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Search of novel molecular
descriptors for antimicrobial peptides
Dottorato di Ricerca in
Scienze del Farmaco
XVI ciclo
Tutor PhD candidate
Prof. 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 conventional
antibiotics
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 Population
Condition
Positive
Condition Negative
Prevalence
86.52
Test outcome positive 543.9 22.1
Precision
96.10
False discovery rate
3.90
Test outcome negative 137 84
False 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
eukaryotic
membrane
Prokaryotic
membrane
… 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
Rosaura parisi ppt progetto
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.
Rosaura parisi ppt progetto
PDB 2LGI PDB 4GU2
Red experimental 3D structure
Blue Predict 3D structure

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Rosaura parisi ppt progetto

Editor's Notes

  1. GIGKFLHSAKKFGKAFVGEIMNS
  2. I peptidi antimicrobici sono un gruppo abbondante e diversificato di molecole attive della risposta immunitaria che costituisce la prima linea di difesa contro i patogeni. Sono definite anche AMP (antimicrobial peptides) e sono prodotti in molti tessuti e tipi di cellule di organismi quali piante, insetti anfibi e dagli organismi superiori. Svolgono un ruolo essenziale nel compartimento naturale dell’imunità innata e soprattutto a livello delle superfici mucosali (epitelio del tratto respiratorio, della pelle e del tratto gastro-enterico. La loro composizione amminoacidica e le loro caratteristiche chimico-fisiche correlate alla struttura consentono loro di interagire in maniera selettiva con il doppio strato lipidico della membrana batterica causando la morte dei microrganismi. I peptidi antimicrobici sembrano avere un alto potenziale d’attività sui ceppi batterici patogeni per l’uomo, essendo in grado di agire singolarmente e in miscela sulle cellule batteriche e non inducendo fenomeni di antibiotico-resistenza, come i farmaci attualmente in uso.Una delle più ricche fonti naturali di peptidici antibiotici sono le secrezioni degli Anfibi e degli insetti prodotte per difendere l’organismo dagli insulti esterni.
  3. The antimicrobial peptide is able to selectively act as there are differences in composition between the charge and the cytoplasmic membranes of higher eukaryotes and the prokaryotes; Sebbene l’azione degli AMP risulti selettiva essa può essere considerata ad ampio spettro poiché si esplica su un’ampia gamma di organismi, batteri Gram-positivi e Gram-negativi, ma anche su virus, funghi e protozoi patogeni e persino contro cellule tumorali (Hoskin et al., 2008) in quanto essi agiscono secondo un meccanismo d’azione che non comprende una classica interazione molecola-recettore (Bulet et al., 2004). However , with the traditional methods has not yet been possible to identify the domains on the peptide sequence that are specific for the iteration with the components of the membrane of the target organism; The goal is to design new antimicrobial peptides extracted from different organisms, specific for the target bacteria and non-toxic for other cells
  4. Non funzionano (per es carica, elicità…) Una possibilità è quella effettuare analisi QSAR su sottoinsiemi omogenei di peptidi It’s a web database dedicated to antimicrobial peptides with detailed information about activities, structural features and biological origin, created to facilitate access to critical information on antimicrobial peptide; It’s a resource for QSAR investigations because allows to create sets of homogeneous peptides It permits to BLAST AMPs as well as free sequences that come from all biological sources; It permits the selection of AMPs with the lowest MIC value. There are important information on 2525 AMPs The most relevant chemical physical properties are calculated, like charge, hydrophobic moment, helicity, flexibility, isoelectric point, Boman and instability index, penetration capabilities, and DG
  5. Solo un piccolo numero di descrittori è correlato alla risposta sperimentale ed è, quindi , rilevante costruire un modello matematico di interesse : passo fondamentale è la selezione del sottoinsieme ottimale di variabili ( descrittori molecolari ) per lo sviluppo del modello: artificial neural networks (ANN) and genetic algorithms (GA)
  6. Training set: 600 peptides chosen randomly Validation set: 250 peptides Test set: 150 peptides Results: This model demonstrated an excellent predictive capability confirmed by an R2 = 0.922
  7. Peptidi 3 Descrittori molecolari Mic Formula : (MIC – VALORE DEL DESCRITTORE MOLECOLARE)^2 Sviluppo di una serie di modelli combinando i vari descrittori molecolari tra loro nelle equazioni Via via si prendono i modelli migliori e si ricombinano finchè non cambia più nulla Questi ottimi risultati sono stati ottenuti dopo aver selezionato insiemi omogenei Grazie a yadamp
  8. Lo scopo è sempre quello di cercare peptidi selettivi e non tossici. La nostra ipotesi di selettività parte da un’idea di basa che … I peptidi sono ben adattati all’intorno lipidico L lipidi sono specifici per i vari batteri La giusta composizione aa potrebbe avere effetto sul batterio The AMPs activity and the selectivity depend on the“match”between peptide composition and membrane composition Consequences TMs are adapted to particular membrane domains TMPs reflect more the physical environment than phylogenetic relationships TMPs are “bioinformatic sensors” of membrane lipid composition A proper metric permits to measure the “distance” between TMPs The same metric permits to measure distances among AMPs Ultimately, permits comparison among TMPs and AMPs
  9. The AMPs activity and the selectivity depend on the“match”between peptide composition and membrane composition
  10. Since the peptides of an organism are tailored to the membrane in which they are inserted, my working hypothesis is that the different membranes will select different peptides;
  11. Bene, con i modelli qsar noi sviluppiamo dei modelli predittivi di azione degli AMPs ma non abbiamo informazioni riguardo l’origine, la storia evolutiva ecc. Quindi nasce la necessità di ricercare nuovi descrittori molecolari come ad esempio pattern ripetuti all’interno delle sequenze. Come migliorare le analisi QSAR? Esistono descrittori migliori? I descrittori molecolari esistenti si riferiscono sempre alla struttura di una molecola. Possono riferirsi alla carica o alla polarizzabilità, al punto isolettrico o al peso molecolare, ma nessuno considera la loro origine. Nessuno considera proprietà emergenti quali la capacità di self-assembly o di riconoscimento di una membrana specifica. Eppure i peptidi (vedi video membrana) sono progettati per bene adattarsi ad un particolare intorno lipidico. I tentativi di individuare dei motif ricorrenti tramite tecniche bioinformatiche tradizionali non ha funzionato e non sono state trovate sequenze ricorrenti se non all’interno di particolari classi di AMP (come le defensine). Un esempio può chiarire il concetto (video peptide ad elica AAKKAAKK…). Dal semplice allineamento per pairwise allignment sequenze apparentemente simili in realtà possono formare strutture diverse e avere anche un diverso meccanismo s’azione. Nasce la necessità di un allignment free … protcomp…dizionari di sequenze… distanza
  12. La differente composizione delle membrane è infatti alla base della selettività che alcuni di questi peptidi hanno per le cellule batteriche. Le cellule eucariotiche, come ad esempio gli ematociti, sono caratterizzate da un alto contenuto di fosfolipidi zwitterionici, come la fosfatidilcolina, la sfingomielina e la fosfatidiletanolammina; sono inoltre ricche di colesterolo, assente nei batteri, che sembra inibire l’azione di tali peptidi conferendo una certa resistenza alle membrane. Un altro fattore importante per la selettività è il valore del potenziale di membrana: un potenziale più negativo all’interno della cellula, tipico delle cellule batteriche (100-150 mV), facilita l’interazione del peptide con lo strato lipidico. Nel caso di batteri Gram-negativi è stato visto che inizialmente il peptide interagisce con le molecole polianioniche di lipopolisaccaride della membrana esterna ed è poi in grado di permeabilizzarla o di essere captato all’interno. Nel caso dei batteri Gram-positivi il peptide è invece probabilmente attratto dagli acidi teicoici e teicuronici e da altri gruppi anionici che si trovano esternamente allo strato di peptidoglicano. toxicity to human cells is one of reasons keeping back AMPs from being introduced into medical practice Need to use a new tool for alignment-free analysis of sequences, PROTCOMP, that creates a dictionary containing the most relevant information in a proteome and It determines how many sequences have in common two organisms compared It is important to determine how a class of peptide sequences, active on a particular bacterium, is away from the proteome or by the portions transmembrane ( TMP ) of the organism to which they are active
  13. Questo lavoro potrebbe consentire la definizione di nuovi descrittori per gli AMP basati sulla “distanza” tra i TMP di diversi organismi.
  14. it is usually defined as the ratio of peptide concentration which lyses 50% of mammalian erythrocytes (HC50) to minimal peptide concentration (MIC) which stops overnight growth of bacteria: TI = HC50/MIC. We can consider the AMPs as biomarkers bioinformatics ? Is there some parameter related to AMP selectivity for bacterial cells that can be easily defined, measured and possibly predicted? How one can reduce AMP toxicity without losing its antimicrobial activity?