Tryptophan Synthase (TSase) is a bi-functional enzyme that catalyzes the last two steps in the synthesis of tryptophan (trp), in different actives site. The active site of the α-subunit catalyzes the formation of indole and gliceraldeyde-3-phosphate (G3P) from indole 3- glycerolphosphate (IGP). Indole is then transported through a 25Å physical tunnel to the active site of the β-subunit where it is added to a molecule of acrylate, derived from serine, to produce trp, in a PLP dependent reaction [1].

In this work, we studied the reaction that takes place in the α-active site of TSase using computational means and QM/MM hybrid methodologies [2]. The results show that the reaction occurs in a stepwise general acid-base mechanism. The first step requires the participation of a water molecule that protonates C3 of the indole ring and receives a proton from αGlu49. In the second step, αGlu49 abstracts a proton from the glycerolyl hydroxyl of IGP through a water molecule, triggering the C–C bond cleavage to give indole and G3P. The rate-limiting step of this reaction is the first one that requires an activation free energy of 17.74 kcal/mol. This result agrees extremely well with the available experimental data that predicts reaction rate of 3.0-3.7 s-1, which corresponds to a free energy barrier of 17.37-17.50 kcal/mol.

The results obtained in this work provide important details about TSase that can now be used for the development of new transition state analogues inhibitors targeting TSase – an important drug target used in the treatment and prophylaxis of tuberculosis that is caused by the Mycobacterium tuberculosis pathogene.

Halogen bonds (XBs) are non-covalent R–X∙∙∙B interactions where heavy halogens (X = Cl, Br, I) act as electrophilic species interacting with Lewis bases (B). This highly directional type of interaction is mostly explained by the existence of a positive region on the molecular electrostatic potential located at the tip of the halogen (called σ-hole), arising from polarization of the R–X covalent bond. Following the recognition of the significance of XBs in biomolecular structures [1], their application in rational drug design, amongst other areas, has been increasingly explored. In this context, the development of computational tools accurately modelling XB is of paramount importance. This is particularly challenging in the case of force field (FF)-based methods, where XBs are typically modelled by introducing a positive extra-point (EP) of charge to mimic the σ-hole [2]. Though different schemes for EP parameterization have been proposed for AMBER or other FFs, their application to lengthy molecular dynamics (MD) simulations is still uncommon. In this work, we assessed the performance of distinct EP models and their transferability to the popular united-atom GROMOS FF, using bacteriophage T4 Lysozyme as a prototype system. The L99A mutant of this enzyme contains a large non-polar cavity that binds iodobenzene and related ligands, via XBs [3]. MD simulations were carried out and the network of intermolecular interactions, particularly XBs targeting different acceptors in the protein, was analysed. The results showed the dramatic impact of varying the X–EP distance and the associated sets of charges on the description of XBs. This, together with the implications for computer-aided drug design will be discussed [4].

Acknowledgements:
Support for this work was provided by FCT through UID/MULTI/00612/2013 and IF/00069/2014. R.N. acknowledges financial support from PhD scholarship SFRH/BD/116614/2016.

References:
[1] P. Auffinger, F. A. Hays, E. Westhof, P. S. Ho, PNAS 101 (2004), 16789-16794.

[2] M. H. Kolář, P. Hobza, Chem. Rev. 116 (2016), 5155−5187.

[3] L. Liu, W. A. Baase, B. W. Matthews, J. Mol. Biol. 385 (2009), 595-605.

[4] R. Nunes, P. J. Costa, et al., in preparation.

“Everything that living things do can be understood in terms of jigglings and wigglings of atoms.” Richard Feynman's remarks in the early 1960’s summarize what is today widely accepted, namely, that biological processes can be described by the dynamics of biomolecules.  Molecular dynamics (MD) simulation, in this regard, is the main methodology employed in structural biology to explore the dynamical behavior of macromolecules at a microscopic level. Aided by MD, researchers have been able, for instance, to resolve atomic structures of multi-protein complexes from cryo-EM densities, thus unveiling the atomistic details of enzymatic mechanisms and characterize the binding of small molecules to proteins. To achieve all this, the capabilities of MD packages are constantly evolving, providing a multitude of complex simulation and analysis techniques, e.g., enhanced sampling and free energy calculations. Although applicable to a wide variety of research problems, a broader usage of MD is hindered by a steep initial learning curve imposed by nearly every MD software. To reduce this initial barrier and make the methodology more accessible to the general community of biomolecular researchers, we developed an intuitive tool named QwikMD (1), which assists the users in the preparation, execution, and analysis of biomolecular MD simulations. Among many other features, QwikMD automatically checks the initial structure for structural inconsistencies, facilitates structure manipulations such as point mutations and partial deletions, simplifies the protein insertion in lipid membranes and enables the visualization and analysis of MD simulations on the fly. The user-friendly graphical interface of QwiKMD allows the preparation of MD simulations in a point-and-click fashion, offering the user multiple MD protocols, such as unbiased MD simulations, Steered MD, MD Flexible Fitting (MDFF), and, most recently, hybrid QM/MM simulations. The latter exploits the recently developed VMD and NAMD interface to common quantum mechanics software packages. QwikMD facilitates performing MD simulations for nearly any user, novice or expert. While assisting the user, QwikMD ensures reproducibility of the results by recording all parameters and steps into two log files, one in a script-like format and another in a “methods section” format. QwikMD also serves as a learning tool, providing the theoretical background of the different MD protocols and options in many “info buttons”.

1. J. V Ribeiro et al., QwikMD — Integrative Molecular Dynamics Toolkit for Novices and Experts. Sci. Rep. 6, 26536 (2016).

Over the last 30 years, HIV has grown to a pandemic status with more than 36 million people infected worldwide. Current therapies provide a significant improvement in the quality of patients’ lives, specifically the Highly Active Anti-Retroviral Therapy (HAART). Yet, viral resistance development towards anti-HIV medication stands as the main obstacle to an effective therapy, having also a substantial economic impact on healthcare systems worldwide. Such viral resistance is primarily related to mutations occurring mainly on the active site of viral key enzymes, capable of decreasing the pocket’s capability to establish the necessary non-covalent interactions with the drugs. Even so, mutations outside the enzyme’s active site can also lead to resistances, by causing changes on its structure and/or chemical environment. Among the two HIV virus types, HIV-1 stands as the most studied and prominent, with HIV‑1 protease being one of the main viral targets for therapy.^[1]

Given the ease of quickly and affordably sequence HIV from infected individuals, considerable progress – in the sense of predicting resistance towards drugs – could be made by developing tools to link specific genetic mutations with the resulting structural and chemical alterations in the active site of the target enzymes.^[2]

In recognition of a serious medical need identified by a team of virologists working at the University of Coimbra teaching Hospital and with the intent of helping rationalize and personalize the choice of anti-HIV therapies, we set out to develop a new computational algorithm to predict resistance to protease inhibitors in HIV‑1 via detection of binding-site Molecular Interactions Field (MIF) dissimilarities. Briefly, the algorithm works by 1) automatically generating high-quality 3D protein model structures from HIV‑1 protease sequences; 2) capturing subtle, mutation-induced, chemical perturbations within the binding sites of resistant and non-resistant HIV‑1 protease structures using a MIF-based approach; and 3) quantifying binding site dissimilarities based on MIF analysis, and translating these into a resistance score. In terms of its predictive power, preliminary testing of the algorithm   using several different HIV protease sequences showed promising levels of sensitivity and specificity.

Despite both sequence- and structure-based computational approaches to the prediction of HIV drug resistance have been proposed in the past, our present work stands out from other known algorithms as a first implementation of a fast structure-based algorithm capable of discriminating between HIV sequences that may be susceptible or resistant to commercially available protease inhibitors. Since the problem of mutation-induced resistance cuts across virtually all pathogenic virus, we believe that our approach may be extended to a wide range of viral targets besides HIV-1.

References:

1. Baxter D. J.; Chasanov M. W.; Adams L. J. J AIDS Clin Res. 2016, 7, 581.
2. Weber I.; Kneller D.; Wong-Sam A.; Future Med Chem., 2015, 7(8), 1023-1038.

M-CSA (Mechanism And Catalytic Site Atlas) is a database of enzyme mechanisms that can be accessed at www.ebi.ac.uk/thornton-srv/m-csa. Our objectives with M-CSA are to provide an open data resource for the community to browse known enzyme reaction mechanisms and catalytic sites, and to use the dataset to understand enzyme function and evolution. Practical applications that could benefit from this data include the design of new enzymes and inhibitors.
M-CSA annotation includes the curly arrow description of the stepwise chemistry, the role of each catalytic residue and any cofactors, and the primary literature that supports such data. We also provide annotation for the associated protein sequences, structures, and their homologues.
M-CSA results from the merging of two previous databases, MACiE (Mechanism, Annotation and Classification in Enzymes), a database of enzyme mechanisms, and CSA (Catalytic Site Atlas), a database of catalytic sites of enzymes. In comparison with the parent databases, M-CSA supports the inclusion of several mechanism proposals, better tools for curators, and the creation and edition of new entries through the website. For people consulting the website, we improved the search and browsing tools, as well as the presentation of database statistics. In addition to the changes in the database and website, we are also carrying out a complete revision of existing data. At the moment, M-CSA contains 961 entries, 423 of these with detailed mechanism information, and 538 with information on the catalytic site residues only. In total, these cover 81% (195/241) of third level EC numbers with a PDB structure, and 30% (840/2793) of fourth level EC numbers with a PDB structure, out of 6028 in total.

The pH (low) insertion peptide (pHLIP) belongs to a family of peptides originated from a segment of the transmembranar C helix of bacteriorhodopsin. The peptide has three major states: state I - soluble and unstructured; state II - adsorbed at the membrane surface and unstructured; state III - inserted in the bilayer as an α-helix at low pH values. One of the major applications of pHLIP befalls on its ability to insert into membrane cells with an acidic vicinity, such as tumoral cells, thus working as an efficient tumor-specific biomarker¹. However, wt-pHLIP has a significant limitation, since it accumulates in the kidneys in considerable amounts due to their naturally acidic extracellular pH. This limitation led to a need for increased pHLIP specificity by delimiting the pH range of insertion, further strengthening its application as a biomarker and possible drug-delivery system for inflammatory tissues.

The stochastic titration constant-pH molecular dynamics (CpHMD) method has been successfully used to sample protonation behaviour of titrable amino acids inserted in a lipid bilayer, presenting, however, an insufficient amount of data to extensively describe pK_a profiles². The newly developed pH-replica exchange (pHRE) method, allows the exchange of pH values between replicas within a certain probability. This approach enhances the transitions between energy minima, improving the sampling of non-favorable protonation states, which leads to a better description of the pK_a profiles. This new method was applied to simulations of wt-pHLIP and L16H variants. 

The pHRE simulations led to more detailed, accurate and consistent pK_a profiles and allowed the identification of Asp14 as the key residue whose protonation state triggers the insertion process. The calculated insertion pK_a value of this residue is in good agreement with the experimental insertion pK value for the wt sequence. Moreover, the simulations of L16H showed that this variant exhibits a second insertion pK_a, at lower pH, indicating that, below this value, the peptide would exit the membrane. These results were corroborated by new experimental data performed by our collaborators, Prof. Oleg Andreev in Rhode Island, USA.

We acknowledge financial support from FCT through projects UID/MULTI/00612/2013, PTDC/QEQ-COM/5904/2014 and grant SFRH/BPD/110491/2015. 

Molecular machines have recently been associated with the development of molecular carriers to enhance drug properties, such as solubility or bioavailability. One possible approach is the drug encapsulation by a host molecule, such as cucurbituril (CB) rings, modifying the environment of the guest molecule. CB rings are able to encapsulate guest molecules providing a hydrophobic cavity and several carbonyl groups that stabilize cationic hosts that interact with this region. This will result in significant pK_a shifts for drugs with titrable (cationic) groups that  can be exploited in order to improve drug bioavailability, whether by enhancing their solubility, stabilizing their active form or by protecting them against external agents. This approach can be used for medical targeting, such as cancer therapy, by designing carriers that deliver guest molecules at specific conditions, knowing the target properties.
Computational tools are a powerful way to help the rational design of CB-guest complexes. In particular, the stochastic titrations constant-pH MD (CpHMD) method allows a molecular dynamics simulation to have the pH value as an external parameter and, consequently, obtain full titration curves and pK_a values. The main goal here is to develop a strategy to model benzimidazole (BZ) pK_a shifts, our «proof-of-concept» molecule, and then extrapolate this process to other host-guest complexes. BZ has a well-known shift of ~3.5 pK_a units when encapsulated by a CB ring and, with the refinement and fine tuning of this process, it is possible to elucidate the molecular details of these host-guest interactions.

We acknowledge financial support from FCT through project UID/MULTI/00612/2013 and grant SFRH/BPD/110491/2015.

pH is a crucial physicochemical property that affects most biomolecules. Changes in protonation equilibrium of susceptible sites will modify the electrostatic environment and, consequently, have an effect on the molecular structure, stability, and catalysis. However, the protonation behavior of pH sensitive biomolecules is difficult to study using experimental techniques and can strongly benefit from using computational approaches. In this context, we have successfully studied several systems using the stochastic constant-pH molecular dynamics (CpHMD) method. In these studies, we were able to obtain titration curves for proteins, membranes, and peptides at the membrane water interface. In the later case, it was observed that, when the titrable groups are deeply inserted in the membrane, the conformational / protonation sampling becomes very limited. In this project, we extended the stochastic CpHMD method to introduce enhanced protonation sampling. We implemented a pH replica exchange scheme and applied it to ethylenediamine, a simple molecule with two strongly coupled macroscopic pKa values, and to hen egg white lysozyme (HEWL), a typical test system for pKa prediction methods. In the future, we will use this method to study challenging pH dependent phenomena in complex biological systems.

α₁-adrenoceptor (α₁AR) is involved in the physiopathology of the central nervous system (CNS) and could constitute a therapeutic target for neurological disorders such as drug addiction or Alzheimer´s disease. α₁AR mainly couples to G_q/11 protein, which activation leads to stimulation of phospholipase C (PLC) and subsequent activation of protein kinase C (PKC). However, other G proteins (G_i, G_s) have also been described to be coupled to α₁AR receptors. The locus coeruleus (LC), the main noradrenergic nucleus in the CNS, has been shown to express α₁AR, but to date functional role of α₁AR in the adult rat brain LC remains unclear. The aim of this study was to characterize, by single-unit extracellular recordings of LC neurons, the role of α₁AR in the regulation of the firing rate (FR) of LC neurons in adult rat brain in vitro. For that purpose, we first characterize the effect of the α_1/α₂AR agonist noradrenaline (NA) in the presence and absence of the α₂AR antagonist RS 79948 (0.1 µM). Then, we investigated the signalling pathway involved in the effect of NA. Perfusion with NA (100 µM) inhibited the FR of LC neurons through activation of α₂AR. However, in the presence of the α₂-adrenoceptor (α₂AR) antagonist RS 79948 (0.1 µM) perfusion with NA increased the FR of NA neurons (stimulatory effect = 114%). The stimulatory effect of NA (100 µM) was blocked by the α₁AR antagonist WB 4101 (0.5 µM). Administration of the PKC inhibitor Go 6976 (1 µM), the G protein-coupled inwardly-rectifying potassium channel (GIRK) blocker BaCl₂ (300 µM) or PKA inhibitor H-89 (10 µM) failed to change the stimulatory effect of NA. However, NA (100 µM) induced stimulation was reduced by 64% in the presence of the G_i/o protein inactivator pertussis toxin (PTX) (500 ng·ml^-1). In conclusion, α₁AR activation stimulates the FR of NA neurons in the adult rat LC through a signalling pathway that involves activation of the G_i/o protein. It remains to be studied the mechanism by which G_i/o proteins stimulates the FR of LC neurons via α₁AR activation.

Cannabidiol (CBD) is the main non-psychoactive cannabinoid found in the Cannabis plant, which exerts several pharmacological effects including anxiolytic, antiemetic, antidepressant, antiepileptic and motor effects. In vivo evidence suggests that these pharmacological effects could be mediated by serotonergic 5-HT_1A receptors. The dorsal raphe nucleus (DRN), which is the main serotonergic cluster in the brain, expresses 5-HT_1A receptor and plays a key role in the regulation of different functions such as mood and anxiety. To date, the nuclei involved in the action of CBD and the mechanisms by which it regulates 5-HT_1A receptor are still unknown. Therefore, the aim of this study was to characterize the effect of CBD on the firing rate of dorsal raphe 5-HT neurons and 5-HT_1A receptor activation by single-unit extracellular electrophysiological recordings in vitro. Direct perfusion with CBD (30 mM) slightly but significantly reduced the firing activity of DRN 5-HT cells. In order to study the effect of CBD on 5-HT_1A receptor activation, we applied the cannabinoid in the presence of two different 5-HT_1A receptor agonists: 8-OH DPAT (10 nM) and ipsapirone (100 nM). Application of 8-OH-DPAT or ipsapirone completely inhibited the firing activity of DRN 5-HT cells. However, in the presence of CBD (30 mM) the inhibitory effects of 8-OH-DPAT and ipsapirone were reduced by 66% and 53%, respectively. Finally, to discard the possible role of CBD as a competitive 5-HT_1A receptor antagonist, we administrated CBD once the cells had been totally inhibited with ipsapirone. Perfusion with CBD (30 mM) failed to recover the firing activity of inhibited 5-HT cells, whereas 5-HT_1A antagonist WAY 100635 (30 nM) recovered the firing rate of all 5-HT cells. In conclusion, these results suggest that CBD regulates the activity of 5-HT_1A receptor in an indirect manner since it does not displace the agonist from the binding site.