PIP Antibodies

Prolactin-inducible protein (PIP)

Gene Information

Human
Gene Name:	PIP
Uniprot:	P12273
Entrez:	5304

Family

Belongs to:
PIP family

Alternative Names

GCDFP15prolactin-inducible protein; GCDFP-15SABP; GPIP4gp17; Gross cystic disease fluid protein 15; prolactin-induced proteinSecretory actin-binding protein

Molecular Weight

Mass (kDA):
16.572 kDA

Genomic Context

Human
Location:	7q34
Sequence:	7; NC_000007.14 (143132077..143139739)

Tissue Specificity

Expressed in pathological conditions of the mammary gland and in several exocrine tissues, such as the lacrimal, salivary, and sweat glands.

Subcellular Localizations

Secreted.

Diseases

• Cystic Disease -retired-
• Mammary Neoplasms
• Carcinoma
• Neoplasms
• Malignant Neoplasm Of Breast
• Malignant Neoplasms
• Neoplasm Metastasis
• Adenocarcinoma
• Malignant Paraganglionic Neoplasm
• Carcinoma, Lobular
• Sweat Gland Neoplasms
• Ductal Carcinoma
• Ductal Breast Carcinoma

• Skin Neoplasms
• Carcinoma Breast Stage Iv
• Osteitis Deformans
• Sensorineural Hearing Loss (disorder)
• Breast Fibrocystic Disease
• Lymphatic Metastasis
• Paget Disease Extramammary

Pathways

• Secretion
• Localization
• Cell Proliferation
• Virulence
• Cell Growth
• Hatching
• Cell Differentiation
• Pathogenesis
• Cell Death
• Excretion
• Transport
• Platelet Aggregation
• Cell Adhesion

• Reflex
• Cell Maturation
• Fermentation
• Oogenesis
• Lactation
• Aging
• Fertilization

PTMs

• Cleavage
• Phosphorylation
• Carboxylation
• Oxidation
• Nitration
• Nitrosylation

• Glycosylation
• Acetylation
• Methylation
• Acylation
• Ubiquitination
• Iodination

Research Areas

• Cancer
• Cellular Markers
• Signal Transduction

For details, visit:
bosterbio.com/bosterbio-gene-info-cards/PIP

Boster Bio - Best Uses Of The PIP Marker

The Boster Bio: The story behind the company that produces the PIP Marker is fascinating. Steven Boster, the man behind the company, has been developing antibodies since 1993. He was known as "he who converted science in the lavatory" because of his ability to create hundreds primary antibodies. In the late 90s, his company was the largest catalog antibody manufacturer in China. His company offers high-sensitivity ELISA tests with proprietary trade secrets.

Ion-based Ion-based IPP

IceR combines ion based PIP with a hybrid approach. It combines global modeling-based kernel density estimation with local feature-specific Ion mobility. IceR allows highly sensitive DICE quantification. It reduces the rate at which missing values occur and allows for comprehensive, label-free proteomics analysis. MaxQuant can visualize the quantification results and show the total number.

IceR, a hybrid feature-based PIP algorithms that extracts quantitative information from sequence data, is called. IceR uses decoy feature based scoring schemes to evaluate the reliability of its quantifications. The algorithm successfully transferred nearly twice the features as MaxQuant, DeMixQ, and other quantitative workflows. Its performance was compared to two benchmarks: a single sample and multiple samples.

MaxQuant, IonStar and IceR all have quantification capabilities. In grey, missing values are highlighted. Each method includes a receiver operating characteristics (ROC) plot. The area under a ROC plot is a measure for the method's performance. IceR, and IonStar can differentiate between the two types data when used to identify protein abundance patterns. Once you have chosen a method, you can use the Boster Bio Ion-based PIP marker.

PIP based upon features

Boster Bio's new feature-based marker for PIP has been created using a hybrid approach. This approach combines both feature and ionbased PIP. Its robust 2-step feature alignement combines global modeling-based feature alignment and local feature-specific KDE based alignment. Its robust features allow it narrow the DICE windows in m/z or RT-space.

IceR improves data quality and completeness in quantitative label-free proteomics by significantly increasing sensitivity and specificity. It uses two key characteristics of feature and ion based PIP and introduces a feature alignment method to detect and correct systematic and local deviations. Boster Bio's PIP marker based on feature has shown such high sensitivity as well as specificity.

IceR is able to improve on both approaches by incorporating peptide sequence information into IceR features. IceR uses a hybrid PIP method to estimate background noise. This is done by counting ions that fall into the decoy feature DEICE windows. These decoy-feature DICE windows can be defined as the m/z the IceR feature plus the alignment window. RT-dependent GAMs were fitted to the observed decoy ion counts. Compared to MaxQuant, IceR significantly reduced missing value rates. Additionally, IceR's RT-dependent approach improved DE analyses.

IceR performs feature specific alignment and uses kernel density estimates ion accumulation maps for narrower DICE windows. The algorithm also allows for ion based PIP scoring schemes. This is not possible using any other quantitative workflow. The resulting data sets are enriched with a variety or peptides. Boster Bio's feature based PIP marker is a useful tool that allows researchers to identify peptides.

Hybrid IPIP

You can increase your PPI level using the Hybrid IP Marker. It's very useful when leveling up your character. This marker will increase your experience by reducing the number you've lost. It can also help you identify potential monster hordes. This marker also makes it possible to travel quickly. It can be used as a weapon if you are a high ranking character.

IceR

There is considerable excitement surrounding low-input proteomics, and early examples have demonstrated the proof-of-principle of single-cell proteome analysis. This technology involves miniaturized sample preparation, narrow-bore chromatography, and increased mass spectrometry sensitivity. TMT multiplexing and booster channels are also part of the technology. Improved bioinformatics solutions play a pivotal role in new single-cell strategies. IceR allows for the re-analysis of single cell proteomics data, increasing the number of consistently quantified prots from a few to hundreds.

A SWATH-MS-type DIA workflow, called hyper reaction monitoring, was developed in a recent study. The authors used eight samples, each containing 12 different proteins. They were then injected into a human background. The protein abundance varied from 10% up to 5000%. Both methods were equally sensitive for detecting 12 proteins. The total change in protein abundance between DIA and DDA was the same for all eight samples.

IceR can also quantify low-abundance characteristics. It can identify 1602 peptides and 433 proteins per single-cell sample, and can rescue low-abundance features. This method improves data quality and decreases missing values by as much as 91% and 33%, respectively. It may be a good choice if your research involves peptide-based pharmacognosy.

IceR enabled differential abundance testing on 491 proteins. CellTrails separated cells based on FM1-43 uptake. To determine the most precise quantification of the proteins in the data, MaxQuant and IceR were used to analyze the results. With this software, the resulting PIP data is stored in tab-delimited text files. These files contain the results of both experiments.

IceR can also be used per sample to select a single peak. This allows it to find the peak that is closest the expected peak location. It is important to ensure that the peak's n-ions do not overlap with other known summits. The algorithm can be made more powerful by increasing the number and quality of grid points in each dimension. It also increases processing time.