De Novo Peptide Sequencing via Probabilistic Network Modeling PepNovo Pepti


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De Novo Peptide Sequencing via Probabilistic Network Modeling PepNovo Pepti

California State University, Bakersfield, US has reference to this Academic Journal, De Novo Peptide Sequencing via Probabilistic Network Modeling PepNovo Peptide Fragmentation A C F E T P G R N C A C F E T N P G R C M PM-M Collision-Induced Dissociation (CID) Peptide Fragmentation A peptide alongside mass PM, that fragments into a prefix of mass m, in addition to a suffix of mass PM-m, can produce different fragment ions: The intensities at the expected offsets from mass m are used so that create an intensity vector:

 Estrich, Susan California State University, Bakersfield


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The Spectrum Graph Scoring in consideration of De Novo Sequencing All masses in spectrum range can be considered putative cleavage sites. Given observed intensities , how so that evaluate if mass m is cleavage site. A common statistical tool used by many scoring functions is the likelihood ratio test (Dancik et al. 99?, Havilio et al. 03?,.) Dancik et al. ?99 ? Hypotheses The main concept: Give premium in consideration of present peaks in addition to penalties in consideration of missing peaks. Uses a probability table: PR ? Probability of observing random peak (~0.1) (Random hypothesis). Fragmentation Hypothesis

Scoring a Cleavage Site (Dancik ?99) Out of k possible ions in consideration of cleavage at m, t are detected (w.l.o.g fragments 1,.,t) in addition to k-t are missing (t+1,.,k). Score using a log ratio test: Probability of cleavage site m according so that Fragmentation hypothesis Probability of cleavage site m according so that Random hypothesis PepNovo Scoring PepNovo implements a similar likelihood ratio test mechanism. Can be viewed as extending the scoring model of Dancik et al. 99?. Includes several factors that are not sufficiently addressed in current scoring functions. Enhancements so that Dancik et al. (?99) Several Intensity values. Combinations of fragment ions. Incorporation of additional chemical knowledge (e.g., preferred cleavage sites). Positional influence of the cleavage site. Improved Random Model.

Radiation Exposure Sources of radiation Means in addition to result of radon exposure

HCID – Fragmentation Network Amino acid influence Ion combinations Positional influence Discrete Intensity Values Peak intensity normalized according so that grass level (average of weakest 33% of peaks in spectrum). Normalized intensities Discretized into 4 intensity levels: zero : I < 0.05 low : 0.05 ? I < 2 (62% of peaks) medium : 2 ? I < 10 (26% of peaks) high : I ? 10 (12% of peaks) Combinations of Fragments Different combinations have significantly different probabilities: P(b=high| y=high) = 0.36, vs. P(b=high| y=low) = 0.03. P(b-H2O > zero | b=high) = 0.5, vs. P(b-H2O > zero | b= zero) = 0.24.

Additional Chemical Knowledge The identity of the flanking amino acids influences the peak intensities: Increased intensities N-terminal so that Proline in addition to Glycine Increased intensities C-terminal so that Aspartic Acid. 400 amino acid combinations reduced so that 15 equivalence sets (X-P,X-G, etc.). Positional Influence Creates separate models in consideration of different locations in the peptide Models phenomena such as: weak b/y ions near the ends. prevalence of a-ions in the first half of the peptides. prevalence of b2 towards the peptide?s C-terminal in addition to y2 near the N-terminal. pos(m) (region in peptide) Probability under HCID From the decomposition properties of probabilistic networks, each node is independent from the rest of the nodes given the value of its parents so: where ?(f) are the parents of node f.

HRandom ? Regional Density w 2? Computing the Random Probability ?=1-(2?)/w , is the probability of a single peak missing the bin. Let ni , 1?i?d, be counts of peaks alongside intensity i in window w: Random Model in consideration of HRandom Peak occurrences are treated as random independent events: The probability of observing a peak at random is estimated from the local density of peaks in the spectrum.

The Likelihood Ratio Score A putative cleavage site is scored according so that the log ratio test: Can be used so that score a peptide by summing the score in consideration of the prefix masses: PepNovo?s De Novo Sequencing A spectrum graph is created from the experimental MS/MS spectrum. The nodes are scored using our method. Highest scoring anti-symmetric path is found using dynamic programming algorithm. Spectrum Graph Acyclic graph. Nodes are cleavage sites, each has a mass m in addition to score s. Edges connect nodes alongside mass differences corresponding so that an amino acid. m:0 s:5.0 m:163.2s: 2.8 m:113 s: -1.2 m:71.2 s: 4.3 m:199.4 s: 5.6 A L m:99.1 s:8.1 V S W Q

Results Benchmarking reported in consideration of 280 spectra. Q & A

Estrich, Susan Host

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