CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials

Fine, Jonathan; Konc, Janez; Samudrala, Ram; Chopra, Gaurav

doi:10.1021/acs.jcim.9b00686

Cited by 44 publications

(60 citation statements)

References 91 publications

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“…We are continuing to enhance the virtual screening pipelines to model reality more accurately, with the goal of increasing compound-proteome signature comparison accuracy. For instance, we are exploring the use different molecular docking programs, such as CANDOCK 84,85 and AutoDock Vina, 86 to populate the compound-proteome interaction signatures. An updated version of the v1 pipeline, v1.5, with parameters optimized for scoring compound-proteome interactions, yields benchmarking performance that is 10% higher relatively at the top10 cutoff (12.8% for v1.5 versus 11.7% for v1).…”

Section: Discussionmentioning

confidence: 99%

Fingerprinting CANDO: Increased Accuracy with Structure and Ligand Based Shotgun Drug Repurposing

Schuler

Samudrala

2019

Preprint

Self Cite

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1We have upgraded our Computational Analysis of Novel Drug Opportunities (CANDO) 2 platform for shotgun drug repurposing to include ligand-based, data fusion, and decision tree 3 pipelines. The first version of CANDO implemented a structure-based pipeline that mod-4 eled interactions between compounds and proteins on a large scale, generating compound-5 proteome interaction signatures used to infer similarity of drug behavior; the new pipelines 6 accomplish this by incorporating molecular fingerprints and the Tanimoto coefficient. We 7 obtain improved benchmarking performance with the new pipelines across all three evalua-8 tion metrics used: average indication accuracy, pairwise accuracy, and coverage. The best 9 performing pipeline achieves an average indication accuracy of 19.0% at the top10 cutoff, 10 compared to 11.7% for v1, and 2.2% for a random control. Our results demonstrate that the 11 CANDO drug recovery accuracy is substantially improved by integrating multiple pipelines, 12 thereby enhancing our ability to generate putative therapeutic repurposing candidates, and 13 increasing drug discovery efficiency. 14 Introduction 15 Drug repurposing 16 Bringing a new drug to the market may costs hundreds of millions of dollars and takes years 17 of work. 1 Drug repurposing is the process of discovering a new use for an existing drug. 2,3 18This process may take advantage of existing data on safety and pharmacokinetic properties 19 from previous trials and clinical use to reduce costs and time associated with traditional drug 20 discovery. Classic examples of drug repurposing include sildenafil (Viagra) and thalidomide, 21 which initially were developed to treat chest pain and morning sickness, but were repurposed 22 to treat erectile dysfunction and erythema nodosum leprosum respectively. 2,4,5 Drugs which 23 have already been repurposed once are being researched for even more novel uses. For exam-24 ple, raloxifene was originally indicated for prevention of osteoporosis and was subsequently 25 approved for risk reduction in the development of breast cancer. 6 More recently, raloxifene 26 has been suggested as a possible treatment for Ebola virus disease 7-9 '. These examples of 27 putative and/or successful drug repurposing underlies the diverse mechanisms through which 28 a single compound may treat a variety of disease types. 10,11 High throughput, target-based, 29 and phenotypic screening of compounds can be used to generate putative candidates for re-30 purposing. 12 For example, potential treatments for Zika virus infection were identified using 31 a phenotypic screen. 13 32 Computational drug discovery and repurposing 33 Finding new drugs or new uses for existing drugs computationally takes advantage of the 34 growing amount of data generated from wet lab experiments accessible on the Internet, 35 increased computational power, and higher fidelity of computational models to reality. Ap-36 proaches to computational drug discovery and repurposing have been classified as structure-37 b...

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Section: Discussionmentioning

confidence: 99%

Fingerprinting CANDO: Increased Accuracy with Structure and Ligand Based Shotgun Drug Repurposing

Schuler

Samudrala

2019

Preprint

Self Cite

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“…Therefore, a useful theoretical strategy is to select promising conformations from the docking study. In this regard, it is worth mentioning that different programs and theoretical methods are available for docking purposes, and each docking program shows advantages and limitations to carry out docking studies and docking-based virtual screening [64][65][66][67]. On the other hand, Hou and co-worker recently illustrated that no single docking program has dominative advantages than other programs [60,67].…”

Section: Molecular Docking Of Polyhalogenated 44 -Bipyridines 1-6 Inmentioning

confidence: 99%

“…Herein, due to the novelty of the 4,4 -bipyridyl motif in this field, a blind docking [21] exploration was performed in order to inspect the disposition of all enantiomers (M) and (P) of compounds 1-5 and the achiral 6 into the whole TTR, focusing on the binding capability toward the T 4 pockets. Despite the fact that flexible docking is frequently used to model ligand-receptor complexes accounting for molecular flexibility [64,65], we do not consider protein flexibility herein, rather focusing on the capability of the new compounds to insert in the same T 4 binding cavity hosting T 4 , as derived from the selected crystal complex. For this purpose, the released crystal structure of TTR complex (PDB ID: 1ICT, chains ABCD) [2] from the RCSB protein databank (www.rcbs.org) was used.…”

Section: Molecular Docking Of Polyhalogenated 44 -Bipyridines 1-6 Inmentioning

confidence: 99%

“…4,4 -Bipyridines 1-10 were constructed by using the standard bond lengths and angles from the fragment database of GaussView 5.0 and optimized with Gaussian 09 (DFT, B3LYP, 6-311G*) [81,86]. The explicit σ-hole (ESH) was used as previously described [51,64,65], in order to account for the charge anisotropy of the electrostatic potential on top of the halogen atoms. On this basis, a massless dummy atom connected to I and Cl was introduced manually, by using distance and charge values as described by Hobza and co-workers [65].…”

Section: In Silico Docking Studymentioning

confidence: 99%

“…The explicit σ-hole (ESH) was used as previously described [51,64,65], in order to account for the charge anisotropy of the electrostatic potential on top of the halogen atoms. On this basis, a massless dummy atom connected to I and Cl was introduced manually, by using distance and charge values as described by Hobza and co-workers [65]. The parameters used for Cl and I were 1.0, 1.6 Å, and 0.1, 0.3 units of positive charge for the extra point (ExP), respectively (Supplementary Information, Table S7).…”

Section: In Silico Docking Studymentioning

confidence: 99%

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Rational Design, Synthesis, Characterization and Evaluation of Iodinated 4,4′-Bipyridines as New Transthyretin Fibrillogenesis Inhibitors

et al. 2020

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The 3,3′,5,5′-tetrachloro-2-iodo-4,4′-bipyridine structure is proposed as a novel chemical scaffold for the design of new transthyretin (TTR) fibrillogenesis inhibitors. In the frame of a proof-of-principle exploration, four chiral 3,3′,5,5′-tetrachloro-2-iodo-2′-substituted-4,4′- bipyridines were rationally designed and prepared from a simple trihalopyridine in three steps, including a Cu-catalysed Finkelstein reaction to introduce iodine atoms on the heteroaromatic scaffold, and a Pd-catalysed coupling reaction to install the 2′-substituent. The corresponding racemates, along with other five chiral 4,4′-bipyridines containing halogens as substituents, were enantioseparated by high-performance liquid chromatography in order to obtain pure enantiomer pairs. All stereoisomers were tested against the amyloid fibril formation (FF) of wild type (WT)-TTR and two mutant variants, V30M and Y78F, in acid mediated aggregation experiments. Among the 4,4′-bipyridine derivatives, interesting inhibition activity was obtained for both enantiomers of the 3,3′,5,5′-tetrachloro-2′-(4-hydroxyphenyl)-2-iodo-4,4′-bipyridine. In silico docking studies were carried out in order to explore possible binding modes of the 4,4′-bipyridine derivatives into the TTR. The gained results point out the importance of the right combination of H-bond sites and the presence of iodine as halogen-bond donor. Both experimental and theoretical evidences pave the way for the utilization of the iodinated 4,4′-bipyridine core as template to design new promising inhibitors of TTR amyloidogenesis.

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Protein-Ligand Binding Affinity Prediction Based on Deep Learning

Liu

Jiang

et al. 2022

Intelligent Computing Theories and Application

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CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials

Abstract: Figure S1: Distribution of RMSD values (Å) for all ligand poses generated by CANDOCK for docked poses in the CASF-2016 benchmark for (a) rigid-protein docking, (b) semi-flexible protein, and (c) fully-flexible protein docking.

Cited by 44 publications

References 91 publications

Fingerprinting CANDO: Increased Accuracy with Structure and Ligand Based Shotgun Drug Repurposing

Fingerprinting CANDO: Increased Accuracy with Structure and Ligand Based Shotgun Drug Repurposing

Rational Design, Synthesis, Characterization and Evaluation of Iodinated 4,4′-Bipyridines as New Transthyretin Fibrillogenesis Inhibitors

Protein-Ligand Binding Affinity Prediction Based on Deep Learning

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