Prediction Of Protein-protein Interactions Research Articles

Intelligence model on sequence-based prediction of PPI using AISSO deep concept with hyperparameter tuning process

Protein–protein interaction (PPI) prediction is vital for interpreting biological activities. Even though many diverse sorts of data and machine learning approaches have been employed in PPI prediction, performance still has to be enhanced. As a result, we adopted an Aquilla Influenced Shark Smell (AISSO)-based hybrid prediction technique to construct a sequence-dependent PPI prediction model. This model has two stages of operation: feature extraction and prediction. Along with sequence-based and Gene Ontology features, unique features were produced in the feature extraction stage utilizing the improved semantic similarity technique, which may deliver reliable findings. These collected characteristics were then sent to the prediction step, and hybrid neural networks, such as the Improved Recurrent Neural Network and Deep Belief Networks, were used to predict the PPI using modified score level fusion. These neural networks’ weight variables were adjusted utilizing a unique optimal methodology called Aquila Influenced Shark Smell (AISSO), and the outcomes showed that the developed model had attained an accuracy of around 88%, which is much better than the traditional methods; this model AISSO-based PPI prediction can provide precise and effective predictions.

ESMDNN-PPI: a new protein–protein interaction prediction model developed with protein language model of ESM2 and deep neural network

ESMDNN-PPI: a new protein–protein interaction prediction model developed with protein language model of ESM2 and deep neural network

Genome-wide identification of oxidosqualene cyclase genes regulating natural rubber in Taraxacum kok-saghyz.

Nine TkOSC genes have been identified by genome-wide screening. Among them, TkOSC4-6 might be more crucial for natural rubber biosynthesis in Taraxacum kok-saghyz roots. Taraxacum kok-saghyz Rodin (TKS) roots contain large amounts of natural rubber, inulin, and valuable metabolites. Oxidosqualene cyclase (OSC) is a key member for regulating natural rubber biosynthesis (NRB) via the triterpenoid biosynthesis pathway. To explore the functions of OSC on natural rubber producing in TKS, its gene family members were identified in TKS genome via genome-wide screening. Nine TkOSCs were identified, which were mainly distributed in the cytoplasm. Their family genes experienced a neutral selection during the evolution process. Overall sequence homology analysis OSC proteins revealed 80.23% similarity, indicating a highly degree of conservation. Pairwise comparisons showed a multiple sequence similarity ranging from 57% to 100%. Protein interaction prediction revealed that TkOSCs may interact with baruol synthase, sterol 1,4-demethylase, lupeol synthase and squalene epoxidase. Phylogenetic analysis showed that OSC family proteins belong to two branches. TkOSC promoter regions contain cis-acting elements related to plant growth, stress response, hormones response and light response. Protein accumulation analysis demonstrated that TkOSC4, TkOSC5 and TkOSC6 proteins had strong expression levels in the root, latex and plumular axis. Comparison of gene expression patterns showed TkOSC1, TkOSC4, TkOSC5, TkOSC6, TkOSC7, TkOSC8 and TkOSC9 might be important in regulating NRB. Combination of gene and protein results revealed TkOSC4-6 might be more crucial, and the data might contribute to a more profound understanding of the roles of OSCs for NRB in TKS roots.

Homeodomain leucine zipper (HD-ZIP) gene family in Apostasia shenzhenica Z. J. Liu & L. J. Chen (Orchidaceae): In silico identification and characterization

The homeodomain leucine zipper (HD-ZIP) gene family constitutes an important class of transcription factors found in plants that play crucial roles in various aspects of growth, development and stress regulation. Despite this, there have been minuscule reports on identification and characterization of HD-ZIP gene family in orchids. The present study identifies 31 putative genes (AsHD-ZIP) in Apostasia shenzhenica, using in silico approaches. All of the identified transcription factors were grouped into four subfamilies (HD-ZIP I to IV) based on the distribution of various conserved domains and motifs i.e., HD-ZIP I (homeodomain and leucine zipper), HD-ZIP II (homeodomain, leucine zipper and CPSCE), HD-ZIP III (homeodomain, leucine zipper, MEKHLA and START) and HD-ZIP IV (homeodomain, leucine zipper and START). Prediction of physicochemical properties, protein structures, protein-protein interactions, gene ontology, and gene structures further validated this classification. Phylogenetic analysis showed clustering of all subgroups separately along with their orthologs in Arabidopsis thaliana, Oryza sativa, Dendrobium catenatum and Phalaenopsis equestris. Furthermore, spatio-temporal expression profiling and presence of cis-regulatory elements confirmed tissue specific expression. As HD-ZIP genes are vital players in regulation of plant growth and development as well as in biotic and abiotic stress responses, this study provides a foundation for genetic improvement in orchids through functional validation and characterization of HD-ZIP gene family.

Funnel graph neural networks with multi-granularity cascaded fusing for protein–protein interaction prediction

The identification of potential protein–protein interactions (PPIs) between humans and viruses is crucial for comprehending viral infection and disease mechanisms at the molecular level. Recently, graph neural networks (GNNs) have emerged as a promising approach to expedite PPI identification. However, GNNs often suffer from over-smoothing when capturing high-order neighbor information. To tackle this issue and effectively capture implicit collaborative information from multi-hop neighbors, we propose FGNN, Funnel Graph Neural Networks with Multi-Granularity Cascaded Fusing, facilitating the distillation of information in a funnel-like manner. Specifically, it enables the mapping of information flow from the full graph to subgraphs and ultimately to nodes. By regarding subgraphs as bridges connecting higher-order neighbors, we ensure the projection of multi-hop neighbors into the same subspace, thereby achieving a comprehensive mapping of the full graph into subgraphs. Moreover, we employ an encoder equipped with a multi-head attention mechanism to effectively map subgraphs onto nodes, facilitating further refinement and compression of information derived from high-order neighbors. FGNN can effectively capture high-order neighbor information whilst relieving over-smoothing. Extensive experiments demonstrate FGNN is superior to the state-of-the-art methods in terms of AUC value. The achieved improvements in the four cardiovascular disease datasets are 7.96 %, 2.3 %, 2.49 %, and 0.82 %, respectively.

Genome-wide analysis of the NYN domain gene family in Brassica napus and its function role in plant growth and development

The NYN domain gene family consists of genes that encode ribonucleases that are characterized by a newly identified NYN domain. Members of the family were widely distributed in all life kingdoms and play a crucial role in various RNA regulation processes, although the wide genome overview of the NYN domain gene family is not yet available in any species. Rapeseed (Brassica napus L.), a polyploid model species, is an important oilseed crop. Here, the phylogenetic analysis of these BnaNYNs revealed five distinct groups strongly supported by gene structure, conserved domains, and conserved motifs. The survey of the expansion of the gene family showed that the birth of BnaNYNs is explained by various duplication events. Furthermore, tissue-specific expression analysis, protein–protein interaction prediction, and cis-element prediction suggested a role for BnaNYNs in plant growth and development. Interestingly, the data showed that three tandem duplicated BnaNYNs (TDBs) exhibited distinct expression patterns from those other BnaNYNs and had a high similarity in protein sequence level. Furthermore, the analysis of one of these TDBs, BnaNYN57, showed that overexpression of BnaNYN57 in Arabidopsis thaliana and B. napus accelerated plant growth and significantly increased silique length, while RNA interference resulted in the opposite growth pattern. It suggesting a key role for the TDBs in processes related to plant growth and development.

Computational analysis of pathogen-host interactome for fast and low-risk in-silico drug repurposing in emerging viral threats like Mpox

Monkeypox (Mpox), a zoonotic illness triggered by the monkeypox virus (MPXV), poses a significant threat since it may be transmitted and has no cure. This work introduces a computational method to predict Protein–Protein Interactions (PPIs) during MPXV infection. The objective is to discover prospective drug targets and repurpose current potential Food and Drug Administration (FDA) drugs for therapeutic purposes. In this work, ensemble features, comprising 2–5 node graphlet attributes and protein composition-based features are utilized for Deep Learning (DL) models to predict PPIs. The technique that is used here demonstrated an excellent prediction performance for PPI on both the Human Integrated Protein–Protein Interaction Reference (HIPPIE) and MPXV-Human PPI datasets. In addition, the human protein targets for MPXV have been identified accurately along with the detection of possible therapeutic targets. Furthermore, the validation process included conducting docking research studies on potential FDA drugs like Nicotinamide Adenine Dinucleotide and Hydrogen (NADH), Fostamatinib, Glutamic acid, Cannabidiol, Copper, and Zinc in DrugBank identified via research on drug repurposing and the Drug Consensus Score (DCS) for MPXV. This has been achieved by employing the primary crystal structures of MPXV, which are now accessible. The docking study is also supported by Molecular Dynamics (MD) simulation. The results of our study emphasize the effectiveness of using ensemble feature-based PPI prediction to understand the molecular processes involved in viral infection and to aid in the development of repurposed drugs for emerging infectious diseases such as, but not limited to, Mpox. The source code and link to data used in this work is available at: https://github.com/CMATERJU-BIOINFO/In-Silico-Drug-Repurposing-Methodology-To-Suggest-Therapies-For-Emerging-Threats-like-Mpox.

The Inhibitory Effects of Maclurin on Fatty Acid Synthase and Adipocyte Differentiation.

Obesity is a complex health condition characterized by excessive adipose tissue accumulation, leading to significant metabolic disturbances such as insulin resistance and cardiovascular diseases. Fatty acid synthase (FAS), a key enzyme in lipogenesis, has been identified as a potential therapeutic target for obesity due to its role in adipocyte differentiation and lipid accumulation. This study employed a multidisciplinary approach involving in silico and in vitro analyses to investigate the anti-adipogenic properties of maclurin, a natural phenolic compound derived from Morus alba. Using SwissDock software (ChEMBL version 23), we predicted protein interactions and demonstrated a high probability (95.6%) of maclurin targeting FAS, surpassing the interaction rates of established inhibitors like cerulenin. Docking simulations revealed maclurin's superior binding affinity to FAS, with a binding score of -7.3 kcal/mol compared to -6.7 kcal/mol for cerulenin. Subsequent in vitro assays confirmed these findings, with maclurin effectively inhibiting FAS activity in a concentration-dependent manner in 3T3-L1 adipocytes, without compromising cell viability. Furthermore, maclurin treatment resulted in significant reductions in lipid accumulation and the downregulated expression of critical adipogenic genes such as PPARγ, C/EBPα, and FAS, indicating the suppression of adipocyte differentiation. Maclurin shows potential as a novel FAS inhibitor with significant anti-adipogenic effects, offering a promising therapeutic avenue for the treatment and prevention of obesity.

Integrated metabolomic and transcriptomic analysis reveals biosynthesis mechanism of flavone and caffeoylquinic acid in chrysanthemum

BackgroundChrysanthemum morifolium ‘HangBaiJu’, a popular medicinal and edible plant, exerts its biological activities primarily through the presence of flavones and caffeoylquinic acids (CQAs). However, the regulatory mechanism of flavone and CQA biosynthesis in the chrysanthemum capitulum remains unclear.ResultsIn this study, the content of flavones and CQAs during the development of chrysanthemum capitulum was determined by HPLC, revealing an accumulation pattern with higher levels at S1 and S2 and a gradual decrease at S3 to S5. Transcriptomic analysis revealed that CmPAL1/2, CmCHS1/2, CmFNS, CmHQT, and CmHCT were key structural genes in flavones and CQAs biosynthesis. Furthermore, weighted gene co-expression correlation network analysis (WGCNA), k-means clustering, correlation analysis and protein interaction prediction were carried out in this study to identify transcription factors (TFs) associated with flavone and CQA biosynthesis, including MYB, bHLH, AP2/ERF, and MADS-box families. The TFs CmERF/PTI6 and CmCMD77 were proposed to act as upstream regulators of CmMYB3 and CmbHLH143, while CmMYB3 and CmbHLH143 might form a complex to directly regulate the structural genes CmPAL1/2, CmCHS1/2, CmFNS, CmHQT, and CmHCT, thereby controlling flavone and CQA biosynthesis.ConclusionsOverall, these findings provide initial insights into the TF regulatory network underlying flavones and CQAs accumulation in the chrysanthemum capitulum, which laid a theoretical foundation for the quality improvement of C. morifolium ‘HangBaiJu’ and the high-quality development of the industry.

The role of GADD45G methylation in endometrial cancer: Insights into CDK1/CCNB1 activation and therapeutic opportunities.

Accumulating evidence suggests the significant involvement of GADD45G in the development of various cancers. This study investigates GADD45G's involvement and methylation status in endometrial cancer (EC), along with molecular mechanisms and potential therapies. The expression of GADD45G in EC tissues and controls was evaluated using RNA-seq, quantitative real-time polymerase chain reaction (qRT-PCR), and western blotting (WB). Methylation-specific PCR (MSP) evaluated GADD45G's methylation status. Protein-protein interaction (PPI) prediction identified potential interactors of GADD45G, and co-immunoprecipitation (co-IP) confirmed GADD45G interact with Cyclin-dependent kinase 1 (CDK1) and cyclin B1 (CCNB1). Several cell behavior assays were conducted in both in vitro and in vivo settings to comprehensively understand the impact of GADD45G dysregulation in EC. Our findings revealed a significant decrease in the expression of GADD45G in endometrial cancer tissues and cells, which was attributed to its methylation status. Reduced GADD45G expression correlated with increased invasive behaviors in EC cells. Furthermore, GADD45G negatively regulated CDK1 and CCNB1, promoting invasive behaviors at transcript and protein levels. This study demonstrated that the downregulation of GADD45G, mediated by methylation, facilitates the invasive behaviors of EC cells through interaction with the CDK1/CCNB1. These findings enhance understanding of the molecular mechanisms underlying endometrial cancer and suggest potential therapeutic strategies targeting GADD45G for treatment.

Protein-Protein Interaction Prediction Model Based on ProtBert-BiGRU-Attention.

The physiological activities within cells are mainly regulated through protein-protein interactions (PPI). Therefore, studying protein interactions has become an essential part of researching protein function and mechanisms. Traditional biological experiments required for PPI prediction are expensive and time consuming. For this reason, many methods based on predicting PPI from protein sequences have been proposed in recent years. However, existing computational methods usually require the combination of evolutionary feature information of proteins to predict PPI docking situations. Because different relevant features of selected proteins are chosen, there may be differences in the predicted results for PPI. This article proposes a PPI prediction method based on the pretrained protein sequence model ProtBert, combined with the Bidirectional Gated Recurrent Unit (BiGRU) and attention mechanism. Only using protein sequence information and leveraging ProtBert's powerful ability to capture amino acid feature information, BiGRU is used for further feature extraction of the amino acid vectors output by ProtBert. The attention mechanism is then applied to enhance the focus on different amino acid features and improve the expression ability of protein sequence features, ultimately obtaining binary classification results for protein interactions. Experimental results show that our proposed ProtBert-BiGRU-Attention model has good predictive performance for PPI. Through relevant comparative experiments, it has been proven that our model performs well in protein binary prediction. Furthermore, through the ablation experiment of the model, different deep learning modules' contributions to the prediction have been demonstrated.

TUnA: an uncertainty-aware transformer model for sequence-based protein-protein interaction prediction.

Protein-protein interactions (PPIs) are important for many biological processes, but predicting them from sequence data remains challenging. Existing deep learning models often cannot generalize to proteins not present in the training set and do not provide uncertainty estimates for their predictions. To address these limitations, we present TUnA, a Transformer-based uncertainty-aware model for PPI prediction. TUnA uses ESM-2 embeddings with Transformer encoders and incorporates a Spectral-normalized Neural Gaussian Process. TUnA achieves state-of-the-art performance and, importantly, evaluates uncertainty for unseen sequences. We demonstrate that TUnA's uncertainty estimates can effectively identify the most reliable predictions, significantly reducing false positives. This capability is crucial in bridging the gap between computational predictions and experimental validation.

African Swine Fever Virus Protein-Protein Interaction Prediction.

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein-protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein-protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein-protein interactions, further validating their relevance. Two protein-protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.

MGPPI: multiscale graph neural networks for explainable protein-protein interaction prediction.

Protein-Protein Interactions (PPIs) involves in various biological processes, which are of significant importance in cancer diagnosis and drug development. Computational based PPI prediction methods are more preferred due to their low cost and high accuracy. However, existing protein structure based methods are insufficient in the extraction of protein structural information. Furthermore, most methods are less interpretable, which hinder their practical application in the biomedical field. In this paper, we propose MGPPI, which is a Multiscale graph convolutional neural network model for PPI prediction. By incorporating multiscale module into the Graph Neural Network (GNN) and constructing multi convolutional layers, MGPPI can effectively capture both local and global protein structure information. For model interpretability, we introduce a novel visual explanation method named Gradient Weighted interaction Activation Mapping (Grad-WAM), which can highlight key binding residue sites. We evaluate the performance of MGPPI by comparing with state-of-the-arts methods on various datasets. Results shows that MGPPI outperforms other methods significantly and exhibits strong generalization capabilities on the multi-species dataset. As a practical case study, we predicted the binding affinity between the spike (S) protein of SARS-COV-2 and the human ACE2 receptor protein, and successfully identified key binding sites with known binding functions. Key binding sites mutation in PPIs can affect cancer patient survival statues. Therefore, we further verified Grad-WAM highlighted residue sites in separating patients survival groups in several different cancer type datasets. According to our results, some of the highlighted residues can be used as biomarkers in predicting patients survival probability. All these results together demonstrate the high accuracy and practical application value of MGPPI. Our method not only addresses the limitations of existing approaches but also can assists researchers in identifying crucial drug targets and help guide personalized cancer treatment.

The identification, evolutionary analysis, and immune roles of Rab family members in red swamp crayfish, Procambarus clarkii

The Rab GTPase constitutes the largest family of small GTPases that regulate intracellular trafficking. Different eukaryotes possess varying numbers of Rab paralogs. However, limited knowledge exists regarding the evolutionary pattern of Rab family in most major eukaryotic supergroups. This study cloned 24 Rab genes from transcriptome data of Procambarus clarkii haemocytes. The multiple sequence alignment and phylogenetic tree analysis revealed a relatively high degree of conservation for PcRab. Furthermore, PcRab exhibited similarities in motif composition with all members showing presence of G, PM, RabF, and RabSF motifs. The tertiary structure indicated that PcRab proteins mainly consisted of α-helices and β-strands, and most PcRab proteins shared similar tertiary structures, and it was indicated that they have similar protein characteristics. Protein-protein interaction prediction identified a total of 20 interacting proteins involved in vesicle trafficking, phagocytosis, and signal transduction with 193 interactions. Expression analysis showed wide expression patterns for PcRab in P. clarkii organs. Upon infection by white spot syndrome virus and Aeromonas veronii, significant induction was observed for PcRab gene expression levels, indicating their involvement in pathogen response mechanisms. The present study represents the pioneering effort in comprehensively identifying and cloning the Rab family genes in crustacean, followed by a systematic investigation into their evolutionary patterns and immune response upon pathogen infection. The results provided valuable insights for further investigation into the molecular mechanism underlying the response of P. clarkii to pathogen infection.

LazyAF, a pipeline for accessible medium-scale in silico prediction of protein-protein interactions.

Artificial intelligence has revolutionized the field of protein structure prediction. However, with more powerful and complex software being developed, it is accessibility and ease of use rather than capability that is quickly becoming a limiting factor to end users. LazyAF is a Google Colaboratory-based pipeline which integrates the existing ColabFold BATCH software to streamline the process of medium-scale protein-protein interaction prediction. LazyAF was used to predict the interactome of the 76 proteins encoded on the broad-host-range multi-drug resistance plasmid RK2, demonstrating the ease and accessibility the pipeline provides.

Genome-wide identification, expression analysis of WRKY transcription factors in Citrus ichangensis and functional validation of CiWRKY31 in response to cold stress

BackgroundIchang papeda (Citrus ichangensis), a wild perennial plant of the Rutaceae family, is a cold-hardy plant. WRKY transcription factors are crucial regulators of plant growth and development as well as abiotic stress responses. However, the WRKY genes in C. ichangensis (CiWRKY) and their expression patterns under cold stress have not been thoroughly investigated, hindering our understanding of their role in cold tolerance.ResultsIn this study, a total of 52 CiWRKY genes identified in the genome of C. ichangensis were classified into three main groups and five subgroups based on phylogenetic analysis. Comprehensive analyses of motif features, conserved domains, and gene structures were performed. Segmental duplication plays a significant role in the CiWRKY gene family expansion. Cis-acting element analysis revealed the presence of various stress-responsive elements in the promoters of the majority of CiWRKYs. Gene ontology (GO) analysis and protein-protein interaction predictions indicate that the CiWRKYs exhibit crucial roles in regulation of both development and stress response. Expression profiling analysis demonstrates that 14 CiWRKYs were substantially induced under cold stress. Virus-induced gene silencing (VIGS) assay confirmed that CiWRKY31, one of the cold-induced WRKYs, functions positively in regulation of cold tolerance.ConclusionSequence and protein properties of CiWRKYs were systematically analyzed. Among the 52 CiWRKY genes 14 members exhibited cold-responsive expression patterns, and CiWRKY31 was verified to be a positive regulator of cold tolerance. These findings pave way for future investigations to understand the molecular functions of CiWRKYs in cold tolerance and contribute to unravelling WRKYs that may be used for engineering cold tolerance in citrus.

A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases.

The ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML). To understand the current state of the field, we compare six representative methods for predicting antibody-antigen complexes from sequence, including two deep learning approaches trained to predict PPIs in general (AlphaFold-Multimer, RoseTTAFold), two composite methods that initially predict antibody and antigen structures separately and dock them (using antibody-mode ClusPro), local refinement in Rosetta (SnugDock) of globally docked poses from ClusPro, and a pipeline combining homology modeling with rigid-body docking informed by ML-based epitope and paratope prediction (AbAdapt). We find that AlphaFold-Multimer outperformed other methods, although the absolute performance leaves considerable room for improvement. AlphaFold-Multimer models of lower-quality display significant structural biases at the level of tertiary motifs (TERMs) towards having fewer structural matches in non-antibody containing structures from the Protein Data Bank (PDB). Specifically, better models exhibit more common PDB-like TERMs at the antibody-antigen interface than worse ones. Importantly, the clear relationship between performance and the commonness of interfacial TERMs suggests that scarcity of interfacial geometry data in the structural database may currently limit application of machine learning to the prediction of antibody-antigen interactions.

Protein-Protein Interaction Prediction via Structure-Based Deep Learning.

Protein-protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end-to-end deep learning framework, RSPPI, combining residual neural network (ResNet) and spatial pyramid pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein three-dimensional structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed-length requirement. The RSPPI model possessed excellent cross-species performance and outperformed several state-of-the-art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategy to develop an AI PPI prediction algorithm.

Democratizing protein language models with parameter-efficient fine-tuning

Proteomics has been revolutionized by large protein language models (PLMs), which learn unsupervised representations from large corpora of sequences. These models are typically fine-tuned in a supervised setting to adapt the model to specific downstream tasks. However, the computational and memory footprint of fine-tuning (FT) large PLMs presents a barrier for many research groups with limited computational resources. Natural language processing has seen a similar explosion in the size of models, where these challenges have been addressed by methods for parameter-efficient fine-tuning (PEFT). In this work, we introduce this paradigm to proteomics through leveraging the parameter-efficient method LoRA and training new models for two important tasks: predicting protein-protein interactions (PPIs) and predicting the symmetry of homooligomer quaternary structures. We show that these approaches are competitive with traditional FT while requiring reduced memory and substantially fewer parameters. We additionally show that for the PPI prediction task, training only the classification head also remains competitive with full FT, using five orders of magnitude fewer parameters, and that each of these methods outperform state-of-the-art PPI prediction methods with substantially reduced compute. We further perform a comprehensive evaluation of the hyperparameter space, demonstrate that PEFT of PLMs is robust to variations in these hyperparameters, and elucidate where best practices for PEFT in proteomics differ from those in natural language processing. All our model adaptation and evaluation code is available open-source at https://github.com/microsoft/peft_proteomics. Thus, we provide a blueprint to democratize the power of PLM adaptation to groups with limited computational resources.