Information

Should there be separate Ramachandran plots for an amino acid in different contexts?

Should there be separate Ramachandran plots for an amino acid in different contexts?



We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

I understand the nomenclature of the phi and the psi angles of the alpha-Carbon atoms in protein stucture, but I am confused by the Ramachandran plot. Each alpha-Carbon atom (magenta) makes two peptide linkages and has two corresponding neighbouring alpha-Carbons (cyan) and side-chains. I would expect the psi and phi values to depend on the interactions of these side-chains, so I would expect that for a single amino acid one would need a separate graph for every possible combination of neighbouring amino acids.

I am not clear whether this the case. The following graph (1) from Harper, Biochemistry, is for “many non-glycine residues from many proteins”. So, suppose a set of phi and psi is allowed for a right-handed helix (a value or data set taken from the plot) does it mean that it is allowed for any amino acid with any other amino acid? I woud expect an alanine adacent to an alanine to have a different interaction to an alanine adjacent to a bulky amino acid such as isoleucine.

I have also seen plots for specific amino acid and their allowed angles, such as that for proline (2), above. This suggests that the neighbours are not being taken into account. If this is so, why not?


The phi and psi dihedrals describe the dihedral on both sides of the c-alpha of a single amino acid, and do not involve any angles of the neighboring amino acid.

The Ramachandran plot is something generated from a set of protein structures, an empirical data set. The top graph represents the dihedrals found for all non-glycine residues in a set of structures. You can filter this for proline only, and you'd get the bottom graph. The top cloud of dihedrals represents those found in beta-sheets, and the bottom cloud those for alpha-helices. Sequence (the amino acid before or after) doesn't really matter that much for what's allowed (although we cannot directly deduce this from the data in those graphs).

If you look a little bit more into the structure of helices and sheets you'll also find out why that's the case. In beta sheets the sidechain of the +1 residue is pointing completely the other way, and also in helices there's little interaction between the sidechains of subsequent residues. Secondary structures are built using the amides, not with the sidechains.


Some further observations about Ramachandran plots in response to the question:

  1. They were originally calculated. This was done by considering the minimum contact distances shown in the diagram (which is from Wikipedia, but based on that in J. Mol. Biol. (1963) 7, 95-99). The side-chains were not considered, except for the Cβ of the central residue. In the case of a glycine side-chain that lacks a Cβ, different values are allowed. The original plot had 'allowed' regions enclosed by a solid line, with an 'outer limit' indicated by a broken line.

  2. The diagram in Fig. 1 in the question, in contrast, is an experimental plot for amino acid residues (other than glycine) in several proteins. Each point represents the dihedral angles for a single instance of an amino acid. Such plots based on very many amino acids are sometimes used as a check for possible errors in experimental values obtained for new proteins - see review by GJ Kleywegt.

  3. When plotting experimental values for very many proteins, the points would superimpose, making it impossible to see which regions were most densely populated. That is why in Fig. 2 in the question a coloured contour map is used to represent the density in particular regions. (More elaborate examples may be found.)

  4. On the general plot of the type originally calculated (1) one often sees particular constrained areas marked for chains in which the same Φ/Ψ angles repeat at every position. These are for structures like α-helices, β-sheets etc. (see image below, taken from Wikipedia entry on Ramachandran Plot.)

  1. The reason Fig. 1 of the question excludes glycine is that its lack of Cβ means it can occur in regions such as Lα, from which most (but not all) larger amino acids are excluded. Likewise, the plot for the imino acid, proline, is different from the other amino acids because of the constraints imposed by the proline ring.

  2. Small, non-repeating hydrogen-bonded structural motifs in proteins may also impose constraints on allowed dihedral angles at certain positions in the motif. Such motifs may exert other constraints which influence the amino acids at particular positions. One might possibly regard this as an influence of amino acid side-chain on allowed dihedral angles, although it is perhaps better to regard it as an influence of amino acids on the occurance of the motif as a whole.


Access to the complete human genome sequence as well as to the complete sequences of pathogenic organisms provides information that can result in an avalanche of therapeutic targets. Structure-based design is one of the first techniques to be used in drug design. Structure based design refers specifically to finding and complementing the 3D structure (binding and/or active site) of a target molecule such as a receptor protein. The aim of this review is to give an outline of studies in the field of structure based drug design that has helped in the discovery process of new drugs. The emphasis will be on comparative/homology modeling.

Discussion of the use of structural biology in drug discovery began over 35 years ago, with the advent of knowledge of the 3D structures of globins, enzymes and polypeptide hormones. Early ideas in circulation were the use of 3D structures to guide the synthesis of ligands of haemoglobin to decrease sickling or to improve storage of blood [1], the chemical modification of insulins to increase half-lives in circulation [2] and the design of inhibitors of serine proteases to control blood clotting. [3] An early and bold venture was the UK Wellcome Foundation programme focussing on haemoglobin structures established in 1975. [4] However, X-ray crystallography was expensive and time consuming. It was not feasible to bring this technique ‘in-house’ into industrial laboratories, and initially the pharmaceutical industry did not embrace it with any real enthusiasm. In time, knowledge of the 3D structures of target proteins found its way into thinking about drug design. Although, in the early days, structures of the relevant drug targets were usually not available directly from X-ray crystallography, comparative models based on homologues began to be exploited in lead optimization in the 1980s. [5] An example was the use of aspartic protease structures to model renin, a target for antihypertensives. [6] It was recognized that 3D structures were useful in defining topographies of the complementary surfaces of ligands and their protein targets, and could be exploited to optimize potency and selectivity. [7] Eventually, crystal structures of real drug targets became available AIDS drugs, such as Agenerase and Viracept, were developed using the crystal structure of HIV protease [8] and the flu drug Relenza was designed using the crystal structure of neuraminidase. [9] There are now several drugs on the market that originated from this structure-based design approach [10] list 㹀 compounds that have been discovered with the aid of structure-guided methods and that have entered clinical trials. The structure-based design methods used to optimize these leads into drugs are now often applied much earlier in the drug discovery process. Protein structure is used in target identification and selection (the assessment of the 𠆍ruggability’ or tractability of a target), in the identification of hits by virtual screening and in the screening of fragments. Additionally, the key role of structural biology during lead optimization to engineer increased affinity and selectivity into leads remains as important as ever.


Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions *

Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.


Module 1 : Introduction to Molecules of Life and Amino Acids

have a solution containing a mixture of three amino acids.

Glycine, alanine and glutamic acid. Suppose the isoelectric point (we are assuming, it

may not be correct, we are not taking any value from the literature), of glycine is 8, the

isoelectric point of alanine is 6.5 and the isoelectric point of glutamic acid is 3. So, that

this pI 3 is less than pI 2 which in turn is less than pI 1. Now, I want to separate. So,

what do I need to do? What I need to do is basically first select a matrix matrix on which

I can apply the solution of the amino acid.

What kind of matrix? It could be a piece of filter paper like the Whatman filter paper or

it could be a gel like substance made up of poly acrylamide or agarose. So, these are the

different matrixes you can use and depending on the matrix we have different names of

the process. The process is that we have the matrix and on two corners we put the

positive and the negative electrode and apply the voltage. The process is called

electrophoresis electrophoresis is basically the mobility or the movement of a molecule

under the influence of an electric field.

So, what I need to do? I need to first apply the solution containing the amino acids in the

middle. And, the one which has got the pI in between the other two that means, the

middle one which is 6.5 (which is for alanine). So, I maintain the pH of the medium on

which this paper is immersed or the gel is immersed, I put it, I keep the pH of that

medium as the pI of the amino acid which is in the middle of the two (in between 8 and

3). So, 6.5 is the pH, I maintained for the buffer system here and then I apply the voltage.

Now what will happen? Alanine may be neutral at 6.5 and it will not move, it will remain

here. And then what will happen to the other two? They will remain as charged species.

Now, the question is what will be their charges? Now isoelectric point it is neutral. In

this case, the pH maintained is 6.5 and the isoelectric point of glycine is 8.

So, if the pH is less than the isoelectric point, then that will be present as a positively

charged species because, it has not crossed the isoelectric point 8. And, if the pH is

greater than the isoelectric point then the species will be present as the anionic form. So,

that means, the rule is that if this is the pH and this matches with the pI, then if you

maintain the pH on this side, the species will be positively charged. And, if you maintain

the pH on this side, the species will be negatively charged. So now, we have to decide

glutamic acid has a pI of 3, which is less than the pH that is maintained. That means, it is

on which side then? It migrates to the anode because its pI is 3, but the pH is much more

than that. So, it will be present as a negative form. So, the glutamic acid will move in this

direction and the glycine will move in the negative direction (towards the cathode).

Because, glycine’s pI is still not crossed (as the pH maintained is 6.5), until you cross the

pH 8 it will not be present as a negative species. So, below 8 it will be present as a

positively charged species.

So now, what will happen? If you apply the voltage for a certain amount of time? You

will see here that the alanine and glycine may move somewhere here and it all depends

on the molecular weight the glutamic acid will be somewhere here. So now, these three

spots (three amino acids) are separated. I hope the principle is clear the principle is that

if you have 3 amino acids, you take the pH same as the pI of the amino acid which is in

between the other two. Now, you can question me that if there are 4 amino acids what

Now, in that case also there is another point which is the mobility of these amino acids.

As I said that molecular weight is an important issue and there are also amino acids

which are having side chains that can form hydrogen bonds, which can form more

interactions with the gel. See when you have applied an electric field there is a

movement of the species because of the charge.

But, something is also dragging it behind. And, what is the dragging force? The dragging

force is the interaction of the molecule that means, in this case amino acids, it the

interaction with the matrix. If it is a paper based matrix, it will be a cellulose (it is a

carbohydrate which can have hydrogen bonded interactions with the amino acid side

chains). So, we can easily pick out that if there is such an amino acid, if the choice is

between glycine and serine, then serine will have a lower mobility than glycine under the

influence of the same applied field.

So, even if you have 4 amino acids, their mobility is equivalent to which is called Rf in

organic chemistry we also have Rf. That means the, the amount of movement related to

the solvent front in this case it is the differential mobility of the amino acids which can

allow you to separate the amino acids. We can do so by applying this simple isoelectric

point rule, ok. For 3 amino acids, you can take the middle one, but for 4 or 5, then you

have to do electrophoresis for a longer time to separate them one after another.

So, that is the use of isoelectric point. Now, how do I know, where is the amino acids in

this piece of paper because, the amino acids are not colored. So, although I have spotted

the amino acid here to start with and they have separated into three spots, but the

question is how do I know where are these spots? So that means, I should have

something to visualize these amino acids.

Now, amino acids unfortunately do not have fluorescence, which means if you shine

light, it will not fluoresce. But there are other chemical tests for which can have color for

the amino acid and that will show the location of the amino acid at a certain place.

So, what is that reaction? The reaction is a well-known reaction which might be already

known to you, ninhydrin test. What is this ninhydrin test? Ninhydrin is a reagent in

which if you add an amino acid and then you heat it, you get a bluish violet color. You

get a bluish violet color for all amino acids except proline. I will show you why proline

does not give positive ninhydrin test it gives some color, but which is different from

other amino acids. But, all the other amino acids give a bluish violet color with the

So now, after the electrophoresis I have bands here. These are the spots of the 3 amino

acids. How to know where are they? So, what I do is that I spray this ninhydrin reagent,

it is just ninhydrin acetone solution. I spray it and then I heat the paper with a hot air gun.

And, what I will see? I will see nothing, but that these spots will be somewhere bluish,

see it is a mixture of something like this bluish violet. Yes, I think you cannot make this

colored here, it is because it is a single color that it shows slightly violet color will be

there, something like this. So, this is the way you can visualize the amino acid.

Now, there are two questions that come from this. First of all if you treat this with

ninhydrin your amino acid is lost, you have basically separated them. But, all the amino

acids have reacted with ninhydrin and you cannot recover the amino acids. Secondly

why does amino acid give color with ninhydrin? What ninhydrin is basically? What type

of reagent it is? What is the structure?

Now, let us first answer one by one. First issue is that if I react all the spots with

ninhydrin then the amino acid is lost. This will only demonstrate that the mixture has 3

amino acids, that much I can tell, but I am not getting to recover the amino acids from

this. So, to bypass that what usually is done is that you take the same piece of paper, but

instead of giving a spot now you apply the solution as a band (like this) and then do the

So now, what will happen? Alanine will remain here I am taking the same example like

earlier. And, there will be glycine somewhere here and aspartic acid depending on their

mobility as I said. One more thing I should say that the mobility not only depends on the

molecular weight, but it also depends on how far away is your pH from the isoelectric

point. This is your starting point, this is the alanine, this is glycine and this is your

Remember if you take the single letter symbol glycine is G, alanine is A, aspartic acid is

D aspardic acid (that is the way you try to pronounce it) so, D so DAG. Now, what you

do in order to isolate back the amino acids, the best way to do is you cut a strip a small

strip from this end and you cut a small strip from this end. So now, I have two strips here

from these two ends and then I spray ninhydrin on both and then again heat with an air

gun. So, what I will see? I will see a band here maybe I will take this, I will see a band

here I will see a band here I will see a band here and the same thing I will see here.

Now what you do? You put those things again back to the place from where you have

taken them. So now, I will see a band here, a band there, a band there actually this is

kind of bluish violet, there is no well-defined color I cannot say that this is pink or this is

magenta. But some books say that it is bluish violet or bluish pink type color, so this is

Now, what you do? Once you reconstruct it, now take a piece of scissor and what do you

do? You cut this piece of paper from here cut this piece of paper from here, taking the

width of here ( the width of your band). And, then cut this piece of paper, just to make

sure that you are taking all the amino acids, you take an extra width over there little bit

extra portion you should cut. Now, it is very easy, you take this piece of paper, cut into

small pieces and put it in a flask add water, these amino acids are mostly soluble in

water and then filter and then from the filtrate if you evaporate the filtrate you get the

So, this is how the separation by paper electrophoresis is done. You cannot do it, if you

have gel here remember that it is not applicable for gel. For gel it is very difficult to cut

this and then again reconstruct it. For paper this is very useful you can use very big size

filter paper to do it even you can isolate milligram quantities of amino acids by using

separation technique via paper electrophoresis. So, you see the importance of isoelectric

So, the first question is solved that is how to detect the amino acid and how can you

isolate it back? So, that is done next is what is the reaction involved? What is ninhydrin?

Why does it give that bluish violet color?

Now, ninhydrin has a structure which is like this it is a trione, it is a benzene and a 5

membered ring with trione. However, from organic chemistry point of view, we know

that if a carbonyl is flanked between 2 electron withdrawing groups, thenthis can exist in

a form which is a geminal diol. So, it can exist as a geminal diol form (like chloral

hydrate there are several other examples like this). So, they are in equilibrium, diol with

this ninhydrin. Now, you are taking an amino acid. So, write the amino acid NH2 CO2H

Now, out of these 3 carbonyls the most reactive carbonyl has to be this middle one, as

this is the most electrophilic carbonyl carbon since this carbonyl is flanked between two

electron withdrawing carbonyls. So, the NH2 is going to react with this carbonyl. So, if

that reacts then what is the reaction between a carbonyl and a primary amine? It forms

the imine. NH2 lone pair of electrons first attack the carbonyl, that goes there, oxygen

takes the hydrogen. And, then in the next step, nitrogen lone pair again flies back to

eliminate water and it give this form which is called an imine.

I am just skipping that step . However, this is not very stable this is a very basic concept

from organic chemistry, that if you have a carboxy group COOH then a carbon and then

one electron withdrawing group like a carbonyl, then what happens? It loses carbon

dioxide very easily, that is called the decarboxylation of β keto acid. So, β keto acids are

very unstable, they lose carbon dioxide. That is why when we buy acetoacetic acid, we

buy it as the ester form (as CO2Et), in order to block the release of the carbon dioxide.

Now, if you look at this structure, it is very similar, this is carboxy, then a carbon, here

there is a carbon, then nitrogen, then a double bond and then another electron

withdrawing group. So, this is even more facile for decarboxylation this structure is such

that it will facilitate the release of carbon dioxide, like the arrow that I am showing. So, it

loses carbon dioxide and as it loses carbon dioxide, this O minus is formed and then you

have this N. So, this is a very good way of decarboxylating amino acids.

Because, the carboxylation is not very easy usually you need a lot of energy to

decarboxylate a carboxylic acid. But, here ninhydrin is acting as an electron sink see

what is happening what is an electron sink? When electrons are going towards the

direction, that direction means there must be something like a black hole that it is trying

to absorb whatever is coming out. Here it is electrons electrons are going to this

direction that means, this is electron sink. So, what is being formed is an imine, like this,

ok. Then there is a hydrolysis of this imine, if there is hydrolysis, then what will happen?

Imines are not very stable unless they are aromatic (you must be knowing that). So, you

have this C double bond O, this is O minus and then that will become NH2 and you have

generated an aldehyde. What is the aldehyde R? The aldehyde R is the same R of the

amino acid, but now that has no function.

Now, what will happen? This amine that has been generated, reacts with another

molecule of ninhydrin, because this amine is now a new nucleophile that is formed. So,

this new nucleophilic amine is going to attack this carbon followed by expulsion of water

to form this highly conjugated molecules..

So, that will be lost and this will form double bond N, that means water is gone and then

double bond N and then you have the other ninhydrin system ok. This is the other

ninhydrin system now you have O minus here, you have this double bond.

So, this is the product which is the colored compound. Why it is colored? Because, you

see the extensive conjugation here, this comes here, that goes there, that can go there,

that can come there or it can go there, that can go there. So, it is an extensively

So, that is why you see the color why do you see the same color for all amino acids?

Because, ultimately the color is not due to the R group, the color is basically due the

compound generated between the 2 ninhydrin molecules which has a nitrogen in-

between. This nitrogen actually is the nitrogen from the alpha amino acid.

So, that is why all amino acids give the same color except proline. Now, what happens

with proline? Let us see what happens during the ninhydrin reaction of proline. Now,

proline being a secondary amine, so we expect some difference and that is the only

secondary amine. And, indeed it gives a brownish yellow color with ninhydrin. Now,

why is that? Because the reaction stops at an intermediate step so, I will write the

reaction along with the mechanism.

So, the ninhydrin is here, it’s a tricarbonyl compound. And, we know that it also exists in

equilibrium with the geminal dihydroxy form because it is flanked between the 2

carbonyls. Now, proline has a structure which is represented by this in the correct

stereochemistry having CO2H in β-bond. But this reaction does not depend on the

stereochemistry, it gives the same color irrespective of whether you use L amino acid or

D amino acid which is an important point.

But, here we are showing either the D amino or the L amino acid. This is 1, that is 2 and

that is 3, hydrogen being α so, this is S configuration. Now, the first reaction is the same

like the earlier cases where primary amine is used. So, that becomes OH and the nitrogen

loses the hydrogen. So, then you have an intermediate which is like this: a carbonyl and

then you have OH, you have this carbonyl and that is linked to the proline nucleus. Now,

what will happen? This nitrogen will utilize its lone pair and remove the OH as water

while the OH minus takes up hydrogen from water.

And, in the process, unlike in the previous cases, here the nitrogen remains as a

positively charged species. So, you have a double bond with the nitrogen and it is an

iminium ion, now OH and this is plus. But, still there are two electron withdrawing

carbonyl groups and this is present at the β position. So, this double bond is under

tremendous stress being linked to positively charged nitrogen and also to electron

withdrawing carbonyl groups.

So, there is now scope for decarboxylation like the previous cases and you have this

relay process of electron shift. And, that gives you the decarboxylation of proline. So

now, the proline has just become a pyrrolidine (a 5 membered heterocyclic nitrogen

containing ring is what is called a pyrrolidine).

So, it becomes a pyrrolidine and you have an intermediate like this, O minus and this is

N and that is the pyrrolidine. So, you see the stereochemistry does not matter because all

these ninhydrin reactions are associated with liberation of carbon dioxide, destroying the

stereochemistry of this stereogenic center.

So now, there is a double bond here and this is the one. So, that becomes a pyrrolidinium

ion and this is brownish yellow in color that I was talking about because this has got a

resonance structure like this. So, because of this resonance, you get a color because

extended conjugation is the main cause of giving coloration and this is the hydrogen.

But, the nitrogen remains positively charged and this is brownish yellow colored.

So, proline behaves little bit differently because it is not able to produce the NH2

attached to the ninhydrin. Earlier primary amine replaces the carbonyl with NH2 and that

reacts with another ninhydrin. But, in this case, only 1 ninhydrin is associated with the

pyrrolidinium ion and this pyrrolidinium ion is coming from the decarboxylation of the

proline ring, so this is brownish yellow. So, that is the situation with ninhydrin reaction

remember all amino acids except proline give a blue violet color.

The color is same for all amino acids except proline this is because the R group is not

there in the final conjugated product since decarboxylation leads to liberation of primary

amine along with the release of R as aldehyde. And so, it is the amine ultimately that

forms the color while it reacts with another ninhydrin.

So, the color is independent of the nature of the side chain. In case of proline, we have a

slight difference because, it cannot liberate the primary amine and so, it stays in these

two resonating forms, so that becomes the brownish yellow. So, that ends up the

detection issues of amino acids. Next, we will go to the synthesis of peptides, how these

building blocks are added one after another.

We know that amino acids (AA1 and AA2) combine that means, 2 amino acids are

combining and that gives what is known as a peptide, ok. Let us give some example

suppose glycine and say alanine (NH2, CO2H and there is a CH3). Now, they react with

each other and form a new bond that is between the carboxy of the glycine and the NH2

(amine) of the alanine and you get what is called a dipeptide. Now, the point to notice

here that this is a very facile reaction it is so because there is elimination of water. A

stable molecule of water that is being eliminated, so that means, its ΔG is highly

So, it should be a spontaneous process however, it is not a spontaneous reaction.

Thermodynamics says that this reaction should go, but as you know that ΔG negative

does not immediately say that the reaction will take place under ordinary conditions. To

have this reaction taking place in a test tube, you have to provide what is called

activation energy. We have to do something to ensure that the activation energy is

lowered and the reaction takes place. So, this amide bond is very stable and this reaction

is very thermodynamically favored because of the loss of water.

However, the reaction does not take place if you mix an amine and an acid because of

high activation barrier. The other point to note is that that this is alanine and this is

glycine they can react in two ways. One that they can form a peptide which is G A, now

we should start writing the symbol G A glycyl alanine or that could be the other way

around, i.e. the alanine carboxy reacting with the glycine amine . Then you get what is

called alanyl glycine, this is glycyl alanine, this is alanyl glycine.

So, 2 amino acids can react to make two different peptides, they are not same. In this

case there is this amine functionality which is free and the carboxy which is free at this

end. And, in this case, the glycine carboxy is free and the amine is free on this other side.

Now, when you write a peptide, then the traditional way of writing is that on the left side

(which is the amino acid), the amine is free and on the right side (the extreme right side)

That means if I have an amino acid suppose X, Y, Z, P, Q (whatever just an arbitrary

sequence), if I write this suppose these are all amino acids, then immediately it says that

this R has carboxy free and this has got NH2 free. And, this will be called N terminus

amino acid and the other will be called the C terminus amino acid C terminus or the

carboxy terminus. And, the other important point (although this is very simple), is that a

dipeptide has a notion, sometimes the students get confused.

Dipeptide means there should be 2 peptide bonds, tripeptide there should be 3 peptides

bonds that is not the case. Dipeptide means a peptide containing 2 amino acids, it is not

the number of peptide bonds. The number of peptide bonds is always less by 1 than the

number of amino acids that are reacting. Of course, there is one intricate point here that

we are only talking about linear peptides, we are not talking about any cyclic systems.

Because, the whole scenario will change, if I start making cyclic peptides which are also

We are only talking about linear peptides so, if there are linear peptides then what

happens? The number of peptide bonds will be one less than the number of amino acids

that are reacting. So now, there are two things which are very important one is how to

synthesize peptides, how do I do this reaction? I said this is thermodynamically favored

but kinetically disfavored. This has got very high activation barrier. So, one issue is the

synthesis, and we are talking about chemical synthesis. We are not talking about

biosynthesis because if you think of the biosynthesis that means, how proteins are made

At that time activation comes from somewhere else because, in the body you cannot

provide high energy, high temperature, high thermal condition that is not possible. So,

by synthesis, we mean chemical synthesis. The other point is that if I have a peptide, then

how do I know the sequence of the different amino acids. So, these are the two

challenges, that the protein chemist faced firstly how to know the sequence of amino

acids? By the way that is what is called the primary structure of proteins and the other

point is how to synthesize proteins as per a design?. The next lecture we will tell about


Discussion

In this study, we generated temporal and spatial genome-wide single-base resolution DNA methylome maps and transcriptome profiles of pineapple leaf tissues, which greatly enhance and complement previous knowledge of CAM pathway studies. Comparing the methylation levels in photosynthetic green tip and non-photosynthetic white base leaf tissues revealed no significant global differences in CG and CHG methylation, but CHH methylation was significantly reduced in white base leaf tissue compared with green tip leaf tissue across different diel time course. In addition to the large number of DMRs located in the intergenic regions, we found that many DMRs were overlapped with gene body and flanking regions. Previous studies have shown that promoter methylation is often associated with downstream gene repression, but the role of gene body DNA methylation is still controversy. However, recent study have shown that gene body DNA methylation can alter gene expression [40]. There are obvious dynamic local DNA methylation changes during diel time course of green tip or white base of pineapple leaf. We hypothesized that dynamic DNA methylation changes in green tip or white base leaf tissue should be related to the CAM-related pathway. Through sampling diel DNA methylation patterns in both green and white pineapple at different diel time course, we were able to identified differential methylation changes related to the CAM photosynthetic pathway. By combining DNA methylation data and transcriptome data, we could identified a large number of DMR-associated DEGs, which are often enriched in several important biological pathways of CAM cycle, such as photosynthesis, lighting harvest, carbohydrate metabolism, transporter and protein phosphorylation. There are three CA gene families annotated in pineapple genome (alpha-CA, beta-CA, and gamma-CA), but only beta-CAs were expressed highly in green tip, and showed diel expression patterns [6]. We found all three beta-CAs in pineapple genome showed different expression between green tip and white base tissues, and were associated with many DMRs, especially CHH contexts.

Many of the presently available research on CAM pathways has focused on evolutionary and transcriptome analyses of CAM pathway-related genes [7, 11]. In Guzmania monostachia, people found that the up-regulated genes of the leaf tip are mainly enriched in the regulation of stomatal movement, tryptophan metabolic process, chlorophyll biosynthetic process, and aspartate metabolic process. However, the up-regulated genes of leaf base are mainly related to response to water deprivation, starch, and sucrose metabolic processes [41]. These results are consistent with our findings, indicating that core CAM-related genes and steps between inducible and constitutive CAM plants are similar.

Bromeliad leaves are described as showing a morpho-physiological gradient from the apex to the base [42,43,44]. In addition, previous studies of leaf segment RNA-seq data in pineapple, rice, and maize all suggest that fructose transporter (SWEET17) plays an essential role in exporting fructose from leaf sheath. They proposed that leaf tip and base within the same pineapple leaf play the role of sink and source cycle [11]. In our transcriptome data, we found that many genes involved in sucrose transporter, hexose transporter, glycolysis and gluconeogenesis were also associated with different methylation divergence, such as Aco005379.1, Aco023036.1, Aco005368.1 and Aco024987.1. We believed that DNA methylation plays a critical role of sink and source cycles daily between leaf tip and base by regulating the expression level of these transporters encoding genes. Gene duplication and expansion was initially proposed to be the driven force for the evolution of the CAM pathway [45]. However, it is still controversial, many studies have shown that CAM pathway should be evolved by differential expression of CAM-related genes or neofunctionalization rather than gene dosage [7, 10, 11].


Biochem Final Review

Correct the size difference indicates that there are four monomers of the same size, making this protein a tetramer.

Protein Molecular Weight (Daltons) Isoelectric point (pI)
YPOI 35,000 7.9
Contaminant 1 32,000 6.0
Contaminant 2 89,500 5.9
Contaminant 3 110,000 8.9

You have three types of column chromatography resins available to separate YPOI from the three contaminants.
- G100: Gel filtration resin (with a fractionation range of 20,000 to 100,000 daltons)
- DEAE cellulose: a positively charged ion exchange resin
- CMC cellulose: a negatively charged ion exchange resin

Your proteins must be in a buffered solution at pH 7.0 to ensure that YPOI remains stable.

All three of these statements are true:

Saquinavir and indinavir do both have a component that mimics the natural Phe-Pro dipeptide substrate of aspartate protease. This dipeptide mimic component is essential to the function of these inhibitors as competitive inhibitors to the enzyme aspartate protease.

Very high local concentrations of proteins with Phe-Pro or Tyr-Pro peptide bonds would reduce the effectiveness of saquinavir and indinavir in limiting HIV's infectivity of new cells because increasing high substrate concentration (proteins with Phe-Pro or Tyr-Pro peptide bonds) can outcompete a competitive inhibitor for binding at an enzyme's active site. Saquinavir and indinavir are competitive inhibitors.


Results

To study the thermodynamic behavior of the chains described in the previous section, we use the method of simulated tempering. This means that we first select a set of allowed temperatures and then perform simulations in which the temperature is a dynamical variable. This is done to speed up low-temperature simulations. In addition, it provides a convenient method for calculating free energies.

An example of a simulated-tempering run is given in Fig. 3, which shows the Monte Carlo evolution of the energy E and radius of gyration Rg (calculated over all backbone atoms) in a simulation of the three-helix sequence. Also shown (Fig. 3 Bottom) is how the system jumps between the different temperatures. Two distinct types of behavior can be seen. In one case, E is high, fluctuations in size are large, and the temperatures visited are high. In the other case, E is low, the size is small and almost frozen, and the temperatures visited are low. Interesting to note is that there is one temperature, the next-lowest one, which is visited in both cases. Apparently, both types of behavior are possible at this temperature.

Monte Carlo evolution of the energy and radius of gyration in a typical simulation of the three-helix sequence. Bottom shows how the system jumps between the allowed temperatures Tj, which are given by Tj = Tmin (Tmax/Tmin) (j−1)/(J−1) (34) with kTmin = 0.625, kTmax = 0.9, and J = 8. The temperature Tmin is chosen to lie just below the collapse transition, whereas Tmax is well into the coil phase (see Fig. 4).

In Fig. 4a, we show the specific heat as a function of temperature for the one-, two-, and three-helix sequences. A pronounced peak can be seen that gets stronger with increasing chain length. In fact, the increase in height is not inconsistent with a linear dependence on chain length, which is what one would have expected if it had been a conventional first-order phase transition with a latent heat.

Thermodynamic functions against temperature for the sequences 1H (◊), 2H (×), and 3H (+) in Table 3. (a) Specific heat Cv = (〈E 2 〉 − 〈E〉 2 )/NkT 2 , N being the number of amino acids. (b) Hydrogen-bond energy per amino acid, Ehb/N. (c) Chain entropy per amino acid, δS/N = [SS(kT = 0.9)]/N. The full lines in a represent single-histogram extrapolations (35). Dotted lines are drawn to guide the eye.

Our results for the radius of gyration (not displayed) show that the specific heat maximum can be viewed as the collapse temperature. The specific heat maximum is also where hydrogen-bond formation occurs, as can be seen from Fig. 4b. Important to note in this figure is that the decrease in hydrogen-bond energy per amino acid with decreasing temperature is most rapid for the three-helix sequence, which implies that, compared to the shorter ones, this sequence forms more stable secondary structure. The results for the chain entropy shown in Fig. 4c provide further support for this the entropy loss per amino acid with decreasing temperature is largest for the three-helix sequence.

It should be stressed that the character of the collapse transition depends strongly on the relative strength of the hydrogen-bond and hydrophobicity terms. Fig. 4 shows that the transition is very abrupt or “first-order-like” for our choice (ɛhb, ɛAA) = (2.8, 2.2). A fairly small decrease of ɛhbAA is sufficient to get a very different behavior with, for example, a much weaker peak in the specific heat. In this case, the chain collapses to a molten globule without specific structure rather than to a three-helix bundle. A substantially weakened transition was observed for ɛhb = ɛAA = 2.5. If, on the other hand, ɛhbAA is too large, then it is evident that the chain will form one long helix instead of a helical bundle.

We now turn to the three-dimensional structure of the three-helix sequence in the collapsed phase. It turns out that it does form a three-helix bundle. This bundle can have two distinct topologies: if we let the first two helices form a U, then the third helix can be either in front of or behind that U. The model is, not unexpectedly, unable to discriminate between these two possibilities. To characterize low-temperature conformations, we therefore determined two representative structures, one for each topology, which, following ref. 18, are referred to as FU and BU, respectively. These structures are shown in Fig. 5. They were generated by quenching a large number of low-T structures to zero temperature, and we feel convinced that they provide good approximations of the energy minima for the respective topologies. Given an arbitrary conformation, we then measure the root-mean-square distances δi (i = FU, BU) to these two structures (calculated over all backbone atoms). These distances are converted into similarity parameters Qi by using 8 At temperatures above the specific heat maximum, both Qi tend to be small. At temperatures below this point, the system is found to spend most of its time close to one or the other of the representative structures either QFU or QBU is close to 1. Finally, at the peak, all three of these regions in the QFU, QBU plane are populated, as can be seen from Fig. 6a. In particular, this implies that the folding transition coincides with the specific heat maximum.

Representative low-temperature structures, FU and BU, respectively. Drawn with rasmol (36).

(a) QFU, QBU (see Eq. 8) scatter plot at the specific heat maximum (kT = 0.658). (b) Free energy F(Q) as a function of Q = max(QFU, QBU) at the same temperature.

The folding transition can be described in terms of a single “order parameter” by taking Q = max(QFU, QBU) as a measure of nativeness. Correspondingly, we put δ = min(δFU, δBU). In Fig. 6b, we show the free-energy profile F(Q) at the folding temperature. The free energy has a relatively sharp minimum at Q ≈ 0.9, corresponding to δ ≈ 3 Å. This is followed by a weak barrier around Q = 0.7, corresponding to δ ≈ 6 Å. Finally, there is a broad minimum at small Q, where Q = 0.2 corresponds to δ ≈ 13 Å.

What does the nonnative population at the folding temperature correspond to in terms of Rg and Ehb? This can be seen from the Q, Rg, and Q, Ehb scatter plots in Fig. 7. These plots show that the low-Q minimum of F(Q) corresponds to expanded structures with a varying but not high secondary-structure content. Although a detailed kinetic study is beyond the scope of this paper, we furthermore note that the free-energy surfaces corresponding to the distributions in Fig. 7 are relatively smooth. Consistent with that, we found that standard fixed-temperature Monte Carlo simulations were able to reach the native state, starting from random coils.

Let us finally mention that we also performed simulations of some random sequences with the same length and composition as the three-helix sequence. The random sequences did not form stable structures and collapsed more slowly with decreasing temperature than the designed three-helix sequence.


Introduction

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), responsible for the ongoing global pandemic, causes a respiratory infection found potentially fatal among elderly and immune-compromised patients 1 , 2 . SARS-CoV-2 is a double-stranded, positive-sense RNA virus that belongs to the Coronaviridae family. The Coronavirus (CoV) genome frequently undergoes recombination and can produce novel strains with variations in virulence 3 . There are seven strains of human coronaviruses (HCoV), namely, HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1, Middle East respiratory syndrome (MERS)-CoV, severe acute respiratory syndrome (SARS-CoV), and the 2019 novel coronavirus (nCoV) or SARS-CoV-2 4 – 6 . The SARS-CoV and the MERS-CoV have been responsible for large-scale epidemics in 2003 and 2012, respectively 3 . As the SARS-CoV-2 pandemic engulfs millions around the world, there is a struggle to find an effective vaccine or drug against the virus. While several drugs, including oseltamivir, lopinavir, ritonavir, arbidol, and chloroquine, have been tried with limited success, the search for effective therapy is still underway 7 – 11 .

Due to the pivotal role that helicases play in the viral life cycle, they represent an attractive target for antiviral therapy. To separate nucleic acid strands, energy derived from ATP hydrolysis is utilized by helicases, nucleic acid unwinding motor proteins. This process is crucial for genome replication 12 , transcription of viral mRNAs, translation, disruption of RNA–protein complexes, and packaging of nucleic acids into virions 12 . Depending on whether they can bind single-stranded (ss) nucleic acid, unwind double-stranded (ds) RNA or dsDNA or both, the polarity of the unwinding (5′ to 3′ or 3′ to 5′), and whether specific signature motifs are present in their primary sequence, helicases are classified into six superfamilies (SF1–SF6) 13 . Helicases belonging to SF1 and SF2 generally act as monomers or dimers on DNA or RNA substrates, whereas most of the SF3–SF6 helicases form ring-shaped hexameric structures that encircle the nucleic acid and have roles mainly in DNA replication 14 , 15 . SARS-CoV-2 helicase enzyme is a member of the SF1 that prefers ATP, dATP, and dCTP as substrates, while hydrolyzing other NTPs as well 12 , 16 .

Several viral helicases have been used as targets in animal models of herpes simplex (HSV) and hepatitis C (HCV) viruses 17 , 18 . The importance of helicase validity as antiviral drug targets was recently corroborated when compounds that inhibit an HSV helicase were shown to block viral replication and disease progression in animal models 19 . Similarly, much effort has been directed towards developing small-molecule inhibitors and chemicals as drug candidates to inhibit the function of SARS-CoV-1 helicase nsP13 (SCV nsP13) 17 , 20 . Unlike the Spike protein that is the key target for antibody-based therapeutics, the nsp13 helicase protein of SARS-CoV-2, perhaps owing to its pivotal role in the virus life cycle, is quite conserved among the human coronavirus family 21 . The conservation and functional importance of helicase makes it an ideal target for antiviral drugs.

Here, using in silico approaches, including homology modeling, molecular docking, and molecular dynamic simulations, we found a panel of 12 drugs that show strong interactions/affinity with SARS-CoV-2 helicase amino acids. The amino acids targeted by the drugs are highly conserved and appear to be crucial for helicase function, indicating that the drugs will be potent against SARS-CoV-2 and that the virus is unlikely to develop resistance mutations against these drugs. Since these drugs are currently used for antiviral and chemotherapeutic purposes, they can be repurposed to treat SARS-CoV-2 without an extensive drug safety profiling process. This will especially benefit regions without high-level biosafety facilities for testing viral drugs, will and provide a timely solution for SARS-CoV-2 therapy 22 .


Author summary

In these early stages of the COVID-19 pandemic it is urgent to understand all features determining the new virus expansion. Two significant factors conditioning infection are ACE2-mediated SARS-CoV-2 cellular entry and viral proteome translation efficiency. Genomic variability across species, including humans, results in ACE2 variants that destabilize its fold, modify ACE2/SARS-CoV-2 recognition, or both. We also point out the importance of considering waters at the interface of protein-protein interactions when performing in silico mutagenesis.

Citation: Delgado Blanco J, Hernandez-Alias X, Cianferoni D, Serrano L (2020) In silico mutagenesis of human ACE2 with S protein and translational efficiency explain SARS-CoV-2 infectivity in different species. PLoS Comput Biol 16(12): e1008450. https://doi.org/10.1371/journal.pcbi.1008450

Editor: Rachel Kolodny, University of Haifa, ISRAEL

Received: April 23, 2020 Accepted: October 19, 2020 Published: December 7, 2020

Copyright: © 2020 Delgado Blanco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The software used in this study is available at http://foldxsuite.crg.es and https://github.com/hexavier/SARSCoV2_species. The authors declare that the data supporting the findings of this study are available within the paper and its Supporting Information files.

Funding: We acknowledge the support of the Centre for Genomic Regulation (CRG) Technology & Business Development Office (TBDO) for support with licensing information, the CRG Tecnologías de Información y Comunicación (TIC) for assistance with web hosting, and the Scientific Information Technologies (SIT) for distributed computing, the Spanish Ministry of Science and Innovation (MICINN), ‘Centro de Excelencia Severo Ochoa’, the CERCA Programme/Generalitat de Catalunya, the Spanish Ministry of Science and Innovation (MICINN) to the EMBL partnership. The project that gave rise to these results was supported by a fellowship from “la Caixa” Foundation (ID 100010434 fellowship code LCF/BQ/DI19/11730061). The work of X.H. has been supported by a PhD fellowship from the Fundación Ramón Areces.

Competing interests: The authors have declared that no competing interests exist.


Should there be separate Ramachandran plots for an amino acid in different contexts? - Biology

a Department of Inorganic & Physical Chemistry, Indian Institute of Science, Bangalore, India-560012
E-mail: [email protected]

b Defence Bioengineering & Electromedical Laboratory, DRDO, C V Raman Nagar, Bangalore, India-560093

c Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India-560012

d Department of Instrumentation & Applied Physics, Indian Institute of Science, Bangalore, India-560012

Abstract

Raman spectroscopy has become an essential tool for chemists, physicists, biologists and materials scientists. In this article, we present the challenges in unravelling the molecule-specific Raman spectral signatures of different biomolecules like proteins, nucleic acids, lipids and carbohydrates based on the review of our work and the current trends in these areas. We also show how Raman spectroscopy can be used to probe the secondary and tertiary structural changes occurring during thermal denaturation of protein and lysozyme as well as more complex biological systems like bacteria. Complex biological systems like tissues, cells, blood serum etc. are also made up of such biomolecules. Using mice liver and blood serum, it is shown that different tissues yield their unique signature Raman spectra, owing to a difference in the relative composition of the biomolecules. Additionally, recent progress in Raman spectroscopy for diagnosing a multitude of diseases ranging from cancer to infection is also presented. The second part of this article focuses on applications of Raman spectroscopy to materials. As a first example, Raman spectroscopy of a melt cast explosives formulation was carried out to monitor the changes in the peaks which indicates the potential of this technique for remote process monitoring. The second example presents various modern methods of Raman spectroscopy such as spatially offset Raman spectroscopy (SORS), reflection, transmission and universal multiple angle Raman spectroscopy (UMARS) to study layered materials. Studies on chemicals/layered materials hidden in non-metallic containers using the above variants are presented. Using suitable examples, it is shown how a specific excitation or collection geometry can yield different information about the location of materials. Additionally, it is shown that UMARS imaging can also be used as an effective tool to obtain layer specific information of materials located at depths beyond a few centimeters.