Meet Multiple Protein Profiler: A New Tool for Analyzing Proteomic Datasets

Dalhousie University, Canada researchers have developed a new web-based tool called Multiple Protein Profiler (MPP) that can efficiently calculate 12 key physicochemical properties of multiple protein sequences uploaded by users. Existing protein analysis tools are limited to single sequences, but MPP enables high-throughput batch analysis of entire proteomic datasets. The computed parameters provide insights into protein structure, interactions, and functions. MPP allows streamlined computational annotation of protein properties to aid functional interpretation of post-genomic data. Its availability promises accelerated research by avoiding the laborious manual characterization of individual proteins within large omics experiments.

Proteins are complex biomolecules that carry out a wide variety of functions within living organisms. From catalyzing metabolic reactions to structural support, transport of molecules, cell signaling, immune protection, and more, proteins play indispensable roles in enabling biological processes. Proteins’ unique structure, interactions, and physicochemical properties directly impact their specific biological functions. Therefore, the ability to compute key physicochemical parameters from protein sequence data provides crucial insights into structure-function relationships for interpreting complex proteomic datasets.

While existing tools can calculate individual protein properties, analysis has been constrained to single proteins. But with advanced proteomics experiments now enabling high-throughput characterization of entire proteomes, a new integrated tool was needed to handle bulk analysis. Researchers developed the Multiple Protein Profiler (MPP) to fill this gap. MPP allows batch analysis to derive 12 key physicochemical properties from proteomic datasets with hundreds or thousands of proteins.

The Pressing Need for New Proteomic Analysis Tools

The field of proteomics has witnessed remarkable technological advances increasing the scale, depth, and throughput by which proteins are profiled for structure and abundance between different biological states. Mass spectrometry methods now enable routine identification and quantification of thousands of distinct proteins within complex samples like biofluids, tissues, or cell lines. Single-cell proteomics techniques are also emerging to capture heterogeneity between individual cells. Parallel progress in next-generation DNA sequencing has yielded thousands of sequenced genomes and transcriptomes across organisms and disease conditions. Translating this wealth of omics data into biomedical insights requires knowing the functional roles and interactions of detected expressed protein products. This creates a pressing need for computational tools to efficiently annotate structural features and physicochemical properties, determining protein functionality on a large scale.

While web servers exist to predict single protein properties, few multi-functional tools allow batch sequence analysis. Traditional programs also lack integrated plotting capacities supporting visualization for proteome-wide inferences. Therefore, the researchers developed MPP 1.0 to address these limitations through an automated pipeline analyzing collections of protein sequences. MPP facilitates large-scale evaluation of 12 physicochemical properties most relevant to describing protein structure and interactions. Integrated plotting capabilities also readily generate publication-quality graphics. The MPP tool empowers cost-efficient annotation and interpretation of high-throughput proteomics data.

Key Features and Functionality of the Multiple Protein Profiler Webserver

The MPP interface allows inputting protein sequences in FASTA format or as simple text through copy-paste or file upload functions. Only standard amino acid single-letter codes are necessary as input without formatting constraints. Upon sequence submission, the backend pipeline handles parsing, multi-threaded computation of physicochemical properties, and output data organization. Results containing all computed parameters for each input sequence are presented in browser-viewable HTML tables. Links directed to external protein visualization tools like NGL are also automatically generated.

The 12 physicochemical properties calculated by MPP span sequence attributes, stability indices, and structural predictions enable diverse functional interpretations:

• Sequence length
• GRAVY hydrophobicity score
• An aliphatic index reflecting hydrophobic residues
• Instability index correlating with in vitro half-life
• Protein stability classification
• Molecular weight
• Aromaticity indicating aromatic residue abundance
• Theoretical isoelectric point (pI)
• Estimated net charge at pH 7
• Secondary structure fractions (helix, sheet, turn)
• Molar extinction coefficients assessing cysteine bonds
• Elemental composition by amino acid (CHONS)

In addition to tabulating per protein measurements, MPP also enables quick visual inspection of global trends through integrated plotting functions that generate histograms, scatter plots, and pie charts. Results can be easily exported as downloadable CSV spreadsheets for further statistical analysis. External links provide a drill-down examination of individual sequences. Overall, the MPP tool lowers barriers to conducting batch computational analysis of protein structural properties on a proteomic scale.

Technical Details Underlying the Multiple Protein Profiler

The MPP tool leverages the Python modules SeqIO and ProteinAnalysis from the packages Bio for parsing submitted protein sequences and iterating property analysis across entries. Pre-existing Biopython modules, including Bio.SeqUtils.ProtParam and Bio.SeqIO handles the requisite protein biochemistry computations and sequence file input/output operations. Outputs are formatted for browser rendering using HTML5 and JavaScript frameworks like Plotly.js for graphical depictions. The Django web framework ties the pipeline together through a Model-View-Controller architecture interaction with the Python backend.

Hosted currently through University of Dalhousie servers, the public MPP site dynamically scales to handle inputs ranging from a few proteins to proteome-level uploads with thousands of entries. Runtimes vary based on sequence length and load but are generally processed within seconds. Usage only requires a modern web browser without local software installations. The tool is platform-agnostic and compatible with desktops and mobile devices across Windows, MacOS, and Linux systems. Ongoing development occurs through a public code repository, allowing community contributions towards expanding capabilities.

Use Cases Demonstrating Applications of the Multiple Protein Profiler

The MPP webserver enables various proteomic analysis workflows spanning basic research, clinical applications, industrial biotechnology, and more. A few exemplary use cases include:

• Genome annotation – High-throughput annotations of theoretical proteomes from newly sequenced genomes. MPP provides key functional clues guiding downstream studies.
• Proteogenomics – Identifying correlations between genomic alterations like SNPs with impacted physicochemical properties altering function. Links genotype to phenotype.
• Biomarker characterization – Systematically assessing putative protein biomarkers in biofluids for desirable stability, interactions and detection properties aiding validation.
• Protein engineering – Comparing engineered variants of therapeutic proteins with wild-type sequences to select optimal candidates exhibiting greater stability or altered interactions.
• Industrial enzymes – Guiding protein engineering or directed evolution efforts towards tailoring enzymatic catalysts for enhanced performance under harsh industrial bioprocessing conditions.

The common theme involves leveraging computational analysis to prioritize downstream targets or reveal functional insights from high-throughput proteomic data. As proteome-scale experiments become mainstream, tools like MPP promise to realize the resultant knowledge gains.

Looking Forward to a Future of Multi-Functional Proteomic Analysis

The initial release of MPP delivers an extensible foundation supporting continued expansion in capabilities. Planned future enhancements include modularity for user-defined custom properties, additional statistical processing options, and support for mutagenesis datasets. Links facilitating programmatic access through APIs may also enable integration into computational analysis pipelines and workflows. Community feature requests and collaborations aimed at empowering proteomic knowledge discovery are welcomed.

The staggering complexity underlying protein structure-function relationships and interactions driving biology necessitate Big Data solutions. As proteomics continues bridging genotype with cellular phenotype, scalable computational tools have become obligatory. Tools like Multiple Protein Profiler aim to fulfill this emerging need by enabling batch quantitative analysis of physicochemical properties. MPP’s efficiency and usability can accelerate proteomics research through intuitive and multi-functional analytics of these fascinating biomolecules encoding life.

Article Source: Reference Paper | Multiple Protein Profiler 1.0 (MPP) is available at the Webserver

Important Note: arXiv releases preprints that have not yet undergone peer review. As a result, it is important to note that these papers should not be considered conclusive evidence, nor should they be used to direct clinical practice or influence health-related behavior. It is also important to understand that the information presented in these papers is not yet considered established or confirmed.

Learn More: