Marlet Martínez-Archundia

Automated docking for novel drug discovery

Introduction: The volume of three-dimensional structural information of macromolecules and the number of computational tools to predict binding modes and affinities of molecular complexes are increasing daily. Molecular docking is a rational structural approach employed to predict thermodynamic parameters based on molecular recognition between two or more molecules. In addition, docking studies have become very important for therapeutic applications in modern structure-based drug design because this computational tool uses few economic resources. However, they omit many biological conditions that critically influence small and macromolecular structural motions. To mimic physiological conditions, it is necessary to consider other environmental factors, such as the presence of water molecules and the flexibility of ligands and side chain residues of proteins. Furthermore, molecular dynamics simulations have been coupled with docking procedures to expand the boundaries and obtain more reliable information. Areas covered: In this article, we review current advances in protein-small molecule docking and possible future directions. Expert opinion: Docking studies include many conformations to predict binding free energies (scoring functions) and to search (scoring sampling) for the most representative binding conformations. Therefore, several biological properties, from side chain residues to complete protein motions, have been included in docking studies to improve theoretical predictions.

Deciphering the GPER/GPR30-agonist and antagonists interactions using molecular modeling studies, molecular dynamics, and docking simulations

The G-protein coupled estrogen receptor 1 GPER/GPR30 is a transmembrane seven-helix (7TM) receptor involved in the growth and proliferation of breast cancer. Due to the absence of a crystal structure of GPER/GPR30, in this work, molecular modeling studies have been carried out to build a three-dimensional structure, which was subsequently refined by molecular dynamics (MD) simulations (up to 120 ns). Furthermore, we explored GPER/GPR30’s molecular recognition properties by using reported agonist ligands (G1, estradiol (E2), tamoxifen, and fulvestrant) and the antagonist ligands (G15 and G36) in subsequent docking studies. Our results identified the E2 binding site on GPER/GPR30, as well as other receptor cavities for accepting large volume ligands, through GPER/GPR30 π–π, hydrophobic, and hydrogen bond interactions. Snapshots of the MD trajectory at 14 and 70 ns showed almost identical binding motifs for G1 and G15. It was also observed that C107 interacts with the acetyl oxygen of G1 (at 14 ns) and that at 70 ns the residue E275 interacts with the acetyl group and with the oxygen from the other agonist whereas the isopropyl group of G36 is oriented toward Met141, suggesting that both C107 and E275 could be involved in the protein activation. This contribution suggest that GPER1 has great structural changes which explain its great capacity to accept diverse ligands, and also, the same ligand could be recognized in different binding pose according to GPER structural conformations.

Identification of the antiepileptic racetam binding site in the synaptic vesicle protein 2A by molecular dynamics and docking simulations

Synaptic vesicle protein 2A (SV2A) is an integral membrane protein necessary for the proper function of the central nervous system and is associated to the physiopathology of epilepsy. SV2A is the molecular target of the anti-epileptic drug levetiracetam and its racetam analogs. The racetam binding site in SV2A and the non-covalent interactions between racetams and SV2A are currently unknown; therefore, an in silico study was performed to explore these issues. Since SV2A has not been structurally characterized with X-ray crystallography or nuclear magnetic resonance, a three-dimensional (3D) model was built. The model was refined by performing a molecular dynamics simulation (MDS) and the interactions of SV2A with the racetams were determined by docking studies. A reliable 3D model of SV2A was obtained; it reached structural equilibrium during the last 15 ns of the MDS (50 ns) with remaining structural motions in the N-terminus and long cytoplasmic loop. The docking studies revealed that hydrophobic interactions and hydrogen bonds participate importantly in ligand recognition within the binding site. Residues T456, S665, W666, D670 and L689 were important for racetam binding within the trans-membrane hydrophilic core of SV2A. Identifying the racetam binding site within SV2A should facilitate the synthesis of suitable radio-ligands to study treatment response and possibly epilepsy progression.

Current tools and methods in Molecular Dynamics (MD) simulations for drug design.

Molecular Dynamics (MD) simulations is a computational method that employs Newtons laws to evaluate the motions of water, ions, small molecules, and macromolecules or more complex systems, for example, whole viruses, to reproduce the behavior of the biological environment, including water molecules and lipid membranes. Specifically, structural motions, such as those that are dependent of the temperature and solute/ solvent are very important to study the recognition pattern of ligandprotein or protein-protein complexes, in that sense, MD simulations are very useful because these motions can be modeled using this methodology. Furthermore, MD simulations for drug design provide insights into the structural cavities required to design novel structures with higher affinity to the target. Also, the employment of MD simulations to drug design can help to refine the three-dimensional (3D) structure of targets in order to obtain a better sampling of the binding poses and more reliable affinity values with better structural advantages, because they incorporate some biological conditions that include structural motions compared to traditional docking procedures. This work analyzes the concepts and applicability of MD simulations for drug design because molecular structural motions are considered, and these help to identify hot spots, decipher structural details in the reported protein sites, as well as to eliminate sites that could be structural artifacts which could be originated from the structural characterization conditions from MD. Moreover, better free energy values for protein ligand recognition can also be obtained, and these can be validated under experimental procedures due to the robustness of the MD simulation methods.

Exploring the Ligand Recognition Properties of the Human Vasopressin V1a Receptor Using QSAR and Molecular Modeling Studies

Vaptans are compounds that act as non‐peptide vasopressin receptor antagonists. These compounds have diverse chemical structures. In this study, we used a combined approach of protein folding, molecular dynamics simulations, docking, and quantitative structure–activity relationship (QSAR) to elucidate the detailed interaction of the vasopressin receptor V1a (V1aR) with some of its blockers (134). QSAR studies were performed using MLR analysis and were gathered into one group to perform an artificial neural network (ANN) analysis. For each molecule, 1481 molecular descriptors were calculated. Additionally, 15 quantum chemical descriptors were calculated. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. Molecular modeling enabled us to obtain a reliable tridimensional model of V1aR. The docking results indicated that the great majority of ligands reach the binding site under π–π, π–cation, and hydrophobic interactions. The QSAR studies demonstrated that the heteroatoms N and O are important for ligand recognition, which could explain the structural diversity of ligands that reach V1aR.

Searching the conformational complexity and binding properties of HDAC6 through docking and molecular dynamic simulations

Histone deacetylases (HDACs) are a family of proteins involved in the deacetylation of histones and other non-histones substrates. HDAC6 belongs to class II and shares similar biological functions with others of its class. Nevertheless, its three-dimensional structure that involves the catalytic site remains unknown for exploring the ligand recognition properties. Therefore, in this contribution, homology modeling, 100-ns-long Molecular Dynamics (MD) simulation and docking calculations were combined to explore the conformational complexity and binding properties of the catalytic domain 2 from HDAC6 (DD2-HDAC6), for which activity and affinity toward five different ligands have been reported. Clustering analysis allowed identifying the most populated conformers present during the MD simulation, which were used as starting models to perform docking calculations with five DD2-HDAC6 inhibitors: Cay10603 (CAY), Rocilinostat (RCT), Tubastatin A (TBA), Tubacin (TBC), and Nexturastat (NXT), and then were also submitted to 100-ns-long MD simulations. Docking calculations revealed that the five inhibitors bind at the DD2-HDAC6 binding site with the lowest binding free energy, the same binding mode is maintained along the 100-ns-long MD simulations. Overall, our results provide structural information about the molecular flexibility of apo and holo DD2-HDAC6 states as well as insight of the map of interactions between DD2-HDAC6 and five well-known DD2-HDAC6 inhibitors allowing structural details to guide the drug design. Finally, we highlight the importance of combining different theoretical approaches to provide suitable structural models for structure-based drug design.

Is there something else besides the proapoptotic AVPI-segment in the Smac/DIABLO protein?

In mammals, apoptosis is the main mechanism to eliminate unwanted cells, securing tissue homeostasis and consequently maintaining the health in the organism. Classically, apoptosis culminates with the activation of caspases, which are enzymes that display cysteine protease activity to degrade specific substrates implied in essential cellular processes. This process is highly regulated. A key regulation mechanism is mediated by the Inhibitor of Apoptosis Proteins (IAPs) family members, which inhibit the activated forms of caspases through physical interaction with them. Smac/DIABLO, a mitochondrial protein that is translocated to the cytoplasm in apoptotic conditions, derepresses the IAP-mediated caspase inhibition through physical interaction with IAPs. The first four amino acids (AVPI) of Smac/DIABLO mediate the interaction with IAPs and subsequent apoptosis induction. This interaction has lead to the creation of small molecules mimicking the AVPI segment for potential anticancer therapy. Nevertheless, several studies have pointed out the existence of AVPI-independent functions of Smac/DIABLO. The aim of this review was to provide a landscape of these underestimated AVPI-independent biological functions that have been observed using different approaches, such as the study of endogenous splice variant isoforms and truncated and mutated artificial proteins.

Study of new interactions of glitazone’s stereoisomers and the endogenous ligand 15d-PGJ2 on six different PPAR gamma proteins

ABSTRACT Diabetes mellitus is a chronic disease characterized by hyperglycemia, insulin resistance and hyperlipidemia. Glitazones or thiazolidinediones (TZD) are drugs that act as insulin‐sensitizing agents whose molecular target is the peroxisome proliferator‐activated receptor gamma (PPAR&ggr;). The euglycemic action of TZD has been linked with the induction of type 4 glucose transporter. However, it has been shown that the effect of TZD depends on the specific stereoisomer that interacts with PPAR&ggr;. Therefore, this work is focused on exploring the interactions and geometry adopted by glitazones stereoisomers and one endogenous ligand on different conformations of the six crystals of the PPAR&ggr; protein using molecular docking and molecular dynamics (MD) simulations accompanied by the MMGBSA approach. Specifically, the 2,4‐thiazolidinedione ring, pioglitazone (PIO), rosiglitazone (ROSI) and troglitazone (TRO) stereoisomers (exogenous ligands), as well as the endogenous ligand 15d‐PGJ2, were evaluated. The six crystallographic structures of PPAR&ggr; are available at Protein Data Bank as the PDB entries 2PRG, 4PRG, 3T03, 1I7I, 1FM6, and 4EMA. According to the results, a boomerang shape and a particular location of ligands were found with low variations according to the protein conformations. The 15d‐PGJ2, TZD, PIO, ROSI and (S,S)‐TRO enantiomers were mostly stabilized by twenty hydrophobic residues: Phe226, Pro227, Leu228, Ile281, Phe282, Cys285, Ala292, Ile296, Ile326, Tyr327, Met329, Leu330, Leu333, Met334, Val339, Ile341, Met348, Leu353, Phe363 and Met364. Most hydrogen bond interactions were found between the polar groups of ligands with Arg288, Ser289, Lys367, Gln286, His323, Glu343 and His449 residues. An energetic analysis revealed binding free energy trends that supported known experimental findings of other authors describing better binding properties for PIO, ROSI and (S,S)‐TRO than for 15d‐PGJ2 and the TZD ring.

Computational Study of the Binding Modes of Diverse DPN Analogues on Estrogen Receptors (ER) and the Biological Evaluation of a New Potential Antiestrogenic Ligand

Estrogen (17β-estradiol) is essential for normal growth and differentiation in the mammary gland. In the last three decades, previous investigations have revealed that Estrogen Receptor Alpha (ERα) plays a critical role in breast cancer. More recently, observations regarding the widespread expression of ERβ-like proteins in normal and neoplastic mammary tissues have suggested that ERβ is also involved in the mentioned pathology. Design of new drugs both steroidal and nonsteroidal that target any of these receptors represents a promise to treat breast cancer although it remains a challenge due to the sequence similarity between their catalytic domains. In this work, we propose a new set of compounds that could effectively target the estrogen receptors ERα and ERβ. These ligands were designed based on the chemical structure of the ERβ-selective agonist Diarylpropionitrile (DPN). The designed ligands were submitted to in silico ADMET studies, yielding in a filtered list of ligands that showed better drug-like properties. Molecular dynamics simulations of both estrogen receptors and docking analysis were carried-out employing the designed compounds, from which two were chosen due to their promising characteristics retrieved from theoretical results (docking analysis or targeting receptor predictions). They were chemically synthetized and during the process, two precursor ligands were also obtained. These four ligands were subjected to biological studies from which it could be detected that compound mol60b dislplayed inhibitory activity and its ability to activate the transcription via an estrogenic mechanism of action was also determined. Interestinly, this observation can be related to theoretical binding free energy calculations, where the complex: ERβ-mol60b showed the highest energy ΔGbind value in comparison to others.

Molecular dynamics simulations reveal initial structural and dynamic features for the A2AR as a result of ligand binding

G-protein-coupled receptors (GPCRs) are membrane proteins that have a wide variety of physiological roles. Adenosine receptors belong to the GPCR family. Adenosine receptors are implicated in many physiological disorders, such as Parkinsons disease, Huntingtons disease, inflammatory and immunes disease and many others. Interestingly, crystal structures of the active and inactive conformations of the A2-subtype adenosine receptor (A2AR) have been solved. These two structures could be used to get insights about the conformational changes that occur during the process of activation/inactivation processes of this receptor. Therefore, two ligand-free simulations of the native active (PDB code: 3QAK) and inactive (PDB code: 3EML) conformations of the A2AR and two halo-simulations were carried out to observe the initial conformational changes induced by coupling adenosine to the inactive conformation and caffeine to the active conformation. Furthermore, we constructed an A2AR model that contained four thermostabilising mutations, L48A, T65A, Q89A and A54L, which had previously been determined to stabilise the bound conformation of the agonist, and we ran molecular dynamics simulations of this mutant to investigate how these point mutations might affect the inactive conformation of this receptor. This study provides insights about the initial structural and dynamic features that occur as a result of the binding of caffeine and adenosine in the active and inactive A2AR structures, respectively, as well as the introduction of some mutations on the inactive structure of the A2AR. Moreover, we provide useful and detailed information regarding structural features such as toggle switch and ionic lock during the activation/inactivation processes of this receptor.