Using spectral moments of spiral networks based on PSA/mass spectra outcomes to derive quantitative proteome–disease relationships (QPDRs) and predicting prostate cancer

An Analysis Model of Protein Mass Spectrometry Data and its Application

Backgroud:

The impact of cancer in society created the necessity of new and faster theoretical models for the early diagnosis of cancer.

Methods:

In this work, a mass spectrometry (MS) data analysis method based on the star-like graph of protein and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the MS data set. Firstly, the MS data is reduced and transformed into the corresponding protein sequence. Then, the topological indexes of the star-like graph are calculated to describe each MS data of the cancer sample. Finally, the SVM model is suggested to classify the MS data.

Results:

Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the model were 96.45%, 96.88%, and 95.67%, respectively, for [0,1] normalization data, and 94.43%, 96.25%, and 91.11% for [-1,1] normalization data.

Conclusion:

The model combined with the SELDI-TOF-MS technology has a prospect in early clinical detection and diagnosis of ovarian cancer.

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.

Naïve Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer

Fast cancer diagnosis represents a real necessity in applied medicine due to the importance of this disease. Thus, theoretical models can help as prediction tools. Graph theory representation is one option because it permits us to numerically describe any real system such as the protein macromolecules by transforming real properties into molecular graph topological indices. This study proposes a new classification model for proteins linked with human colon cancer by using spiral graph topological indices of protein amino acid sequences. The best quantitative structure-disease relationship model is based on eleven Shannon entropy indices. It was obtained with the Naïve Bayes method and shows excellent predictive ability (90.92%) for new proteins linked with this type of cancer. The statistical analysis confirms that this model allows diagnosing the absence of human colon cancer obtaining an area under receiver operating characteristic of 0.91. The methodology presented can be used for any type of sequential information such as any protein and nucleic acid sequence.

Analysis of synovial fluid in knee joint of osteoarthritis:5 proteome patterns of joint inflammation based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

PURPOSE

The purpose of this study was to use matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in osteoarthritis research. Our aim was to find differentially expressed disease-related and condition-specific peptide in synovial fluid in the knee joint of patients suffering from osteoarthritis (OA), and to develop and validate the peptide classification model for OA diagnosis.

METHODS

Based on the American College of Rheumatology criteria, 30 OA cases and ten healthy donors were enrolled and underwent analysis. Magnetic beads-based weak cation exchange chromatography (MB-WCX) was performed for sample processing, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) was conducted for peptide profile. ClinProt software 2.2 was used for data analysis and a genetic algorithm was created for class prediction.

RESULTS

Two peptide peaks were found which may be characterised as the potential diagnostic markers for OA. Two other significantly different peptide peaks were found in OA patients at a medium stage compared to the early and late stages. A genetic algorithm (GA) was used to establish differential diagnosis models of OA. As a result, the algorithm models marked 100% of OA, and of 97.92% of medium-stage OA.

CONCLUSION

This study demonstrated that use of proteomics methods to identify potential biomarkers of OA is possible, and the identified potential biomarkers may be potential markers for diagnosis and monitoring the progression of OA.

MISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens

Infections caused by human parasites (HPs) affect the poorest 500 million people worldwide but chemotherapy has become expensive, toxic, and/or less effective due to drug resistance. On the other hand, many 3D structures in Protein Data Bank (PDB) remain without function annotation. We need theoretical models to quickly predict biologically relevant Parasite Self Proteins (PSP), which are expressed differentially in a given parasite and are dissimilar to proteins expressed in other parasites and have a high probability to become new vaccines (unique sequence) or drug targets (unique 3D structure). We present herein a model for PSPs in eight different HPs (Ascaris, Entamoeba, Fasciola, Giardia, Leishmania, Plasmodium, Trypanosoma, and Toxoplasma) with 90% accuracy for 15 341 training and validation cases. The model combines protein residue networks, Markov Chain Models (MCM) and Artificial Neural Networks (ANN). The input parameters are the spectral moments of the Markov transition matrix for electrostatic interactions associated with the protein residue complex network calculated with the MARCH-INSIDE software. We implemented this model in a new web-server called MISS-Prot (MARCH-INSIDE Scores for Self-Proteins). MISS-Prot was programmed using PHP/HTML/Python and MARCH-INSIDE routines and is freely available at: . This server is easy to use by non-experts in Bioinformatics who can carry out automatic online upload and prediction with 3D structures deposited at PDB (mode 1). We can also study outcomes of Peptide Mass Fingerprinting (PMFs) and MS/MS for query proteins with unknown 3D structures (mode 2). We illustrated the use of MISS-Prot in experimental and/or theoretical studies of peptides from Fasciola hepatica cathepsin proteases or present on 10 Anisakis simplex allergens (Ani s 1 to Ani s 10). In doing so, we combined electrophoresis (1DE), MALDI-TOF Mass Spectroscopy, and MASCOT to seek sequences, Molecular Mechanics + Molecular Dynamics (MM/MD) to generate 3D structures and MISS-Prot to predict PSP scores. MISS-Prot also allows the prediction of PSP proteins in 16 additional species including parasite hosts, fungi pathogens, disease transmission vectors, and biotechnologically relevant organisms.

Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica

There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately, most QSAR models predict activity against only one protein. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 32:32-15-1:1. This MLP classifies correctly 623 out of 678 DTPs (Sensitivity = 91.89%) and 2995 out of 3234 nDTPs (Specificity = 92.61%), corresponding to training Accuracy = 92.48%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 313 out of 338 DTPs (Sensitivity = 92.60%) and 1411 out of 1534 nDTP (Specificity = 91.98%) in validation series, corresponding to total Accuracy = 92.09% for validation series (Predictability). This model favorably compares with other LDA and ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. These mt-QSARs offer also a good opportunity to construct drug-protein Complex Networks (CNs) that can be used to explore large and complex drug-protein receptors databases. Finally, we illustrated two practical uses of this model with two different experiments. In experiment 1, we report prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of 10 rasagiline derivatives promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, SEC and 1DE sample preparation, MALDI-TOF MS and MS/MS analysis, MASCOT search, MM/MD 3D structure modeling, and QSAR prediction for different peptides of hemoglobin found in the proteome of the human parasite Fasciola hepatica; which is promising for anti-parasite drug targets discovery.

Machine learning techniques for single nucleotide polymorphism--disease classification models in schizophrenia

Single nucleotide polymorphisms (SNPs) can be used as inputs in disease computational studies such as pattern searching and classification models. Schizophrenia is an example of a complex disease with an important social impact. The multiple causes of this disease create the need of new genetic or proteomic patterns that can diagnose patients using biological information. This work presents a computational study of disease machine learning classification models using only single nucleotide polymorphisms at the HTR2A and DRD3 genes from Galician (Northwest Spain) schizophrenic patients. These classification models establish for the first time, to the best knowledge of the authors, a relationship between the sequence of the nucleic acid molecule and schizophrenia (Quantitative Genotype – Disease Relationships) that can automatically recognize schizophrenia DNA sequences and correctly classify between 78.3–93.8% of schizophrenia subjects when using datasets which include simulated negative subjects and a linear artificial neural network.

Serum peptidome patterns of human systemic lupus erythematosus based on magnetic bead separation and MALDI-TOF mass spectrometry analysis

OBJECTIVES

Currently few studies have been reported to utilize matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in rheumatic disease, especially in systemic lupus erythematosus (SLE). Our aim was to find differentially expressed disease-related and condition-specific peptides in sera from patients with SLE, as well as to develop and validate the peptide classification model for the diagnosis of SLE.

METHODS

Based on the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) 2000, 50 SLE patients were divided into two subgroups: 25 were defined as stable SLE (SLEDAI < or = 8) and 25 as active SLE (SLEDAI > 8). Twenty-five patients with rheumatoid arthritis (RA) and 24 healthy donors were also included and underwent analysis. We performed magnetic beads-based weak cation exchange chromatography (MB-WCX) for sample processing and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for peptide profiling. ClinProt software 2.1 was used for data analysis and a genetic algorithm was modelled for class prediction.

RESULTS

A series of significant short peptides was detected. Classification models were developed to classify samples across normal controls, active SLE patients, and stable SLE patients, and achieved high capability of prediction and cross-validation. Blinded verification of the classification model showed 91.7% sensitivity in active SLE, 83.3% sensitivity in stable SLE, and 86.7% specificity in normal controls.

CONCLUSION

We have completed a preliminary study to describe the serum peptide profile of SLE and improve the diagnosis of SLE from an integrated perspective of peptide mass patterns.

Scoring function for DNA-drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories

We introduce here a new class of invariants for MD trajectories based on the spectral moments pi(k)(L) of the Markov matrix associated to lattice network-like (LN) graph representations of Molecular Dynamics (MD) trajectories. The procedure embeds the MD energy profiles on a 2D Cartesian coordinates system using simple heuristic rules. At the same time, we associate the LN with a Markov matrix that describes the probabilities of passing from one state to other in the new 2D space. We construct this type of LNs for 422 MD trajectories obtained in DNA-drug docking experiments of 57 furocoumarins. The combined use of psoralens+ultraviolet light (UVA) radiation is known as PUVA therapy. PUVA is effective in the treatment of skin diseases such as psoriasis and mycosis fungoides. PUVA is also useful to treat human platelet (PTL) concentrates in order to eliminate Leishmania spp. and Trypanosoma cruzi. Both are parasites that cause Leishmaniosis (a dangerous skin and visceral disease) and Chagas disease, respectively; and may circulate in blood products collected from infected donors. We included in this study both lineal (psoralens) and angular (angelicins) furocoumarins. In the study, we grouped the LNs on two sets; set1: DNA-drug complex MD trajectories for active compounds and set2: MD trajectories of non-active compounds or no-optimal MD trajectories of active compounds. We calculated the respective pi(k)(L) values for all these LNs and used them as inputs to train a new classifier that discriminate set1 from set2 cases. In training series the model correctly classifies 79 out of 80 (specificity=98.75%) set1 and 226 out of 238 (Sensitivity=94.96%) set2 trajectories. In independent validation series the model correctly classifies 26 out of 26 (specificity=100%) set1 and 75 out of 78 (sensitivity=96.15%) set2 trajectories. We propose this new model as a scoring function to guide DNA-docking studies in the drug design of new coumarins for anticancer or antiparasitic PUVA therapy.

Generalized lattice graphs for 2D-visualization of biological information

Several graph representations have been introduced for different data in theoretical biology. For instance, complex networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein–protein interaction networks. In addition, Randic, Liao, Nandy, Basak, and many others developed some special types of graph-based representations. This special type of graph includes geometrical constrains to node positioning in space and adopts final geometrical shapes that resemble lattice-like patterns. Lattice networks have been used to visually depict DNA and protein sequences but they are very flexible. However, despite the proved efficacy of new lattice-like graph/networks to represent diverse systems, most works focus on only one specific type of biological data. This work proposes a generalized type of lattice and illustrates how to use it in order to represent and compare biological data from different sources. We exemplify the following cases: protein sequence; mass spectra (MS) of protein peptide mass fingerprints (PMF); molecular dynamic trajectory (MDTs) from structural studies; mRNA microarray data; single nucleotide polymorphisms (SNPs); 1D or 2D-Electrophoresis study of protein polymorphisms and protein-research patent and/or copyright information. We used data available from public sources for some examples but for other, we used experimental results reported herein for the first time. This work may break new ground for the application of Graph theory in theoretical biology and other areas of biomedical sciences.

Study of peptide fingerprints of parasite proteins and drug-DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks

Since the advent of Molecular Dynamics (MD) in biopolymers science with the study by Karplus et al. on protein dynamics, MD has become the by foremost well established, computational technique to investigate structure and function of biomolecules and their respective complexes and interactions. The analysis of the MD trajectories (MDTs) remains, however, the greatest challenge and requires a great deal of insight, experience, and effort. Here, we introduce a new class of invariants for MDTs based on the spatial distribution of Mean-Energy values ξ_k (L) on a 2D Euclidean space representation of the MDTs. The procedure forces one MD trajectory to fold into a 2D Cartesian coordinates system using a step-by-step procedure driven by simple rules. The ξ_k (L) values are invariants of a Markov matrix (¹ Π), which describes the probabilities of transition between two states in the new 2D space; which is associated to a graph representation of MDTs similar to the lattice networks (LNs) of DNA and protein sequences. We also introduce a new algorithm to perform phylogenetic analysis of peptides based on MDTs instead of the sequence of the polypeptide. In a first experiment, we illustrate this algorithm for 35 peptides present on the Peptide Mass Fingerprint (PMF) of a new protein of Leishmania infantum studied in this work. We report, by the first time, 2D Electrophoresis isolation, MALDI TOF Mass Spectroscopy characterization, and MASCOT search results for this PMF. In a second experiment, we construct the LNs for 422 MDTs obtained in DNA-Drug Docking simulations of the interaction of 57 anticancer furocoumarins with a DNA oligonucleotide. We calculated the respective ξ_k (L) values for all these LNs and used them as inputs to train a new classifier with Accuracy = 85.44% and 84.91% in training and validation respectively. The new model can be used as scoring function to guide DNA-Drug Docking studies in drug design of new coumarins for PUVA therapy. The new phylogenetics analysis algorithms encode information different from sequence similarity and may be used to analyze MDTs obtained in Docking or modeling experiments for any classes of biopolymers. The work opens new perspective on the analysis and applications of MD in polymer sciences.

Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices

The cancer diagnostic is a complex process and, sometimes, the specific markers can interfere or produce negative results. Thus, new simple and fast theoretical models are required. One option is the complex network graphs theory that permits us to describe any real system, from the small molecules to the complex genetic, neural or social networks by transforming real properties in topological indices. This work converts the protein primary structure data in specific Randic's star networks topological indices using the new sequence to star networks (S2SNet) application. A set of 1054 proteins were selected from previous works and contains proteins related or not with two types of cancer, human breast cancer (HBC) and human colon cancer (HCC). The general discriminant analysis method generates an input-coded multi-target classification model with the training/predicting set accuracies of 90.0% for the forward stepwise model type. In addition, a protein subset was modified by single amino acid mutations with higher log-odds PAM250 values and tested with the new classification if can be related with HBC or HCC. In conclusion, we shown that, using simple input data such is the primary protein sequence and the simples linear analysis, it is possible to obtain accurate classification models that can predict if a new protein related with two types of cancer. These results promote the use of the S2SNet in clinical proteomics.