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BCL::Conf: Improved Open-Source Knowledge-Based Conformation Sampling Using the Crystallography Open Database.

ABSTRACT: We previously described BCL::Conf, a knowledge-based conformation sampling algorithm utilizing a small molecule fragment rotamer library derived from the Cambridge Structural Database (CSD, license required), as a component of the BioChemical Library (BCL) cheminformatics toolkit. This paper describes substantial improvements made to the BCL::Conf algorithm and a transition to a rotamer library derived from molecules in the Crystallography Open Database (COD, no license required). We demonstrate the performance of the new BCL::Conf on native conformer recovery in the Platinum dataset of high-quality protein-ligand complexes. This set of 2859 structures has previously been used to assess the performance of over a dozen conformer generation algorithms, including the Conformator, Balloon, RDKit DG, ETKDG, Confab, Frog2, MultiConf-DOCK, CSD conformer generator, ConfGenX-OPSL3 force field, Omega, excalc, iCon, and MOE. These benchmarks suggest that the CSD conformer generator is at the state of the art of reported conformer generators. Our results indicate that the improved BCL::Conf significantly outperforms the CSD conformer generation algorithm at binding conformer recovery across a range of ensemble sizes and with similarly fast rates of conformer generation. BCL::Conf is now distributed with the COD-derived rotamer library and is free for academic use. The BCL can be downloaded at http://meilerlab.org/bclcommons for Windows, Linux, or Apple operating systems. BCL::Conf can now also be accessed via webserver at http://meilerlab.org/bclconf.

SUBMITTER: Mendenhall J

PROVIDER: S-EPMC8130828 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundLack of accurate data on the distribution of sub-national populations in low- and middle-income countries impairs planning, monitoring, and evaluation of interventions. Novel, low-cost methods to develop unbiased survey sampling frames at sub-national, sub-provincial, and even sub-district levels are urgently needed. This article details our experience using remote satellite imagery to develop a provincial-level representative community survey sampling frame to evaluate the effects of a 7-year health system intervention in Sofala Province, Mozambique.MethodsMozambique's most recent census was conducted in 2007, and no data are readily available to generate enumeration areas for representative health survey sampling frames. To remedy this, we partnered with the Humanitarian OpenStreetMap Team to digitize every building in Sofala and Manica provinces (685,189 Sofala; 925,713 Manica) using up-to-date remote satellite imagery, with final results deposited in the open-source OpenStreetMap database. We then created a probability proportional to size sampling frame by overlaying a grid of 2.106 km resolution (0.02 decimal degrees) across each province, and calculating the number of buildings within each grid square. Squares containing buildings were used as our primary sampling unit with replacement. Study teams navigated to the geographic center of each selected square using geographic positioning system coordinates, and then conducted a standard "random walk" procedure to select 20 households for each time a given square was selected. Based on sample size calculations, we targeted a minimum of 1500 households in each province. We selected 88 grids within each province to reach 1760 households, anticipating ongoing conflict and transport issues could preclude the inclusion of some clusters.ResultsCivil conflict issues forced the exclusion of 8 of 31 subdistricts in Sofala and 15 of 39 subdistricts in Manica. Using Android tablets, Open Data Kit software, and a remote RedCap data capture system, our final sample included 1549 households in Sofala (4669 adults; 4766 children; 33 missing age) and 1538 households in Manica (4422 adults; 4898 children; 33 missing age).ConclusionsOther implementation or evaluation teams may consider employing similar methods to track population distributions for health systems planning or the development of representative sampling frames using remote satellite imagery.

Dataset Information

BCL::Conf: Improved Open-Source Knowledge-Based Conformation Sampling Using the Crystallography Open Database.

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