Finding optimal interaction interface alignments between biological complexes

Cui, Xuefeng; Naveed, Hammad; Gao, Xin

doi:10.1093/bioinformatics/btv242

Cited by 18 publications

(13 citation statements)

References 49 publications

(93 reference statements)

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“…Briefly, CASTp webserver was used to extract the pocket that the drug binds to [39], referred to as the ‘bound pocket’ from here on. Sequence order-independent alignment was used to find the pocket similar to the bound pocket [40,41] using the distance function described below. We extracted the conserved (positive and negative) structural signatures by applying pairwise sequence order-independent structure alignment followed by hierarchical clustering.…”

Section: Methodsmentioning

confidence: 99%

Identifying Novel Targets by using Drug-binding Site Signature: A Case Study of Kinase Inhibitors

Naveed

Reglin²,

Schubert³

et al. 2019

Preprint

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7Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to 2 8 the mechanism-independent effects of these drugs. Identifying these mechanism-independent 2 9 interactions could improve drug safety and support drug repurposing. We have developed 3 0 "iDTPnd", a computational approach for large-scale discovery of novel targets for known drugs. 1For a given drug, we construct a positive and a negative structural signature that captures the 3 2 weakly conserved structural features of drug binding sites. To facilitate assessment of unintended 3 3 targets iDTPnd also provides a docking-based interaction score and its statistical significance. 4We were able to confirm the interaction of sorafenib, imatinib, dasatinib, sunitinib, and 3 5 pazopanib with their known targets at a sensitivity and specificity of 52% and 55% respectively. 6We have validated 10 predicted novel targets, using in vitro experiments. Our results suggest that 3 7 proteins other than kinases, such as nuclear receptors, cytochrome P450 or MHC Class I 3 8 molecules can also be physiologically relevant targets of kinase inhibitors. Our method is general 3 9 and broadly applicable for the identification of protein-small molecule interactions, when 4 0 sufficient drug-target 3D data are available. 4 1 Keywords 4 2 Protein-drug interactions; iDTPnd; Kinase inhibitors; Drug binding site signatures 4 3

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Section: Methodsmentioning

confidence: 99%

Identifying Novel Targets by using Drug-binding Site Signature: A Case Study of Kinase Inhibitors

Naveed

Reglin²,

Schubert³

et al. 2019

Preprint

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“…While the local subsets tend to model local shapes of the backbone, the remote subsets tend to model the global topology of the backbone. It can be shown that remote topology similarities are more robust to less similar protein structure models with little local structure similarities [3,4].…”

Section: Initial Superposition Samplingmentioning

confidence: 99%

Compare local pocket and global protein structure models by small structure patterns

Cui

Kuwahara

et al. 2015

Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

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Researchers proposed several criteria to assess the quality of predicted protein structures because it is one of the essential tasks in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) competitions. Popular criteria include root mean squared deviation (RMSD), MaxSub score, TM-score, GDT-TS and GDT-HA scores. All these criteria require calculation of rigid transformations to superimpose the the predicted protein structure to the native protein structure. Yet, how to obtain the rigid transformations is unknown or with high time complexity, and, hence, heuristic algorithms were proposed.In this work, we carefully design various small structure patterns, including the ones specifically tuned for local pockets. Such structure patterns are biologically meaningful, and address the issue of relying on a sufficient number of backbone residue fragments for existing methods. We sample the rigid transformations from these small structure patterns; and the optimal superpositions yield by these small structures are refined and reported. As a result, among 11, 669 pairs of predicted and native local protein pocket models * To whom all correspondence should be addressed (email: shuaicli@cityu.edu.hk). † To whom all correspondence should be addressed (email: xin.gao@kaust.edu.sa).Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. BCB '15, September 9-12, 2015 from the CASP10 dataset, the GDT-TS scores calculated by our method are significantly higher than those calculated by LGA. Moreover, our program is computationally much more efficient.Source codes and executables are publicly available at

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“…One intuitive way to find remote homologous proteins is to perform pairwise structure alignments between the query protein and all proteins in the database. In the past two decades, many successful methods have been developed for the pairwise structure alignment problem [6]- [14]. The main challenge here is to find the optimal superposition and the optimal residue matching simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Learning Protein Structural Fingerprints under the Label-Free Supervision of Domain Knowledge

Min

Liu

Cui

2018

Preprint

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Finding homologous proteins is the indispensable first step in many protein biology studies. Thus, building highly efficient "search engines" for protein databases is a highly desired function in protein bioinformatics. As of August 2018, there are more than 140,000 protein structures in PDB, and this number is still increasing rapidly. Such a big number introduces a big challenge for scanning the whole structure database with high speeds and high sensitivities at the same time. Unfortunately, classic sequence alignment tools and pairwise structure alignment tools are either not sensitive enough to remote homologous proteins (with low sequence identities) or not fast enough for the task. Therefore, specifically designed computational methods are required for quickly scanning structure databases for homologous proteins.Here, we propose a novel ContactLib-DNN method to quickly scan structure databases for homologous proteins. The core idea is to build structure fingerprints for proteins, and to perform alignment-free comparisons with the fingerprints. Specifically, the fingerprints are low-dimensional vectors representing the contact groups within the proteins. Notably, the Cartesian distance between two fingerprint vectors well matches the RMSD between the two corresponding contact groups. This is done by using RMSD as the domain knowledge to supervise the deep neural network learning. When comparing to existing methods, ContactLib-DNN achieves the highest average AUROC of 0.959. Moreover, the best candidate found by ContactLib-DNN has a probability of 70.0% to be a true positive. This is a significant improvement over 56.2%, the best result produced by existing methods.GitHub: https://github.com/Chenyao2333/contactlib/ Index Terms-homologous proteins, protein structures, remote protein homolog detection, alignment-free comparisons

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Finding optimal interaction interface alignments between biological complexes

Cited by 18 publications

References 49 publications

Identifying Novel Targets by using Drug-binding Site Signature: A Case Study of Kinase Inhibitors

Identifying Novel Targets by using Drug-binding Site Signature: A Case Study of Kinase Inhibitors

Compare local pocket and global protein structure models by small structure patterns

Learning Protein Structural Fingerprints under the Label-Free Supervision of Domain Knowledge

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