Karina Martinez-Mayorga

Bio: Karina Martinez-Mayorga is an academic researcher from National Autonomous University of Mexico. The author has contributed to research in topics: Rhodopsin & Chemical space. The author has an hindex of 27, co-authored 82 publications receiving 2320 citations. Previous affiliations of Karina Martinez-Mayorga include University of Arizona & Torrey Pines Institute for Molecular Studies.

Recognizing Pitfalls in Virtual Screening: A Critical Review

Abstract: The aim of virtual screening (VS) is to identify bioactive compounds through computational means, by employing knowledge about the protein target (structure-based VS) or known bioactive ligands (ligand-based VS). In VS, a large number of molecules are ranked according to their likelihood to be bioactive compounds, with the aim to enrich the top fraction of the resulting list (which can be tested in bioassays afterward). At its core, VS attempts to improve the odds of identifying bioactive molecules by maximizing the true positive rate, that is, by ranking the truly active molecules as high as possible (and, correspondingly, the truly inactive ones as low as possible). In choosing the right approach, the researcher is faced with many questions: where does the optimal balance between efficiency and accuracy lie when evaluating a particular algorithm; do some methods perform better than others and in what particular situations; and what do retrospective results tell us about the prospective utility of a part...

How to recognize and workaround pitfalls in QSAR studies: a critical review.

Abstract: Quantitative Structure-Activity Relationships (QSAR) are based on the hypothesis that changes in molecular structure reflect proportional changes in the observed response or biological activity. In order to successfully conduct QSAR studies certain conditions have to be met that are not frequently reported in the literature. This suggests that some authors are not aware of the principle flaws, occasional shortcomings, and circumstantial downsides of QSAR methods. The present paper focuses on prerequisites to set up correct models and on limitations of model applications. Their implications are systematically described and illustrated as pitfalls that have strong implications in QSAR, and possible solutions are suggested. The paper is focused on small scale 2D- and 3D-QSAR studies for lead optimization. The work is enriched with comprehensive comments and non-mathematical explanations for the computer practitioner in Medicinal Chemistry.

Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs.

Abstract: Activity landscape characterization has been demonstrated to be a valuable tool in lead optimization, virtual screening, and computational modeling of active compounds. In this work, we present a general protocol to explore systematically the activity landscape of a lead series using 11 2D and 3D structural representations. As a test case we employed a set of 48 bicyclic guanidines (BCGs) with kappa-opioid receptor binding affinity, identified in our group. MACCS keys, graph-based three point pharmacophores, circular fingerprints, ROCS shape descriptors, and the TARIS approach, that compares structures based on molecular electrostatic potentials, were employed as orthogonal descriptors. Based on 'activity cliffs' common to a series of descriptors, we introduce the concept of consensus activity cliffs. Results for the current test case suggest that the presence or absence of a methoxybenzyl group may lead to different modes of binding for the active BCGs with the kappa-opioid receptor. The most active compound (IC50 = 37 nM) is involved in a number of consensus cliffs making it a more challenge query for future virtual screening than would be expected from affinity alone. Results also reveal the importance of screening high density combinatorial libraries, especially in the "cliff-rich" regions of activity landscapes. The protocol presented here can be applied to other data sets to develop a consensus model of the activity landscape.

Two protonation switches control rhodopsin activation in membranes

Abstract: Activation of the G protein-coupled receptor (GPCR) rhodopsin is initiated by light-induced isomerization of the retinal ligand, which triggers 2 protonation switches in the conformational transition to the active receptor state Meta II. The first switch involves disruption of an interhelical salt bridge by internal proton transfer from the retinal protonated Schiff base (PSB) to its counterion, Glu-113, in the transmembrane domain. The second switch consists of uptake of a proton from the solvent by Glu-134 of the conserved E(D)RY motif at the cytoplasmic terminus of helix 3, leading to pH-dependent receptor activation. By using a combination of UV–visible and FTIR spectroscopy, we study the activation mechanism of rhodopsin in different membrane environments and show that these 2 protonation switches become partially uncoupled at physiological temperature. This partial uncoupling leads to ≈50% population of an entropy-stabilized Meta II state in which the interhelical PSB salt bridge is broken and activating helix movements have taken place but in which Glu-134 remains unprotonated. This partial activation is converted to full activation only by coupling to the pH-dependent protonation of Glu-134 from the solvent, which stabilizes the active receptor conformation by lowering its enthalpy. In a membrane environment, protonation of Glu-134 is therefore a thermodynamic rather than a structural prerequisite for activating helix movements. In light of the conservation of the E(D)RY motif in rhodopsin-like GPCRs, protonation of this carboxylate also may serve a similar function in signal transduction of other members of this receptor family.

Visualization of the Chemical Space in Drug Discovery

Abstract: Chemical space has become a key concept in drug discovery. The continued growth in the number of molecules available raises the question regarding how many compounds may exist and which ones have the potential to become drugs. Analysis and visualization of the chemical space covered by public, commercial, in-house and virtual compound collections have found multiple applications in diversity analysis, in silico property profiling, data mining, virtual screening, library design, prioritization in screening campaigns, and acquisition of compound collections, among others. This review covers several techniques, computational programs and approaches that have been developed to visualize, navigate and study the chemical space of molecular databases. Techniques developed in our group are presented including a quantitative assessment of the multi-fusion similarity maps. Additionally an application of 3D-similarity, based on the overlay of chemical structures, to represent the chemical space is introduced. Several comparisons of the chemical space covered by compound collections from different sources such as combinatorial libraries, drugs and natural products, or directed to specific therapeutic areas are also discussed.

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Abstract: We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. A deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete molecular representations. The predictor estimates chemical properties from the latent continuous vector representation of the molecule. Continuous representations of molecules allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous represent...

Small-Molecule Library Screening by Docking with PyRx

Abstract: Virtual molecular screening is used to dock small-molecule libraries to a macromolecule in order to find lead compounds with desired biological function. This in silico method is well known for its application in computer-aided drug design. This chapter describes how to perform small-molecule virtual screening by docking with PyRx, which is open-source software with an intuitive user interface that runs on all major operating systems (Linux, Windows, and Mac OS). Specific steps for using PyRx, as well as considerations for data preparation, docking, and data analysis, are also described.

Automatic chemical design using a data-driven continuous representation of molecules

Abstract: We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. A deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder and a predictor. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete molecular representations. The predictor estimates chemical properties from the latent continuous vector representation of the molecule. Continuous representations allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the domain of drug-like molecules and also in the set of molecules with fewer that nine heavy atoms.