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View Preview. Learn more Check out. We also discuss the potential applications of our methodology to the design of nanoparticles for medical imaging. Central to such a simulation is the interplay between the order parameters and the atomic scale variables which is captured via stochastic equations derived rigorously from the fundamental laws of molecular physics I. Fokker-Planck or Smoluchowski and the corresponding Langevin equations.

The thermal average forces and diffusion coefficients that appear in the order parameter dynamics equations, evolve along with the system and are automatically generated within the dynamics loop. NanoX is built on the CHARMM inter-atomic force-field which has been well developed and optimized thus avoiding the need for recalibration of the model for different systems, and is available as free open-source software. Avenues for future development are also discussed.

Skip to main content. Wednesday, November 19, - pmpm. Adhangale, P. Each time, the sequences for the internal dataset were randomly selected from the main dataset, and the remaining sequences were included in external dataset. Finally, we evaluated the performance of our models using various features on both the internal as well as the external datasets as described in above sections.


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This process gave 10 performance values using internal and 10 performance values using external validation from 10 rounds of sampling. We computed the mean and standard deviations of these performance values to check for bias in performance of the models depending on the choice of sequences on which the models were trained or independently evaluated.

As described above, initially the negative dataset consisted of the experimentally identified endogenous human serum peptides as non-epitopes constituting the negative dataset. We further wanted to check whether the performances of the classification models were dependent on the choice and size of the negative datasets.

This was necessary as the negative dataset does not contain the experimentally verified non-epitopes. For this, we created an alternative negative dataset of random peptides derived from the human proteins obtained from the Swiss-Prot database. As mentioned in the previous section, for each of the 10 rounds of sampling, a different set of random peptides 10 times the number of the positive sequences A-cell epitopes from the human proteins was kept as the negative dataset.

One of the objectives of this study is to understand the nature of A-cell epitopes regarding the residues preferred in A-cell epitopes. Thus, we computed the average residue composition of A-cell epitopes and the non-epitopes.

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The non-epitope dataset consists of peptides occurring in the normal human serum assumed to be non-immunomodulatory. In addition, the average residue composition of the Swiss-Prot Human proteins was also computed and compared with that of the A-cell epitopes. In the A-cell epitope dataset, the percentage composition of an amino acid residue was calculated for each epitope, and the average of these values was plotted in Fig. Similarly, the average percentage composition was calculated for all the amino acids in the non-epitope dataset and the Swiss-Prot Human proteins.


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As shown in Fig. In decreasing order of significance increasing adjusted p-value , the residues R, E, T, S, D, V, W, L, I and C showed the most significant difference between the A-cell epitopes and non-epitopes among all of the residue types with adjusted p-values 1. In particular, when compared to the human proteins taken from Swiss-Prot; R was found to have a higher average composition in A-cell epitopes. The average composition of R in non-epitopes is lower than that in the human proteins.

Overall, the residues I, R, V and W were found to be more abundant in the A-cell epitopes as compared to the non-epitopes and Swiss-Prot Human proteins. Barplots showing the comparison of percent average amino acid composition of A-cell epitopes blue with non-epitopes red and Swiss-Prot human proteins green. Similarly, the dipeptide and tripeptide compositions of the A-cell epitopes and non-epitopes were also compared with the Swiss-Prot Human proteins.

Additional file 1 : Table S3 gives the average composition for each dipeptide in the A-cell epitopes, non-epitopes as well as the Swiss-Prot Human proteins. After sorting the table according to descending order of difference of dipeptide composition between the A-cell epitopes and the human proteins, top 10 dinucleotide include the residues I, R and V.

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But these motifs also contain other amino acids that show less significant difference of abundance as compared to the non-epitopes and human proteins. Similar analysis of tripeptide composition is shown in Additional file 1 : Table S4. In this case too, the top 10 tripeptide motifs include less abundant residues apart from I, R and V.

We performed position-specific analysis of residues in A-cell epitopes to understand the type of residues preferred at different positions in A-cell epitopes. Since the minimum peptide length in the dataset was 3, the N-terminal 3 residues of both the negative and the positive sequences were taken as input to build the N-terminus TSL. C-terminus TSL was obtained using the C-terminal 3 residues from the dataset.

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Two-sample logo of the 3 residue positions at the a N-terminus and b C-terminus of the A-cell epitopes and non-epitopes. Enriched label represents the positive dataset whereas depleted label represents the negative dataset. In a two-sample logo, the height of a symbol at a residue position is proportional to the difference in symbol frequency between the positive and the negative datasets at that residue position.

In the case of A-cell epitopes as positives and non-epitopes as negatives , R is a preferred amino acid at terminal positions apart from I and V. Top 10 ungapped motifs with respect to the occurrence in the A-cell epitope sequences have a frequent occurrence of I, R and V. First, the occurrence of each type of n -mer was counted in all of the Swiss-Prot proteins, and the n -mers were arranged in increasing order of occurrence.

In this order, the n -mers were divided into 8 bins such that the 1st bin contained the n -mers least abundant in Swiss-Prot while the 8th bin contained the most abundant n -mers occurring in the Swiss-Prot.

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Next, the percentage of n -mers in a particular bin that occur in the Swiss-Prot was calculated with respect to the total number of n -mers in Swiss-Prot. Similar percentage value was calculated for A-cell epitopes and non-epitopes for each bin, and the values were presented in the form of a plot in Fig. Comparison of occurrence of a tripeptides, b tetrapeptides, c pentapeptides and d hexapeptides divided into 8 bins in the ascending order of occurrence most rarely occurring to most abundant in Swiss-Prot proteins.

For tetrapeptides Fig. The bins having distinctly more pentapeptides in the A-cell epitopes than non-epitopes are again the first four bins. On the other hand, the percentage occurrence of hexapeptides of A-cell epitopes is lower than non-epitopes and Swiss-Prot proteins only in the 8th bin Fig. The sequence-based analyses like residue composition preferences; position-wise residue preference and motif search indicated that these features could help in discriminating the A-cell epitopes from non-epitopes.

We developed SVM-based prediction models using SVM light by from the dataset of A-cell epitopes as positive sequences and non-epitopes as negative sequences. Thus, the training—testing dataset had positive and negative sequences. The best performing models were selected on the basis of highest Matthews correlation coefficient values and a minimal difference between the sensitivity and specificity values.

In order to understand, which machine learning technique will be most efficient for predicting A-cell epitopes, models were developed using different machine learning techniques. These models were developed using amino acid composition AAC and dipeptide composition DPC of peptide sequences epitope and non-epitope.

As shown in Additional file 1 : Table S7, SVM based model performed better than models developed using any other machine learning technique. Similarly, performance was evaluated on the independent dataset. Thus, in this study, we used SVM for developing models using various features of peptides.