Bioinformatics and the Language of DNA Aydin Tozeren aydin.tozeren@drexel.edu Center for Integrated Bioinformatics Drexel University, Philadelphia, PA,

Bioinformatics and the Language of DNA

Aydin Tozerenaydin.tozeren@drexel.edu

Center for Integrated BioinformaticsDrexel University, Philadelphia,

PA, USAwww.gpba-bio.com

Yeni Hayat/ New Life by Orhan Pamuk

• Bir kitap okudum butun hayatim degisti

• Read a book and my life has changed forever

Living systems have the same building blocks: C, N, H, O, P, Su, minerals.

Five different macromolecules: DNA, RNA, Protein, Carbohydrates, Fat (lipid)

Information Flow: DNA to RNA (template) to Protein back to DNADNA has four basic building blocks, arranged in a sequence.Proteins have 20 building blocks, arranged in various orders in a linear chain.

Proteins: Molecular Machines

• There are about 40,000 types of proteins in a cell.

• Proteins change configuration upon actively resulting in movement and motion. They are responsible for heart beat, neuron firing, muscular movement, etc.

DNA can be viewed as a long string of four letters in various combinations: ACGTTACCGCGCTCA.....

Billions of letters with no coma or period, just arranged serially. 

Genome: Collection of DNA in the nucleus of a cell.

Next Generation Sequences can human genome in 6 weeks.

BACTERIA has single (circular) DNA organized into OPERONS

EUKARYOTES (plants, yeast, mammals) have DNA organized into chapters (single linear DNA molecules or chromosomes).

DNA : Book of Life

Each and every cell in the body has the same book of life

DNA is the hard drive and the information storage unit of the living. Cells from different tissue types may use (read) different sections (pages) of the DNA (book of life).

DNA various only so slightly between individuals in a species.

The sequence of letters along DNA is similar among species such as dogs, human, monkey, and even mouse.

Gene is a segment of DNA that provides a recipe for a protein. typically it is 300 letters (nucleotides) but can be much longer. A three letter along a gene (CODON) represents an amino acid, one of the 20 building blocks of proteins. 

gatcaggtcc ttatgatgac agattggggc ccactttgtt gtgctttttc ttattggttg ctgtcattat caactttata ttaagattga agtacaatga cgctaacact aagttatgaa attgtaattc caatatcgta agcgtgggtt acgcacaaac tgtattttca agatgctcac aaataattta gtttcatata tacgcatata tagaaagtat ccatctatag gtaatcatga acaataaaaa tattcacgtt tcaggagcta ttgtttgtac tcattacgtt tttggatatc aagttgaaaa tcagcccctt tcactagata tcaagcgcta taaaaaaatt ttaatttcga tgaggcatct ttcttttctc ttgtggctat gtaagcctaa gaagccgttt acacatcaat gataaataag tatacaaaaa gggttccatt ttttttttgg ccgctaccgg actagcaagg gcctaatggt acgctgagcg tagtacaacc aagcgcttgt  

/

translation="MSDAVTIRTRKVISNPLLARKQFVVDVLHPNRANVSKDELREKLAEVYKAEKDAVSVFGFRTQFGGGKSVGFGLVYNSVAEAKKFEPTYRLVRYGLAEKVEKASRQQRKQKKNRDKKIFGTGKRLAKKVARRNAD"

EUKARYOTE GENE SEQUENCES ARE HIGHLY CONSERVED BUT VIRAL SEQUENCES VARY WITH TIMESequence measurement is fast and accurate with next generation sequencing

Will Dampier: SNP Islands on HIV-1 Genomemutations vary from one HIV-1 protein after another. Less the mutation density more important the protein is for viral survival.

Will Dampier: Homology Islands Along HIV-1 Genome GenomGenome

Will Dampier, Invariant Sequences on HIV-1

• Original Seqquence

• AAGGAGAGAGATGGGTGCGAGAGCGTC AATAAAATAGTAAGAATGTA TAGAAGAAATGATGACAGCATG CAGGCTAATTTTTTAGGGAA ACAGGAGCAGATGATACAGT ATGGAAACCAAAAATGATAGG ATTGGAGGAAATGAACAAGT TTAGCAGGAAGATGGCCAGT ATTCCCTACAATCCCCAAAG CACAATTTTAAAAGAAAAGGGGGGATTGGGGGG TACAGTGCAGGGGAAAGAATA AAAATTCAAAATTTTCGGGT TTCAAAATTTTCGGGTTTATT AATTTTCGGGTTTATTACAG CTCTGGAAAGGTGAAGGGGCAGTAGTAAT

eukaryotic linear binding motifs (ELMs): ISNP, LLARKQ, FVVDV

4 - 7 amino acid long sequence segments of a protein. it has been shown to be involved in binding interactions with other proteins in the same species.

there are about 130 such motifs conserved across eukaryotes

Yichuan Liu: Domains/Motifs stabilities across Eukaryotics

Perry Evans: Conserved ELM locations on HIV-1 ENV

Will Dampier: Patient Progression

Will Dampier: Predictive Ability

Will Dampier: ELM Locations

HIV1 Sequence Motifs

Conserved regions can be targeted with micro RNA to prevent HIV-1 from multiplying.

Presence of Linear Binding motifs at certain locations in the alignment is correlated with the severity of the disease.

Given the sequence of a virus, we can predict motifs on the sequence relevant to clinical outcome

CROSSTALKaydin.tozeren@drexel.edu

www.gpba-bio.com

• VIRUS/HOST CELL

• CELL-CELL

• CELL EXTRACELLULAR MATRIX

Kar/ Snow –Orhan Pamuk

• Kadife, Lacivert ve Tiyatro trubu Karsta

• People in Kars becomes obsessed with a visiting theatre group, anticipating and watching a new show every night. Talk of the town.

Virus proteins

• Virus proteins hijack binding motifs found in the host cells.

• One mode of protein interaction is due to binding of an ELM on one protein to a domain on the other.

Evans: Conserved ELM locations on HIV-1 NEF

HIV Host Protein Interactions

Evans: Host-pathogen KEGG proj.

Given Viral Sequence

• We can determine with good accuracy the host proteins targeted by the virus.

• One can then search for optimized therapies for the virus.

Yichuan Liu: Predicting Protein Interactions

Yichuan Liu: Heat Graph of PPI separated by BP GO terms in 5 different Cell Compartments

Machine learning

What are the sequence motifs that are enriched in proteins known to interact with other proteins?

Answer: ELMs and Counter Domains explain only 20% of known protein interactions. Therefore the language and grammar of crosstalk between two proteins are yet to be discovered.

Microarray ChipsA slide with thousands of dots with each dot sticky for a product of a gene higher the number of gene product copy shinier is the dot. Question is which subset of genes we should use to differentiate between disease subtypes?

Noor: Examples of Gene Enrichment

each microarray experiment provides thousands of values for the activities of genes at a specific tissue at a given time

Adam Ertel: Switch-like gene expression

Adam Ertel: Switch-like genes involved in cell communication pathways

Adam Ertel: Clustering tissue by switch high/low state

1265 Bimodal Genes

ECM-MEM Bimodal Genes

Michael Gormley: Model- based clustering of tissue type

KMeans Hierarchical Model-Based

Michael Gormley: Bimodal genes expressed in the “on” mode in specific tissues

Michael Gormley: Bimodal genes expressed in the “on” mode in HIV infection

Michael Gormley: Model- based clustering of infectious disease

• Effect size (μ1-μ2/σ2)• Regression coefficients (β)• Number of samples (n) • Number of genes (p)• Number of significant genes (M) • Number of selected features (N)

Parameters

Michael Gormley: Simulation of supervised classification with bimodal genes

Mahdi Sarmady: Sample Model

1.Species and interaction initial value: inactive

2.Effect of TLR1/TLR2 activation by binding of a bacterial lipoprotein

Noor: Model and Data Description• Main Model Description:

o Comprises of 78 flux equations used to simulate the kinetics of 98 variables found in blood, cytoplasm, mitochondrial intermembrane space and matrix

o Simualtions take into consideration significant bindings of reactants to the cations H+, K+ and Mg2+, and are therefore pH sensitive

• Data Description:o Microarray data was collected for each of the 4 tissues and

significantly altered genes representing enzymes from aforementioned metabolic pathways were identified

o Fold change values from SAM test where used to adjust enzymatic Vmax values assuming Vmax is directly proportional to enzyme concentration Vmax = k[E]

Glycolysis: Glucose  • Hexokinase: A = Mg2+-bound ATP, B = GLC_c, P = G6P_c, Q = Mg2+-bound

ADP           • GLUT4: A = GLC_b, P = GLC_c

Noor: Examples of ODEs Used

TCA: Fumarate  • Succinyl Dehydrogenase: A = SUC_x, B = COQ_x, P = QH2_x, Q = FUM_x

Noor: Examples of ODEs Used

Mitochandria Energy Flux

Pathway for Diabetes

Genomics Summary

• Given: Sequence Data

• Microarray Data

• A priori information (curated literature, pathways)

• Show: Host proteins targeted by virus

• Discover new protein-protein interactions

• Investigate side effects and treatment potential of drugs

Sevgi Soysal

• Kadinin Adi Yok!

• A Woman Has No Name!

• More and more women’s names are embedded onto the marble stones of science.