4 research outputs found
Molecular Descriptor Subset Selection in Theoretical Peptide Quantitative StructureâRetention Relationship Model Development Using Nature-Inspired Optimization Algorithms
In
this work, performance of five nature-inspired optimization
algorithms, genetic algorithm (GA), particle swarm optimization (PSO),
artificial bee colony (ABC), firefly algorithm (FA), and flower pollination
algorithm (FPA), was compared in molecular descriptor selection for
development of quantitative structureâretention relationship
(QSRR) models for 83 peptides that originate from eight model proteins.
The matrix with 423 descriptors was used as input, and QSRR models
based on selected descriptors were built using partial least squares
(PLS), whereas root mean square error of prediction (RMSEP) was used
as a fitness function for their selection. Three performance criteria,
prediction accuracy, computational cost, and the number of selected
descriptors, were used to evaluate the developed QSRR models. The
results show that all five variable selection methods outperform interval
PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA
is superior because of its lowest computational cost and higher accuracy
(RMSEP of 5.534%) with a smaller number of variables (nine descriptors).
The GA-QSRR model was validated initially through Y-randomization.
In addition, it was successfully validated with an external testing
set out of 102 peptides originating from <i>Bacillus subtilis</i> proteomes (RMSEP of 22.030%). Its applicability domain was defined,
from which it was evident that the developed GA-QSRR exhibited strong
robustness. All the sources of the modelâs error were identified,
thus allowing for further application of the developed methodology
in proteomics
Qualitative and Quantitative Analysis of Proteome and Peptidome of Human Follicular Fluid Using Multiple Samples from Single Donor with LCâMS and SWATH Methodology
Human
follicular fluid (hFF) is a natural environment of oocyte
maturation, and some components of hFF could be used to judge oocyte
capability for fertilization and further development. In our pilot
small-scale study three samples from four donors (12 samples in total)
were analyzed to determine which hFF proteins/peptides could be used
to differentiate individual oocytes and which are patient-specific.
Ultrafiltration was used to fractionate hFF to high-molecular-weight
(HMW) proteome (>10 kDa) and low-molecular-weight (LMW) peptidome
(<10 kDa) fractions. HMW and LMW compositions were analyzed using
LCâMS in SWATH data acquisition and processing methodology.
In total we were able to identify 158 proteins, from which 59 were
never reported before as hFF components. 55 (45 not reported before)
proteins were found by analyzing LMW fraction, 67 (14 not reported
before) were found by analyzing HMW fraction, and 36 were identified
in both fractions of hFF. We were able to perform quantitative analysis
for 72 proteins from HMW fraction of hFF. We found that concentrations
of 11 proteins varied substantially among hFF samples from single
donors, and those proteins are promising targets to identify biomarkers
useful in oocyte quality assessment
Qualitative and Quantitative Analysis of Proteome and Peptidome of Human Follicular Fluid Using Multiple Samples from Single Donor with LCâMS and SWATH Methodology
Human
follicular fluid (hFF) is a natural environment of oocyte
maturation, and some components of hFF could be used to judge oocyte
capability for fertilization and further development. In our pilot
small-scale study three samples from four donors (12 samples in total)
were analyzed to determine which hFF proteins/peptides could be used
to differentiate individual oocytes and which are patient-specific.
Ultrafiltration was used to fractionate hFF to high-molecular-weight
(HMW) proteome (>10 kDa) and low-molecular-weight (LMW) peptidome
(<10 kDa) fractions. HMW and LMW compositions were analyzed using
LCâMS in SWATH data acquisition and processing methodology.
In total we were able to identify 158 proteins, from which 59 were
never reported before as hFF components. 55 (45 not reported before)
proteins were found by analyzing LMW fraction, 67 (14 not reported
before) were found by analyzing HMW fraction, and 36 were identified
in both fractions of hFF. We were able to perform quantitative analysis
for 72 proteins from HMW fraction of hFF. We found that concentrations
of 11 proteins varied substantially among hFF samples from single
donors, and those proteins are promising targets to identify biomarkers
useful in oocyte quality assessment
Qualitative and Quantitative Analysis of Proteome and Peptidome of Human Follicular Fluid Using Multiple Samples from Single Donor with LCâMS and SWATH Methodology
Human
follicular fluid (hFF) is a natural environment of oocyte
maturation, and some components of hFF could be used to judge oocyte
capability for fertilization and further development. In our pilot
small-scale study three samples from four donors (12 samples in total)
were analyzed to determine which hFF proteins/peptides could be used
to differentiate individual oocytes and which are patient-specific.
Ultrafiltration was used to fractionate hFF to high-molecular-weight
(HMW) proteome (>10 kDa) and low-molecular-weight (LMW) peptidome
(<10 kDa) fractions. HMW and LMW compositions were analyzed using
LCâMS in SWATH data acquisition and processing methodology.
In total we were able to identify 158 proteins, from which 59 were
never reported before as hFF components. 55 (45 not reported before)
proteins were found by analyzing LMW fraction, 67 (14 not reported
before) were found by analyzing HMW fraction, and 36 were identified
in both fractions of hFF. We were able to perform quantitative analysis
for 72 proteins from HMW fraction of hFF. We found that concentrations
of 11 proteins varied substantially among hFF samples from single
donors, and those proteins are promising targets to identify biomarkers
useful in oocyte quality assessment