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The Role of Protein Structural Analysis in the Next Generation Sequencing Era [chapter]

Wyatt W. Yue, D. Sean Froese, Paul E. Brennan
2012 Topics in current chemistry  
Proteins also serve as the predominant target class for the design of small molecule drugs to modulate their activity.  ...  In this chapter we outline, with the wide readership of non-structural biologists in mind, the various experimental and computational methods available for protein structure determination.  ...  , Lilly Canada, the Novartis Research Foundation, Pfizer, Abbott, Takeda, the Ontario Ministry of Research and Innovation and the Wellcome Trust.  ... 
doi:10.1007/128_2012_326 pmid:22610134 pmcid:PMC7121836 fatcat:2bw4vf5hn5f5zdkhurpl72dfey

A Multidimensional Strategy to Detect Polypharmacological Targets in the Absence of Structural and Sequence Homology

Jacob D. Durrant, Rommie E. Amaro, Lei Xie, Michael D. Urbaniak, Michael A. J. Ferguson, Antti Haapalainen, Zhijun Chen, Anne Marie Di Guilmi, Frank Wunder, Philip E. Bourne, J. Andrew McCammon, Robert B. Russell
2010 PLoS Computational Biology  
Additionally, identifying multiple protein targets is also critical for side-effect prediction.  ...  In the current work, we introduce a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with  ...  Keck Foundation, the National Biomedical Computation Resource, and the Center for Theoretical Biological Physics are gratefully acknowledged.  ... 
doi:10.1371/journal.pcbi.1000648 pmid:20098496 pmcid:PMC2799658 fatcat:gkypligfcvbqtca2hc77wjcuf4

Prediction of Drug-Target Interactions by Ensemble Learning Method from Protein Sequence and Drug Fingerprint

Xinke Zhan, Zhu-Hong You, Jinfan Cai, Liping Li, Changqing Yu, Jie Pan, Jiangkun Kong
2020 IEEE Access  
Predicting the target-drug interactions (DITs) is of great important for screening new drug candidate and understanding biological processes.  ...  Specifically, the target protein sequence is firstly transformed as the PSSM, in which the evolutionary information of protein is retained.  ...  The comprehensive experimental results demonstrate that the proposed method is feasible and effectively for identifying drug-target interactions on a large scale. II. Materials and Methodology A.  ... 
doi:10.1109/access.2020.3026479 fatcat:3hoeffsxrjbkpdmdfjbkiialti

Confronting the catalytic dark matter encoded by sequenced genomes

Kenneth W. Ellens, Nils Christian, Charandeep Singh, Venkata P. Satagopam, Patrick May, Carole L. Linster
2017 Nucleic Acids Research  
We extensively review classical biochemical as well as more recent systematic experimental and computational approaches that can be used to support enzyme function discovery research.  ...  The post-genomic era has provided researchers with a deluge of protein sequences. However, a significant fraction of the proteins encoded by sequenced genomes remains without an identified function.  ...  Jung for critical reading of the manuscript and Prof. Rudi Balling for helpful comments.  ... 
doi:10.1093/nar/gkx937 pmid:29059321 pmcid:PMC5714238 fatcat:oya4agqq6fbv3kfirizk4cjr6u

Phenotype Sequencing: Identifying the Genes That Cause a Phenotype Directly from Pooled Sequencing of Independent Mutants

Marc A. Harper, Zugen Chen, Traci Toy, Iara M. P. Machado, Stanley F. Nelson, James C. Liao, Christopher J. Lee, Raphael Valdivia
2011 PLoS ONE  
Random mutagenesis and phenotype screening provide a powerful method for dissecting microbial functions, but their results can be laborious to analyze experimentally.  ...  Our statistical analysis of these data (4099 mutations from 32 mutant genomes) successfully identified 3 genes (acrB, marC, acrA) that have been independently validated as causing this experimental phenotype  ...  Miller, Matteo Pellegrini, and Rich Roberts for helpful discussions on this work.  ... 
doi:10.1371/journal.pone.0016517 pmid:21364744 pmcid:PMC3041756 fatcat:omeha6dptbhexnobpky4wkwgpa

MPSM-DTI: Prediction of Drug-Target Interaction via Machine Learning based on Chemical Structure and Protein Sequence

Yayuan Peng, Jiye Wang, Zengrui Wu, Lulu Zheng, Biting Wang, Guixia Liu, Weihua Li, Yun Tang
2022 Digital Discovery  
Drug-target interaction (DTI) plays a central role in drug discovery. How to predict DTI quickly and accurately is a key issue.  ...  Traditional structure-based and ligand-based methods have some inherent deficiencies....  ...  Furthermore, to verify whether the model can predict compounds exactly for new targets, the experimentally validated DTIs were gathered from a list of recent publications, in which the proteins were completely  ... 
doi:10.1039/d1dd00011j fatcat:xkhgst6ysvaabe3xn6qfgrfime

Functional Census of Mutation Sequence Spaces: The Example of p53 Cancer Rescue Mutants

S.A. Danziger, S.J. Swamidass, Jue Zeng, L.R. Dearth, Qiang Lu, J.H. Chen, Jianlin Cheng, V.P. Hoang, H. Saigo, R. Luo, P. Baldi, R.K. Brachmann (+1 others)
2006 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
We devised a general methodology for conducting a functional census of a mutation sequence space by choosing informative mutants early.  ...  Many biomedical problems relate to mutant functional properties across a sequence space of interest, e.g., flu, cancer, and HIV.  ...  Hits from the screens would provide an initial training set for computational predictors of mutant p53 activity.  ... 
doi:10.1109/tcbb.2006.22 pmid:17048398 pmcid:PMC2748235 fatcat:knhggyly7ndwfiltpkrceuuffe

AREM: Aligning Short Reads from ChIP-Sequencing by Expectation Maximization

Daniel Newkirk, Jacob Biesinger, Alvin Chon, Kyoko Yokomori, Xiaohui Xie
2011 Journal of Computational Biology  
other DNA binding proteins.  ...  Although several methods have been proposed for ChIP-Seq analysis, most existing methods only consider reads that can be uniquely placed in the reference genome, and therefore have low power for detecting  ...  Session 3: Protein-Protein Interactions and Target Identification Computational Prediction and Experimental Verification of New MAP Kinase Docking Sites and Substrates Including Gli Transcription Factors  ... 
doi:10.1089/cmb.2011.0185 pmid:22035330 pmcid:PMC3216101 fatcat:lmzgyguv4bhini6o5gegycwgwi

Investigative mining of sequence data for novel enzymes: A case study with nitrilases

Jennifer L. Seffernick, Sudip K. Samanta, Tai Man Louie, Lawrence P. Wackett, Mani Subramanian
2009 Journal of Biotechnology  
Thus, predictions from sequence analysis and distant superfamily structures yielded enzyme activities with high selectivity for mandelonitrile.  ...  However, when relying on sequence data alone, prediction of the reaction catalyzed by a specific protein sequence is often elusive, and substrate specificity is far from trivial.  ...  We thank Kailin Chew for her assistance during the course of this study, Chi Li Yu for help with HPLC, the Romas Kazlauskas laboratory for the use of their chiral HPLC column, and Jack Richman for help  ... 
doi:10.1016/j.jbiotec.2009.06.004 pmid:19539670 fatcat:73d4277725hurdlpd6cghyyjxq

Prediction of Protein–ligand Interaction Based on Sequence Similarity and Ligand Structural Features

Dmitry Karasev, Boris Sobolev, Alexey Lagunin, Dmitry Filimonov, Vladimir Poroikov
2020 International Journal of Molecular Sciences  
Computationally predicting the interaction of proteins and ligands presents three main directions: the search of new target proteins for ligands, the search of new ligands for targets, and predicting the  ...  We tested our approach on five protein groups, which represented promised targets for drug-like ligands and differed in functional peculiarities.  ...  Computational prediction of the interaction between the proteins and drug-like ligands significantly decreases the time consumption and costs required for experimental studies.  ... 
doi:10.3390/ijms21218152 pmid:33142754 pmcid:PMC7663273 fatcat:dk2t6antrrdsbkyvb6xpo7rcvy

DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences

Ingoo Lee, Jongsoo Keum, Hojung Nam, James M. Briggs
2019 PLoS Computational Biology  
In conclusion, our prediction model for detecting local residue patterns of target proteins successfully enriches the protein features of a raw protein sequence, yielding better prediction results than  ...  In several computational models, conventional protein descriptors have been shown to not be sufficiently informative to predict accurate DTIs.  ...  Thus, drug developers screen for compounds that interact with specified targets with biological activities of interest.  ... 
doi:10.1371/journal.pcbi.1007129 pmid:31199797 pmcid:PMC6594651 fatcat:g2neuk3lk5f5ldvahannupjyuq

Flanking region sequence information to refine microRNA target predictions

Russiachand Heikham, Ravi Shankar
2010 Journal of Biosciences  
Our methodology attained a higher average accuracy of 0.88, average sensitivity and specifi city of 0.81 and 0.94, respectively, and areas under the curves (AUCs) for all the four models scored above 0.9  ...  , suggesting better performance by our methodology and a possible role of fl anking regions in microRNA targeting control.  ...  Acknowledgements We thank Mr Amit Chaurasia and Dr Mitali Mukerji of Institute of Genomics and Integrative Biology, Delhi, for sharing TFBS data on human sequences using the TP database.  ... 
doi:10.1007/s12038-010-0013-7 pmid:20413915 fatcat:dklnccihenduflpzn5uljfcbvm

Proteins: Sequence to Structure and Function – Current Status

Sandhya R. Shenoy, B. Jayaram
2010 Current protein and peptide science  
interactions, metabolic networks, potential drug targets based on simple sequence properties, disordered proteins, the sequence-structure relationship and chemical logic of protein sequences.  ...  The central challenge of Computational Structural Biology is therefore to rationalize the mass of sequence information into biochemical and biophysical knowledge and to decipher the structural, functional  ...  Of note, Becker et al. used the predicted structural models of the serotonin receptors to screen a compound library [242] .  ... 
doi:10.2174/138920310794109094 pmid:20887265 fatcat:d7jfqzg7lvg5bf3lt4uefm25me

Next-Generation Sequencing to Guide Clinical Trials

L. L. Siu, B. A. Conley, S. Boerner, P. M. LoRusso
2015 Clinical Cancer Research  
Rapidly accruing knowledge of the mutational landscape of malignant neoplasms, the increasing facility of massively parallel genomic sequencing, and the availability of drugs targeting many "driver" molecular  ...  It is clear that clinical (histology and stage) eligibility criteria are not sufficient for most clinical trials using agents that target mutations that are present in only a minority of patients.  ...  Future Directions and Conclusions Inspired by the recent approval and high activity of targeted agents against driver mutations in cancers formerly considered "untreatable," the desire to use genomic sequencing  ... 
doi:10.1158/1078-0432.ccr-14-3215 pmid:26473189 pmcid:PMC4610034 fatcat:ocsw6vdhafhmbgxcuf3f2oqc4e

Bayesian modelling of high-throughput sequencing assays with malacoda

Andrew R. Ghazi, Xianguo Kong, Ed S. Chen, Leonard C. Edelstein, Chad A. Shaw, Jian Ma
2020 PLoS Computational Biology  
We also used luciferase assays to experimentally validate several hits from our primary data, as well as variants for which the various methods disagree and variants detectable only with the aid of external  ...  The model uses the negative binomial distribution with gamma priors to model sequencing counts while accounting for effects from input library preparation and sequencing depth.  ...  NPRL3 is part of the GTP-ase activating protein activity toward Rags [22] (GATOR1) complex.  ... 
doi:10.1371/journal.pcbi.1007504 pmid:32692749 fatcat:we6krdf5jrgl5gkbgseqtehrla
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