Pérdida de proteínas en líquido peritoneal de pacientes críticos con abdomen abierto / Protein loss in peritoneal fluid of critically ill patients with open abdomen

Introducción. Los pacientes con patología abdominal quirúrgica que requieren manejo con abdomen abierto son susceptibles a la pérdida de proteínas desde la cavidad expuesta. El objetivo de este estudio fue caracterizar la pérdida proteica a través de dos tipos de cierre temporal abdominal. Métodos. Se realizó un estudio decohorte prospectivo, con pacientes críticos manejados durante el año 2021 con abdomen abierto mediante dos tipos de cierre temporal: bolsa de Bogotá y ABThera™. Se recolectaron muestras intraoperatorias seriadas de líquido peritoneal (días 1, 3 y 5). Se calcularon frecuencias y promedios, y se compararon con las pruebas de Chi cuadrado y t de Student. Resultados. Se incluyeron 25 pacientes. El promedio de pérdida de proteínas en líquido peritoneal fue mayor con el sistema ABThera™ (44,38 g/L) comparado con la bolsa de Bogotá (25,18 g/L; p=0,0185). Durante el seguimiento se observó la tendencia a la disminución del promedio de proteínas perdidas por ambos sistemas, pero con ABThera™ se perdieron en promedio 15,47 gr/L más de proteínas, independientemente del estado nutricional y del aporte proteico recibido (p=0,042). No hubo diferencias según la etiología que llevó al manejo con abdomen abierto, los procedimientos quirúrgicos realizados o el estado de infección por COVID-19. Conclusiones. El abdomen abierto representa una fuente importante de pérdida de proteínas, que es diferente según el tipo de cierre temporal usado. Estas pérdidas deberían considerarse en los cálculos de soporte nutricional en la unidad de cuidado intensivo.

Introduction. Patients with surgical abdominal pathology requiring management with an open abdomen are susceptible to protein loss from the exposed cavity. The objective of this study was to characterize protein loss through two types of temporary abdominal closure. Methods. A prospective cohort study was carried out with critically ill patients managed during 2021 with an open abdomen using two types of temporary closure: Bogota bag and ABThera™. Serial intraoperative peritoneal fluid samples were collected (days 1, 3, and 5). Frequencies and averages were calculated and compared with the Chi square and Student's t tests. Results. Twenty-five patients were included. The average protein loss in peritoneal fluid was higher with the ABThera™ system (44.38 g/L) compared to the Bogota bag (25.18 g/L; p-value=0.0185). During follow-up, a tendency to decrease the average protein lost by both systems was observed, but with ABThera™ an average of 15.47 gr/L more protein was lost, regardless of the nutritional status and protein intake received (p=0.042). There were no differences based on etiology leading to open abdomen management, surgical procedures performed, or Covid-19 infection status. Conclusions. The open abdomen represents an important source of protein loss, which is different depending on the type of temporary closure used. These losses should be considered in calculations of nutritional support in the intensive care unit.

DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening.

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.

BAT2: an Open-Source Tool for Flexible, Automated, and Low Cost Absolute Binding Free Energy Calculations.

Absolute binding free energy (ABFE) calculations with all-atom molecular dynamics (MD) have the potential to greatly reduce costs in the first stages of drug discovery. Here, we introduce BAT2, the new version of the Binding Affinity Tool (BAT.py), designed to combine full automation of ABFE calculations with high-performance MD simulations, making it a potential tool for virtual screening. We describe and test several changes and new features that were incorporated into the code, such as relative restraints between the protein and the ligand instead of using fixed dummy atoms, support for the OpenMM simulation engine, a merged approach to the application/release of restraints, support for cobinders and proteins with multiple chains, and many others. We also reduced the simulation times for each ABFE calculation, assessing the effect on the expected robustness and accuracy of the calculations.

Rp3: Ribosome profiling-assisted proteogenomics improves coverage and confidence during microprotein discovery.

There has been a dramatic increase in the identification of non-canonical translation and a significant expansion of the protein-coding genome. Among the strategies used to identify unannotated small Open Reading Frames (smORFs) that encode microproteins, Ribosome profiling (Ribo-Seq) is the gold standard for the annotation of novel coding sequences by reporting on smORF translation. In Ribo-Seq, ribosome-protected footprints (RPFs) that map to multiple genomic sites are removed since they cannot be unambiguously assigned to a specific genomic location. Furthermore, RPFs necessarily result in short (25-34 nucleotides) reads, increasing the chance of multi-mapping alignments, such that smORFs residing in these regions cannot be identified by Ribo-Seq. Moreover, it has been challenging to identify protein evidence for Ribo-Seq. To solve this, we developed Rp3, a pipeline that integrates proteogenomics and Ribosome profiling to provide unambiguous evidence for a subset of microproteins missed by current Ribo-Seq pipelines. Here, we show that Rp3 maximizes proteomics detection and confidence of microprotein-encoding smORFs.

Proteins and Peptides Studied In Silico and In Vivo for the Treatment of Diabetes Mellitus: A Systematic Review.

Bioinformatics has expedited the screening of new efficient therapeutic agents for diseases such as diabetes mellitus (DM). The objective of this systematic review (SR) was to understand naturally occurring proteins and peptides studied in silico and subsequently reevaluated in vivo for treating DM, guided by the question: which peptides or proteins have been studied in silico for the treatment of diabetes mellitus? The RS protocol was registered in the International Prospective Register of Systematic Reviews database. Articles meeting the eligibility criteria were selected from the PubMed, ScienceDirect, Scopus, Web of Science, Virtual Health Library (VHL), and EMBASE databases. Five studies that investigated peptides or proteins analyzed in silico and in vivo were selected. Risk of bias assessment was conducted using the adapted Strengthening the Reporting of Empirical Simulation Studies (STRESS) tool. A diverse range of assessed proteins and/or peptides that had a natural origin were investigated in silico and corresponding in vivo reevaluation demonstrated reductions in glycemia and/or insulin, morphological enhancements in pancreatic ß cells, and alterations in the gene expression of markers associated with DM. The in silico studies outlined offer crucial insights into therapeutic strategies for DM, along with promising leads for screening novel therapeutic agents in future trials.

Analysis of human biological samples using porous graphitic carbon columns and liquid chromatography-mass spectrometry: a review.

Liquid chromatography-mass spectrometry (LC-MS) has emerged as a powerful analytical technique for analyzing complex biological samples. Among various chromatographic stationary phases, porous graphitic carbon (PGC) columns have attracted significant attention due to their unique properties-such as the ability to separate both polar and non-polar compounds and their stability through all pH ranges and to high temperatures-besides the compatibility with LC-MS. This review discusses the applicability of PGC for SPE and separation in LC-MS-based analyses of human biological samples, highlighting the diverse applications of PGC-LC-MS in analyzing endogenous metabolites, pharmaceuticals, and biomarkers, such as glycans, proteins, oligosaccharides, sugar phosphates, and nucleotides. Additionally, the fundamental principles underlying PGC column chemistry and its advantages, challenges, and advances in method development are explored. This comprehensive review aims to provide researchers and practitioners with a valuable resource for understanding the capabilities and limitations of PGC columns in LC-MS-based analysis of human biological samples, thereby facilitating advancements in analytical methodologies and biomedical research.

Streamlined integrated protein isoelectric focusing using microfluidic paper-based device.

An innovative integrated paper-based microdevice was developed for protein separation by isoelectric focusing (IEF), allowing for robust design thanks to a 3D-printed holder integrating separation channel, reservoirs, and electrodes. To reach robustness and precision, the optimization focused on the holder geometry, the paper nature, the reservoir design, the IEF medium, and various focusing parameters. A well-established and stable pH gradient was obtained on a glass-fiber paper substrate with simple sponge reservoirs, and the integration of the electrodes in the holder led to a straightforward system. The separation medium composed of water/glycerol (85/15, v/v) allowed for reducing medium evaporation while being an efficient medium for most hydrophobic and hydrophilic proteins, compatible with mass spectrometry detection for further proteomics developments. To our knowledge, this is the first report of the use of glycerol solutions as a separation medium in a paper-based microdevice. Analytical performances regarding pH gradient generation, pI determination, separation efficiency, and resolution were estimated while varying the IEF experimental parameters. The overall process led to an efficient separation within 25 min. Then, this methodology was applied to a sample composed of saliva doped with proteins. A minimal matrix effect was evidenced, underscoring the practical viability of our platform. This low-cost, versatile and robust paper-based IEF microdevice opens the way to various applications, ranging from sample pre-treatment to integration in an overall proteomic-on-a-chip device.

Unconventional sourced proteins in 3D and 4D food printing: Is it the future of food processing?

Following consumer trends and market needs, the food industry has expanded the use of unconventional sources to obtain proteins. In parallel, 3D and 4D food printing have emerged with the potential to enhance food processing. While 3D and 4D printing technologies show promising prospects for improving the performance and applicability of unconventional sourced proteins (USP) in food, this combination remains relatively unexplored. This review aims to provide an overview of the application of USP in 3D and 4D printing, focusing on their primary sources, composition, rheological, and technical-functional properties. The drawbacks, challenges, potentialities, and prospects of these technologies in food processing are also examined. This review underscores the current necessity for greater regulation of food products processed by 3D and 4D printing. The data presented here indicate that 3D and 4D printing represent viable, sustainable, and innovative alternatives for the food industry, emphasizing the potential for further exploration of 4D printing in food processing. Additional studies are warranted to explore their application with unconventional proteins.

The power of computational proteomics platforms to decipher protein-protein interactions.

Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.

Reassessing the exon-foldon correspondence using frustration analysis.

Protein folding and evolution are intimately linked phenomena. Here, we revisit the concept of exons as potential protein folding modules across a set of 38 abundant and conserved protein families. Taking advantage of genomic exon-intron organization and extensive protein sequence data, we explore exon boundary conservation and assess the foldon-like behavior of exons using energy landscape theoretic measurements. We found deviations in the exon size distribution from exponential decay indicating selection in evolution. We show that when taken together there is a pronounced tendency to independent foldability for segments corresponding to the more conserved exons, supporting the idea of exon-foldon correspondence. While 45% of the families follow this general trend when analyzed individually, there are some families for which other stronger functional determinants, such as preserving frustrated active sites, may be acting. We further develop a systematic partitioning of protein domains using exon boundary hotspots, showing that minimal common exons correspond with uninterrupted alpha and/or beta elements for the majority of the families but not for all of them.

Analysis of proteins in the light of mutations.

Proteins have evolved through mutations-amino acid substitutions-since life appeared on Earth, some 109 years ago. The study of these phenomena has been of particular significance because of their impact on protein stability, function, and structure. This study offers a new viewpoint on how the most recent findings in these areas can be used to explore the impact of mutations on protein sequence, stability, and evolvability. Preliminary results indicate that: (1) mutations can be viewed as sensitive probes to identify 'typos' in the amino-acid sequence, and also to assess the resistance of naturally occurring proteins to unwanted sequence alterations; (2) the presence of 'typos' in the amino acid sequence, rather than being an evolutionary obstacle, could promote faster evolvability and, in turn, increase the likelihood of higher protein stability; (3) the mutation site is far more important than the substituted amino acid in terms of the marginal stability changes of the protein, and (4) the unpredictability of protein evolution at the molecular level-by mutations-exists even in the absence of epistasis effects. Finally, the Darwinian concept of evolution "descent with modification" and experimental evidence endorse one of the results of this study, which suggests that some regions of any protein sequence are susceptible to mutations while others are not. This work contributes to our general understanding of protein responses to mutations and may spur significant progress in our efforts to develop methods to accurately forecast changes in protein stability, their propensity for metamorphism, and their ability to evolve.

Robust assessment of sample preparation protocols for proteomics of cells and tissues.

In proteomic studies, the reliability and reproducibility of results hinge on well-executed protein extraction and digestion protocols. Here, we systematically compared three established digestion methods for macrophages, namely filter-assisted sample preparation (FASP), in-solution, and in-gel digestion protocols. We also compared lyophilization and manual lysis for liver tissue protein extraction, each of them tested using either sodium deoxycholate (SDC)- or RIPA-based lysis buffer. For the macrophage cell line, FASP using passivated filter units outperformed the other tested methods regarding the number of identified peptides and proteins. However, a careful standardization has shown that all three methods can yield robust results across a wide range of starting material (even starting with 1 µg of proteins). Importantly, inter and intra-day coefficients of variance (CVs) were determined for all sample preparation protocols. Thus, the median inter-day CVs for in-solution, in-gel and FASP protocols were respectively 10, 8 and 9%, very similar to the median CVs obtained for the intra-day analysis (9, 8 and 8%, respectively). Moreover, FASP digestion presented 80% of proteins with a CV lower than 25%, followed closely by in-gel digestion (78%) and in-solution sample preparation (72%) protocols. For tissue proteomics, both manual lysis and lyophilization presented similar proteome coverage and reproducibility, but the efficiency of protein extraction depended on the lysis buffer used, with RIPA buffer showing better results. In conclusion, although each sample preparation method has its own particularity, they are all suited for successful proteomic experiments if a careful standardization of the sample preparation workflow is carried out.

Comparison of the precursor, amino acid oxidation, and end-product methods for the evaluation of protein turnover in senior dogs.

Stable isotope methods have been used to study protein metabolism in humans; however, there application in dogs has not been frequently explored. The present study compared the methods of precursor (13C-Leucine), end-products (15N-Glycine), and amino acid oxidation (13C-Phenylalanine) to determine the whole-body protein turnover rate in senior dogs. Six dogs (12.7 ± 2.6 years age, 13.6 ± 0.6 kg bodyweight) received a dry food diet for maintenance and were subjected to all the above-mentioned methods in succession. To establish 13C and 15N kinetics, according to different methodologies blood plasma, urine, and expired air were collected using a specifically designed mask. The volume of CO2 was determined using respirometry. The study included four methods viz. 13C-Leucine, 13C-Phenylalanine evaluated with expired air, 13C-Phenylalanine evaluated with urine, and 15N-Glycine, with six dogs (repetitions) per method. Data was subjected to variance analysis and means were compared using the Tukey test (P<0.05). In addition, the agreement between the methods was evaluated using Pearson correlation and Bland-Altman statistics. Protein synthesis (3.39 ± 0.33 g.kg-0,75. d-1), breakdown (3.26 ± 0.18 g.kg-0.75.d-1), and flux estimations were similar among the four methods of study (P>0.05). However, only 13C-Leucine and 13C-Phenylalanine (expired air) presented an elevated Pearson correlation and concordance. This suggested that caution should be applied while comparing the results with the other methodologies.

Extraction of Proteins from Fermented Food.

The metaproteomic approach allows a deep microbiome characterization in different complex systems. Based on metaproteome data, microbial communities' composition, succession, and functional role in different environmental conditions can be established.The main challenge in metaproteomic studies is protein extraction, and although many protocols have been developed, a few are focused on the protein extraction of fermented foods. In this chapter, a reproducible and efficient method for the extraction of proteins from a traditionally fermented starchy food is described. The method can be applied to any fermented food and aims to enrich the extraction of proteins from microorganisms for their subsequent characterization.

SAnDReS 2.0: Development of machine-learning models to explore the scoring function space.

Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.

Evaluating large language models for annotating proteins.

In UniProtKB, up to date, there are more than 251 million proteins deposited. However, only 0.25% have been annotated with one of the more than 15000 possible Pfam family domains. The current annotation protocol integrates knowledge from manually curated family domains, obtained using sequence alignments and hidden Markov models. This approach has been successful for automatically growing the Pfam annotations, however at a low rate in comparison to protein discovery. Just a few years ago, deep learning models were proposed for automatic Pfam annotation. However, these models demand a considerable amount of training data, which can be a challenge with poorly populated families. To address this issue, we propose and evaluate here a novel protocol based on transfer learningThis requires the use of protein large language models (LLMs), trained with self-supervision on big unnanotated datasets in order to obtain sequence embeddings. Then, the embeddings can be used with supervised learning on a small and annotated dataset for a specialized task. In this protocol we have evaluated several cutting-edge protein LLMs together with machine learning architectures to improve the actual prediction of protein domain annotations. Results are significatively better than state-of-the-art for protein families classification, reducing the prediction error by an impressive 60% compared to standard methods. We explain how LLMs embeddings can be used for protein annotation in a concrete and easy way, and provide the pipeline in a github repo. Full source code and data are available at https://github.com/sinc-lab/llm4pfam.

Understanding the Energy Landscape of Intrinsically Disordered Protein Ensembles.

A substantial portion of various organisms' proteomes comprises intrinsically disordered proteins (IDPs) that lack a defined three-dimensional structure. These IDPs exhibit a diverse array of conformations, displaying remarkable spatiotemporal heterogeneity and exceptional conformational flexibility. Characterizing the structure or structural ensemble of IDPs presents significant conceptual and methodological challenges owing to the absence of a well-defined native structure. While databases such as the Protein Ensemble Database (PED) provide IDP ensembles obtained through a combination of experimental data and molecular modeling, the absence of reaction coordinates poses challenges in comprehensively understanding pertinent aspects of the system. In this study, we leverage the energy landscape visualization method (JCTC, 6482, 2019) to scrutinize four IDP ensembles sourced from PED. ELViM, a methodology that circumvents the need for a priori reaction coordinates, aids in analyzing the ensembles. The specific IDP ensembles investigated are as follows: two fragments of nucleoporin (NUL: 884-993 and NUS: 1313-1390), yeast sic 1 N-terminal (1-90), and the N-terminal SH3 domain of Drk (1-59). Utilizing ELViM enables the comprehensive validation of ensembles, facilitating the detection of potential inconsistencies in the sampling process. Additionally, it allows for identifying and characterizing the most prevalent conformations within an ensemble. Moreover, ELViM facilitates the comparative analysis of ensembles obtained under diverse conditions, thereby providing a powerful tool for investigating the functional mechanisms of IDPs.

APEX2-based proximity proteomic analysis identifies candidate interactors for Plasmodium falciparum knob-associated histidine-rich protein in infected erythrocytes.

The interaction of Plasmodium falciparum-infected red blood cells (iRBCs) with the vascular endothelium plays a crucial role in malaria pathology and disease. KAHRP is an exported P. falciparum protein involved in iRBC remodelling, which is essential for the formation of protrusions or "knobs" on the iRBC surface. These knobs and the proteins that are concentrated within them allow the parasites to escape the immune response and host spleen clearance by mediating cytoadherence of the iRBC to the endothelial wall, but this also slows down blood circulation, leading in some cases to severe cerebral and placental complications. In this work, we have applied genetic and biochemical tools to identify proteins that interact with P. falciparum KAHRP using enhanced ascorbate peroxidase 2 (APEX2) proximity-dependent biotinylation and label-free shotgun proteomics. A total of 30 potential KAHRP-interacting candidates were identified, based on the assigned fragmented biotinylated ions. Several identified proteins have been previously reported to be part of the Maurer's clefts and knobs, where KAHRP resides. This study may contribute to a broader understanding of P. falciparum protein trafficking and knob architecture and shows for the first time the feasibility of using APEX2-proximity labelling in iRBCs.

Orientational Pathways during Protein Translocation through Polymer-Modified Nanopores.

Protein translocation through nanopores holds significant promise for applications in biotechnology, biomolecular analysis, and medicine. However, the interpretation of signals generated by the translocation of the protein remains challenging. In this way, it is crucial to gain a comprehensive understanding on how macromolecules translocate through a nanopore and to identify what are the critical parameters that govern the process. In this study, we investigate the interplay between protein charge regulation, orientation, and nanopore surface modifications using a theoretical framework that allows us to explicitly take into account the acid-base reactions of the titrable amino acids in the proteins and in the polyelectrolytes grafted to the nanopore surface. Our goal is to thoroughly characterize the translocation process of different proteins (GFP, ß-lactoglobulin, lysozyme, and RNase) through nanopores modified with weak polyacids. Our calculations show that the charge regulation mechanism exerts a profound effect on the translocation process. The pH-dependent interactions between proteins and charged polymers within the nanopore lead to diverse free energy landscapes with barriers, wells, and flat regions dictating translocation efficiency. Comparison of different proteins allows us to identify the significance of protein isoelectric point, size, and morphology in the translocation behavior. Taking advantage of these insights, we propose pH-responsive nanopores that can load proteins at one pH and release them at another, offering opportunities for controlled protein delivery, separation, and sensing applications.

Structured Tandem Repeats in Protein Interactions.

Tandem repeats (TRs) in protein sequences are consecutive, highly similar sequence motifs. Some types of TRs fold into structural units that pack together in ensembles, forming either an (open) elongated domain or a (closed) propeller, where the last unit of the ensemble packs against the first one. Here, we examine TR proteins (TRPs) to see how their sequence, structure, and evolutionary properties favor them for a function as mediators of protein interactions. Our observations suggest that TRPs bind other proteins using large, structured surfaces like globular domains; in particular, open-structured TR ensembles are favored by flexible termini and the possibility to tightly coil against their targets. While, intuitively, open ensembles of TRs seem prone to evolve due to their potential to accommodate insertions and deletions of units, these evolutionary events are unexpectedly rare, suggesting that they are advantageous for the emergence of the ancestral sequence but are early fixed. We hypothesize that their flexibility makes it easier for further proteins to adapt to interact with them, which would explain their large number of protein interactions. We provide insight into the properties of open TR ensembles, which make them scaffolds for alternative protein complexes to organize genes, RNA and proteins.