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Over the last three decades, Computer Aided Drug Design (CADD) has positioned as one of the more useful approaches aiding the research at early stages of drug discovery process [1]. Particularly, small molecule docking algorithms have been employed exhaustively to identify the possible atomic interactions, between the protein target and a suggested small molecule, that support the formation of the protein-ligand complex. This evaluation is performed by employing a scoring function that relates geometric patterns of the interacting molecules to free energy values. However, the accurate and exact prediction of the binding free energy, along with the complex conformation, remains as an open problem [2]. As consequence, docking results present a high rate of false positive cases. The high complexity of the physicochemical process, together with the vast amount of structural experimental information available, renders the use of machine learning algorithms an attractive possibility [3, 4, 5]. In the present work, we briefly describe the problems associated with current docking scoring functions and posit the idea of pruning the false positive cases using machine learning algorithms. Then, we expose the limitations the docking algorithm (not only of the scoring function) of AutodockVina [6] by analyzing a set of approximately 15000 crystallographic complexes retrieved from Protein Data Bank [7]. Finally, we present preliminary results obtained with our tools designed for false positive identification. Specifically, we show how a relatively simple Bayesian Network, based on interaction fingerprints, can be used to infer the badly placed fragment molecules (with molecular weights in the range of 150-350 Da). Additionally, we present the results obtained of a Convolutional Neural Network to analyze docking poses (molecules with molecular weights in the range of 150-850 Da). Both networks show promising results by improving Receiver Operating Characteristic metrics as compared with the use of the docking protocol alone. <br> <br> Acknowledgments: This project was supported by DireccioÌ n General de Asuntos del Personal AcadeÌ mico at Universidad Nacional AutoÌ noma de MeÌ xico (PAPIIT-IA202917). The authors thank DireccioÌ n General de CoÌ mputo y de Tecnologías de Información y Comunicación at Universidad Nacional AutoÌ noma de MeÌ xico for granting the use of the supercomputer Miztli (LANCAD-UNAM-DGTIC-320). <br> <br> [1] G. Sliwoski, S. Kothiwale, J. Meiler, E. W. Lowe-Jr. Pharmacol. Rev., 66, 334-395, 2014. <br> [2] HA Carlson, et. al. J. Chem. Inf. Model., 56, 1063-1077, 2016. <br> [3] M. Arciniega, O. F. Lange. J. Chem. Inf. Model., 54, 1401-1411, 2014. <br> [4] J. C. Pereira, et. al. J. Chem. Inf. Model., 56, 2495-2506, 2016. <br> [5] M. Wójcikowski, P. J. Ballester, P. Siedlecki. Sci. Rep. 7, 46710, 2017. <br> [6] O. Trott, A. J. Olson. J. Compt. Chem. 31, 455-461, 2010. <br> [7] www.rcsb.org.
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The attached file is a supplement to the author’s doctoral dissertation at https://circle.library.ubc.ca/handle/2429/72961
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We consider the macroscopic disordered system of free lattice fermions with the one-body Hamiltonian, which is the Schrödinger operator with i.i.d. potential in d>1. Assuming that the fractional moment criteria for the Anderson localization is satisfied, we prove Central Limit Theorem for the large block entanglement entropy.
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The dynamics of speciesâ densities depend both on internal and external variables. Internal variables include frequencies of individuals exhibiting different phenotypes or living in different spatial locations. External variables include abiotic factors or non-focal species. These internal or external variables may fluctuate due to stochastic fluctuations in environmental conditions. The interplay between these variables and species densities can determine whether a particular population persists or goes extinct. I will present recent theorems for stochastic persistence and exclusion for stochastic ecological difference equations accounting for internal and external variables, and will illustrate their utility with applications to models of eco-evolutionary dynamics
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We consider quantum systems coupled simultaneously to multiple environments. Examples include solid-state photon emitters, with coupling both to vibrations and the electromagnetic field, and molecular nanojunctions, with coupling both to vibrations and electronic leads. We show that enforcing additivity of such combined influences results in non-equilibrium dynamics that does not respect the Franck-Condon principle in the former case, and can lead to unphysical electronic current under equilibrium conditions in the latter. We overcome these shortcomings by employing a collective coordinate representation of the vibrational environment, which permits the derivation of a non-additive master equation. When applied to a two-level emitter our treatment predicts decreasing photon emission rates with increasing vibrational coupling, consistent with Franck-Condon physics. Applied to a molecular nanojunction we employ counting statistics techniques to track electron flow between the system and the electronic leads, revealing both strong-coupling and non-additive effects in the electron current, noise and Fano factor.
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Frequencies of synonymous codons are typically non-uniform, despite the fact that such codons correspond to the same amino acid in the genetic code. This phenomenon, known as codon bias, is broadly believed to be due to a combination of factors including genetic drift, mutational effects, and selection for speed and accuracy of codon translation; however, quantitative modeling of codon bias has been somewhat elusive. I will present a biophysical model which explains genome-wide codon frequencies observed across 20 organisms. Our model implements detailed codon-level treatment of mutations and includes two contributions to codon fitness which describe codon translation speed and accuracy. We find that the observed patterns of genome-wide codon usage are consistent with a strong selective penalty for mistranslated amino acids, while the dependence of codon fitness on translation speed is much weaker on average. Treating the translation process explicitly in the context of a finite ribosomal pool has allowed us to highlight the biophysical underpinnings of codon-level selective pressures. Overall, our approach offers a unified biophysical and population genetics framework for understanding the origin of codon bias.
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We investigate the problem of differentially private hypothesis selection: Given i.i.d. samples from an unknown probability distribution P and a set of m probability distributions H, the goal is to privately output a distribution from H whose total variation distance to P is comparable to that of the best such distribution. We present several algorithms for this problem which achieve sample complexity similar to those of the best non-private algorithms. These give new and improved learning algorithms for a number of natural distribution classes. Our results also separate the sample complexities of private mean estimation under product vs. non-product distributions.
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Features of the CRISPR-Cas system, in which bacteria integrate small segments of phage genome (spacers) into their DNA to neutralize future attacks, suggest that its effect is not limited to individual bacteria but may control the fate and structure of whole populations [1]. In our model, we find that early dynamics of large phage clones is largely independent of bacterial dynamics but crucially depends on the burst-size of phage infections. In contrast, the fates of early phage mutants are strongly influenced by the feedback from bacterial population that creates a time-dependent fitness landscape for that phage type. Taken together, we quantify the role of population parameters in maintaining phage and bacterial diversity where CRISPR-cas is in the play. [1] https://www.pnas.org/content/115/32/E7462)
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