Proteomics Pipeline Research Articles

Comparative Analysis of Data-Driven Rescoring Platforms for Improved Peptide Identification in HeLa Digest Samples.

Mass spectrometry is a critical tool to understand complex changes in biological processes. Despite significant advances in search engine technology, many spectra remain unassigned. This research evaluates the performance of three rescoring platforms, Oktoberfest, MS2Rescore, and inSPIRE, using MaxQuant output. The results indicated a substantial increase in identifications at the peptide level (40%-53%) and PSM level (64%-67%). However, some peptides were lost due to limitations in processing posttranslational modifications (PTMs)-with up to 75% of lost peptides exhibiting PTMs. Each platform displayed distinct strengths and weaknesses. For instance, inSPIRE performed best in terms of peptide identifications and unique peptides, while MS2Rescore performed better for PSMs at higher FDR values. Differences in platform performance stemmed from different sources: original search engine feature selection, type of ion series predicted, retention time predictor, and PTMs compatibility. Overall, inSPIRE showed a superior ability to harness original search engine results. Taken all together, rescoring platforms clearly outperformed original search results; however, they demanded additional computation time (up to 77%) and manual adjustments. The findings here underline the necessity of integrating rescoring platforms into current proteomics pipelines but also address some challenges in their implementation and optimization. Future integrated platforms may help enhance adoption.

Proteomics: An In-Depth Review on Recent Technical Advances and Their Applications in Biomedicine.

Proteins hold pivotal importance since many diseases manifest changes in protein activity. Proteomics techniques provide a comprehensive exploration of protein structure, abundance, and function in biological samples, enabling the holistic characterization of overall changes in organisms. Nowadays, the breadth of emerging methodologies in proteomics is unprecedentedly vast, with constant optimization of technologies in sample processing, data collection, data analysis, and its scope of application is steadily transitioning from the bench to the clinic. Here, we offer an insightful review of the technical developments in proteomics and its applications in biomedicine over the past 5 years. We focus on its profound contributions in profiling disease spectra, discovering new biomarkers, identifying promising drug targets, deciphering alterations in protein conformation, and unearthing protein-protein interactions. Moreover, we summarize the cutting-edge technologies and potential breakthroughs in the proteomics pipeline and provide the principal challenges in proteomics. Based on these, we aspire to broaden the applicability of proteomics and inspire researchers to enhance our understanding of complex biological systems by utilizing such techniques.

A Proteomics Pipeline for Generating Clinical Grade Biomarker Candidates from Data-Independent Acquisition Mass Spectrometry (DIA-MS) Discovery.

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package that uses data-independent acquisition analysis from a discovery cohort to select precursors, peptides, and proteins that adhere to analytical criteria required for established targeted assays. TEAQ was applied to DIA-MS data from plasma samples acquired on a new high resolution accurate mass (HRAM) mass spectrometry platform where precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation based on 8- or 11-point loading curves at three throughputs. This data can be used as a general resource for developing other targeted assays. TEAQ analysis of data from a case and control cohort for inflammatory bowel disease (n=492) identified 1110 signature peptides for 326 quantifiable proteins from the 1179 identified proteins. Applying TEAQ analysis to discovery data will streamline targeted assay development and the transition to validation and clinical studies.

A Proteomics Pipeline for Generating Clinical Grade Biomarker Candidates from Data‐Independent Acquisition Mass Spectrometry (DIA‐MS) Discovery

AbstractClinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package that uses data‐independent acquisition analysis from a discovery cohort to select precursors, peptides, and proteins that adhere to analytical criteria required for established targeted assays. TEAQ was applied to DIA‐MS data from plasma samples acquired on a new high resolution accurate mass (HRAM) mass spectrometry platform where precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra‐protein correlation based on 8‐ or 11‐point loading curves at three throughputs. This data can be used as a general resource for developing other targeted assays. TEAQ analysis of data from a case and control cohort for inflammatory bowel disease (n=492) identified 1110 signature peptides for 326 quantifiable proteins from the 1179 identified proteins. Applying TEAQ analysis to discovery data will streamline targeted assay development and the transition to validation and clinical studies.

Spatial proteomics of human diabetic kidney disease, from health to class III.

Diabetic kidney disease (DKD) is the leading cause of chronic and end-stage kidney disease in the USA and worldwide. Animal models have taught us much about DKD mechanisms, but translation of this knowledge into treatments for human disease has been slowed by the lag in our molecular understanding of human DKD. Using our Spatial TissuE Proteomics (STEP) pipeline (comprising curated human kidney tissues, multiplexed immunofluorescence and powerful analysis tools), we imaged and analysed the expression of 21 proteins in 23 tissue sections from individuals with diabetes and healthy kidneys (n=5), compared to those with DKDIIA, IIA-B and IIB (n=2 each) and DKDIII (n=1). These analyses revealed the existence of 11 cellular clusters (kidney compartments/cell types): podocytes, glomerular endothelial cells, proximal tubules, distal nephron, peritubular capillaries, blood vessels (endothelial cells and vascular smooth muscle cells), macrophages, myeloid cells, other CD45+ inflammatory cells, basement membrane and the interstitium. DKD progression was associated with co-localised increases in inflammatory cells and collagen IV deposition, with concomitant loss of native proteins of each nephron segment. Cell-type frequency and neighbourhood analyses highlighted a significant increase in inflammatory cells and their adjacency to tubular and αSMA+ (α-smooth muscle actin-positive) cells in DKD. Finally, DKD progression showed marked regional variability within single tissue sections, as well as inter-individual variability within each DKD class. Using the STEP pipeline, we found alterations in protein expression, cellular phenotypic composition and microenvironment structure with DKD progression, demonstrating the power of this pipeline to reveal the pathophysiology of human DKD.

A proximity proteomics pipeline with improved reproducibility and throughput

Proximity labeling (PL) via biotinylation coupled with mass spectrometry (MS) captures spatial proteomes in cells. Large-scale processing requires a workflow minimizing hands-on time and enhancing quantitative reproducibility. We introduced a scalable PL pipeline integrating automated enrichment of biotinylated proteins in a 96-well plate format. Combining this with optimized quantitative MS based on data-independent acquisition (DIA), we increased sample throughput and improved protein identification and quantification reproducibility. We applied this pipeline to delineate subcellular proteomes across various compartments. Using the 5HT2A serotonin receptor as a model, we studied temporal changes of proximal interaction networks induced by receptor activation. In addition, we modified the pipeline for reduced sample input to accommodate CRISPR-based gene knockout, assessing dynamics of the 5HT2A network in response to perturbation of selected interactors. This PL approach is universally applicable to PL proteomics using biotinylation-based PL enzymes, enhancing throughput and reproducibility of standard protocols.

Pharmacophore modelling based virtual screening and molecular dynamics identified the novel inhibitors and drug targets against Waddlia chondrophila

Waddlia chondrophila is a possible cause of fetal death in humans. This Chlamydia-related bacterium is an emergent pathogen that causes human miscarriages and ruminant abortions, which results in financial losses. Despite the years of efforts, the underlying mechanism behind the pathogenesis of W. chondrophila is little known which hindered the development of novel treatment options. In the framework of current study, computational approaches were used to identify novel inhibitors (phytocompounds) and drug targets against W. chondrophila. At first, RNA polymerase sigma factor SigA and 3-deoxy-d-manno-octulosonic acid transferase were identified through subtractive proteomics pipeline. Afterwards, extensive docking and simulation analyses were conducted to optimize potentially novel phytocompounds by assessing their binding affinity to target proteins. A 100ns molecular dynamics simulation well complimented the compound's binding affinity and indicated strong stability of predicted compounds at the docked site. The calculation of binding free energies with MMGBSA corroborated the significant binding affinity between phytocompounds and target protein binding sites. The proposed phytocompounds may be a viable treatment option for patients infected with W. chondrophila; however, further research is required to ensure their safety.

Effect of an immune challenge and two feed supplements on broiler chicken individual breast muscle protein synthesis rate

Optimization of broiler chicken breast muscle protein accretion is key for the efficient production of poultry meat, whose demand is steadily increasing. In a context where antimicrobial growth promoters use is being restricted, it is important to find alternatives as well as to characterize the effect of immunological stress on broiler chicken's growth. Despite its importance, research on broiler chicken muscle protein dynamics has mostly been limited to the study of mixed protein turnover. The present study aims to characterize the effect of a bacterial challenge and the feed supplementation of citrus and cucumber extracts on broiler chicken individual breast muscle proteins fractional synthesis rates (FSR) using a recently developed dynamic proteomics pipeline. Twenty-one day-old broiler chickens were administered a single 2H2O dose before being culled at different timepoints. A total of 60 breast muscle protein extracts from five experimental groups (Unchallenged, Challenged, Control Diet, Diet 1 and Diet 2) were analysed using a DDA proteomics approach. Proteomics data was filtered in order to reliably calculate multiple proteins FSR making use of a newly developed bioinformatics pipeline. Broiler breast muscle proteins FSR uniformly decreased following a bacterial challenge, this change was judged significant for 15 individual proteins, the two major functional clusters identified as well as for mixed breast muscle protein. Citrus or cucumber extract feed supplementation did not show any effect on the breast muscle protein FSR of immunologically challenged broilers. The present study has identified potential predictive markers of breast muscle growth and provided new information on broiler chicken breast muscle protein synthesis which could be essential for improving the efficiency of broiler chicken meat production. SignificanceThe present study constitutes the first dynamic proteomics study conducted in a farm animal species which has characterized FSR in a large number of proteins, establishing a precedent for biomarker discovery and assessment of health and growth status. Moreover, it has been evidenced that the decrease in broiler chicken breast muscle protein following an immune challenge is a coordinated event which seems to be the main cause of the decreased growth observed in these animals.

Extracellular Matrix Orchestration of Tissue Remodeling in the Chronically Inflamed Mouse Colon

Background & AimsChronic inflammatory illnesses are debilitating and recurrent conditions associated with significant co-morbidities, including an increased risk of developing cancer. Extensive tissue remodelling is a hallmark of such illnesses, and is both a consequence and a mediator of disease progression. Despite previous characterisation of epithelial and stromal remodelling during Inflammatory Bowel Disease, a complete understanding of its impact on disease progression is still lacking. MethodsA comprehensive proteomic pipeline using data-independent acquisition was applied to decellularized colon samples from the Muc2KO mouse model of colitis, for an in-depth characterisation of extracellular matrix remodelling. Unique proteomic profiles of the matrisomal landscape were extracted from pre- and pathological colitis. Integration of proteomics and transcriptomics datasets extracted from the same murine model produced network maps describing the orchestrating role of matrisomal proteins in tissue remodelling during the progression of colitis. ResultsThe in-depth proteomic workflow used here allowed the addition of 34 proteins to the known colon matrisomal signature. Protein signatures of pre- and pathological colitic states were extracted, differentiating the two states by expression of small leucine rich proteoglycans. We outlined the role of this class and other matrisomal proteins in tissue remodelling during colitis, as well as the potential for co-ordinated regulation of cell types by matrisomal ligands. ConclusionOur work highlights a central role for matrisomal proteins in tissue remodelling during colitis and defines orchestrating nodes that can be exploited in selection of therapeutic targets.

Defining the Cell Surface Cysteinome Using Two-Step Enrichment Proteomics.

The plasma membrane proteome is a rich resource of functionally important and therapeutically relevant protein targets. Distinguished by high hydrophobicity, heavy glycosylation, disulfide-rich sequences, and low overall abundance, the cell surface proteome remains undersampled in established proteomic pipelines, including our own cysteine chemoproteomics platforms. Here, we paired cell surface glycoprotein capture with cysteine chemoproteomics to establish a two-stage enrichment method that enables chemoproteomic profiling of cell Surface Cysteinome. Our "Cys-Surf" platform captures >2,800 total membrane protein cysteines in 1,046 proteins, including 1,907 residues not previously captured by bulk proteomic analysis. By pairing Cys-Surf with an isotopic chemoproteomic readout, we uncovered 821 total ligandable cysteines, including known and novel sites. Cys-Surf also robustly delineates redox-sensitive cysteines, including cysteines prone to activation-dependent changes to cysteine oxidation state and residues sensitive to addition of exogenous reductants. Exemplifying the capacity of Cys-Surf to delineate functionally important cysteines, we identified a redox sensitive cysteine in the low-density lipoprotein receptor (LDLR) that impacts both the protein localization and uptake of low-density lipoprotein (LDL) particles. Taken together, the Cys-Surf platform, distinguished by its two-stage enrichment paradigm, represents a tailored approach to delineate the functional and therapeutic potential of the plasma membrane cysteinome.

High-throughput proteomics uncovers exercise training and type 2 diabetes-induced changes in human white adipose tissue.

White adipose tissue (WAT) is important for metabolic homeostasis. We established the differential proteomic signatures of WAT in glucose-tolerant lean and obese individuals and patients with type 2 diabetes (T2D) and the response to 8 weeks of high-intensity interval training (HIIT). Using a high-throughput and reproducible mass spectrometry-based proteomics pipeline, we identified 3773 proteins and found that most regulated proteins displayed progression in markers of dysfunctional WAT from lean to obese to T2D individuals and were highly associated with clinical measures such as insulin sensitivity and HbA1c. We propose that these distinct markers could serve as potential clinical biomarkers. HIIT induced only minor changes in the WAT proteome. This included an increase in WAT ferritin levels independent of obesity and T2D, and WAT ferritin levels were strongly correlated with individual insulin sensitivity. Together, we report a proteomic signature of WAT related to obesity and T2D and highlight an unrecognized role of human WAT iron metabolism in exercise training adaptations.

Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO.

The metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy, and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model that can efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxation algorithms in a package called geckopy. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. In addition, to ensure that enzyme-constrained models follow the community standards, a format for the proteins is postulated. We hope that the package and algorithms presented here will be useful for the constraint-based modeling community.

Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale.

The growing complexity and volume of proteomics data necessitate the development of efficient software tools for peptide identification and quantification from mass spectra. Given their central role in proteomics, it is imperative that these tools are auditable and extensible─requirements that are best fulfilled by open-source and permissively licensed software. This work presents Sage, a high-performance, open-source, and freely available proteomics pipeline. Scalable and cloud-ready, Sage matches the performance of state-of-the-art software tools while running an order of magnitude faster.

Microanalytical Mass Spectrometry with Super-Resolution Microscopy Reveals a Proteome Transition During Development of the Brain's Circadian Pacemaker.

During brain development, neuronal proteomes are regulated in part by changes in spontaneous and sensory-driven activity in immature neural circuits. A longstanding model for studying activity-dependent circuit refinement is the developing mouse visual system where the formation of axonal projections from the eyes to the brain is influenced by spontaneous retinal activity prior to the onset of vision and by visual experience after eye-opening. The precise proteomic changes in retinorecipient targets that occur during this developmental transition are unknown. Here, we developed a microanalytical proteomics pipeline using capillary electrophoresis (CE) electrospray ionization (ESI) mass spectrometry (MS) in the discovery setting to quantify developmental changes in the chief circadian pacemaker, the suprachiasmatic nucleus (SCN), before and after the onset of photoreceptor-dependent visual function. Nesting CE-ESI with trapped ion mobility spectrometry time-of-flight (TOF) mass spectrometry (TimsTOF PRO) doubled the number of identified and quantified proteins compared to the TOF-only control on the same analytical platform. From 10 ng of peptide input, corresponding to <∼0.5% of the total local tissue proteome, technical triplicate analyses identified 1894 proteins and quantified 1066 proteins, including many with important canonical functions in axon guidance, synapse function, glial cell maturation, and extracellular matrix refinement. Label-free quantification revealed differential regulation for 166 proteins over development, with enrichment of axon guidance-associated proteins prior to eye-opening and synapse-associated protein enrichment after eye-opening. Super-resolution imaging of select proteins using STochastic Optical Reconstruction Microscopy (STORM) corroborated the MS results and showed that increased presynaptic protein abundance pre/post eye-opening in the SCN reflects a developmental increase in synapse number, but not presynaptic size or extrasynaptic protein expression. This work marks the first development and systematic application of TimsTOF PRO for CE-ESI-based microproteomics and the first integration of microanalytical CE-ESI TimsTOF PRO with volumetric super-resolution STORM imaging to expand the repertoire of technologies supporting analytical neuroscience.

A fully automated FAIMS-DIA mass spectrometry-based proteomic pipeline

Here, we present a standardized, "off-the-shelf" proteomics pipeline working in a single 96-well plate to achieve deep coverage of cellular proteomes with high throughput and scalability. This integrated pipeline streamlines a fully automated sample preparation platform, a data-independent acquisition (DIA) coupled with high-field asymmetric waveform ion mobility spectrometer (FAIMS) interface, and an optimized library-free DIA database search strategy. Our systematic evaluation of FAIMS-DIA showing single compensation voltage (CV) at -35V not only yields the deepest proteome coverage but also best correlates with DIA without FAIMS. Our in-depth comparison of direct-DIA database search engines shows that Spectronaut outperforms others, providing the highest quantifiable proteins. Next, we apply three common DIA strategies in characterizing human induced pluripotent stem cell (iPSC)-derived neurons and show single-shot mass spectrometry (MS) using single-CV (-35 V)-FAIMS-DIA results in >9,000 quantifiable proteins with <10% missing values, as well as superior reproducibility and accuracy compared with other existing DIA methods.

Semi-Automated Phenotypic Analysis of Functional 3D Spheroid Cell Cultures.

We present a protocol that describes the properties and advantages of using a standalone clinostat incubator for growing, treating, and monitoring 3D cell cultures. The clinostat mimics an environment where cells can assemble as highly reproducible spheroids with low shear forces and active nutrient diffusion. We demonstrate that both cancer and non-cancer hepatocytes (HepG2/C3A and THLE-3 cell lines) require 3 weeks of growth prior to achieving functionalities comparable to liver cells. This protocol highlights the convenience of utilizing incubators for 3D cells with cameras monitoring the cell growth, as snapshots can be taken to count and measure spheroids upon treatment. We describe the comparison of THLE-3 and HepG2/C3A cell lines, showing how non-cancerous cell lines can be grown as well as immortalized cancer cells. We demonstrate and illustrate how proteomics experiments can be conducted from a few spheroids, which can be collected without perturbing cell signaling, i.e., no trypsinization required. We show that proteomics analysis can be used to monitor the typical liver phenotype of respiratory chain metabolism and the production of proteins involved in metal detoxification and describe a semi-automated system to count and measure the spheroid's area. Altogether, the protocol presents a toolbox that comprises a phenotypic characterization via image capture and a proteomics pipeline to experiment on 3D cell culture models.

An improved sulfur-nitroso-proteome strategy for global profiling of sulfur-nitrosylated proteins and sulfur-nitrosylation sites in mice

Comprehensive sulfur-nitrosylation (SNO) proteome coverage in complex biological systems remains challenging as a result of the low level of endogenous S-nitrosylation and its chemical instability. Herein, we optimized the synthesis route of SNOTRAP (SNO trapping by triaryl phosphine) probe and the proteomics pipeline (including preventing over-alkylation, sample washing, trypsin digestion). Preventing overalkylation was found to be the key factor resulting in a higher number of identified SNO proteins by evaluating various experimental conditions. With the improved SNOTRAP-based proteomic pipeline, we achieved an improvement of ∼10-fold on identification efficiency, and identified 1181 SNO proteins (1714 SNO sites) in mouse brain, representing the largest repository of endogenous S-nitrosylation. Moreover, we identified the consensus motif of SNO sites, suggesting the correlation with local hydrophobicity, acid-base catalysis, and the surrounding secondary structures for modification of specific cysteines by NO. Collectively, we provide a universal pipeline for the high-coverage identification of low-abundance SNO proteins with high enrichment efficiency, high specificity (98%), good reproducibility, and easy implementation, contributing to the elucidation of the mechanism(s) of nitrosative stress in multiple diseases.

Proteome analysis of patients with early onset colorectal cancer in Ireland.

e15639 Background: Early onset colorectal cancer (EOCRC) is defined as colorectal cancer (CRC) in those under the age of 50. The incidence of EOCRC is rising at an alarming rate. Unlike in the USA, screening for CRC in Ireland begins at 60 years of age. It has been hypothesized that the signatures of CRC in younger populations could differ from that in elderly patients. Deep characterization of the tissue using omics profiling, including proteomics, could provide the answer to this question. Moreover, omics profiling could identify new biomarkers for patient stratification and earlier diagnosis. Methods: Forty micrograms of healthy and matched frozen tumors were taken from 8 patients with locally advanced EOCRC for whole proteome analysis. The samples were digested with trypsin and mass spectrometry was performed using data dependent analysis parallel accumulation serial fragmentation (DDA-PASEF). The raw data was searched against the Homo sapiens subset of the Uniprot Swissprot database (reviewed) with the proteomics pipeline software Fragpipe (Version 19.1) Statistical analysis was performed using Perseus software. Results: When we compared proteomes of 8 tumor tissues versus matched normal tissues, out of 5,717 protein IDs, there were more than 1,300 differentially expressed proteins. Our pathway enrichment analysis, using differentially expressed proteins, has identified a number of molecular functions altered in EOCRC samples. The most enriched signaling pathways were associated with FAT10 signaling, neutrophil extracellular trap (NET) signaling and oxidative stress response. The upstream regulator analysis (URA) has identified LASP1 and LONP1 proteins. LASP1 is known to promote CRC progression via the Hippo signaling pathway, while LONP1 is known to play a role in mitochondrial functions, metabolism, and epithelial–mesenchymal transition (EMT) in CRC cells. Importantly, the NET signaling activation in all samples suggests unfavorable immunomodulation that could stimulate cell proliferation and invasiveness. The observed activation of oxidative stress signaling components is in line with the inflammatory nature of CRC, which could signify potential response to immune checkpoint inhibitors. Conclusions: Our findings in a small cohort of patients indicate that proteomics can provide a deeper insight into aberrant signalling in EOCRC. We have identified novel vulnerabilities that might be used as therapeutic targets as well as patient stratification biomarkers. Additional proteome studies with corresponding genomic and transcriptomic analysis are needed to fully understand the potential of this approach.

Accurate Label-Free Quantification by directLFQ to Compare Unlimited Numbers of Proteomes

Recent advances in mass spectrometry–based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks in the bioinformatics pipeline. Although peptide identification is already scalable, most label-free quantification (LFQ) algorithms scale quadratic or cubic with the sample numbers, which may even preclude the analysis of large-scale data. Here we introduce directLFQ, a ratio-based approach for sample normalization and the calculation of protein intensities. It estimates quantities via aligning samples and ion traces by shifting them on top of each other in logarithmic space. Importantly, directLFQ scales linearly with the number of samples, allowing analyses of large studies to finish in minutes instead of days or months. We quantify 10,000 proteomes in 10 min and 100,000 proteomes in less than 2 h, a 1000-fold faster than some implementations of the popular LFQ algorithm MaxLFQ. In-depth characterization of directLFQ reveals excellent normalization properties and benchmark results, comparing favorably to MaxLFQ for both data-dependent acquisition and data-independent acquisition. In addition, directLFQ provides normalized peptide intensity estimates for peptide-level comparisons. It is an important part of an overall quantitative proteomic pipeline that also needs to include high sensitive statistical analysis leading to proteoform resolution. Available as an open-source Python package and a graphical user interface with a one-click installer, it can be used in the AlphaPept ecosystem as well as downstream of most common computational proteomics pipelines.

Seasonal and water restriction-related changes in Eucalyptus grandis leaf proteins: Shedding light on the dark proteome

Climate change is escalating the frequency and intensity of warming and drought periods around the globe, currently representing a threat to many plant species. Understanding how plants cope with such abiotic stresses is crucial.We investigate how Eucalyptus grandis, a plant species with several industry applications, copes with seasonal variation and water restriction imposition at the proteomic level under field conditions. Therefore, we attempted to identify known proteins and novel peptides associated with the effect of seasonality and water restriction impositions, as well as to provide insights into how novel peptides behave under such conditions. The leaf proteome of E. grandis plants was studied under both a conventional proteomic workflow and a dedicated proteogenomics approach. The highest proteomic variability was identified in the summer season and the most abundant known proteins associated with seasonal variation were related to photosynthesis. Post-translational modifications, protein turnover, and chaperones were the main functional classifications identified among biological treatments. Furthermore, 144 novel peptides not predicted by current proteomics pipelines, were identified by both spectral correlations against modified databases (43) and a de novo peptide sequencing approach (101). It is predicted that most single amino acid substituted (SAS) peptides, mainly associated with the photosynthesis process, decrease protein stability by altering the quantitative change upon ΔΔG values and non-covalent interactions. Multiple reaction monitoring validation assays were performed for selected novel peptide identifications demonstrating that it is a very robust mass spectrometry-based method of validation. Data are available via ProteomeXchange with identifier PXD031100.