ClusPro in rounds 38 to 45 of CAPRI: Toward combining template‐based methods with free docking

Padhorny, Dzmitry; Porter, Kathryn A.; Ignatov, Mikhail; Alekseenko, Andrey; Beglov, Dmitri; Kotelnikov, Sergei; Ashizawa, Ryota; Desta, Israel; Alam, Nawsad; Sun, Zhuyezi; Brini, Emiliano; Dill, Ken A.; Schueler‐Furman, Ora; Vajda, Sándor; Kozakov, Dima

doi:10.1002/prot.25887

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…It includes only heterodimeric targets from the PDB released after May 2018, since such protein structures were certainly not used for training AlphaFold2. Furthermore, we used ClusPro TBM to look for homologous templates for potential targets and limited our selection to those with no appropriate template in the PDB [13]. This was done to ensure that our data set consisted of truly novel protein-protein interactions.…”

Section: Benchmark Setsmentioning

confidence: 99%

Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro

Ghani¹,

Desta²,

Jindal³

et al. 2021

Preprint

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It has been demonstrated earlier that the neural network based program AlphaFold2 can be used to dock proteins given the two sequences separated by a gap as the input. The protocol presented here combines AlphaFold2 with the physics based docking program ClusPro. The monomers of the model generated by AlphaFold2 are separated, re-docked using ClusPro, and the resulting 10 models are refined by AlphaFold2. Finally, the five original AlphaFold2 models are added to the 10 AlphaFold2 refined ClusPro models, and the 15 models are ranked by their predicted aligned error (PAE) values obtained by AlphaFold2. The protocol is applied to two benchmark sets of complexes, the first based on the established protein-protein docking benchmark, and the second consisting of only structures released after May 2018, the cut-off date for training AlphaFold2. It is shown that the quality of the initial AlphaFold2 models improves with each additional step of the protocol. In particular, adding the AlphaFold2 refined ClusPro models to the AlphaFold2 models increases the success rate by 23% in the top 5 predictions, whereas considering the 10 models obtained by the combined protocol increases the success rate to close to 40%. The improvement is similar for the second benchmark that includes only complexes distinct from the proteins used for training the neural network.

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Section: Benchmark Setsmentioning

confidence: 99%

Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro

Ghani¹,

Desta²,

Jindal³

et al. 2021

Preprint

Self Cite

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show abstract

“…The structural models of protein complexes were prepared using a combination of AlphaFold2 61 and ClusProTBM 62 , 63 . For AlphaFold2, the multiple sequence alignments were prepared using the MMseqs2 tool 64 (Version 13-45111), and model selection was done based on the cutoff value of 10.0 for the Predicted Aligned Error (PAE) of interface residues.…”

Section: Methodsmentioning

confidence: 99%

Scalable multiplex co-fractionation/mass spectrometry platform for accelerated protein interactome discovery

et al. 2022

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Co-fractionation/mass spectrometry (CF/MS) enables the mapping of endogenous macromolecular networks on a proteome scale, but current methods are experimentally laborious, resource intensive and afford lesser quantitative accuracy. Here, we present a technically efficient, cost-effective and reproducible multiplex CF/MS (mCF/MS) platform for measuring and comparing, simultaneously, multi-protein assemblies across different experimental samples at a rate that is up to an order of magnitude faster than previous approaches. We apply mCF/MS to map the protein interaction landscape of non-transformed mammary epithelia versus breast cancer cells in parallel, revealing large-scale differences in protein-protein interactions and the relative abundance of associated macromolecules connected with cancer-related pathways and altered cellular processes. The integration of multiplexing capability within an optimized workflow renders mCF/MS as a powerful tool for systematically exploring physical interaction networks in a comparative manner.

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“…Further, it was validated experimentally by hydroxyl radical-mediated oxidative protein foot-printing [ 40 , 41 ]. Padhorny et al [ 42 ] established the validity and accuracy of docking predictions in the protein docking experiment CAPRI (Critical Assessment of Predicted Interactions).…”

Section: Methodsmentioning

confidence: 99%

In silico study of some selective phytochemicals against a hypothetical SARS-CoV-2 spike RBD using molecular docking tools

Nag

Paul

Banerjee

et al. 2021

Computers in Biology and Medicine

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Background This world is currently witnessing a pandemic outbreak of 'COVID-19' caused by a positive-strand RNA virus 'SARS-CoV-2’. Millions have succumbed globally to the disease, and the numbers are increasing day by day. The viral genome enters into the human host through interaction between the spike protein (S) and host angiotensin-converting enzyme-2 (ACE2) proteins. S is the common target for most recently rolled-out vaccines across regions. A recent surge in single/multiple mutations in S region is of great concern as it may escape vaccine induced immunity. So far, the treatment regime with repurposed drugs has not been too successful. Hypothesis Natural compounds are capable of targeting mutated spike protein by binding to its active site and destabilizing the spike-host ACE2 interaction. Materials and methods A hypothetical mutated spike protein was constructed by incorporating 12 different mutations from twelve geographical locations simultaneously into the receptor-binding domain (RBD) and docked with ACE2 and seven phytochemicals namely allicin, capsaicin, cinnamaldehyde, curcumin, gingerol, piperine and zingeberene. Molecular Dynamic (MD) simulation and Principal Component Analysis (PCA) were finally used for validation of the docking results. Result The docking results showed that curcumin and piperine were most potent to bind ACE2, mutated spike, and mutated spike-ACE2 complex, thereby restricting viral entry. ADME analysis also proved their drug candidature. The docking complexes were found to be stable by MD simulation. Conclusion This result provides a significant insight about the phytochemicals' role, namely curcumin and piperine, as the potential therapeutic entities against mutated spike protein of SARS-CoV-2.

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ClusPro in rounds 38 to 45 of CAPRI: Toward combining template‐based methods with free docking

Abstract: This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Cited by 8 publications

References 41 publications

Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro

Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro

Scalable multiplex co-fractionation/mass spectrometry platform for accelerated protein interactome discovery

In silico study of some selective phytochemicals against a hypothetical SARS-CoV-2 spike RBD using molecular docking tools

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