Acetyl-CoA

Protein is all you need
Enzymatic Imagination real as reality
Like dust to dust,
Even proteins give-up the ghost to Amino acids
Likewise polysaccharides to mono
Lipids to glycerol & fatty acids.
I am a biochemist, are you…?

Do you feel the energy-
Our polymers are dead!!!
Chaperoned by Acetyl-CoA
Sailing them south; I think “TCA”
And then electron transport chain
Death is so stingy & greedy
Making ATP from such tragedy.

Do you feel accomplished?
You can’t resist it
CO2 is released in catabolism
Great metabolites
Great Acetyl-CoA
The reverse could also be the case!!
Anabolism.

Expression Eruption: Biochemistry II

http://wp.me/pQaGb-Ms

the struggle

Anytime Success Becomes 100 Percent, I Hope I Will Never Forget These Difficult Times: For Its Not So Easy I Swear

My View Of Biochemistry; What is yours?

-Biochemistry a distraction like incantation to a diabolical magic ward; searching so deep, making it seem uneasy, while the main magic is seen as a manifestation of either Growth or Retardation- ᴼᶰʸᵉᶜhᴱ ᵛᴵᶰᶜᵉᶰᵗ ᴼᶰᵞᵉᵏᵃ

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Δ

Enzyme Inhibition

There are 3 different mechanisms of enzyme inhibitions; irreversible, competitive and noncompetitive.

Agents that binds covalently to enzymes and disrupt their function are irreversible inhibitors. A few do bind non-covalently and they are highly toxic.

 

Competitive inhibition: Compete with the substrate for the active site by binding reversibly with non covalent bond at the active site.

They block the substrate from binding to the active site, and forming the ES complex. The Comparative inhibitor will increase the Km but Vmax doesn’t change. The Inhibition can be overcome by adding more of the substrate.

 Non-competitive inhibitors: binds non covalently to a site other than the active site and change the conformation of the enzyme. They do not prevent the substrate from binding to the Enzyme.

unlike competitive inhibitors they cannot be overcome by increasing the amount of substrate.

The non covalent inhibitor will also lower the Vmax due to the inability of the reaction to proceed as efficiently. They do not lower the enzyme affinity for the substrate. so the Km remains the same.

 

Uncompetitive; they bind only to the enzyme-substrate  complex to change the conformation of the enzyme. in uncompetitive inhibition, the Vmax  decreases and the Km decreases due to the availability of the active complex.

 

Greenhouse Effect cons

The greenhouse effect is like the gods that give with the righthand and takes with the left: At first it makes plants grow nicely Then melts the ice cap, increase the sea level and course flooding at the coasts wiping away the plant. https://instituteofbch.wordpress.com

Biochemistry

Biochemistry

• Biochemistry
• Of which I study
• Results never encouraging
• Should I be bionic
• For you to accept wit

• Day and night
• Robber of an off day
• No rest
• My witty wit been dead beaten
• Oh … Biochemistry; It is not that am lazy

• Back in space remember
• Remember, my forefathers never studied
• Yet they knew the usefulness of flavones
• Including consequences of starvation
• And added vegetables to their dishes

• Genetics and traits
• Its bug is demonic
• Body a temple
• They had no concern with metabolism
• However, palm wine sharpened their vision

• Crops grew by the pathway
• They knew not of the simple-complex pathways
• Now I cram and draw structures
• Of sugary sugar, chlorophylls, fats, and proteins
• Who sent me down this pathway?

• Unto the last-minute on the pathway
• You can never be the last-ditch
• You can never be my last wish
• We shall split wit like the last slice
• Biochemistry the brainteaser

• It is not that am lazy
• Results never encouraging
• Metabolism they understood not. But energy.
• Glycolysis big grammar, lipolysis a misery
• Oh … Biochemistry; it is not that am lazy.

Protein Folding Prediction: Secondary Structure

Protein Folding Prediction

Secondary Structure

Presented By Vincent

 

 

 

Table of Contents

Protein Folding Prediction: Secondary Structure. 2

Introduction. 2

Steps Involved in Prediction. 2

Prediction in 1D involves. 2

Prediction in 2D involves. 3

Prediction in 3D involves. 3

Prediction in 1D.. 3

Secondary structure prediction. 3

First generation SSP. 3

Second generation SSP. 4

Third generation SSP. 4

Prediction of solvent accessibility. 5

Prediction of Trans-membrane helices. 5

Prediction in 2D.. 5

Prediction of inter-residue and strand contacts. 5

Prediction in 3D.. 6

Summary. 6

 

Protein Folding Prediction: Secondary Structure.

 

Introduction

Proteins are building blocks of life. Proteins exhibit more sequence and chemical complexity than DNA or RNA. A protein sequence is a linear hetero polymer made up of one of the 20 different amino acids. They perform a wide variety of functions in the living organism: catalytic, structural, regulatory, differentiation, replication and signaling roles required for the cellular development. The key to the wide variety of functions exhibited by the individual proteins is not its linear sequence but its three dimensional structure.

The 3D structure of proteins can be studied either by experimental methods or structure prediction.

Protein structure prediction is not as easy as it sounds. There are a number of facts that exist that make structure prediction a difficult task. These are:

• A protein could fold in several ways to attain the native state.

• The physical basis of protein structural stability is not fully understood.

• The primary sequence may not fully specify the tertiary structure.

There are proteins called chaperones that induce the protein to fold in specific ways

Steps Involved in Prediction

Although there are many methods and algorithms to predict the structure, the general steps involved can be summarized as follows:

Prediction in 1D involves

• Prediction of secondary structure (SSP)

• Prediction of solvent accessibility

• Prediction of trans-membrane helices

Prediction in 2D involves

• Prediction of the inter-residue and strand contacts

Prediction in 3D involves

• Searching the database to find a suitable template for modeling.

 

Prediction in 1D

Secondary structure prediction

The secondary structure of a protein has three regular forms, α-helix, β-sheet and loop or turns. SSP involves predicting the secondary structure state for each amino acid residue. The most widely used accuracy index for SSP is the 3state accuracy which gives the percentage of the correctly predicted residues in any of the three states.

Q = (P_α + P_β + P_loop)/T x 100

Where T is the total number of residues, P_α is the number of residues predicted correctly to be in alpha helix, P_β is the number of residues predicted correctly to be in beta sheet and P_loop is the number of residues predicted correctly to be in loops or turns.

The quality of the prediction is assessed by the number of segments in a protein, the average segment length and the distribution of the number of segments with the length.

First generation SSP

Most of the methods in this generation were based on single residue statistics. In the Chou-Fasman method developed in 1974, the residues were aligned according to their ability to form or break a secondary structure. He identified an α-helix by locating a clusters within 6 residues which was extended in both directions until terminated by a tetra-peptide with an average α propensity of less than 1.

For β-sheet, he looked in a cluster that had 3 out of 5 residues and then extended in both directions. Turns were predicted in a window of 4 residues first with an overall score that is significantly greater than that for helix and then by a position specific score for each of the 4 residues in reverse turn.

The GOR algorithm (Garnier, Osuguthorpe, and Robson) was developed in 1978 to improve upon the Chou-Fasman method. The GOR method not only took the relative occurrence of residue in a particular element of the structure but also took the accuracy of the data into consideration. The method first analyzed the protein of a known structure based on the query. It then considered the effect that a residue has on the secondary structure of another residue say ‘n’ residues from it. This gave the likelihood of a residue and its neighbors being in particular secondary structure.

Second generation SSP

These methods depended on sequence structure relationship and modeled using algorithms based on statistical information, physio-chemical properties, sequence patterns, multilayered neural networks, graph theory, multivariate statistics and nearest neighbor algorithm. The neural network based algorithm by Qian and Sejnowksi predicted the α-helix, a β-sheet of 15 test proteins.

The first generation method gave an accuracy of 50-60% and the second generation methods gave an improved accuracy of about 70%, this method had some drawbacks. The secondary structures differ even between crystals of the same protein. Moreover the long range interaction plays a role in secondary structure formation.

Third generation SSP

Superior in terms of accuracy (76%) and also dealing with the drawbacks of the other two generation is the third generation SSP.  Developed by Rost and Sander in 1993 it is composed of several cascading neural networks. In it, aligned homologous sequences of known structures are used to “train” the network, which then can be used to predict the secondary structure of the aligned sequences of the unknown protein. The homologous sequences are determined by BLAST and are aligned using MaxHom.

In this case, the network will be trained not to predict unreasonably short segments of secondary structure. Another step consists of averaging the output from independently trained network. Some of the best secondary structure prediction programs are PHD with an approximate 72% accuracy, Jpred with about 73-75% accuracy, PREDATOR with about 75% accuracy, Sam T99 with about 74% accuracy.

One of the difficulties in predicting secondary structures at high accuracy is the presence of non-local contacts in protein folding. This is because amino acids which are quite distant in the primary sequence may be close to each other in the 3D structure as the protein folds. Bayesian network which is based on parameterization of the sequence structure relationship in terms of structural segments can be used for predicting secondary structures.

Prediction of solvent accessibility

Usually non-polar amino acids tend to be buried inside the protein and the polar amino acids are in contact with the solvent. Although residue solvent accessibility is not as well conserved within a structural family as secondary structure, prediction can be improved by including evolutionary information. A neural network prediction of accessibility has been shown to be superior to simple hydrophobicity analyses. Prediction of solvent accessibility has been used successfully in prediction based threading as well.

The average accuracy of predicting the solvent accessibility is around 70-75%

 

Prediction of Trans-membrane helices

There are two main classes of membrane protein

• 17 to 27 residue forming transmembrane helices that spans the membrane
• 16 strand beta barrel fold that forms a pore through the membrane.

 

Prediction in 2D

Prediction of inter-residue and strand contacts

The NMR spectroscopy produces experimental data of distances between the protons. Using these distances, the 3D structure can be reconstructed using distance geometry or molecular dynamics. Hence if the secondary structure can be predicted successfully, some fraction (helices and strands which can be assigned based on hydrogen bonding pattern) of the contacts is known and its 3D structure can be determined by distance geometry. But the contacts predicted by secondary structure are short range contacts. For application of distance geometry, contacts between residues far apart in sequence should also be considered. One of the methods to predict such long range inter-residue contacts is by analyzing correlated mutations.

 

Prediction in 3D

The tertiary structure of proteins involves the folding of the secondary structural. The physical properties that determine fold are the backbone rigidity, interaction between the amino acids which include the electrostatic interaction, the vander-waals interaction, hydrogen and disulphide bonds and interaction with water.

There are three methods for protein structure prediction namely:

1. homology modeling
2. Fold recognition or threading and
3. Ab-initio method.

All these methods involve searching the database for a homologue to the target protein.

Summary

Proteins are formed by polymerization of amino acid molecules to provide a primary structure which further folds to form a helix (secondary structure). Protein secondary structure prediction remains an important step on the way to full tertiary structure prediction in computational biology. Predicting the structure of a protein is a difficult task. Different approaches to predict the structure take into account different chemical and physical properties. This has given rise to a number of tools and techniques, some of which being specialized to work on either some aspects of predictions or some categories of proteins. Nevertheless these are not significantly accurate or reliable enough to predict all kinds of proteins..

END

Secondary Structure Of Proteins

Protein Folding Prediction

Secondary Structure

Onyeche Vincent Onyeka

 

 

Table of Contents

Secondary Structure of Protein. 1

Introduction. 1

Secondary Structure Of protein. 2

The β sheet. 2

The helices. 3

The α-helix. 3

 

Secondary Structure of Protein

By Onyeche Vincent Onyeka

Introduction

There are four structures of proteins, the primary, secondary, tertiary and quaternary. The primary is made up of sequence of amino acids; the tertiary is a side chain packing in 3D structure containing monomers of amino acids while the quaternary is an association of subunits held by a non-covalent bond but forms as a result of aggregation.

All proteins would have a primary structure, secondary and tertiary but not all proteins would have a quaternary structure.

Before we begin the secondary structure of protein, let’s have a recap on amino acids.  Amino acids are monomers of proteins, upon polymerization they form proteins. Amino acids are classified by their polarity, non-polarity, aromatic, aliphatic and charges. The nonpolar amino acids are hydrophobic in nature and they are found in clusters at the interior part of the protein. The polar amino acids are hydrophilic in nature and are found in the exterior part of the peptide on the solvent.

Classes of Proteins

Based on structures and solubility.

Proteins can be grouped into 3 classes; Fibrous, globular & membrane.

Non-polar aliphatic amino acid: Glycine, Alanine, Proline, Valine, Leucine, Isoleucine, Methionine,

Non-polar aromatic amino acid: Phenylalanine, Tyrosine, Tryptophan

Polar uncharged Amino acid: Serine, threonine, Cysteine, asparagine, Glutamine

Positive charge amino acid: lysine, Arginine, histidine

Negative Amino acid: Aspartate, Glutamate

Secondary Structure Of protein

A secondary structure is said to be formed when peptide chains form a helix shape in the course of attaining a native state. It is known to have to helices,sheet and a loop.

The β sheet

As a result of hydrogen bond between peptide chains, the βpleated sheet forms either a parallel or an antiparallel sheet.

The helices

There are two kinds of helices; left handed and right handed. The helices are of three regular structure kinds: 3₁₀Helice (I, i+3 H⁺ bonding pattern), α-helix and π helix (I, i+5 H⁺ bonding pattern).

The 3₁₀Helice contains three amino residues and ten turns

The α-helix contains four amino residues (3.6) and thirteen turns (most common)

The π helix contains six amino residues and sixteen turns

Helical wheel is used to predict the secondary structure of protein

Each amino acid residue makes a complete turn in 100◦. With this feature, the hydrophobic and hydrophilic ends of the amino acid residue can be predicted using a helical wheel. A PITCH is the height achieved by the polypeptide chain in one complete rotation.

The α-helix

Alpha helix is antiparallel in nature as it tends to counter the dipole bond. The α-helix is one of two structures (the other being the β sheet) predicted and discovered by Linus Pauling in 1951.

It is a right-handed helix with the following spatial paraments:

Φ = -57’

Ψ = -47’

N = 3.6 (number of residues per turn)

Pitch 0.5nm (or 5.4A)

Solving Protein Structures

 

Only 2 kinds of techniques allow one to get atomic resolution picture of macromolecules.

• X-ray crystallography (first applied in 1961 – Kendrew & Perutz)
• NMR spectroscopy (first applied in 1983 – Ernest & Wuthrich)

 

Knowing the structure of the protein helps in the following reasons;

 

 

 

 

The X-ray crystallography technique is difficult because it demands a protein crystal ball and that is the reason people go into protein structure prediction which requires amino acid sequence. For the sequence of amino acid proteins are easily found.

 The protein folding problems

Levinthal’s paradox – consider a 100residue protein. If each residue can take 3 positions, there is 3¹⁰⁰ = 5 × 10^e possible conformations.

If it takes 10^-13s to convert from 1 structure to another, xnhanitive search would take 1.6 × 10²⁷ years

The whole question problem about protein is given a particular sequence of amino acid residue (primary structure) what will the tertiary / quaternary structure of the resulting protein going to be?

The only way to answer such question is by first predicting the secondary structure.  This is achieved using a hydropathy plot.

B.           Hydropathy plots (sliding window approach)

• An hydropathy plot is a graphical display of the local hydrophobicity of amino acid side chain in a protein.
• A positive value indicate local hydrophobicity and a negative value suggest a water exposed region on the face of a protein
• Hydrophathy plots are generally most useful in predicting Trans membrane segments n-terminal secretion signal sequences.

 

 

Note:

In an α-helix the rotation is 100 degrees per amino acid.

The rise per amino acid is 1.5A Å (angstrom) in height during when span  transverse

To span a membrane of 30 Å (angstrom) approximately

 30/1.5 = 20amino acids are needed.

30 Å is the thickness of the membrane.

Next would be to plot the hydropathy plot.

First calculate property for first sub sequence:

i.e.; I, L, I, K, E, I, R, G, A:

Where the values are 4.5, 3.80, 4.5, (-3.9), (-3.5), 4.5, (-4.5), (-0.4), 1.8

Taking the first window of 7,

(4.5 + 3.80 + 4.5 + (-3.9) + (-3.5) +4.5 + (-4.5) + (-0.4) +1.8) = 5.4 /7 = 0.77

0.77 is a sign to the central residue.

K (#4) will have a hydrophathy index of 0.77

It then is repeated for the next slide

0.07 will be a sign to residue E (#5) and so on

Then a plot is made with the index on the y axis and amino acid sequence on the x axis.

The window size can be changed; a small window produces ‘noisier’ plots that more accurately reflect highly local hydrophobicity.

A window of 9 or 11 is generally optimal for recognizing the long hydrophobic stretches that typify trans-membrane stretches.

 

c.         Chou-Fasman Paraments

Three-state model

 

This is applied to determine the point where the helix is going to be. The Chou-fasman paraments tells us that the propensity of the amino acid to be in the helix, beta or loop.

Propensity: it is sort of a probability to see numbers greater than 100. The propensity values were gotten by Chou-Fasman using a statistical analysis on available structures (crystal structures solved for proteins are available in the Protein Data Bank (PDB)).

Propensity = {#Ala^α /#Res^α) / {#Ala ^database /#residues ^database}

 

 

 

The Chou -fasman algorithm

• Identify α helices

–          4 out of 6 contiguous amino acids have P_(a) > 100

–          Extend the region until 4 amino acids with P_(a) < 100 found

–          Complete ƸP_a and ƸP_b, if the region is >5residue and ƸP_a > ƸP_b, identify as a helix.

• Repeat for β sheet (use P_b)
• If an α and β region overlap, the overlapping region is predicted according to the sum of P_a & P_b (ƸPa and ƸPb)

After the determination of the helix, loop and beta sheet, a helical wheel can be constructed to determine the polarity (hydrophobic and hydrophilic part of the secondary protein structure).