Optimizing the PROTREC network-based missing protein prediction algorithm

This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.

Dealing with missing values in proteomics data

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

Resolving missing protein problems using functional class scoring

Despite technological advances in proteomics, incomplete coverage and inconsistency issues persist, resulting in “data holes”. These data holes cause the missing protein problem (MPP), where relevant proteins are persistently unobserved, or sporadically observed across samples, hindering biomarker discovery and proper functional characterization. Network-based approaches can provide powerful solutions for resolving these issues. Functional Class Scoring (FCS) is one such method that uses protein complex information to recover missing proteins with weak support. However, FCS has not been evaluated on more recent proteomic technologies with higher coverage, and there is no clear way to evaluate its performance. To address these issues, we devised a more rigorous evaluation schema based on cross-verification between technical replicates and evaluated its performance on data acquired under recent Data-Independent Acquisition (DIA) technologies (viz. SWATH). Although cross-replicate examination reveals some inconsistencies amongst same-class samples, tissue-differentiating signal is nonetheless strongly conserved, confirming that FCS selects for biologically meaningful networks. We also report that predicted missing proteins are statistically significant based on FCS p values. Despite limited cross-replicate verification rates, the predicted missing proteins as a whole have higher peptide support than non-predicted proteins. FCS also predicts missing proteins that are often lost due to weak specific peptide support.

Protein Structure Prediction Using Quantile Dragonfly and Structural Class-Based Deep Learning

Predicting three-dimensional structure of a protein in the field of computational molecular biology has received greater attention. Most of the recent research works aimed at exploring search space, however with the increasing nature and size of data, protein structure identification and prediction are still in the preliminary stage. This work is aimed at exploring search space to tackle protein structure prediction with minimum execution time and maximum accuracy by means of quantile regressive dragonfly and structural class homolog-based deep learning (QRD-SCHDL). The proposed QRD-SCHDL method consists of two distinct steps. They are protein structure identification and prediction. In the first step, protein structure identification is performed by means of QRD optimization model to identify protein structure with minimum error. Here the protein structure identification is first performed as the raw database contains sequence information and does not contain structural information. An optimization model is designed to obtain the structural information from the database. However, protein structure gives much more insight than its sequence. Therefore, to perform computational prediction of protein structure from its sequence, actual protein structure prediction is made. The second step involves the actual protein structure prediction via structural class and homolog-based deep learning. For each protein structure prediction, a scoring matrix is obtained by utilizing structural class maximum correlation coefficient. Finally, the proposed method is tested on a set of different unique numbers of protein data and compared to the state-of-the-art methods. The obtained results showed the potentiality of the proposed method in terms of metrics, error rate, protein structure prediction time, protein structure prediction accuracy, precision, specificity, recall, ROC, Kappa coefficient and [Formula: see text]-measure, respectively. It also shows that the proposed QRD-SCHDL method attains comparable results and outperformed in certain cases, thereby signifying the efficiency of the proposed work.

PROTREC: A probability-based approach for recovering missing proteins based on biological networks

A novel network-based approach for predicting missing proteins (MPs) is proposed here. This approach, PROTREC (short for PROtein RECovery), dominates existing network-based methods - such as Functional Class Scoring (FCS), Hypergeometric Enrichment (HE), and Gene Set Enrichment Analysis (GSEA) - across a variety of proteomics datasets derived from different proteomics data acquisition paradigms: Higher PROTREC scores are much more closely correlated with higher recovery rates of MPs across sample replicates. The PROTREC score, unlike methods reporting p-values, can be directly interpreted as the probability that an unreported protein in a proteomic screen is actually present in the sample being screened. SIGNIFICANCE: Mass spectrometry (MS) has developed rapidly in recent years; however, an obvious proportion of proteins is still undetected, leading to missing protein problems. A few existing protein recovery methods are based on biological networks, but the performance is not satisfactory. We propose a new protein recovery method, PROTREC, a Bayesian-inspired approach based on biological networks, which shows exceptional performance across multiple validation strategies. It does not rely on peptide information, so it avoids the ambiguity issue that most protein assembly methods face.