rosaura parisi ppt progetto

CNJEWDUHFYGRYBJDSKLHJUGFHGJBVUEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJBVUEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJCEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJBVUCEDWHFYUGWIEGFIUWHEIGEOHMNVBICNJEWDUHFYGRYBJDSKLHJUGFHGJBVUEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJBVUEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJCEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJBVUCEDWHFYUGWIEGFIUWHEIGEOHMNVBICNJEWDUHFYGRYBJDSKLHJUGFHGJBVUEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJBVUEDWHFYUGWIEGFHGJBVUEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJCEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJBVUCEDWHFYUGWIEGFIUWHEIGEOHMNVBICNJEWDUHFYGRYBJDSKLHJUGFHGJBVUEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJBVUEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJCEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJBVUCEDWHFYUGWIEGFIUWHEIGEOHMNVBICNJEWDUHFYGRYBJDSKLHJUGFHGJBVUEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJBVUEDWHFYUGWIEGFHGJBVUEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJCEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJBVUCEDWHFYUGWIEGFIUWHEIGEOHMNVBICNJEWDUHFYGRYBJDSKLHJUGFHGJBVUEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJBVUEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJCEDWHFYUGWIEGFIUWBJDSKLHJUGFHGJBVUCEDWHFYUGWIEGFIUWHEIGEOHEGFIUEGFIUEGFIUEGFIUEGFIU

S e a rc h o f n o v e l m o l e c u l a rd e s c r i p t o rs fo r a nt i m i c ro b i a l p e p t i d e s

Dottorato di Ricerca in

Scienze del Farmaco

XVI ciclo

Tutor PhD candidateProf. Stefano Piotto Rosaura Parisi

Small peptides produced by multicellular organisms

12-50 amino acids in their L-configuration

α-helix structure

Amphipathic conformation

Positively charged

They are able to kill or to inhibit growth of various microorganisms

<3000 natural AMPs have been isolated and characterized from different sources

What are AMPs?

AMPs Limits Selectivity YADAMP QSAR GOALS PROTCOMP Validation Workflow

Advantages Disadvantages

Potential substitute of conventionalantibiotics

Protease susceptibility

They aren’t hindered by resistance High cost of production

Broad spectra of activity

Specificity

Limits of AMPs as therapeutic agents

AMPs Limits SelectivityYADAMP QSAR GOALS PROTCOMP Validation Workflow

Limits of current models

AMPs Limits YADAMP selectivityQSAR GOALS PROTCOMP Validation Workflow

Artificial Neural Networks

AMPs Limits SelectivityYADAMP QSAR GOALS PROTCOMP Validation Workflow

How to obtain reliable QSAR models?

An ANN with 10 hidden neurons was applied to 1000 antimicrobial peptides with less than 60 amino acids active against S. aureus

ANN model on AMPs active against S. aureus

Artificial Neural Networks (ANN)

AMPs Limits SelectivityYADAMP QSAR GOALS PROTCOMP Validation Workflow

Statistical evaluation of a GA model of AMP activity against S. aureus

Genetic Algorithms model

AMPs Limits SelectivityYADAMP QSAR GOALS PROTCOMP Validation Workflow

Training set: peptides with 6 < Lenght < 12 aa

MODEL2 performance

Condition

Total PopulationCondition

PositiveCondition Negative

Prevalence

86.52

Test outcome positive 543.9 22.1Precision

96.10

False discovery rate

3.90

Test outcome negative 137 84False omission rate

61.99

Negative predictive

value

38.01

Positive Likelihood Ratio

3.83

Sensitivity

79.88

False positive Rate

20.83

Accuracy

79.78

Negative Likelihood Ratio

0.25

False negative rate

20.12

Specificity

79.17

Diagnostic odd ratio

1508.99

Peptides are adapted to their lipid environment

AMPs Limits SelectivityYADAMP QSAR GOALS PROTCOMP Validation Workflow

eukaryoticmembrane

Prokaryoticmembrane

… better said: an example of a small portion of the lipid membrane of a particular organism

The selectivity depends on the “match”

My goal is to investigate any regularity between TMPs of

different organisms and see if they can be correlated with AMPs;

I will use the ‘distances’ among TMPs and AMPs as novel

descriptors in QSAR analysis

Main goals

AMPs Limits SelectivityYADAMP QSAR GOALS PROTCOMP Validation Workflow

How to improve QSAR models?

AAKKAAKKAAKK

IIIIIII-IIII

AAKKAAK-AAKKA

AMPs Limits SelectivityYADAMP QSAR GOALS PROTCOMP Validation Workflow

Define the variable that describes a biological entity (BE)

X = sequence (nucleotides, amino acids, ...) of the entity (such as a proteome)

Ci⊆X one possible substring of the sequence X (feature)

|Ci| occurrence number of characteristic

| X | = length of the sequence. Since | X | = Σ | x | ∀xCi

|Ci|/ | X | Weight feature

Define metrics

Calculate the distances among BEs

How it works

AMPs Limits SelectivityYADAMP QSAR goals PROTCOMP Validation Workflow

Validation 1: proteomes comparison

AMPs Limits SelectivityYADAMP QSAR goals PROTCOMP Validation Workflow

This work could allow the definition of new molecular descriptors for AMP based on the "distance"between the TMP of different organisms

Validation 2: TMPs comparison

AMPs Limits SelectivityYADAMP QSAR GOALS PROTCOMP Validation Workflow

Workflow

AMPs Limits SelectivityYADAMP QSAR goals PROTCOMP Validation Workflow

Work organization

Tasks I year II year III year

1 2 3 4 5 6

Selection of homogeneous AMP sets

QSAR analysis by means of GAs and ANNs

Definition of metrics within Protcomp

Definition and test of molecular descriptors

Design of AMPs with high selectivity

(Synthesis and test of peptides)

Stage abroad

AMPs Limits SelectivityYADAMP QSAR goals PROTCOMP Validation Workflow

> Thanks peptide in FASTA format

THANKSFORTHEATTENTION

Four models of interaction between cationic AMPs and cytoplasmic membrane

a) Barrel-stave pore

b) Carpet mechanism

c) Toroidal pore

d) Disordered toroidal pore

Mechanism of action: proposed models

Priddy, Kevin L., and Paul E. Keller. Artificial neural networks: an introduction. Vol. 68. SPIE Press, 2005.

PDB 2LGI PDB 4GU2

Red experimental 3D structure

Blue Predict 3D structure