Exam 1 Flashcards

Question

RNA polymerase prokaryotes vs eukaryotes

Answer 1

**prokaryotes**- have a single type **eukaryotes**- RNA pol 1 (most rRNA genes) RNA pol 2 (protein-encodoing genes --> makes mRNA) RNA pol 3: tRNA, 5S rRNA, small structural RNA genes

Answer 2

**pro**: RNA pol can initiate without helper protein **euk**: require general transcription factors

Answer 3

**pro**- transcripts are generally NOT processed **euk**- mRNAs are processed

Answer 4

1. helicase-like function opens up DNA 2. promoter escape: phosphorylates C-terminal end of RNA polymerase --> RNA pol activates and leaves promoter

Answer 5

1. RNA polymerase recognizes and binds to promoter 2. TF 2D comes with TBP and TAF 3. TF 2A/ 2B: help recruit RNA polymerase to promoter region 4. TF 2F (escort protein) binds, which recognizes RNA polymerase, and RNA polymerase binds to promoter and stabilizes the structure 5. TF 2E/ 2H

Answer 6

**TBF**: TATA box binding protein **TAF**: TATA box binding protein associated factor

Answer 7

1. Small ribosome subunit scans newly synthesized RNA transcript looking for translation initiator site (AUG) 2. Once found, large ribosomal subunit binds 3. First amino acid binds to P region 4. New amino acids come to bind to A site. That amino acid move to P site and binds through peptide bond to existing amino acid 5. tRNA released through E site 1. Reaches stop codon (UAG) 2. Binding of release factor to A site 3. Termination

Answer 8

Prokaryotic mRNAs lack 5’ caps, so instead, ribosomes search for specific ribosome-binding sequences (Shine Delgarno sequence), and there can be multiple bindingsites within an mRNA (polycistronic-- ribosome can bind anywhere)

Answer 9

1. RNA polymerase cant recognize promoter sequence unless sigma factor is attached to it 2. RNA polymerase (apoenzyme: not active) + sigma factor = holoenzyme (active). All attached together, closed structure. Needs to be opened up 3. Sigma factor leaves RNA polymerase → open structure → Rna polymerase can start transcription

Answer 10

**Rho independent** Have palindromic sequence to form hairpin-like structure GC (3 bonds) very stable and strong vs weak AU (2 bonds) Change of forces help to dissociate the newly formed RNA from DNA-RNA hybrid **Rho protein dependent** Stretch of CCCCCC (Rut - Rho utilization site) recognized by rho protein Cleaves RNA polymerase from hybrid

Answer 11

1. CPSF binds to AAUAA 2. CSTF binds G/U-rich sequences 3. Cleavage factor chops RNA in half to release TRAN transcript

Answer 12

help go to nuclear pore to cytoplasm

Answer 13

**7 methylguanylate cap**: 5’ cap at the end of 5’ that helps navigate through the nuclear pore to cytoplasm; Prevent from exonuclease ** 3’ poly A polymerase**: poly A tail at 3’ end (where RNA polymerase cut through the RNA) **Splicing**: chopping introns and attaching exons ; Exons go to cytoplasm for translation

Answer 14

amino acid

Answer 15

RNA pol 2?? (double check)

Answer 16

addition of functional groups

Answer 17

**Genomics** -Global analysis of genome structure and function, including gene expression **Proteomics** -Global analysis of the proteome, including protein expression and modification **Metabolomics**- Global analysis of metabolic processes

Answer 18

discovery of catalytic RNA, ribozymes, by Tom Cech and Sidney Altman put an entirely different spin on this question. RNA can both store genetic info and has catalytic functions (ribozymes)- want RNA because already spliced CDNA = DNA and do reverse transcriptase

Answer 19

* Have nucleotides that are translated into amino acids of proteins * Separated from one another by introns. * segments of mRNA spliced back together after the introns are removed

Answer 20

* Intervening segments of DNA * Are not translated into protein. They are removed when eukaryotic mRNA is processed.

Answer 21

a template

Answer 22

when a functional product is made from the gene

Answer 23

gene-expression analysis if want to see expression pattern of 3 different genes: 1. collect RNA from tumor and control cells 2. reverse transcription --> cDNA 3. tagged with different colors and mixed together 4. genes on plates can only fluorescence if fluorecing cDNA comes to bind with them (can be 1, 2, or none colors); probes indicate A, B, or C

Answer 24

equal amounts of red and green (A and B)

Answer 25

generalized (DNA, proteins and carbs, 3D structures) specialized (genomes, RNA, protein families, pathways)

Answer 26

* National center for biotechnology info * primary sequence database * understanding of molecular processing affecting human health and disease

Answer 27

* sequence-based (BLAST, FASTA) * motif-based * structure-based * mass-based protein search

Answer 28

* basic local alignment search tool * produce local alignments: short significant stretches of similarity, irrespective of where they are in the sequence; more useful in searching whole genome

Answer 29

* Good balance of sensitivity and speed * Reliable * Flexible

Answer 30

binary- they are either homologous or not

Answer 31

* have: similar sequence, 3D structures and functions, and share a common ancestral sequence * Proteins with >30% sequence identity

Answer 32

Same protein, different species Human and chimpanzee histone h1.1= orthologs (same in different species) retain similar functions during evolution

Answer 33

* Same species, result of gene duplication * same species, different function

Answer 34

* Proteins are more complex than DNA (20 aa vs 4 nt) * DNA contains protein coding sequences, untranslated sequences and regulatory sequences and additional sequences with mostly unknown but important function

Answer 35

Trying to see similarity for the entirety -Attempt to align every residue in every sequence -Most useful when the sequences in the query set are similar and are of roughly equal length. -**Needleman-Wunsch algorithm** -Best score from among alignments of full-length sequences Can only use if length is similar. So if there is huge mismatch, cannot use global Intersequence- how much similarity it has

Answer 36

Identify the optimal “best local” alignments and then extends those alignments until search parameters fall below a threshold, T -More useful for dissimilar sequences and possibly dissimilar sequence lengths **Smith–Waterman algorithm** -Best score from among the partial sequences

Answer 37

A speculative explanation serving as a guide in the solution of a problem **FASTA**: First fast sequence searching algorithm for comparing a query sequence against a database **BLAST**: Improvement of FASTA: Search speed, ease of use, statistical rigor

Answer 38

Guaranteed to find the optimal local alignment (with respect to the scoring system being used). Costly: to align two sequences of lengths m and n, O(mn) time and space are required.

Answer 39

Heuristic methods that are much more efficient than SW useful in large-scale database searches where a large proportion of stored sequences will have essentially no significant match with the query sequence

Answer 40

1. Identify orthologs/ paralogs 2. Identify protein/genes in a genome 3. Identify new genes 4. Structure/function analysis 5. Primer design (Primer-BLAST)

Answer 41

* heuristic search that approximates the S-W algorithm, identifies HSPs * pairwise analysis of a query sequence against an entire databse

Answer 42

1. removes low-complexity region or sequence repeats in query sequence 2. makes a list of word pairs (K-letter word) for each query sequence 3. list possible matching words 4. identification of exact word match method (scans database of word pairs that meet T score or greater) 5. maximum segment pair alignment method (word hits are extended in either direction to generate alignment with score exceeding S 6. list all HSPs in database whose score is high enough to be considered 7. determine E value: significance of HSP score 8. report every match whose expect score is lower than threshold parameter E scores are calculated from scoring matrices like BLOSUM62

Answer 43

L-W+1 W=11 for nucleotides W=3 for proteins

Answer 44

**score** S: calculates from substitution matrix **E-value**: expected HSPs of at least S **Alignment**: visual assessment of a BLAST result

Answer 45

* expected number of distinct alignments (HSPs), with a score of at least S * the lower the E value, the more significant/ reliable the score is * anything less than 10^-4 * decreases exponentially as the score S of the match increases

Answer 46

* verify locations of sequence identities, similarities, and mismatches * identify gap locations and distributions * illustrate extent of sequence between query sequence and a "hit"

Answer 47

* estimate of frequency of occurrence and the likelihood of some aas to substitute for other aas * BLAST default matrix is BLOSUM62 * the higher the score the better the alignment

Answer 48

constant: +1 each match, -1 whole gap linear: +1 each match, -1 per gap affine: gap open and extension penalty different

Answer 49

top: query bottom: hit or subject sequence line between: + for similar aas; spaces for gaps

Answer 50

Nedleman Winsch (global) Smith Waterman; Word (K-tuple) method (local)

Answer 51

BLOSSum62 for protein sequence

Answer 52

Phylogenetic analysis * Domain identification * Structure/Function Analysis * Regulatory element identification

Answer 53

1. All MSA apps are approximate 2. To be effective, MSAs are iterative 3. Use multiple MSA applications to find robust and stable alignments

Answer 54

1. Apply filtering of biased composition regions 2. Adjust E value from 0.001 (default) to a lower value such as E = 0.0001. 3. Visually inspect the output from each iteration. Remove suspicious hits by unchecking the box.