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.

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.

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.

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.

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.

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.

Increase in plasma resveratrol levels and in acid-resistant proteins in the acquired enamel pellicle after use of resveratrol-containing orodispersible tablets.

OBJECTIVE: This study evaluated the effect of administration of trans-resveratrol-containing orodispersible tablets on the protein composition of the AEP and on blood plasma trans-resveratrol concentrations. METHODS: Ten volunteers participated in two crossover double-blind phases. In each phase, after dental prophylaxis, they received a trans-resveratrol (15 mg) orodispersible tablet, or a placebo tablet (without actives). The AEP formed after 120 min was collected with electrode filter papers soaked in 3 % citric acid. Blood samples were collected 30, 45, 60 and 120 min after the use of the tablet. After protein extraction, AEP samples were analyzed by shotgun labelfree quantitative proteomics and plasma samples were analyzed by high-performance liquid chromatography (HPLC). RESULTS: Eight hundred and two proteins were identified in the AEP. Among them, 336 and 213 were unique to the trans-resveratrol and control groups, respectively, while 253 were common to both groups. Proteins with important functions in the AEP had increased expression in the trans-resveratroltreated group, such as neutrophil defensins, S100 protein isoforms, lysozyme C, cystatin-D, mucin-7, alphaamylase, albumin, haptoglobin and statherin. Trans-resveratrol was detected in the plasma at all the times evaluated, with the peak at 30 min. CONCLUSIONS: The administration of trans-resveratrol in sublingual orodispersible tablets was effective both to increase the bioavailability of the polyphenol and the expression of antibacterial and acid-resistant proteins in the AEP, which might benefit oral and general health.

Mass Spectrometry-Based Characterization of Protein Aggregates in Tissues and Biofluids.

Protein aggregation is a common mechanism in multiple neurodegenerative and heart diseases and the accumulation of proteins in aggregates is toxic to cells, causing injury and death. The degree of protein aggregation directly correlates with the severity of the disease. Misfolded proteins present thermodynamic barriers that culminate in the loss of structure and function and the exposure of hydrophobic residues. The exposure of hydrophobic residues is the driving force behind protein aggregation, as it reduces surface free energy and increases the propensity for the formation of large insoluble aggregates. Exploring the protein content of aggregates is fundamental to understanding their formation mechanism and pathophysiological effects. We demonstrate here a method for isolating aggregated protein content in human plasma and mouse brain samples. The samples were characterized by mass spectrometry analysis, transmission electron microscopy, and western blotting. We report the identification of proteins associated with neurodegenerative diseases in the isolated pellets. The western blotting analyses of the isolated pellet showed the positivity for CD89 and CD63, consolidated markers of exosomes, confirming the presence of exosomes within the pellet but not in the supernatant in human plasma. Notably, the concomitant isolation of exosomes together with the protein aggregates was feasible starting from 200 µL of human plasma. Moreover, the presented methodology separated albumin from the aggregated pellet, allowing identification of larger diversity of proteins through mass spectrometry analysis.

Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes.

Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modeling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints. The resulting protein-constrained models consider data on turnover numbers (kcat ) and facilitate the integration of protein abundances. In this systematic review, we present and discuss the current state-of-the-art regarding the estimation of kinetic parameters used in protein-constrained models. We also highlight how data-driven and constraint-based approaches can aid the estimation of turnover numbers and their usage in improving predictions of cellular phenotypes. Finally, we identify standing challenges in protein-constrained metabolic models and provide a perspective regarding future approaches to improve the predictive performance.

Dynamics and Function of sRNA/mRNAs Under the Scrutiny of Computational Simulation Methods.

Molecular dynamics simulations have proved extremely useful in investigating the functioning of proteins with atomic-scale resolution. Many applications to the study of RNA also exist, and their number increases by the day. However, implementing MD simulations for RNA molecules in solution faces challenges that the MD practitioner must be aware of for the appropriate use of this tool. In this chapter, we present the fundamentals of MD simulations, in general, and the peculiarities of RNA simulations, in particular. We discuss the strengths and limitations of the technique and provide examples of its application to elucidate small RNA's performance.

Thermal Stabilization of a Bacterial Zn(II)-Dependent Phospholipase C through Consensus Sequence Design.

Proteins' extraordinary performance in recognition and catalysis has led to their use in a range of applications. However, proteins obtained from natural sources are oftentimes not suitable for direct use in industrial or diagnostic setups. Natural proteins, evolved to optimally perform a task in physiological conditions, usually lack the stability required to be used in harsher conditions. Therefore, the alteration of the stability of proteins is commonly pursued in protein engineering studies. Here, we achieved a substantial thermal stabilization of a bacterial Zn(II)-dependent phospholipase C by consensus sequence design. We retrieved and analyzed sequenced homologues from different sources, selecting a subset of examples for expression and characterization. A non-natural consensus sequence showed the highest stability and activity among those tested. Comparison of the stability parameters of this stabilized mutant and other natural variants bearing similar mutations allows us to pinpoint the sites most likely to be responsible for the enhancement. Point mutations in these sites alter the unfolding process of the consensus sequence. We show that the stabilized version of the protein retains full activity even in harsh oil degumming conditions, making it suitable for industrial applications.

MLOsMetaDB, a meta-database to centralize the information on liquid-liquid phase separation proteins and membraneless organelles.

Over the past few years, there has been a focus on proteins that create separate liquid phases in the intracellular liquid environment, known as membraneless organelles (MLOs). These organelles allow for the spatiotemporal associations of macromolecules that dynamically exchange within the cellular milieu. They provide a form of compartmentalization crucial for organizing key functions in many cells. Metabolic processes and signaling pathways in both the cytoplasm and nucleus are among the functions performed by MLOs, which are facilitated by diverse combinations of proteins and nucleic acids. However, disruptions in these liquid-liquid phase separation processes (LLPS) may lead to several diseases, such as neurodegenerative disorders and cancer, among others. To foster the study of this process and MLO function, we present MLOsMetaDB (http://mlos.leloir.org.ar), a comprehensive resource of information on MLO- and LLPS-related proteins. Our database integrates and centralizes available information for every protein involved in MLOs, which is otherwise disseminated across a plethora of different databases. Our manuscript outlines the development and features of MLOsMetaDB, which provides an interactive and user-friendly environment with modern biological visualizations and easy and quick access to proteins based on LLPS role, MLO location, and organisms. In addition, it offers an advanced search for making complex queries to generate customized information. Furthermore, MLOsMetaDB provides evolutionary information by collecting the orthologs of every protein in the same database. Overall, MLOsMetaDB is a valuable resource as a starting point for researchers studying the many processes driven by LLPS proteins and membraneless organelles.

Differential modulation of digestive enzymes and energy reserves at different times after feeding in juveniles of the marine estuarine-dependent flatfish Paralichthys orbignyanus (Valenciennes, 1839).

Integrative studies are lacking on the responses of digestive enzymes and energy reserves in conjunction with morphological traits at distinct postprandial times in marine estuarine-dependent flatfishes of ecological and economic importance, such as Paralichthys orbignyanus. We determined total weight (TW), hepato-somatic index (IH), activities of digestive enzymes in the intestine, and the concentration of energy reserves in the liver and the muscle at 0, 24, 72, and 360 h after feeding in juveniles of P. orbignyanus. Amylase activity decreased at 72 h (about 30%). Maltase, sucrose, and lipase activities reached peak at 24 h (67%, 600%, and 35%, respectively). Trypsin and aminopeptidase-N activities at 24 and 72 h, respectively, were lower than those at t = 0 (53% and 30%). A peak increase in the concentration of glycogen and triglycerides in the liver (24 h) (86% and 89%, respectively) occurred. In muscle, glycogen and triglyceride concentrations were unchanged at 24 h and higher at 72 and 360 h (100% and 60%). No changes were found in TW, IH, free glucose in the liver and muscle, and protein in the liver. The protein concentration in the muscle sharply increased at 24 and 360 h after feeding (60%). The results indicate a distinct and specific response of central components of carbohydrate, lipid, and protein metabolism that could be adjustments at the biochemical level upon periods of irregular feeding and even of long-term food deprivation inside coastal lagoons or estuaries. The distinct responses of digestive enzymes in the intestine and energy reserves in the liver and muscle suggest the differential modulation of tissue-specific anabolic and catabolic pathways that would allow the maintenance of physical conditions.

Local energetic frustration conservation in protein families and superfamilies.

Energetic local frustration offers a biophysical perspective to interpret the effects of sequence variability on protein families. Here we present a methodology to analyze local frustration patterns within protein families and superfamilies that allows us to uncover constraints related to stability and function, and identify differential frustration patterns in families with a common ancestry. We analyze these signals in very well studied protein families such as PDZ, SH3, ɑ and ß globins and RAS families. Recent advances in protein structure prediction make it possible to analyze a vast majority of the protein space. An automatic and unsupervised proteome-wide analysis on the SARS-CoV-2 virus demonstrates the potential of our approach to enhance our understanding of the natural phenotypic diversity of protein families beyond single protein instances. We apply our method to modify biophysical properties of natural proteins based on their family properties, as well as perform unsupervised analysis of large datasets to shed light on the physicochemical signatures of poorly characterized proteins such as the ones belonging to emergent pathogens.

Proteolytic signaling in cancer.

INTRODUCTION: Cancer is a disease of (altered) biological pathways, often driven by somatic mutations and with several implications. Therefore, the identification of potential markers of disease is challenging. Given the large amount of biological data generated with omics approaches, oncology has experienced significant contributions. Proteomics mapping of protein fragments, derived from proteolytic processing events during oncogenesis, may shed light on (i) the role of active proteases and (ii) the functional implications of processed substrates in biological signaling circuits. Both outcomes have the potential for predicting diagnosis/prognosis in diseases like cancer. Therefore, understanding proteolytic processing events and their downstream implications may contribute to advances in the understanding of tumor biology and targeted therapies in precision medicine. AREAS COVERED: Proteolytic events associated with some hallmarks of cancer (cell migration and proliferation, angiogenesis, metastasis, as well as extracellular matrix degradation) will be discussed. Moreover, biomarker discovery and the use of proteomics approaches to uncover proteolytic signaling events will also be covered. EXPERT OPINION: Proteolytic processing is an irreversible protein post-translational modification and the deconvolution of biological data resulting from the study of proteolytic signaling events may be used in both patient diagnosis/prognosis and targeted therapies in cancer.

Accurate prediction of in vivo protein abundances by coupling constraint-based modelling and machine learning.

Quantification of how different environmental cues affect protein allocation can provide important insights for understanding cell physiology. While absolute quantification of proteins can be obtained by resource-intensive mass-spectrometry-based technologies, prediction of protein abundances offers another way to obtain insights into protein allocation. Here we present CAMEL, a framework that couples constraint-based modelling with machine learning to predict protein abundance for any environmental condition. This is achieved by building machine learning models that leverage static features, derived from protein sequences, and condition-dependent features predicted from protein-constrained metabolic models. Our findings demonstrate that CAMEL results in excellent prediction of protein allocation in E. coli (average Pearson correlation of at least 0.9), and moderate performance in S. cerevisiae (average Pearson correlation of at least 0.5). Therefore, CAMEL outperformed contending approaches without using molecular read-outs from unseen conditions and provides a valuable tool for using protein allocation in biotechnological applications.

State of the art in protein-protein interactions within the fungi kingdom.

Proteins rarely exert their function by themselves. Protein-protein interactions (PPIs) regulate virtually every biological process that takes place in a cell. Such interactions are targets for new therapeutic agents against all sorts of diseases, through the screening and design of a variety of inhibitors. Here we discuss several aspects of PPIs that contribute to prediction of protein function and drug discovery. As the high-throughput techniques continue to release biological data, targets for fungal therapeutics that rely on PPIs are being proposed worldwide. Computational approaches have reduced the time taken to develop new therapeutic approaches. The near future brings the possibility of developing new PPI and interaction network inhibitors and a revolution in the way we treat fungal diseases.

Assessment of the toxic effects of levetiracetam on biochemical, functional, and redox parameters of salivary glands in male Wistar rats.

Levetiracetam (LEV) is an anticonvulsant for epilepsy. The toxic effects of this medication in tissues have been associated with redox state imbalance, which can lead to salivary gland dysfunction. Therefore, the current work investigated the effects of LEV on the biochemical, functional, and redox parameters of the parotid and submandibular glands in rats. For this, male Wistar rats (Rattus norvegicus albinus) were randomly divided into 3 groups (n = 10/group): Control (0.9% saline solution), LEV100 (100 mg/kg), and LEV300 (300 mg/kg). After 21 consecutive days of intragastric gavage treatments, pilocarpine stimulated saliva secretion was collected for salivary biochemical analysis. The extracted salivary glands were utilized for histomorphometry and redox state analyses. Our results showed that LEV300 increased plasma hepatotoxicity markers and reduced salivary amylase activity and the acinar surface area of the parotid gland. Total oxidant capacity and oxidative damage to lipids and proteins were higher in the parotid gland, while total antioxidant capacity and uric acid levels were reduced in the submandibular gland of the LEV100 group compared to Control. On the other hand, total oxidant capacity, oxidative damage to lipids and proteins, total antioxidant capacity, and uric acid levels were lower in both salivary glands of the LEV300 group compared to Control. Superoxide dismutase and glutathione peroxidase activities were lower in the salivary glands of treated animals compared to Control. In conclusion our data suggest that treatment with LEV represents a potentially toxic agent, that contributes to drug-induced salivary gland dysfunction.

Expanded bioinformatic analysis of Oximouse dataset reveals key putative processes involved in brain aging and cognitive decline.

The theory that aging is driven by the damage produced by reactive oxygen species (ROS) derived from oxidative metabolism dominated geroscience studies during the second half of the 20th century. However, increasing evidence that ROS also plays a key role in the physiological regulation of numerous processes through the reversible oxidation of cysteine residues in proteins, has challenged this notion. Currently, the scope of redox signaling has reached proteomic dimensions through mass spectrometry techniques. Here, we perform a comprehensive bioinformatics analysis of cysteine oxidation changes during mouse brain aging, using the quantitative data provided in the Oximouse dataset. Interestingly, our unbiased analysis identified hundreds of putative cysteine redox switches covering several pathways previously associated with aging. These include the ubiquitin-proteasome pathway and one-carbon metabolism (folate cycle, methionine cycle, transsulfuration and polyamine pathways). Surprisingly, cysteine oxidation changes are enriched in synaptic proteins in a highly asymmetric distribution: while postsynaptic proteins tend to increase cysteine oxidation with age, the opposite occurs for presynaptic proteins. Additionally, cysteine oxidation changes during aging are associated with proteins involved in the regulation of the mitochondrial transition pore opening and synaptic calcium homeostasis. Our analysis reinforces the concept that brain aging is associated with selective changes in the oxidation state of key proteins, rather than an overall trend toward increased oxidation. Also, we provide a prioritized list of specific cysteine residues with putative impact in aging processes for future experimental validation.