PypKa server: online pKa predictions and biomolecular structure preparation with precomputed data from PDB and AlphaFold DB.

When preparing biomolecular structures for molecular dynamics simulations, pKa calculations are required to provide at least a representative protonation state at a given pH value. Neglecting this step and adopting the reference protonation states of the amino acid residues in water, often leads to wrong electrostatics and nonphysical simulations. Fortunately, several methods have been developed to prepare structures considering the protonation preference of residues in their specific environments (pKa values), and some are even available for online usage. In this work, we present the PypKa server, which allows users to run physics-based, as well as ML-accelerated methods suitable for larger systems, to obtain pKa values, isoelectric points, titration curves, and structures with representative pH-dependent protonation states compatible with commonly used force fields (AMBER, CHARMM, GROMOS). The user may upload a custom structure or submit an identifier code from PBD or UniProtKB. The results for over 200k structures taken from the Protein Data Bank and the AlphaFold DB have been precomputed, and their data can be retrieved without extra calculations. All this information can also be obtained from an application programming interface (API) facilitating its usage and integration into existing pipelines as well as other web services. The web server is available at pypka.org.

Multiplexed cellular profiling identifies an organoselenium compound as an inhibitor of CRM1-mediated nuclear export.

Chromosomal region maintenance 1 (CRM1 also known as Xpo1 and exportin-1) is the receptor for the nuclear export controlling the intracellular localization and function of many cellular and viral proteins that play a crucial role in viral infections and cancer. The inhibition of CRM1 has emerged as a promising therapeutic approach to interfere with the lifecycle of many viruses, for the treatment of cancer, and to overcome therapy resistance. Recently, selinexor has been approved as the first CRM1 inhibitor for the treatment of multiple myeloma, providing proof of concept for this therapeutic option with a new mode of action. However, selinexor is associated with dose-limiting toxicity and hence, the discovery of alternative small molecule leads that could be developed as less toxic anticancer and antiviral therapeutics will have a significant impact in the clinic. Here, we report a CRM1 inhibitor discovery platform. The development of this platform includes reporter cell lines that monitor CRM1 activity by using red fluorescent protein or green fluorescent protein-labeled HIV-1 Rev protein with a strong heterologous nuclear export signal. Simultaneously, the intracellular localization of other proteins, to be interrogated for their capacity to undergo CRM1-mediated export, can be followed by co-culturing stable cell lines expressing fluorescent fusion proteins. We used this platform to interrogate the mode of nuclear export of several proteins, including PDK1, p110α, STAT5A, FOXO1, 3, 4 and TRIB2, and to screen a compound collection. We show that while p110α partially relies on CRM1-dependent nuclear export, TRIB2 is exported from the nucleus in a CRM1-independent manner. Compound screening revealed the striking activity of an organoselenium compound on the CRM1 nuclear export receptor.

High Charge Density in Peptide Dendrimers is Required to Destabilize Membranes: Insights into Endosome Evasion.

Peptide dendrimers are a type of branched, symmetric, and topologically well-defined molecule that have already been used as delivery systems for nucleic acid transfection. Several of the most promising sequences showed high efficiency in many key steps of transfection, namely, binding siRNA, entering cells, and evading the endosome. However, small changes to the peptide dendrimers, such as in the hydrophobic core, the amino acid chirality, or the total available charges, led to significantly different experimental results with unclear mechanistic insights. In this work, we built a computational model of several of those peptide dendrimers (MH18, MH13, and MH47) and some of their variants to study the molecular details of the structure and function of these molecules. We performed CpHMD simulations in the aqueous phase and in interaction with a lipid bilayer to assess how conformation and protonation are affected by pH in different environments. We found that while the different peptide dendrimer sequences lead to no substantial structural differences in the aqueous phase, the total charge and, more importantly, the total charge density are key for the capacity of the dendrimer to interact and destabilize the membrane. These dendrimers become highly charged when the pH changes from 7.5 to 4.5, and the presence of a high charge density, which is decreased for MH47 that has four fewer titratable lysines, is essential to trigger membrane destabilization. These findings are in excellent agreement with the experimental data and help us to understand the high efficiency of some dendrimers and why the dendrimer MH47 is unable to complete the transfection process. This evidence provides further understanding of the mode of action of these peptide dendrimers and will be pivotal for the future design of new sequences with improved transfection capabilities.

Is There a Functional Role for the Knotted Topology in Protein UCH-L1?

Knotted proteins are present in nature, but there is still an open issue regarding the existence of a universal role for these remarkable structures. To address this question, we used classical molecular dynamics (MD) simulations combined with in vitro experiments to investigate the role of the Gordian knot in the catalytic activity of UCH-L1. To create an unknotted form of UCH-L1, we modified its amino acid sequence by truncating several residues from its N-terminus. Remarkably, we find that deleting the first two N-terminal residues leads to a partial loss of enzyme activity with conservation of secondary structural content and knotted topological state. This happens because the integrity of the N-terminus is critical to ensure the correct alignment of the catalytic triad. However, the removal of five residues from the N-terminus, which significantly disrupts the native structure and the topological state, leads to a complete loss of enzymatic activity. Overall, our findings indicate that UCH-L1's catalytic activity depends critically on the integrity of the N-terminus and the secondary structure content, with the latter being strongly coupled with the knotted topological state.

pKa Calculations of GPCRs: Understanding Protonation States in Receptor Activation.

The increase in the available G protein-coupled receptor (GPCR) structures has been pivotal in helping to understand their activation process. However, the role of protonation-conformation coupling in GPCR activation still needs to be clarified. We studied the protonation behavior of the highly conserved Asp2.50 residue in five different class A GPCRs (active and inactive conformations) using a linear response approximation (LRA) pKa calculation protocol. We observed consistent differences (1.3 pK units) for the macroscopic pKa values between the inactive and active states of the A2AR and B2AR receptors, indicating the protonation of Asp2.50 during GPCR activation. This process seems to be specific and not conserved, as no differences were observed in the pKa values of the remaining receptors (CB1R, NT1R, and GHSR).

Arginine Residues Modulate the Membrane Interactions of pHLIP Peptides.

Most processes at the water-membrane interface often involve protonation events in proteins or peptides that trigger important biological functions and events. This is the working principle behind the pHLIP peptide technology. A key titrating aspartate (Asp14 in wt) is required to protonate to induce the insertion process, increase its thermodynamic stability when membrane-embedded, and trigger the peptide's overall clinical functionality. At the core of pHLIP properties, the aspartate pKa and protonation are a consequence of the residue side chain sensing the changing surrounding environment. In this work, we characterized how the microenvironment of the key aspartate residue (Asp13 in the investigated pHLIP variants) can be modulated by a simple point mutation of a cationic residue (ArgX) at distinct sequence positions (R10, R14, R15, and R17). We carried out a multidisciplinary study using pHRE simulations and experimental measurements. Fluorescence and circular dichroism measurements were carried out to establish the stability of pHLIP variants in state III and establish the kinetics of the insertion and exit of the peptide from the membrane. We estimated the contribution of the arginine to the local electrostatic microenvironment, which promotes or hinders other electrostatic players from coexisting in the Asp interaction shell. Our data indicate that the stability and kinetics of the peptide insertion and exit from the membrane are altered when Arg is topologically available for a direct salt-bridge formation with Asp13. Hence, the position of arginine contributes to fine-tuning the pH responses of pHLIP peptides, which finds wide applications in clinics.

Interfacial Dynamics and Growth Modes of ß2-Microglobulin Dimers.

Protein aggregation is a complex process, strongly dependent on environmental conditions and highly structurally heterogeneous, both at the final level of fibril structure and intermediate level of oligomerization. Since the first step in aggregation is the formation of a dimer, it is important to clarify how certain properties of the latter (e.g., stability or interface geometry) may play a role in self-association. Here, we report a simple model that represents the dimer's interfacial region by two angles and combine it with a simple computational method to investigate how modulations of the interfacial region occurring on the ns-µs time scale change the dimer's growth mode. To illustrate the proposed methodology, we consider 15 different dimer configurations of the ß2m D76N mutant protein equilibrated with long Molecular Dynamics simulations and identify which interfaces lead to limited and unlimited growth modes, having, therefore, different aggregation profiles. We found that despite the highly dynamic nature of the starting configurations, most polymeric growth modes tend to be conserved within the studied time scale. The proposed methodology performs remarkably well taking into consideration the nonspherical morphology of the ß2m dimers, which exhibit unstructured termini detached from the protein's core, and the relatively weak binding affinities of their interfaces, which are stabilized by nonspecific apolar interactions. The proposed methodology is general and can be applied to any protein for which a dimer structure has been experimentally determined or computationally predicted.

Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer.

Globally with over 10 million deaths per year, cancer is the most transversal disease across countries, cultures, and ethnicities, affecting both developed and developing regions. Tumorigenesis is dynamically altered by distinct events and can be lethal when untreated. Despite the innovative therapeutics available, multidrug resistance (MDR) to chemotherapy remains the major hindrance to the success of cancer therapy. The multiple mechanisms by which cancer cells evade cell death are diverse, indicating that MDR involves complex interconnected biological networks. Molecular profiling is currently able to stratify cancer into its distinct subtypes and help identify the best therapeutics, leading to "translational systems medicine". Highly specialized methodologies are generating a large amount of "omics" data - including epigenetics, genomics, transcriptomics, proteomics, metabolomics, as well as pharmacogenomics. Many of the resulting databases store data in non-standard formats, which need to be converted, interpreted, and merged into readable formats. The latest development of artificial intelligence (AI) methodologies and tools, coupled with advancements in large-scale data management and powerful graphic processing computing units, potentiate the integration of these large data sources into relevant biological networks, which will enhance our understanding of cancer MDR. In this review, we revisit common MDR mechanisms and compile a list of the most relevant "omics" public databases. We highlight examples of AI methods that are now decisively contributing to clear advances in cancer research, such as identification of new drugs from large databases and prediction of relevant drug, target, and system properties. An overview of several freely available "ready-to-use" algorithms is also provided. The described molecular scale AI algorithms and tools will undoubtedly guide important improvements in efficiency and efficacy of traditional methods of cancer diagnostics and treatment.

Synthesis and the In Vitro Evaluation of Antitumor Activity of Novel Thiobenzanilides.

Cancer is a generic term for a large group of diseases that are the second-leading cause of death worldwide, accounting for nearly 10 million deaths in 2020. Melanoma is a highly aggressive skin tumor with an increasing incidence and poor prognosis in the metastatic stage. Breast cancer still stands as one of the major cancer-associated deaths among women, and diagnosed cases are increasing year after year worldwide. Despite the recent therapeutic advances for this type of cancer, novel drugs and treatment strategies are still urgently needed. In this paper, the synthesis of 18 thiobenzanilide derivatives (17 of them new) is described, and their cytotoxic potential against melanoma cells (A375) and hormone-dependent breast cancer (MCF-7) cells is evaluated using the MTT assay. In the A375 cell line, most of the tested thiobenzanilides derivatives showed EC50 values in the order of µM. Compound 17 was the most promising, with an EC50 (24 h) of 11.8 µM. Compounds 8 and 9 are also interesting compounds that deserve to be further improved. The MCF-7 cell line, on the other hand, was seen to be less susceptible to these thiobenzanilides indicating that these compounds show different selectivity towards skin and breast cancer cells. Compound 15 showed the highest cytotoxic potential for MCF-7 cells, with an EC50 (24 h) of 43 µM, a value within the range of the EC50 value determined for tamoxifen (30.0 µM). ADME predictions confirm the potential of the best compounds. Overall, this work discloses a new set of thiobenzanilides that are worth being considered as new scaffolds for the further development of anticancer agents.

pKPDB: a protein data bank extension database of pKa and pI theoretical values.

SUMMARY: pKa values of ionizable residues and isoelectric points of proteins provide valuable local and global insights about their structure and function. These properties can be estimated with reasonably good accuracy using Poisson-Boltzmann and Monte Carlo calculations at a considerable computational cost (from some minutes to several hours). pKPDB is a database of over 12 M theoretical pKa values calculated over 120k protein structures deposited in the Protein Data Bank. By providing precomputed pKa and pI values, users can retrieve results instantaneously for their protein(s) of interest while also saving countless hours and resources that would be spent on repeated calculations. Furthermore, there is an ever-growing imbalance between experimental pKa and pI values and the number of resolved structures. This database will complement the experimental and computational data already available and can also provide crucial information regarding buried residues that are under-represented in experimental measurements. AVAILABILITY AND IMPLEMENTATION: Gzipped csv files containing p Ka and isoelectric point values can be downloaded from https://pypka.org/pKPDB. To query a single PDB code please use the PypKa free server at https://pypka.org. The pKPDB source code can be found at https://github.com/mms-fcul/pKPDB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mutant p53 reactivator SLMP53-2 hinders ultraviolet B radiation-induced skin carcinogenesis.

The growing incidence of skin cancer (SC) has prompted the search for additional preventive strategies to counteract this global health concern. Mutant p53 (mutp53), particularly with ultraviolet radiation (UVR) signature, has emerged as a promising target for SC prevention based on its key role in skin carcinogenesis. Herein, the preventive activity of our previously disclosed mutp53 reactivator SLMP53-2 against UVR-induced SC was investigated. The pre-treatment of keratinocyte HaCaT cells with SLMP53-2, before UVB exposure, depleted mutp53 protein levels with restoration of wild-type-like p53 DNA-binding ability and subsequent transcriptional activity. SLMP53-2 increased cell survival by promoting G1-phase cell cycle arrest, while reducing UVB-induced apoptosis through inhibition of c-Jun N-terminal kinase (JNK) activity. SLMP53-2 also protected cells from reactive oxygen species and oxidative damage induced by UVB. Moreover, it enhanced DNA repair through upregulation of nucleotide excision repair pathway and depletion of UVB-induced DNA damage, as evidenced by a reduction of DNA in comet tails, γH2AX staining and cyclobutane pyrimidine dimers (CPD) levels. SLMP53-2 further suppressed UVB-induced inflammation by inhibiting the nuclear translocation and DNA-binding ability of NF-κB, and promoted the expression of key players involved in keratinocytes differentiation. Consistently, the topical application of SLMP53-2 in mice skin, prior to UVB irradiation, reduced cell death and DNA damage. It also decreased the expression of inflammatory-related proteins and promoted cell differentiation, in UVB-exposed mice skin. Notably, SLMP53-2 did not show signs of skin toxicity for cumulative topical use. Overall, these results support a promising protective activity of SLMP53-2 against UVB-induced SC.

Novel US-CpHMD Protocol to Study the Protonation-Dependent Mechanism of the ATP/ADP Carrier.

We have designed a protocol combining constant-pH molecular dynamics (CpHMD) simulations with an umbrella sampling (US) scheme (US-CpHMD) to study the mechanism of ADP/ATP transport (import and export) by their inner mitochondrial membrane carrier protein [ADP/ATP carrier (AAC)]. The US scheme helped overcome the limitations of sampling the slow kinetics involved in these substrates' transport, while CpHMD simulations provided an unprecedented realism by correctly capturing the associated protonation changes. The import of anionic substrates along the mitochondrial membrane has a strong energetic disadvantage due to a smaller substrate concentration and an unfavorable membrane potential. These limitations may have created an evolutionary pressure on AAC to develop specific features benefiting the import of ADP. In our work, the potential of mean force profiles showed a clear selectivity in the import of ADP compared to ATP, while in the export, no selectivity was observed. We also observed that AAC sequestered both substrates at longer distances in the import compared to the export process. Furthermore, only in the import process do we observe transient protonation of both substrates when going through the AAC cavity, which is an important advantage to counteract the unfavorable mitochondrial membrane potential. Finally, we observed a substrate-induced disruption of the matrix salt-bridge network, which can promote the conformational transition (from the C- to M-state) required to complete the import process. This work unraveled several important structural features where the complex electrostatic interactions were pivotal to interpreting the protein function and illustrated the potential of applying the US-CpHMD protocol to other transport processes involving membrane proteins.

Optimization of an in Silico Protocol Using Probe Permeabilities to Identify Membrane Pan-Assay Interference Compounds.

Membrane pan-assay interference compounds (PAINS) are a class of molecules that interact nonspecifically with lipid bilayers and alter their physicochemical properties. An early identification of these compounds avoids chasing false leads and the needless waste of time and resources in drug discovery campaigns. In this work, we optimized an in silico protocol on the basis of umbrella sampling (US)/molecular dynamics (MD) simulations to discriminate between compounds with different membrane PAINS behavior. We showed that the method is quite sensitive to membrane thickness fluctuations, which was mitigated by changing the US reference position to the phosphate atoms of the closest interacting monolayer. The computational efficiency was improved further by decreasing the number of umbrellas and adjusting their strength and position in our US scheme. The inhomogeneous solubility-diffusion model (ISDM) used to calculate the membrane permeability coefficients confirmed that resveratrol and curcumin have distinct membrane PAINS characteristics and indicated a misclassification of nothofagin in a previous work. Overall, we have presented here a promising in silico protocol that can be adopted as a future reference method to identify membrane PAINS.

Improvement of conventional anti-cancer drugs as new tools against multidrug resistant tumors.

Multidrug resistance (MDR) is the dominant cause of the failure of cancer chemotherapy. The design of antitumor drugs that are able to evade MDR is rapidly evolving, showing that this area of biomedical research attracts great interest in the scientific community. The current review explores promising recent approaches that have been developed with the aim of circumventing or overcoming MDR. Encouraging results have been obtained in the investigation of the MDR-modulating properties of various classes of natural compounds and their analogues. Inhibition of P-gp or downregulation of its expression have proven to be the main mechanisms by which MDR can be surmounted. The use of hybrid molecules that are able to simultaneously interact with two or more cancer cell targets is currently being explored as a means to circumvent drug resistance. This strategy is based on the design of hybrid compounds that are obtained either by merging the structural features of separate drugs, or by conjugating two drugs or pharmacophores via cleavable/non-cleavable linkers. The approach is highly promising due to the pharmacokinetic and pharmacodynamic advantages that can be achieved over the independent administration of the two individual components. However, it should be stressed that the task of obtaining successful multivalent drugs is a very challenging one. The conjugation of anticancer agents with nitric oxide (NO) donors has recently been developed, creating a particular class of hybrid that can combat tumor drug resistance. Appropriate NO donors have been shown to reverse drug resistance via nitration of ABC transporters and by interfering with a number of metabolic enzymes and signaling pathways. In fact, hybrid compounds that are produced by covalently attaching NO-donors and antitumor drugs have been shown to elicit a synergistic cytotoxic effect in a variety of drug resistant cancer cell lines. Another strategy to circumvent MDR is based on nanocarrier-mediated transport and the controlled release of chemotherapeutic drugs and P-gp inhibitors. Their pharmacokinetics are governed by the nanoparticle or polymer carrier and make use of the enhanced permeation and retention (EPR) effect, which can increase selective delivery to cancer cells. These systems are usually internalized by cancer cells via endocytosis and accumulate in endosomes and lysosomes, thus preventing rapid efflux. Other modalities to combat MDR are described in this review, including the pharmaco-modulation of acridine, which is a well-known scaffold in the development of bioactive compounds, the use of natural compounds as means to reverse MDR, and the conjugation of anticancer drugs with carriers that target specific tumor-cell components. Finally, the outstanding potential of in silico structure-based methods as a means to evaluate the ability of antitumor drugs to interact with drug transporters is also highlighted in this review. Structure-based design methods, which utilize 3D structural data of proteins and their complexes with ligands, are the most effective of the in silico methods available, as they provide a prediction regarding the interaction between transport proteins and their substrates and inhibitors. The recently resolved X-ray structure of human P-gp can help predict the interaction sites of designed compounds, providing insight into their binding mode and directing possible rational modifications to prevent them from becoming P-gp drug substrates. In summary, although major efforts were invested in the search for new tools to combat drug resistant tumors, they all require further implementation and methodological development. Further investigation and progress in the abovementioned strategies will provide significant advances in the rational combat against cancer MDR.

Computational Analysis of the Interactions between the S100B Extracellular Chaperone and Its Amyloid ß Peptide Client.

S100B is an astrocytic extracellular Ca2+-binding protein implicated in Alzheimer's disease, whose role as a holdase-type chaperone delaying Aß42 aggregation and toxicity was recently uncovered. Here, we employ computational biology approaches to dissect the structural details and dynamics of the interaction between S100B and Aß42. Driven by previous structural data, we used the Aß25-35 segment, which recapitulates key aspects of S100B activity, as a starting guide for the analysis. We used Haddock to establish a preferred binding mode, which was studied with the full length Aß using long (1 µs) molecular dynamics (MD) simulations to investigate the structural dynamics and obtain representative interaction complexes. From the analysis, Aß-Lys28 emerged as a key candidate for stabilizing interactions with the S100B binding cleft, in particular involving a triad composed of Met79, Thr82 and Glu86. Binding constant calculations concluded that coulombic interactions, presumably implicating the Lys28(Aß)/Glu86(S100B) pair, are very relevant for the holdase-type chaperone activity. To confirm this experimentally, we examined the inhibitory effect of S100B over Aß aggregation at high ionic strength. In agreement with the computational predictions, we observed that electrostatic perturbation of the Aß-S100B interaction decreases anti-aggregation activity. Altogether, these findings unveil features relevant in the definition of selectivity of the S100B chaperone, with implications in Alzheimer's disease.

PypKa: A Flexible Python Module for Poisson-Boltzmann-Based pKa Calculations.

The protonation of titratable residues has a significant impact on the structure and function of biomolecules, influencing many physicochemical and ADME properties. Thus, the importance of the estimation of protonation free energies (pKa values) is paramount in different scientific communities, including bioinformatics, structural biology, or medicinal chemistry. Here, we introduce PypKa, a flexible tool to predict Poisson-Boltzmann/Monte Carlo-based pKa values of titratable sites in proteins. This application was benchmarked using a large data set of experimental values to show that our single structure-based method is fast and has a competitive performance. This is a free and open-source tool that provides a simple, reusable, and extensible Python API and CLI for pKa calculations with a valuable trade-off between fast and accurate predictions. PypKa allows pKa calculations in existing protocols with the addition of a few extra lines of code. PypKa supports CPU parallel computing on solvated proteins obtained from the PDB repository but also from MD simulations using three common naming schemes: GROMOS, AMBER, and CHARMM. The code and documentation to this open-source project is publicly available at https://github.com/mms-fcul/PypKa.

The multi-factorial nature of clinical multidrug resistance in cancer.

Curative cancer therapy remains a major challenge particularly in cancers displaying multidrug resistance (MDR). The MDR phenotype is characterized by cross-resistance to a wide array of anticancer drugs harboring distinct structures and mechanisms of action. The multiple factors involved in mediating MDR may include host factors, tumor factors as well as tumor-host interactions. Among the host factors are genetic variants and drug-drug interactions. The plethora of tumor factors involves decreased drug uptake primarily via impaired influx transporters, increased drug efflux predominantly due to the overexpression of MDR efflux transporters of the ATP-binding cassette superfamily or due to drug efflux mediated by extracellular vesicles (EVs) or drug-loaded lysosomes undergoing exocytosis, deregulation of cell death mechanisms (i.e. anti-apoptotic modalities), enhanced DNA damage repair, epigenetic alterations and/or deregulation of microRNAs. The intratumor heterogeneity and dynamics, along with cancer stem cell plasticity, are important tumor factors. Among the tumor-host interactions are the role of the tumor microenvironment, selective pressure of various stressor conditions and agents, acidic pH and the intracellular transfer of traits mediated by EVs. The involvement of these diverse factors in MDR, highlights the need for precision medicine and real-time personalized treatments of individual cancer patients. In this review, written by a group of researchers from COST Action STRATAGEM "New diagnostic and therapeutic tools against multidrug resistant tumors", we aim to bring together these multidisciplinary and interdisciplinary features of MDR cancers. Importantly, it is becoming increasingly clear that deciphering the molecular mechanisms underlying anticancer drug resistance, will pave the way towards the development of novel precision medicine treatment modalities that are able to surmount distinct and well-defined mechanisms of anticancer drug resistance.

Evaluation of EGCG Loading Capacity in DMPC Membranes.

Catechins are molecules with potential use in different pathologies such as diabetes and cancer, but their pharmaceutical applications are often hindered by their instability in the bloodstream. This issue can be circumvented using liposomes as their nanocarriers for in vivo delivery. In this work, we studied the molecular details of (-)-epigallocatechin-3-gallate (EGCG) interacting with 1,2-dimyristoyl- sn-glycero-3-phosphocholine (DMPC) monolayer/bilayer systems to understand the catechin loading ability and liposome stability, using experimental and computational techniques. The molecular dynamics simulations show the EGCG molecules deep inside the lipid bilayer, positioned below the lipid ester groups, generating a concentration-dependent lipid condensation. This effect was also inferred from the surface pressure isotherms of DMPC monolayers. In the polarization-modulated infrared reflection absorption spectra assays, the predominant effect at higher concentrations of EGCG (e.g., 20 mol %) was an increase in lipid tail disorder. The steady-state fluorescence data confirmed this disordered state, indicating that the catechin-induced liposome aggregation outweighs the condensation effects. Therefore, by adding more than 10 mol % EGCG to the liposomes, a destabilization of the vesicles occurs with the ensuing release of entrapped catechins. The loading capacity for DMPC seems to be limited by its disordered lipid arrangements, typical of a fluid phase. To further increase the clinical usefulness of liposomes, lipid bilayers with more stable and organized assemblies should be employed to avoid aggregation at large concentrations of catechin.

Bioactivities of Centaurium erythraea (Gentianaceae) Decoctions: Antioxidant Activity, Enzyme Inhibition and Docking Studies.

Centaurium erythraea is recommended for the treatment of gastrointestinal disorders and to reduce hypercholesterolemia in ethno-medicinal practice. To perform a top-down study that could give some insight into the molecular basis of these bioactivities, decoctions from C. erythraea leaves were prepared and the compounds were identified by liquid chromatography-high resolution tandem mass spectrometry (LC-MS/MS). Secoiridoids glycosides, like gentiopicroside and sweroside, and several xanthones, such as di-hydroxy-dimethoxyxanthone, were identified. Following some of the bioactivities previously ascribed to C. erythraea, we have studied its antioxidant capacity and the ability to inhibit acetylcholinesterase (AChE) and 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR). Significant antioxidant activities were observed, following three assays: free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) reduction; lipoperoxidation; and NO radical scavenging capacity. The AChE and HMGR inhibitory activities for the decoction were also measured (56% at 500 µg/mL and 48% at 10 µg/mL, respectively). Molecular docking studies indicated that xanthones are better AChE inhibitors than gentiopicroside, while this compound exhibits a better shape complementarity with the HMGR active site than xanthones. To the extent of our knowledge, this is the first report on AChE and HMGR activities by C. erythraea decoctions, in a top-down analysis, complemented with in silico molecular docking, which aims to understand, at the molecular level, some of the biological effects ascribed to infusions from this plant.

The Catalase Activity of Catalase-Peroxidases Is Modulated by Changes in the pKa of the Distal Histidine.

The unusual Met-Tyr-Trp adduct composed of cross-linked side chains along with an associated mobile Arg is essential for catalase activity in catalase-peroxidases. In addition, acidic residues in the entrance channel, in particular an Asp and a Glu ∼7 and ∼15 Å, respectively, from the heme, significantly enhance catalase activity. The mechanism by which these channel carboxylates influence catalase activity is the focus of this work. Seventeen new variants with fewer and additional acidic residues have been constructed and characterized structurally and for enzymatic activity, revealing that their effect on activity is roughly inversely proportional to their distance from the heme and adduct, suggesting that the electrostatic potential of the heme cavity may be affected. A discrete group of protonable residues are contained within a 15 Å sphere surrounding the heme iron, and a computational analysis reveals that the pKa of the distal His112, alone, is modulated within the pH range of catalase activity by the remote acidic residues in a pattern consistent with its protonated form having a key role in the catalase reaction cycle. The electrostatic potential also impacts the catalatic reaction through its influence on the charged status of the Met-Tyr-Trp adduct.