PI brings improvements in looks, usability and performance as well as adding new functionality and features. For humans here is a visual guide to some of the best improvements.
GUI of PI
The factors influencing global fragmentation
The training set was categorized into several subsets with respect to the length of peptide. On some subsets containing peptides with same length L=7,8,9,10,11,12,13, the relationship between amino acids position and affinity ability are calculated. Fig. 1 shows the cases where L=12 and L=13. Fig. 1 demonstrates that fragmentation occurs more often towards the middle of a peptide than at its ends, which is consistent with the observation given previously.
The statistical results of the preference of fragmentation at all the 400 peptide bonds are calculated. Examination of the preference data rediscovers some knowledge already known to mass spectrum experts. First, some amino acids prefer cleavage at N-terminus over C-terminus bond. For example, it is well-known that cleavage at Pro's N-terminus is preferred than that at C-terminus because attacking of the adjacent carbonyl oxygen at the electropositive carbon is hindered due to the molecular structure of Pro.
In Table 1, Xaa-Pro has the highest possibility (0.1994) of fragmentation, while Pro-Xaa has a lower one(0.017), supporting that Pro tends to cleavage at its N-terminal than C-terminal bond. And cleavage at Asp-Xaa(0.09), Ile-Xaa(0.09) and His-Xaa(0.07565) are also much more often than others, which is consistent with the conclusion that cleavage at the C-terminal bond of His is enhanced. Second, fragmentation of the Xaa-Pro bond is encouraged when Xaa is Ile(0.025), His(0.019), Trp(0.0166) or Asp(0.0139), while is hindered when Xaa is Gly(0.0036), Pro(0.0006) or Met(0.0032).
In conclusion, the above results are consistent with "mobile proton model", i.e., the more basic the residue, the more large the affinity of proton, and then the more facile the fragmentation.
Probabilities of neutral losses
For each amino acid, the possibility of isotopic shift and loss of water or ammonia were estimated through the EM method. Here, isotope probability were used to benchmark our algorithm since its theoretical value can be easily calculated with known element isotopic abundance and amino acid molecular formula. In Figure 1, both theoretical and experimental value of isotopic shift probability are shown, the coincidence of which demonstrates the feasibility of the EM method.
Figure 2 shows the ammonia loss probability for each amino acid. From Figure 2, we note that there are four amino acids with a high probability of ammonia loss, i.e., Arg, Lys, Asn, Gln, while the others are very lower compared to these ones. This observation is consistent with the reaction pathway analysis reported in . Moreover, Arg and Gln have about a two fold higher ammonia loss probability than Lys and Asn, supporting the observation that y-ions often suffer an ammonia loss for tryptic peptides.
Figure 3,4,5 show the probability of water loss, which supports the theoretical and practical observation that Asp and Glu have tendency to lose water from C-terminus, Ser and Thr often lose a water from side-chain, etc. Additionally, we also note that other amino acids, such as ???, may also lose water at backbone amide oxygen.
Predicting spectrum from sequence accurately
We used this model to predict theoretical spectrum for a testing set and made comparison with practical ones. Intensity was estimated for the ions with neutral losses, thus, a more complete theoretical spectrum could be predicted for a peptide sequence. Experimental results show that this model could predict a more 'realistic' spectrum.