein content of the diet is clearly below that of requirement, deliberately done to maximise existing differences in quality. Differences may however become considerably minimised, if not completely masked, when proteins are fed at levels above or close to requirement, since requirements of all EAA can be completely met even from a poor quality protein, when enough is consumed to satisfy the needs of its most limiting aminoacid. Thus what BV and NPU measure is the near maximal potential ability of the protein. That the utilisation of a protein - the % retained, falls with its increasing concentration in the diet was first shown over three decades ago (7) and subsequently repeatedly confirmed. BV can vary by a factor of two-form over 90% at low intakes (100 mg N/kg) to around 40% at high intakes (500 mg/kg) (8). In young men, BV of wheat gluten fell from 100 (intake 100 mg/kg) to 45 (intake 400 mg/kg) and 25 (intake 1.09/kg) when intakes progressively increased. (9). Similarly the BV of egg protein fell from a value of 100 at an intake of 200 mg/kg to around 60 and 70 when intakes increased to 400 and 500 mg/kg (10). As importantly, the dose-response relationship was found not to be linear through all ranges of intake, but curvilinear at both low and high intakes and at intakes approaching requirements. Differences in protein value as judged by BV were not evident when wheat gluten and egg were fed at levels below 200 mg/kg, but became clear at higher levels. Differences progressively increased as protein intake increased (11). The degree of carvature depends upon the most limiting aminoacid.
At intakes of protein approaching requirement, BV is considerably lower than maximal. To use values obtained under conditions designed to evaluate maximal potential, for purposes of calculating protein requirements would therefore have a high degree of inaccuracy built in. This would also be true when data on BV of two proteins obtained at levels meant to demonstrate maximal differences in quality are used to arrive at requirement of those two proteins, because proportionately between proteins seen at lower levels will not be maintained at higher levels. Requirements of any protein cannot be estimated from an extrapolation of the dose-response line obtained using low levels of the protein. If BV of a protein is to be used to compute human protein requirements, the value then, has to be measured at several intake levels close to requirement levels.
Another limitation of the use of BV as a measure of protein quality is that proteins which are completely devoid of one EAA can still have a BV of up to 40, because of the capacity of the organism to conserve and recycle EAA as an adaptation of inadequate intake of the aminoacid; also EAA needs for growth and maintenance are different (12).
Determination of BV of a single protein is of limited use for application to human protein requirements. No population derives all of its protein exclusively from a single food. Proteins come from a mixed bag of animal and vegetable foods or from a mixture of several vegetable foods. Mixtures of protein foods frequently promote better growth than anticipated from the performance of individual components of the mixture. This has been explained on the basis of a partial or complete correction of the constraint imposed by the limiting aminoacid present in individual proteins. That this may not be the sole explanation, is suggested by the observation that in some mixtures which promote better growth, levels of some EAA are lower than that seen in the better component of the mixture. A better aminoacid balance which improves utilisation of existing aminoacids has been suggested as a possible explanation.
When two or more protein sources are mixed, the outcome in terms of quality may be that the mixture has:
a. a value which lies between those of the components predicted by changes in EAA composition,
b. a value close to or identical with that of the better component or
c. a value which is even higher than that of the better component.
There is no instance where the value falls below that of the poorest component. Various explanations, including changes in the ratio of total EAA to non EAA and digestibility of protein have been offered (13). The important point from the application angle, is that the determination of BV of a single protein has limited value, and that BV should be determined on combinations of proteins present in habitual diets. Such determination should be made at levels of protein intake needed for N balance in adults and for growth in children. It would be best to evaluate protein quality of the diet in the form in which it is actually eaten. This would be the procedure which would provide the most direct estimate of protein quality, but even such data suffer from some limitations. Such studies per force are done under controlled conditions, usually in a metabolic ward. There is evidence that some of the conditions under which such studies are done influence the results and that these conditions may not apply to real life situations.
4. Protein Calorie Interaction
One of them relates to an important facet of the protein-calorie interaction. Inadequate energy intake lowers the efficiency of protein utilisation and in most N balance studies, calorie adequacy is ensured. Although early data have suggested that excess calories promote better N retention, its practical implications had by and large been overlooked until recently. Results of some recent studies have reemphasised the importance of this interaction. The utilisation of both egg and polished rice protein in young men was about 30% higher and protein required to maintain N balance lower when fed at a calorie intake which was about 25% higher than that needed for maintenance (14). Similarly, when egg protein was fed at the “FAO/WHO 1973 safe level”, young men were in negative balance when their calorie intake was at maintenance level and they achieved positive balance only when calorie intakes were raised by 9 to 14% (15). Using two levels of protein at constant calorie intake and three levels of calorie at constant N intake, N balance was found to be influenced more by energy than by protein at marginal intakes (16). In all these studies, better N retention was due exclusively to decreased urinary N excretion. It needs to be ensured therefore that protein quality is always evaluated when calorie intake is at maintenance level and not in excess.
5. Frequency of food intake
The frequency of food intake appears to have an equally striking effect. In young women who consumed 62 g protein/day, 53 of which came from animal sources, the distribution of this protein into three meals instead of two, improved N utilisation through a reduction in urinary N (17). Similarly in 8 children who consumed 63 g of protein daily, 60% of which came from animal foods (2050 Kc) distribution of this into four meals instead of two, raised N retention almost three fold (18), again through a marked fall in urinary N. These findings are similar to that made in a single subject that at an intake of 1.14 g protein/kg, but not at a lower level, NPU was higher when the protein was consumed in four meals instead of two (19). The uniformity of ingestion of the day's total protein would appear to improve its utilisation. This factor is perhaps not taken care of sufficiently, in N balance studies, and emphasis the need to simulate as far as possible, the eating patterns of the populations for whose use N balance studies are carried out. There do not appear to be any data on what happens to N balance when exclusively vegetable proteins are fed in this fashion. The findings of such a study may have more than mere academic interest.
6. N utilisation from mixed proteins
A review of data on N balance studies in humans shows that at levels of intake of N between 70 and 100 mg/kg, through a wide variety of protein sources, such as egg, milk, meat and plant foods, either singly or in combination, efficiency of utilisation varies between a relatively narrow range of 60% and 72%. To maintain N balance, 77 mg N/kg/day was needed if the protein source was either of animal origin or a mixture of animal and vegetable protein, 93 mg/day of the source was a mixture of vegetable proteins and over 110 mg/day if a single vegetable protein was ingested (20). It is important to recognise that the differences between these figures is less than what would have been predicted on the basis of their known aminoacid content or BV obtained at low levels of intake, - a finding which emphasises the need for studies to be done directly on man, with diets which are habitually consumed.
It would be obviously be impossible to evaluate all combinations and permutations of even the major food protein sources and varying proportions of each of the components in mixtures which population groups consume the world over, let alone the minor sources which have some nutritional significance. It may however be possible to group the sources into major categories - those of animal origin such as milk, meat, fish and eggs - those of vegetable origin: - cereals - wheat, rice and barley, millets - maize, sorghum and finger millet, legumes, and oil seeds, look at the relative amounts of these which go into dietaries of large segments of the population in a broad way and determine the efficiency of utilisation of their protein as maesured by N balance at several levels close to requirement. This procedure will also take into account the valid criticism that biological value of a protein is not identical with its nutritive value since while the former is influenced solely by its aminoacid content and profile, the latter is influenced by additional factors outside those related to protein alone - a situation which from the practical view point is more realistic.
A recent committee of the National Academy of Sciences has in fact used this approach to formulate RDA for proteins (21). The committee has assumed that protein in diets of the type consumed in USA, are utilised to the extent of 75%. The committee states that “Further quality correlations are not required, except possible for young children who are subsisting almost exclusively on diets composed largely of cereal grains and root crops, an unlikely situation in the United States”. This approach has much to commend it and is perhaps at present the most realistic approach to the practical problem of recommending intakes which meet protein requirements. This would call for a new approach and each country would be required to carry out studies relevant to their food sources, the composition of habitual diets as well as cooking and eating practices. It would involve a considerable number of diet combinations to be evaluated.
N balance studies are time consuming, tedious and expensive. Development of methods which can rapidly evaluate N balances would therefore constitute an advance over existing methods. An adaptation of the conventional balance technique in which three or four levels of protein in the region of requirement are used, and small increments or decrements made either daily or once in two days is employed has been reported to yield data on BV for egg, spray dried whole milk and casein protein which are not statistically different from those obtained with observations made over longer duration (22). Values however tended to be slightly higher in the short term assay and is likely to be due to incomplete adaptation to rapidly changing protein levels. Wider application of this method may be expected to establish its usefulness and validity.
7. Summary
Several methods are currently in use for evaluating protein quality. Some are based upon chemical methods while others are biological assays. The biological value of a protein derived from nitrogen balance data, is at present considered as the best available measure of protein quality. The concept of a standard protein with an EAA profile that matches human needs, against which the quality of all proteins can be judged has the advantage of simplicity and ease of application, but its use to predict quality would be valid and appropriate only when protein digestion, and availability of aminoacid, both for absorption and utilisation are not critical.
Biological value measures the proportion of absorbed nitrogen which is retained and presumably utilised for protein synthesis and therefore reflects true protein quality. The practical application of data on BV which is determined by conventional methods, for purposes of correcting for differences in quality has limitations, since what is measured is maximal potential of quality and not a true estimate of quality at requirement level. It would be more appropriate to evaluate protein quality of diets as consumed by N balance technique in humans. Such a procedure calls for neither the definition and use of a standard, nor does it make use of the calculated biological value of the ingested protein based on an unrealistic intake level. It will also take care of the valid criticism that the biological value of a protein and its nutritive value are not identical.
To evaluate the large number of representative diets consumed by different population groups all over the world, never short term N balance techniques need to be developed and their validity established.
1. Block R.J. and Mitchell H.H., Nutr. Abs & Rev 16, 249, 1946
2. Report of the FAO Committee, FAO Nutritional Studies No. 16, 1957
3. Joint FAO/WHO Expert Group on Protein Requirements FAO Nut. meeting Rep. Series No. 37, W.H.O. TRS 301, 1965
4. Joint FAO/WHO Expert Committee, WHO TRS 522, 1973
5. Carpenter K.J., Nutr. Abst and Rev 43, 423, 1973
6. Harper, A.E. and Benevenga, N.J. in ‘Proteins as Human Food’ Ed: Lawrie, R.A. Av. Pub. 1970
Harper, A.E. in ‘Improvements of protein nutriture’, Committee on aminoacids, NAS, 1974
Scrimshaw, N.S., Brassani, R., Behar, M. and Viteri, F., J. Nutr., 66, 485 1958
Gopalan, C. Am.J.Clin.Nutr., 23, 35, 1970
7. Barnes, R.H., Bates, M.J. and Maack, J.E., J. Nutr., 32, 535, 1946
8. Bressani, R. in ‘Evaluation of Proteins for Humans’ loc.cit.
9. Inoue et al., Nutr.Rep.Inter., 10, 201, 1974
10. Young, V.R. et al., J. Nutr., 103, 1164, 1973
11. Inoue et al., 1973 loc cit
12. Said, A.K. and Hegsted, D.M., J. Nutr., 99, 474, 1969
13. Woodham, A.A. in ‘Nutritional Improvement of foods and feeds’, 1977, loc cit
14. Inoue et al., J. Nutr., 103, 1673, 1973
15. Garza, C.G., Scrimshaw, N.S. and Young, V.R., Am.J.Clin.Nutr., 29, 280, 1976
16. Calloway, D.H., J. Nutr., 105, 914, 1975
17. Leverton, R.M. and Gram, M.R., J. Nutr., 39, 57, 1949
18. I. Barja et al., Am.J.Clin.Nutr., 25, 506, 1972
19. Wu,H. and Wu D.Y., Proc.Soc.Exp.Biol.Med., 74, 78, 1950
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