Better together: Elements of successful scientific software development in a distributed collaborative community

Leman, Julia Koehler; Weitzner, Brian D.; Renfrew, P. Douglas; Lewis, Steven M.; Moretti, Rocco; Watkins, Andrew; Mulligan, Vikram Khipple; Lyskov, Sergey; Adolf-Bryfogle, Jared; Labonte, Jason W.; Krys, Justyna; Bystroff, Christopher; Schief, William R.; Gront, Dominik; Schueler‐Furman, Ora; Baker, David; Bradley, Philip; Dunbrack, Roland L.; Kortemme, Tanja; Leaver‐Fay, Andrew; Strauss, Charlie E. M.; Meiler, Jens; Kuhlman, Brian; Gray, Jeffrey J.; Bonneau, Richard

doi:10.1371/journal.pcbi.1007507

Cited by 32 publications

(44 citation statements)

References 37 publications

(54 reference statements)

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“…Currently, the BCL is only able to support LB design algorithms. Ultimately, we anticipate that increasing the capabilities of the BCL to perform both LB and SB design tasks will make it a valuable companion to other academic molecular modeling software projects, such as the Rosetta 42 macromolecular modeling and design software suite.…”

Section: Introductionmentioning

confidence: 99%

General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps

Brown

Mendenhall

Geanes

et al. 2021

J. Chem. Inf. Model.

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The BioChemical Library (BCL) is an academic open-source cheminformatics toolkit comprising ligand-based virtual high-throughput screening (vHTS) tools such as quantitative structure–activity/property relationship (QSAR/QSPR) modeling, small molecule flexible alignment, small molecule conformer generation, and more. Here, we expand the capabilities of the BCL to include structure-based virtual screening. We introduce two new score functions, BCL-AffinityNet and BCL-DockANNScore, based on novel distance-dependent signed protein–ligand atomic property correlations. Both metrics are conventional feed-forward dropout neural networks trained on the new descriptors. We demonstrate that BCL-AffinityNet is one of the top performing score functions on the comparative assessment of score functions 2016 affinity prediction and affinity ranking tasks. We also demonstrate that BCL-AffinityNet performs well on the CSAR-NRC HiQ I and II test sets. Furthermore, we demonstrate that BCL-DockANNScore is competitive with multiple state-of-the-art methods on the docking power and screening power tasks. Finally, we show how our models can be decomposed into human-interpretable pharmacophore maps to aid in hit/lead optimization. Altogether, our results expand the utility of the BCL for structure-based scoring to aid small molecule discovery and design. BCL-AffinityNet, BCL-DockANNScore, and the pharmacophore mapping application, as well as the remainder of the BCL cheminformatics toolkit, are freely available with an academic license at the BCL Commons site hosted on .

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Section: Introductionmentioning

confidence: 99%

General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps

Brown

Mendenhall

Geanes

et al. 2021

J. Chem. Inf. Model.

Self Cite

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“…Such programs are often designed and written by interdisciplinary groups including, e.g., chemists, biochemists, and informaticians. The design and validation of codes is a crucial step in the workflow [ 73 ].…”

Section: Conversion Of the Biological Code Into Smilesmentioning

confidence: 99%

Proposal of the Annotation of Phosphorylated Amino Acids and Peptides Using Biological and Chemical Codes

et al. 2021

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Phosphorylation represents one of the most important modifications of amino acids, peptides, and proteins. By modifying the latter, it is useful in improving the functional properties of foods. Although all these substances are broadly annotated in internet databases, there is no unified code for their annotation. The present publication aims to describe a simple code for the annotation of phosphopeptide sequences. The proposed code describes the location of phosphate residues in amino acid side chains (including new rules of atom numbering in amino acids) and the diversity of phosphate residues (e.g., di- and triphosphate residues and phosphate amidation). This article also includes translating the proposed biological code into SMILES, being the most commonly used chemical code. Finally, it discusses possible errors associated with applying the proposed code and in the resulting SMILES representations of phosphopeptides. The proposed code can be extended to describe other modifications in the future.

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“…RosettaCommons (founded in 2001) is an example of a collaborative initiative of > 500 developers that began in the mid-1990 s, where the scientific community is sharing a codebase for development of computational algorithms [101] . Eventually, this library of over 3.1 million lines of code have grown to become one of the largest programs in molecular modeling.…”

Section: Collective Intelligence and Protein Foldingmentioning

confidence: 99%

Homology modeling in the time of collective and artificial intelligence

Hameduh

Haddad

Adam

et al. 2020

Computational and Structural Biotechnology Journal

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Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.

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Better together: Elements of successful scientific software development in a distributed collaborative community

Cited by 32 publications

References 37 publications

General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps

General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps

Proposal of the Annotation of Phosphorylated Amino Acids and Peptides Using Biological and Chemical Codes

Homology modeling in the time of collective and artificial intelligence

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