How sticky are our proteins? Quantifying hydrophobicity of the human proteome

Creating Chemiluminescence Signature Arrays Coupled with Machine Learning for Alzheimer's Disease Serum Diagnosis

Although omics and multi-omics approaches are the most used methods to create signature arrays for liquid biopsy, the high cost of omics technologies still largely limits their wide applications for point-of-care. Inspired by the bat echolocation mechanism, we propose an "echoes" approach for creating chemiluminescence signatures via screening of a compound library, and serum samples of Alzheimer's disease (AD) were used for our proof-of-concept study. We first demonstrated the discrepancy in physicochemical properties between AD and healthy control serums. On this basis, we developed a simple, cost-effective, and versatile platform termed UNICODE (UNiversal Interaction of Chemiluminescence echOes for Disease Evaluation). The UNICODE platform consists of a "bat" probe, which generates different chemiluminescence intensities upon interacting with various substrates, and a panel/array of "flag" molecules that are selected from library screening. The UNICODE array could enable the reflecting/"echoing" of the signatures of various serum components and intact physicochemical interactions between serum substrates. In this study, we screened a library of over 1,000 small molecules and identified 12 "flag" molecules (top 12) that optimally depict the differences between AD and healthy control serums. Finally, we employed the top 12 array to conduct tests on serum samples and utilized machine learning methods to optimize detection performance. We successfully distinguished AD serums, achieving the highest area under the curve of 90.24% with the random forest method. Our strategy could provide new insights into biofluid abnormality and prototype tools for developing liquid biopsy diagnoses for AD and other diseases.

Allostery and inter-domain dynamics in NXF1: An insight into viral CTE-RNA binding

Nucleocytoplasmic export of mRNA is a fundamental process in eukaryotic cells, facilitating the transportation of mRNA transcripts from nucleus to cytoplasm. Central to this pathway is Nuclear Export Factor 1 (NXF1), a key RNA binding protein (RBP) mediating mRNA export through the Nuclear Pore Complex (NPC). The significance of NXF1 in the export pathway extends to viral infections and neurodegenerative diseases where aberrations in nucleocytoplasmic transport have been identified as critical factors in disease progression. This study focuses on the structural dynamics and binding interactions of NXF1 with Constitutive Transport Element (CTE) RNA. Through molecular dynamics simulation, we explore the conformational shift and stability of NXF1 upon RNA binding and assess the impact of point mutations on the binding affinity. Moreover, our study highlights allosteric communication between RNA Recognition Motif (RRM) and Leucine-Rich Repeat (LRR) domains of NXF1 upon RNA binding. Using network analysis, we identify potential allosteric sites and assess the impact of point mutations, showing their dual roles in RNA binding and allosteric regulation. This study advances the understanding of RNA recognition by NXF1 and lays the foundation for future therapeutic strategies targeting impaired NXF1-RNA interactions in diseases associated with nucleocytoplasmic transport defects.

Computational electrostatic engineering of nanobodies for enhanced SARS-CoV-2 receptor binding domain recognition

This study presents a novel computational approach for engineering nanobodies (Nbs) for improved interaction with receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Using Protein Structure Reliability reports, RBD (7VYR_R) was selected and refined for subsequent Nb-RBD interactions. By leveraging electrostatic complementarity (EC) analysis, we engineered and characterized five Electrostatically Complementary Nbs (ECSb1-ECSb5) based on the CeVICA library's SR6c3 Nb. Through targeted modifications in the complementarity-determining regions (CDR) and framework regions (FR), we optimized electrostatic interactions to improve binding affinity and specificity. The engineered Nbs (ECSb3, ECSb4, and ECSb5) demonstrated high binding specificity for AS3, CA1, and CA2 epitopes. Interestingly, ECSb1 and ECSb2 selectively engaged with AS3 and CA1 instead of AS1 and AS2, respectively, due to a preference for residues that conferred superior binding complementarities. Furthermore, ECSbs significantly outperformed SR6c3 Nb in MM/GBSA results, notably, ECSb4 and ECSb3 exhibited superior binding free energies of -182.58 kcal.mol-1 and -119.07 kcal.mol-1, respectively, compared to SR6c3 (-105.50 kcal.mol-1). ECSbs exhibited significantly higher thermostability (100.4-148.3 kcal·mol⁻1) compared to SR6c3 (62.6 kcal·mol⁻1). Similarly, enhanced electrostatic complementarity was also observed for ECSb4-RBD and ECSb3-RBD (0.305 and 0.390, respectively) relative to SR6c3-RBD (0.233). Surface analyses confirmed optimized electrostatic patches and reduced aggregation propensity in the engineered Nb. This integrated EC and structural engineering approach successfully developed engineered Nbs with enhanced binding specificity, increased thermostability, and reduced aggregation, laying the groundwork for novel therapeutic applications targeting the SARS-CoV-2 spike protein.

Plasmonic Single-Molecule Affinity Detection at 10-20 Molar

DNA can be readily amplified through replication, enabling the detection of a single-target copy. A comparable performance for proteins in immunoassays has yet to be fully assessed. Surface-plasmon-resonance (SPR) serves as a probe capable of performing assays at concentrations typically around 10⁻⁹ molar. In this study, plasmonic single-molecule assays for both proteins and DNA are demonstrated, achieving limits-of-detections (LODs) as low as 10⁻2⁰ molar (1 ± 1 molecule in 0.1 mL), even in human serum, in 1 h. This represents an improvement in typical SPR LODs by eleven orders-of-magnitude. The single-molecule SPR assay is achieved with a millimeter-wide surface functionalized with a physisorbed biolayer comprising trillions of recognition-elements (antibodies or protein-probe complexes) which undergo an acidic or alkaline pH-conditioning. Potentiometric and surface-probing imaging experiments reveal the phenomenon underlying this extraordinary performance enhancement. The data suggest an unexplored amplification process within the biomaterial, where pH-conditioning, driving the biolayer in a metastable state, induces a self-propagating aggregation of partially misfolded proteins, following single-affinity binding. This process triggers an electrostatic rearrangement, resulting in the displacement of a charge equivalent to 1.5e per 102 recognition elements. Such findings open new opportunities for reliable SPR-based biosensing at the physical detection limits, with promising applications in point-of-care plasmonic systems.

Electric Field Cycling of Physisorbed Antibodies Reduces Biolayer Polarization Dispersion

The electric dipoles of proteins in a biolayer determine their dielectric properties through the polarization density P. Hence, its reproducibility is crucial for applications, particularly in bioelectronics. Biolayers encompassing capturing antibodies covalently bound at a biosensing interface are generally preferred for their assumed higher stability. However, surface physisorption is shown to offer advantages like easily scalable fabrication processes and high stability. The present study investigates the effects of electric-field (EF)-cycling of anti-Immunoglobulin M (anti-IgM) biolayers physisorbed on Au. The impact of EF-cycling on the dielectric, optical, and mechanical properties of anti-IgM biolayer is investigated. A reduction of the dispersion (standard deviation over a set of 31 samples) of the measured P values is observed, while the set median stays almost constant. Hence, physisorption combined with EF cycling, results in a biolayer with highly reproducible bioelectronic properties. Additionally, the study provides important insights into the mechanisms of dielectric rearrangement of dipole moments in capturing biolayers after EF-cycling. Notably, EF-cycling acts as an annealing process, driving the proteins in the biolayer into a statistically more probable and stable conformational state. Understanding these phenomena enhances the knowledge of the properties of physisorbed biolayers and can inform design strategies for bioelectronic devices.

Protein aggregation in health and disease: A looking glass of two faces

Protein molecules organize into an intricate alphabet of twenty amino acids and five architecture levels. The jargon "one structure, one functionality" has been challenged, considering the amount of intrinsically disordered proteins in the human genome and the requirements of hierarchical hetero- and homo-protein complexes in cell signaling. The assembly of large protein structures in health and disease is now viewed through the lens of phase separation and transition phenomena. What drives protein misfolding and aggregation? Or, more fundamentally, what hinders proteins from maintaining their native conformations, pushing them toward aggregation? Here, we explore the principles of protein folding, phase separation, and aggregation, which hinge on crucial events such as the reorganization of solvents, the chemical properties of amino acids, and their interactions with the environment. We focus on the dynamic shifts between functional and dysfunctional states of proteins and the conditions that promote protein misfolding, often leading to disease. By exploring these processes, we highlight potential therapeutic avenues to manage protein aggregation and reduce its harmful impacts on health.

PatchProt: hydrophobic patch prediction using protein foundation models

Motivation

Hydrophobic patches on protein surfaces play important functional roles in protein-protein and protein-ligand interactions. Large hydrophobic surfaces are also involved in the progression of aggregation diseases. Predicting exposed hydrophobic patches from a protein sequence has shown to be a difficult task. Fine-tuning foundation models allows for adapting a model to the specific nuances of a new task using a much smaller dataset. Additionally, multitask deep learning offers a promising solution for addressing data gaps, simultaneously outperforming single-task methods.

Results

In this study, we harnessed a recently released leading large language model Evolutionary Scale Models (ESM-2). Efficient fine-tuning of ESM-2 was achieved by leveraging a recently developed parameter-efficient fine-tuning method. This approach enabled comprehensive training of model layers without excessive parameters and without the need to include a computationally expensive multiple sequence analysis. We explored several related tasks, at local (residue) and global (protein) levels, to improve the representation of the model. As a result, our model, PatchProt, cannot only predict hydrophobic patch areas but also outperforms existing methods at predicting primary tasks, including secondary structure and surface accessibility predictions. Importantly, our analysis shows that including related local tasks can improve predictions on more difficult global tasks. This research sets a new standard for sequence-based protein property prediction and highlights the remarkable potential of fine-tuning foundation models enriching the model representation by training over related tasks.

Availability and implementation

https://github.com/Deagogishvili/chapter-multi-task.

Structural flexibility and heterogeneity of recombinant human glial fibrillary acidic protein (GFAP)

Glial fibrillary acidic protein (GFAP) is a promising biomarker for brain and spinal cord disorders. Recent studies have highlighted the differences in the reliability of GFAP measurements in different biological matrices. The reason for these discrepancies is poorly understood as our knowledge of the protein's 3-dimensional conformation, proteoforms, and aggregation remains limited. Here, we investigate the structural properties of GFAP under different conditions. For this, we characterized recombinant GFAP proteins from various suppliers and applied hydrogen-deuterium exchange mass spectrometry (HDX-MS) to provide a snapshot of the conformational dynamics of GFAP in artificial cerebrospinal fluid (aCSF) compared to the phosphate buffer. Our findings indicate that recombinant GFAP exists in various conformational species. Furthermore, we show that GFAP dimers remained intact under denaturing conditions. HDX-MS experiments show an overall decrease in H-bonding and an increase in solvent accessibility of GFAP in aCSF compared to the phosphate buffer, with clear indications of mixed EX2 and EX1 kinetics. To understand possible structural interface regions and the evolutionary conservation profiles, we combined HDX-MS results with the predicted GFAP-dimer structure by AlphaFold-Multimer. We found that deprotected regions with high structural flexibility in aCSF overlap with predicted conserved dimeric 1B and 2B domain interfaces. Structural property predictions combined with the HDX data show an overall deprotection and signatures of aggregation in aCSF. We anticipate that the outcomes of this research will contribute to a deeper understanding of the structural flexibility of GFAP and ultimately shed light on its behavior in different biological matrices.

Bacterial nucleoid is a riddle wrapped in a mystery inside an enigma

Bacterial chromosome, the nucleoid, is traditionally modeled as a rosette of DNA mega-loops, organized around proteinaceous central scaffold by nucleoid-associated proteins (NAPs), and mixed with the cytoplasm by transcription and translation. Electron microscopy of fixed cells confirms dispersal of the cloud-like nucleoid within the ribosome-filled cytoplasm. Here, I discuss evidence that the nucleoid in live cells forms DNA phase separate from riboprotein phase, the "riboid." I argue that the nucleoid-riboid interphase, where DNA interacts with NAPs, transcribing RNA polymerases, nascent transcripts, and ssRNA chaperones, forms the transcription zone. An active part of phase separation, transcription zone enforces segregation of the centrally positioned information phase (the nucleoid) from the surrounding action phase (the riboid), where translation happens, protein accumulates, and metabolism occurs. I speculate that HU NAP mostly tiles up the nucleoid periphery-facilitating DNA mobility but also supporting transcription in the interphase. Besides extruding plectonemically supercoiled DNA mega-loops, condensins could compact them into solenoids of uniform rings, while HU could support rigidity and rotation of these DNA rings. The two-phase cytoplasm arrangement allows the bacterial cell to organize the central dogma activities, where (from the cell center to its periphery) DNA replicates and segregates, DNA is transcribed, nascent mRNA is handed over to ribosomes, mRNA is translated into proteins, and finally, the used mRNA is recycled into nucleotides at the inner membrane. The resulting information-action conveyor, with one activity naturally leading to the next one, explains the efficiency of prokaryotic cell design-even though its main intracellular transportation mode is free diffusion.

Proteome encoded determinants of protein sorting into extracellular vesicles

Extracellular vesicles (EVs) are membranous structures released by cells into the extracellular space and are thought to be involved in cell-to-cell communication. While EVs and their cargo are promising biomarker candidates, sorting mechanisms of proteins to EVs remain unclear. In this study, we ask if it is possible to determine EV association based on the protein sequence. Additionally, we ask what the most important determinants are for EV association. We answer these questions with explainable AI models, using human proteome data from EV databases to train and validate the model. It is essential to correct the datasets for contaminants introduced by coarse EV isolation workflows and for experimental bias caused by mass spectrometry. In this study, we show that it is indeed possible to predict EV association from the protein sequence: a simple sequence-based model for predicting EV proteins achieved an area under the curve of 0.77 ± 0.01, which increased further to 0.84 ± 0.00 when incorporating curated post-translational modification (PTM) annotations. Feature analysis shows that EV-associated proteins are stable, polar, and structured with low isoelectric point compared to non-EV proteins. PTM annotations emerged as the most important features for correct classification; specifically, palmitoylation is one of the most prevalent EV sorting mechanisms for unique proteins. Palmitoylation and nitrosylation sites are especially prevalent in EV proteins that are determined by very strict isolation protocols, indicating they could potentially serve as quality control criteria for future studies. This computational study offers an effective sequence-based predictor of EV associated proteins with extensive characterisation of the human EV proteome that can explain for individual proteins which factors contribute to their EV association.

Associating Compositional, Nutritional and Techno-Functional Characteristics of Faba Bean (Vicia faba L.) Protein Isolates and Their Production Side-Streams with Potential Food Applications

Faba beans (Vicia faba L.) show exciting prospects as a sustainable source of protein and fibre, with the potential to transition to a more sustainable food production. This study reveals the compositional, nutritional and techno-functional characteristics of two protein isolates from faba beans (Vicia faba L.), a high-starch fraction and a high-fibre side-stream. During the analysis of those four ingredients, particular attention was paid to the isolates' protein profile and the side-streams' carbohydrate composition. The isoelectric precipitated protein isolate 1 showed a protein content of 72.64 ± 0.31% DM. It exhibited low solubility but superior digestibility and high foam stability. High foaming capacity and low protein digestibility were observed for protein isolate 2, with a protein content of 71.37 ± 0.93% DM. This fraction was highly soluble and consisted primarily of low molecular weight proteins. The high-starch fraction contained 83.87 ± 3.07% DM starch, of which about 66% was resistant starch. Over 65% of the high-fibre fraction was insoluble dietary fibre. The findings of this study provide a detailed understanding of different production fractions of faba beans, which is of great value for future product development.

ProteinGLUE multi-task benchmark suite for self-supervised protein modeling

Self-supervised language modeling is a rapidly developing approach for the analysis of protein sequence data. However, work in this area is heterogeneous and diverse, making comparison of models and methods difficult. Moreover, models are often evaluated only on one or two downstream tasks, making it unclear whether the models capture generally useful properties. We introduce the ProteinGLUE benchmark for the evaluation of protein representations: a set of seven per-amino-acid tasks for evaluating learned protein representations. We also offer reference code, and we provide two baseline models with hyperparameters specifically trained for these benchmarks. Pre-training was done on two tasks, masked symbol prediction and next sentence prediction. We show that pre-training yields higher performance on a variety of downstream tasks such as secondary structure and protein interaction interface prediction, compared to no pre-training. However, the larger base model does not outperform the smaller medium model. We expect the ProteinGLUE benchmark dataset introduced here, together with the two baseline pre-trained models and their performance evaluations, to be of great value to the field of protein sequence-based property prediction. Availability: code and datasets from https://github.com/ibivu/protein-glue .

Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE)

Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to accelerate the validation and ultimate implementation of novel biomarkers in clinical practice, using proteomics-based biomarker development for main dementias as experimental case studies. We address several knowledge gaps that have been identified in the field. First, there is the technology-translation gap of different technologies for the discovery (e.g., mass spectrometry) and the large-scale validation (e.g., immunoassays) of biomarkers. In addition, there is a limited understanding of conformational states of biomarker proteins in different matrices, which affect the selection of reagents for assay development. In this review, we aim to understand the decisions taken in the initial steps of biomarker development, which is done via an interim narrative update of the work of each ESR subproject. The results describe the decision process to shortlist biomarkers from a proteomics to develop immunoassays or mass spectrometry assays for Alzheimer's disease, Lewy body dementia, and frontotemporal dementia. In addition, we explain the approach to prepare the market implementation of novel biomarkers and assays. Moreover, we describe the development of computational protein state and interaction prediction models to support biomarker development, such as the prediction of epitopes. Lastly, we reflect upon activities involved in the biomarker development process to deduce a best-practice roadmap for biomarker development.