Screening and insilico analysis of deleterious nsSNPs (missense) in human CSF3 for their effects on protein structure,stability and function

Human granulocyte colony stimulating factor (hG-CSF), known as CSF3, plays an important role in the growth, differentiation, proliferation, survival, and activation of the granulocyte cell lineage such as neutrophils and their precursors. Functional reduction in native CSF3 protein results in reduced proliferation and activation of neutrophils and leads to neutropenia. Single nucleotide polymorphisms (SNPs) in the CSF3 gene may have deleterious effects on the CSF3 protein function. This study was undertaken to find the functional SNPs in the human CSF3 gene. Results suggest that 18.9% of all the SNPs in the dbSNP database for CSF3 gene were present in the coding region. Out of 59 non-synonymous SNPs (nsSNPs), 26 nsSNPs were predicted to be non-tolerable by SIFT whereas 18 and 7 nsSNPs were predicted as probably damaging and possibly damaging, respectively by PolyPhen. Among this 31 nsSNPs, 16 nsSNPs were identified to be potentially deleterious by PANTHER server, and 4 nsSNPs were found to be neutral by PROVEAN. SNPAnalyzer predicted 7 nsSNPs to be neutral phenotype and the remaining 24 nsSNPs to be associated with diseases. Among the predicted nsSNPs, rs144408123, rs144408123, rs145136406, rs145311241, rs373191696, rs762945096, rs763688260, rs767572172, rs775326370, rs777777864, rs777983866, rs781596455, rs139072004, rs757612684, rs772911210, rs139072004, rs746634544, rs749993200, rs763426127, rs772466210 were identified as deleterious and potentially damaging. I-Mutant analysis revealed that th 20 nsSNPs were important for protein stability of CSF3. Therefore, th 20 nsSNPs may be used for the wider population-based genetic studies and also should be taken into account while engineering the recombinant CSF3 protein for clinical use.     

Computational algorithmic and molecular dynamics study of functional and structural impacts of non-synonymous single nucleotide polymorphisms in human DHFR gene

Human dihydrofolate reductase (DHFR) is a conserved enzyme that is central to folate metabolism and is widely targeted in pathogenic diseases as well as cancers. Although studies have reported the fact that genetic mutations in DHFR leads to a rare autosomal recessive inborn error of folate metabolism and drug resistance, there is a lack of an extensive study on how the deleterious non-synonymous SNPs (nsSNPs) disrupt its phenotypic effects. In this study, we aim at discovering the structural and functional consequences of nsSNPs in DHFR by employing a combined computational approach consisting of ten recently developed in silico tools for identification of damaging nsSNPs and molecular dynamics (MD) simulation for getting deeper insights into the magnitudes of damaging effects. Our study revealed the presence of 12 most deleterious nsSNPs affecting the native phenotypic effects, with three (R71T, G118D, Y122D) identified in the co-factor and ligand binding active sites. MD simulations also suggested that these three SNPs particularly Y122D, alter the overall structural flexibility and dynamics of the native DHFR protein which can provide more understandings into the crucial roles of these mutants in influencing the loss of DHFR function.     

Identification and structural characterization of deleterious non-synonymous single nucleotide polymorphisms in the human SKP2 gene

In SCF (Skp, Cullin, F-box) ubiquitin-protein ligase complexes, S-phase kinase 2 (SKP2) is one of the major players of F-box family, that is responsible for the degradation of several important cell regulators and tumor suppressor proteins. Despite of having significant evidence for the role of SKP2 on tumorgenesis, there is a lack of available data regarding the effect of non-synonymous polymorphisms. In this communication, the structural and functional consequences of non-synonymous single nucleotide polymorphisms (nsSNPs) of SKP2 have been reported by employing various computational approaches and molecular dynamics simulation. Initially, several computational tools like SIFT, PolyPhen-2, PredictSNP, I-Mutant 2.0 and ConSurf have been implicated in this study to explore the damaging SNPs. In total of 172 nsSNPs, 5 nsSNPs were identified as deleterious and 3 of them were predicted to be decreased the stability of protein. Guided from ConSurf analysis, P101L (rs761253702) and Y346C (rs755010517) were categorized as the highly conserved and functional disrupting mutations. Therefore, these mutations were subjected to three dimensional model building and molecular dynamics simulation study for the detailed structural consequences upon the mutations. The study revealed that P101L and Y346C mutations increased the flexibility and changed the structural dynamics. As both these mutations are located in the most functional regions of SKP2 protein, these computational insights might be helpful to consider these nsSNPs for wet-lab confirmatory analysis as well as in rationalizing future population based studies and structure based drug design against SKP2.     

A comprehensive in Silico analysis of the functional and structural impact of single nucleotide polymorphisms (SNPs) in the human IL-33 gene

Interleukin 33 (IL-33) is the latest member of the IL-1 cytokine family, which plays both pro - and anti-inflammatory functions. Numerous Single-nucleotide polymorphisms (SNPs) in the IL-33 gene have been recognized to be associated with a vast variety of inflammatory disorders. SNPs associated studies have become a crucial approach in uncovering the genetic background of human diseases. However, distinguishing the functional SNPs in a disease-related gene from a pool of both functional and neutral SNPs is a major challenge and needs multiple experiments of hundreds or thousands of SNPs in candidate genes. This study aimed to identify the possible deleterious SNPs in the IL-33 gene using bioinformatics predictive tools. The nonsynonymous SNPs (nsSNPs) were analyzed by SIFT, PolyPhen, PROVEAN, SNP&GO, MutPred, SNAP, PhD SNP, and I-Mutant tools. The Non-coding SNPs (ncSNPs) were also analyzed by SNPinfo and RegulomeDB tools. In conclusion, our in-silico analysis predicted 5 nsSNPs and 22 ncSNPs as potential candidates in the IL-33 gene for future genetic association studies.     

Predicting protein-protein interactions using graph invariants and a neural network

The PDZ domain of proteins mediates a protein-protein interaction by recognizing the hydrophobic C-terminal tail of the target protein. One of the challenges put forth by the DREAM (Discussions on Reverse Engineering Assessment and Methods) 2009 Challenge consists of predicting a position weight matrix (PWM) that describes the specificity profile of five PDZ domains to their target peptides. We consider the primary structures of each of the five PDZ domains as a numerical sequence derived from graph-theoretic models of each of the individual amino acids in the protein sequence. Using available PDZ domain databases to obtain known targets, the graph-theoretic based numerical sequences are then used to train a neural network to recognize their targets. Given the challenge sequences, the target probabilities are computed and a corresponding position weight matrix is derived. In this work we present our method. The results of our method placed second in the DREAM 2009 challenge.     

Effective detection of peptides containing cysteine sulfonic acid using matrix-assisted laser desorption/ionization and laser desorption/ionization on porous silicon mass spectrometry

Cysteine sulfonic acid-containing peptides, being typical acidic peptides, exhibit low response in matrix-assisted laser desorption/ionization (MALDI) mass spectrometry. In this study, matrix conditions and the effect of diammonium hydrogencitrate (DAHC) as additive were investigated for ionization of cysteine sulfonic acid-containing peptides in MALDI. A matrix-free ionization method, desorption/ionization on porous silicon (DIOS), was also utilized to evaluate the effect of DAHC. When equimolar three-component mixtures of peptides carrying free cysteine, cysteine sulfonic acid, and carbamidomethyl cysteine were measured by MALDI using a common matrix, alpha-cyano-4-hydroxycinnamic acid (CHCA), no signal corresponding to cysteine sulfonic acid-containing peptide could be observed in the mass spectrum. However, by addition of DAHC to CHCA, the peaks of cysteine sulfonic acid-containing peptides were successfully observed, as well as when using 2,4,6-trihydroxyacetophenone (THAP) and 2,6-dihydroxyacetophenone with DAHC. In the DIOS mass spectra of these analytes, the use of DAHC also enhanced the peak intensity of the cysteine sulfonic acid-containing peptides. On the basis of studies with these model peptides, tryptic digests of oxidized peroxiredoxin 6 were examined as a complex peptide mixture by MALDI and DIOS. In MALDI, the peaks of cysteine sulfonic acid-containing peptides were observed when using THAP/DAHC as the matrix, but this was not so with CHCA. In DIOS, the signal from cysteine sulfonic acid-containing peptides was suppressed; however, the use of DAHC significantly enhanced the signal intensity with an increase in the number of observed peptides and increased signal-to-noise ratio in the DIOS spectra. The results show that DAHC in the matrix or on the DIOS chip decreases discrimination and suppression effects in addition to suppressing alkali-adduct ions, which leads to a beneficial effect on protonation of peptides containing cysteine sulfonic acid.     

Assessment of structurally and functionally high-risk nsSNPs impacts on human bone morphogenetic protein receptor type IA (BMPR1A) by computational approach

BMPR1A (BMP type 1 receptor) is a transmembrane cell-surface receptor also known as ALK3 (activin-like kinases-3) encodes for a type I serine/threonine kinase receptor and a member of the transforming growth-factor β–receptor (TGF-β) super family. The BMPR1A has a significant interaction with BMP-2 for protein activity and also has a low affinity with growth and differentiation factor 5 (GDF5); positively regulates chondrocyte differentiation. The genetic variations can alter the structure and function of the BMPR1A gene that causes several diseases such as juvenile polyposis syndrome or hereditary cancer-predisposing syndrome. The current study was carried out to identify potential deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in BMPR1A by implementing different computational algorithms such as SIFT, PolyPhen2, SNAP2, PROVEAN, PhD-SNP, SNPs&GO, nsSNPAnalyzer, and P-Mut. From 205 nsSNPs in BMPR1A, 7 nsSNPs (C76Y, C124R, C124Y, C376Y, R443C, R480W, and W487R) were predicted as deleterious in 8 prediction algorithms. The Consurf analysis showed that selected 7 nsSNPs were present in the highly conserved regions. Molecular dynamics simulation analysis also performed to explore conformational changes in the variant structure with respect to its native structure. According to the MDS result, all variants flexibility and rigidity were unbalanced, which may alter the structural and functional behavior of the native protein. Although, three nsSNPs i.e., C124R, C376Y, and R443C have already been reported in patients associated with JPS, but their structural and functional molecular studies remain uncharacterized. Therefore, the findings of this study can provide a better understanding of uncharacterized nsSNPS and to find their association with disease susceptibility and also facilitate to the researchers for designing or developing the target dependent drugs.     

Computational analyses prioritize and reveal the deleterious nsSNPs in human angiotensinogen gene

Angiotensinogen (AGT) is a key component of renin-angiotensin-aldosterone system (RAAS), which plays central role in blood pressure homeostasis. Association of AGT polymorphisms have been investigated in different ethnic populations in variety of cardiovascular and non-cardiovascular conditions. In this study, 354 non-synonymous SNPs (nsSNPs) of AGT were evaluated to predict damaging and structurally important variants. Majority of the deleterious nsSNPs occurred in the evolutionary conserved regions. Several of these nsSNPs were found to affect post-translational modifications like methylation, glycosylation, phosphorylation, ubiquitination etc. Structural evaluations predicted 19 variants as destabilizing and some of them were also predicted to destabilize the renin-AGT interaction. Therefore, the present computational investigation predicted pathogenic and functionally important variants of human AGT gene. The study has also shown that AGT deregulation is associated with survival outcome in patients with gastric and breast cancer, using microarray gene expression profile. Furthermore, the computationally screened nsSNPs can be analyzed in population based genotyping studies and may help futuristic drug development in the area of AGT pharmacogenomics.     

Improved spectra for MALDI MSI of peptides using ammonium phosphate monobasic in MALDI matrix

MALDI mass spectrometry imaging (MSI) enables analysis of peptides along with histology. However, there are several critical steps in MALDI MSI of peptides, 1 of which is spectral quality. Suppression of MALDI matrix clusters by the aid of ammonium salts in MALDI experiments is well known. It is asserted that addition of ammonium salts dissociates potential matrix adducts and thereafter decreases matrix cluster formation. Consequently, MALDI MS sensitivity and mass accuracy increase. Up to our knowledge, a limited number of MALDI MSI studies used ammonium salts as matrix additives to suppress matrix clusters and enhance peptide signals. In this work, we investigated the effect of ammonium phosphate monobasic (AmP) as alpha‐cyano‐4‐hydroxycinnamic acid (α‐CHCA) matrix additive in MALDI MSI of peptides. Prior to MALDI MSI, the effect of varying concentrations of AmP in α‐CHCA was assessed in bovine serum albumin tryptic digests and compared with the control (α‐CHCA without AmP). Based on our data, the addition of AmP as matrix additive decreased matrix cluster formation regardless of its concentration, and specifically, 8 mM AmP and 10 mM AmP increased bovine serum albumin peptide signal intensities. In MALDI MSI of peptides, both 8 and 10 mM AmP in α‐CHCA improved peptide signals especially in the mass range of m/z 2000 to 3000. In particular, 9 peptide signals were found to have differential intensities within the tissues deposited with AmP in α‐CHCA (AUC > 0.60). To the best of our knowledge, this is the first MALDI MSI of peptides work investigating different concentrations of AmP as α‐CHCA matrix additive to enhance peptide signals in formalin‐fixed paraffin‐embedded (FFPE) tissues. Further, AmP as part of α‐CHCA matrix could enhance protein identifications and support MALDI MSI‐based proteomic approaches.     

Mesoporous tungsten titanate as matrix for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis of biomolecules

In this paper, mesoporous tungsten titanate (WTiO) with different nano-pore structures was utilized as matrix for the analysis of short peptides by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS). Effect of characteristic features of mesoporous matrices on laser desorption/ionization process was investigated. Experiments showed that the ordered two-dimensional and three-dimensional mesoporous matrices were superior in performance to the non-ordered WTiO matrix. The dramatic enhancement of signal sensitivity by the ordered mesoporous matrices can be reasonably attributed to the ordered structure, which facilitated the understanding on structure-function relationship in mesoporous cavity for laser desorption process of adsorbed biomolecules. With the ordered mesoporous matrix, the short peptides are successfully detected. The presence of trace alkali metal salt effectively increased the analyte ion yields and the MALDI-TOFMS using the inorganic mesoporous matrices displayed a high salt tolerance. The developed technique also showed a satisfactory performance in peptide-mapping and amino-acid sequencing analysis.     

Investigating quantitation of phosphorylation using MALDI-TOF mass spectrometry

Despite advances in methods and instrumentation for analysis of phosphopeptides using mass spectrometry, it is still difficult to quantify the extent of phosphorylation of a substrate because of physiochemical differences between unphosphorylated and phosphorylated peptides. Here we report experiments to investigate those differences using MALDI-TOF mass spectrometry for a set of synthetic peptides by creating calibration curves of known input ratios of peptides/phosphopeptides and analyzing their resulting signal intensity ratios. These calibration curves reveal subtleties in sequence-dependent differences for relative desorption/ionization efficiencies that cannot be seen from single-point calibrations. We found that the behaviors were reproducible with a variability of 5-10% for observed phosphopeptide signal. Although these data allow us to begin addressing the issues related to modeling these properties and predicting relative signal strengths for other peptide sequences, it is clear that this behavior is highly complex and needs to be further explored.     

Trace LC/MS quantitative analysis of polypeptide biomarkers: impact of 1-D and 2-D chromatography on matrix effects and sensitivity

We investigated the impact of one dimension (single reverse phase (RP) column) and two dimension (two different RP columns) chromatographic methods on SIM (MS) and multiple reaction monitoring (MRM; MS/MS) performance from human plasma. We find that MRM analysis is clearly preferable for 1-D applications; however, implementation of SIM detection in conjunction with 2-D separation technique resulted in an over 60-fold increase in analyte peak area and improved S/N compared to MRM for our analyte, human C-peptide. Implementation of a 2-D RP-RP technique with SIM detection is capable of eliminating matrix effects and greatly increases signal response and data quality. For two large peptides in complex biological samples, we found that a 2-D approach performed better than high quality sample preparation together with 1-D chromatography and MRM, even on a high-end mass spectrometer.     

Structure prediction of cyclic peptides by molecular dynamics + machine learning

Recent computational methods have made strides in discovering well-structured cyclic peptides that preferentially populate a single conformation. However, many successful cyclic-peptide therapeutics adopt multiple conformations in solution. In fact, the chameleonic properties of some cyclic peptides are likely responsible for their high cell membrane permeability. Thus, we require the ability to predict complete structural ensembles for cyclic peptides, including the majority of cyclic peptides that have broad structural ensembles, to significantly improve our ability to rationally design cyclic-peptide therapeutics. Here, we introduce the idea of using molecular dynamics simulation results to train machine learning models to enable efficient structure prediction for cyclic peptides. Using molecular dynamics simulation results for several hundred cyclic pentapeptides as the training datasets, we developed machine-learning models that can provide molecular dynamics simulation-quality predictions of structural ensembles for all the hundreds of thousands of sequences in the entire sequence space. The prediction for each individual cyclic peptide can be made using less than 1 second of computation time. Even for the most challenging classes of poorly structured cyclic peptides with broad conformational ensembles, our predictions were similar to those one would normally obtain only after running multiple days of explicit-solvent molecular dynamics simulations. The resulting method, termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), is the first technique capable of efficiently predicting complete structural ensembles of cyclic peptides without relying on additional molecular dynamics simulations, constituting a seven-order-of-magnitude improvement in speed while retaining the same accuracy as explicit-solvent simulations.

The StrEAMM method enables predicting the structural ensembles of cyclic peptides that adopt multiple conformations in solution.     

Scoring multiple features to predict drug disease associations using information fusion and aggregation

Prediction of drug–disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug–disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug–drug level and the drug–disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug–disease interactions. The method was validated against a well-established drug–disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.     

HIV-1 protease cleavage site prediction based on amino acid property

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired is useful for designing specific and efficient HIV protease inhibitors. Recently, several works have approached the HIV-1 protease specificity problem by applying a number of classifier creation and combination methods. The pace in searching for the proper inhibitors of HIV protease will be greatly expedited if one can find an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this article, we selected HIV-1 protease as the subject of the study. 299 oligopeptides were chosen for the training set, while the other 63 oligopeptides were taken as a test set. The peptides are represented by features constructed by AAIndex (Kawashima et al., Nucleic Acids Res 1999, 27, 368; Kawashima and Kanehisa, Nucleic Acids Res 2000, 28, 374). The mRMR method (Maximum Relevance, Minimum Redundancy; Ding and Peng, Proc Second IEEE Comput Syst Bioinformatics Conf 2003, 523; Peng et al., IEEE Trans Pattern Anal Mach Intell 2005, 27, 1226) combining with incremental feature selection (IFS) and feature forward search (FFS) are applied to find the two important cleavage sites and to select 364 important biochemistry features by jackknife test. Using KNN (K-nearest neighbors) to combine the selected features, the prediction model obtains high accuracy rate of 91.3% for Jackknife cross-validation test and 87.3% for independent-set test. It is expected that our feature selection scheme can be referred to as a useful assistant technique for finding effective inhibitors of HIV protease, especially for the scientists in this field.     

Solid-phase immunoassay detection of peptides from complex matrices without a separation

A simple and sensitive solid-phase fluorescence immunoassay method was developed to detect peptides without separating them from a biological matrix. A near infrared fluorescence detection system was constructed for scanning analyte spots blotted onto protein binding membranes. Hydrophobic membranes were used with a modified vacuum spot blotting system to concentrate the peptide solution into a small area and the overall assay time was thus reduced by eliminating blocking steps. Both direct and indirect immunoassay methods are demonstrated; the indirect is more sensitive and features a 1 pmol detection limit of neat dynorphin A solutions. To further increase the immunoassay sensitivity, a novel capillary blotting system with hydrophilic membranes was designed where optimized sample volumes of 167 nL were deposited for each spot. The area-reduced blotting method shows a 1000-fold improved, 1.3 fmol spot(-1) detection limit of a dynorphin A diluted in a buffered solution of 150 mg L(-1) of casein. Low-flow push-pull perfusates with volumes of 1 microL sampled from the striatum of the rat were assayed for dynorphin A by the method of standard addition. The detection limit was estimated to be 1.9 fmol in the low-flow push-pull perfusates. These data demonstrate a solid-phase near infrared immunofluorescence strategy for the study of peptides directly blotted from chemically complex biological fluid matrices.     

Improvement of in-gel digestion protocol for peptide mass fingerprinting by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

High-sensitivity, high-throughput analysis of proteins for proteomics studies is usually performed by polyacrylamide gel electrophoresis in combination with mass spectrometry. However, the quality of the data obtained depends on the in-gel digestion procedure employed. This work describes an improvement in the in-gel digestion efficiency for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) analysis. A dramatic improvement in the coverage of tryptic peptides was observed when n-octyl glucoside was added to the buffer. Whole cell extracted proteins from S. cerevisiae were separated by two-dimensional gel electrophoresis and stained with silver. Protein spots were identified using our improved in-gel digestion method and MALDI-TOFMS. In addition, the mass spectra obtained by using the matrix alpha-cyano-4-hydroxycinnamic acid (CHCA) were compared with those obtained using 2,5-dihydroxybenzoic acid (DHB). The DHB matrix usually gave more peaks, which led to higher sequence coverage and, consequently, to higher confidence in protein identification. This improved in-gel digestion protocol is simple and useful for protein identification by MALDI-TOFMS.