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.

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.

Dual FGFR-targeting and pH-activatable ruthenium-peptide conjugates for targeted therapy of breast cancer.

Dysregulation of Fibroblast Growth Factor Receptors (FGFRs) signaling has been associated with breast cancer, yet employing FGFR-targeted delivery systems to improve the efficacy of cytotoxic agents is still sparsely exploited. Herein, we report four new bi-functional ruthenium-peptide conjugates (RuPCs) with FGFR-targeting and pH-dependent releasing abilities, envisioning the selective delivery of cytotoxic Ru complexes to FGFR(+)-breast cancer cells, and controlled activation at the acidic tumoral microenvironment. The antiproliferative potential of the RuPCs and free Ru complexes was evaluated in four breast cancer cell lines with different FGFR expression levels (SKBR-3, MDA-MB-134-VI, MCF-7, and MDA-MB-231) and in human dermal fibroblasts (HDF), at pH 6.8 and pH 7.4 aimed at mimicking the tumor microenvironment and normal tissues/bloodstream pHs, respectively. The RuPCs showed higher cytotoxicity in cells with higher level of FGFR expression at acidic pH. Additionally, RuPCs showed up to 6-fold higher activity in the FGFR(+) breast cancer lines compared to the normal cell line. The release profile of Ru complexes from RuPCs corroborates the antiproliferative effects observed. Remarkably, the cytotoxicity and releasing ability of RuPCs were shown to be strongly dependent on the conjugation of the peptide position in the Ru complex. Complementary molecular dynamic simulations and computational calculations were performed to help interpret these findings at the molecular level. In summary, we identified a lead bi-functional RuPC that holds strong potential as a FGFR-targeted chemotherapeutic agent.

Recent advances on molecular dynamics-based techniques to address drug membrane permeability with atomistic detail.

Several factors affect the passive membrane permeation of small molecules, including size, charge, pH, or the presence of specific chemical groups. Understanding these features is paramount to identifying or designing drug candidates with optimal ADMET properties and this can be achieved through experimental/knowledge-based methodologies or using computational approaches. Empirical methods often lack detailed information about the underlying molecular mechanism. In contrast, Molecular Dynamics-based approaches are a powerful strategy, providing an atomistic description of this process. This technique is continuously growing, featuring new related methodologies. In this work, the recent advances in this research area will be discussed.

The World of GPCR dimers - Mapping dopamine receptor D2 homodimers in different activation states and configuration arrangements.

G protein-coupled receptors (GPCRs) are known to dimerize, but the molecular and structural basis of GPCR dimers is not well understood. In this study, we developed a computational framework to generate models of symmetric and asymmetric GPCR dimers using different monomer activation states and identified their most likely interfaces with molecular details. We chose the dopamine receptor D2 (D2R) homodimer as a case study because of its biological relevance and the availability of structural information. Our results showed that transmembrane domains 4 and 5 (TM4 and TM5) are mostly found at the dimer interface of the D2R dimer and that these interfaces have a subset of key residues that are mostly nonpolar from TM4 and TM5, which was in line with experimental studies. In addition, TM2 and TM3 appear to be relevant for D2R dimers. In some cases, the inactive configuration is unaffected by the partnered protomer, whereas in others, the active protomer adopts the properties of an inactive receptor. Additionally, the ß-arrestin configuration displayed the properties of an active receptor in the absence of an agonist, suggesting that a switch to another meta-state during dimerization occurred. Our findings are consistent with the experimental data, and this method can be adapted to study heterodimers and potentially extended to include additional proteins such as G proteins or ß-arrestins. In summary, this approach provides insight into the impact of the conformational status of partnered protomers on the overall quaternary GPCR macromolecular structure and dynamics.

Fighting Multidrug Resistance with Ruthenium-Cyclopentadienyl Compounds: Unveiling the Mechanism of P-gp Inhibition.

The search for more effective and selective drugs to overcome cancer multidrug resistance is urgent. As such, a new series of ruthenium-cyclopentadienyl ("RuCp") compounds with the general formula [Ru(η5-C5H4R)(4,4'-R'-2,2'-bipy)(PPh3)] were prepared and fully characterized. All compounds were evaluated toward non-small cell lung cancer cells with different degrees of cisplatin sensitivity (A549, NCI-H2228, Calu-3, and NCI-H1975), showing better cytotoxicity than the first-line chemotherapeutic drug cisplatin. Compounds 2 and 3 (R' = -OCH3; R = CHO (2) or CH2OH (3)) further inhibited the activity of P-gp and MRP1 efflux pumps by impairing their catalytic activity. Molecular docking calculations identified the R-site P-gp pocket as the preferred one, which was further validated using site-directed mutagenesis experiments in P-gp. Altogether, our results unveil the first direct evidence of the interaction between P-gp and "RuCp" compounds in the modulation of P-gp activity and establish them as valuable candidates to circumvent cancer MDR.

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.

Histidine Protonation and Conformational Switching in Diphtheria Toxin Translocation Domain.

Protonation of key histidine residues has been long implicated in the acid-mediated cellular action of the diphtheria toxin translocation (T-) domain, responsible for the delivery of the catalytic domain into the cell. Here, we use a combination of computational (constant-pH Molecular Dynamics simulations) and experimental (NMR, circular dichroism, and fluorescence spectroscopy along with the X-ray crystallography) approaches to characterize the initial stages of conformational change happening in solution in the wild-type T-domain and in the H223Q/H257Q double mutant. This replacement suppresses the acid-induced transition, resulting in the retention of a more stable protein structure in solutions at pH 5.5 and, consequently, in reduced membrane-disrupting activity. Here, for the first time, we report the pKa values of the histidine residues of the T-domain, measured by NMR-monitored pH titrations. Most peaks in the histidine side chain spectral region are titrated with pKas ranging from 6.2 to 6.8. However, the two most up-field peaks display little change down to pH 6, which is a limiting pH for this protein in solution at concentrations required for NMR. These peaks are absent in the double mutant, suggesting they belong to H223 and H257. The constant-pH simulations indicate that for the T-domain in solution, the pKa values for histidine residues range from 3.0 to 6.5, with those most difficult to protonate being H251 and H257. Taken together, our experimental and computational data demonstrate that previously suggested cooperative protonation of all six histidines in the T-domain does not occur.

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.

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.

Corrigendum to "Unveiling molecular details behind improved activity at neutral to alkaline pH of an engineered DyP-type peroxidase" Comput Struct Biotechnol J, 20 (2022), 3899-3910.

[This corrects the article DOI: 10.1016/j.csbj.2022.07.032.].

In Silico Characterization of African Swine Fever Virus Nucleoprotein p10 Interaction with DNA.

African swine fever virus (ASFV) is the etiological agent of a highly contagious, hemorrhagic infectious swine disease, with a tremendous sanitary and economic impact on a global scale. Currently, there are no globally available vaccines or treatments. The p10 protein, a structural nucleoprotein encoded by ASFV, has been previously described as capable of binding double-stranded DNA (dsDNA), which may have implications for viral replication. However, the molecular mechanism that governs this interaction is still unknown, mostly due to the lack of a structural model for this protein. In this work, we have generated an ab initio model of the p10 protein and performed extensive structural characterization, using molecular dynamics simulations to identify the motifs and residues regulating DNA recognition. The helix-turn-helix motif identified at the C-terminal region of the protein was shown to be crucial to the dsDNA-binding efficiency. As with other DNA-binding proteins, two distinct serine and lysine-rich regions found in the two helices were identified as key players in the binding to DNA, whose importance was later validated using experimental binding assays. Altogether, these findings may contribute to a better understanding of the p10 function in ASFV replication.

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.

Extending the Stochastic Titration CpHMD to CHARMM36m.

The impact of pH on proteins is significant but often neglected in molecular dynamics simulations. Constant-pH Molecular Dynamics (CpHMD) is the state-of-the-art methodology to deal with these effects. However, it still lacks widespread adoption by the scientific community. The stochastic titration CpHMD is one of such methods that, until now, only supported the GROMOS force field family. Here, we extend this method's implementation to include the CHARMM36m force field available in the GROMACS software package. We test this new implementation with a diverse group of proteins, namely, lysozyme, Staphylococcal nuclease, and human and E. coli thioredoxins. All proteins were conformationally stable in the simulations, even at extreme pH values. The RMSE values (pKa prediction vs experimental) obtained were very encouraging, in particular for lysozyme and human thioredoxin. We have also identified a few residues that challenged the CpHMD simulations, highlighting scenarios where the method still needs improvement independently of the force field. The CHARMM36m all-atom implementation was more computationally efficient when compared with the GROMOS 54A7, taking advantage of a shorter nonbonded interaction cutoff and a less frequent neighboring list update. The new extension will allow the study of pH effects in many systems for which this force field is particularly suited, i.e., proteins, membrane proteins, lipid bilayers, and nucleic acids.

Increasing the Realism of in Silico pHLIP Peptide Models with a Novel pH Gradient CpHMD Method.

The pH-low insertion peptides (pHLIP) are pH-dependent membrane inserting peptides, whose function depends on the cell microenvironment acidity. Several peptide variants have been designed to improve upon the wt-sequence, particularly the state transition kinetics and the selectivity for tumor pH. The variant 3 (Var3) peptide is a 27 residue long peptide, with a key titrating residue (Asp-13) that, despite showing a modest performance in liposomes (pKins ∼ 5.0), excelled in tumor cell experiments. To help rationalize these results, we focused on the pH gradient in the cell membrane, which is one of the crucial properties that are not present in liposomes. We extended our CpHMD-L method and its pH replica-exchange (pHRE) implementation to include a pH gradient and mimic the pHLIP-membrane microenvironment in a cell where the internal pH is fixed (pH 7.2) and the external pH is allowed to change. We showed that, by properly modeling the pH-gradient, we can correctly predict the experimentally observed loss and gain of performance in tumor cells experiments by the wt and Var3 sequences, respectively. In sum, the pH gradient implementation allowed for more accurate and realistic pKa estimations and was a pivotal step in bridging the in silico data and the in vivo cell experiments.

Unveiling molecular details behind improved activity at neutral to alkaline pH of an engineered DyP-type peroxidase.

DyP-type peroxidases (DyPs) are microbial enzymes that catalyze the oxidation of a wide range of substrates, including synthetic dyes, lignin-derived compounds, and metals, such as Mn2+ and Fe2+, and have enormous biotechnological potential in biorefineries. However, many questions on the molecular basis of enzyme function and stability remain unanswered. In this work, high-resolution structures of PpDyP wild-type and two engineered variants (6E10 and 29E4) generated by directed evolution were obtained. The X-ray crystal structures revealed the typical ferredoxin-like folds, with three heme access pathways, two tunnels, and one cavity, limited by three long loops including catalytic residues. Variant 6E10 displays significantly increased loops' flexibility that favors function over stability: despite the considerably higher catalytic efficiency, this variant shows poorer protein stability compared to wild-type and 29E4 variants. Constant-pH MD simulations revealed a more positively charged microenvironment near the heme pocket of variant 6E10, particularly in the neutral to alkaline pH range. This microenvironment affects enzyme activity by modulating the pK a of essential residues in the heme vicinity and should account for variant 6E10 improved activity at pH 7-8 compared to the wild-type and 29E4 that show optimal enzymatic activity close to pH 4. Our findings shed light on the structure-function relationships of DyPs at the molecular level, including their pH-dependent conformational plasticity. These are essential for understanding and engineering the catalytic properties of DyPs for future biotechnological applications.

A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven pKa Predictions in Proteins.

Existing computational methods for estimating pKa values in proteins rely on theoretical approximations and lengthy computations. In this work, we use a data set of 6 million theoretically determined pKa shifts to train deep learning models, which are shown to rival the physics-based predictors. These neural networks managed to infer the electrostatic contributions of different chemical groups and learned the importance of solvent exposure and close interactions, including hydrogen bonds. Although trained only using theoretical data, our pKAI+ model displayed the best accuracy in a test set of ∼750 experimental values. Inference times allow speedups of more than 1000× compared to physics-based methods. By combining speed, accuracy, and a reasonable understanding of the underlying physics, our models provide a game-changing solution for fast estimations of macroscopic pKa values from ensembles of microscopic values as well as for many downstream applications such as molecular docking and constant-pH molecular dynamics simulations.

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.

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.

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.