Antimicrobial Peptide Features #

Introduction: #

Small peptides (~50 AA) synthesized at the post translational state of central dogma can act as antimicrobial peptides (AMP). Those peptides need to fish out from the wide array of the peptides. At present validate the peptides against to microbes is not a cost effective and manual evaluation is not possible for every sequenced genome. To overcome the problem computational methods such as QSAR and machine learning technique contributes much to identify the AMP candidates. Here I am only list out the significant AMP features which used for effective classification of AMP from the given pool of peptides. There is N number of data driven features used till now in the machine learning techniques.

Bactericidal Propensity #

AMPA tool is an antimicrobial sequence scanning system from the long protein which deploy from calculating the bacterial propensity index for each amino acid using the experimental data reported for the high-throughput screening assay. [ http://tcoffee.crg.cat/apps/ampa/do]. It use to predict the AMP spots from long protein Physiochemical properties General properties of each peptide, i.e. Length, size, residue composition, charge. Those can be easily calculated by pepstat [http://emboss.bioinformatics.nl/cgi-bin/emboss/pepstats].

Aliphatic index #

The aliphatic index of a protein is defined as the relative volume occupied by aliphatic side chains (alanine, valine, isoleucine, and leucine). It may be regarded as a positive factor for the increase of thermo-stability of globular proteins. The aliphatic index of a protein is calculated according to the following formula.

           Aliphatic index = X(Ala) + a * X(Val) + b * ( X(Ile) + X(Leu) )

where X(Ala), X(Val), X(Ile), and X(Leu) are mole percent (100 X mole fraction) of alanine, valine, isoleucine, and leucine. The coefficients of a and b are the relative volume of valine side chain (a = 2.9) and of Leu/Ile side chains (b = 3.9) to the side chain of alanine. (http://www.camp.bicnirrh.res.in/campHelp.php)

Instability index #

The instability index provides an estimate of the stability of your protein in a test tube. The authors of this method have assigned a weight value of instability to each of the 400 different dipeptides (DIWV). Using these weight values it is possible to compute an instability index (II) which is defined as:

                    i=L-1
   II = (10/L) x Sum { DIWV(x[i]x[i+1])}
                    i=1

Where: L is the length of sequence, DIWV(x[i] x [i+1]) is the instability weight value for the dipeptide starting in position i. A protein whose instability index is smaller than 40 is predicted as stable, a value above 40 predicts that the protein may be unstable. ( http://www.camp.bicnirrh.res.in/campHelp.php)

Hydrophobicity #

The Hydrophobicity of a peptide or protein is represented as Grand Average Hydrophobicity Value (GRAVY), calculated as the sum of hydropathy values of all the amino acids, divided by the number of residues in the sequence. Positive value of the score indicates hydrophobic and negative score indicates hydrophilic peptide. ( http://www.gravy-calculator.de/)

Secondary Structure Propensity #

AMP structures can be stable based on the secondary structure (SS) of the peptides. And based on the SS those were classified in to multiple families

Aggregation Propensity #

Structure folding stabilities on lipid bilayer

Invivo aggregation (http://bioinf.uab.es/aap/) #

In vivo aggregation can computed using AGGRESCAN, an algorithm based on an amino acid aggregation-propensity scale derived from in vivo experiments and on the assumption that short and specific sequence stretches modulate protein aggregation.

Invitro aggregation (http://tango.crg.es/about.jsp) #

In vitro aggregation and secondary structure prediction were calculated by using the TANGO software Tango calculates the partition function of the conformational phase space assuming that every segment on the protein populates one state: random coil, b-turn, a-helix, a-helix aggregation and b-sheet aggregation. Therefore, TANGO software can predict aggregation in solution, taking into account only structural parameters determined by the peptide sequence

Reference #

• https://en.wikipedia.org/wiki/Antimicrobial_peptides