konglutyna.bib

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@article{Sanchez-Aparicio2021,
abstract = {With a large amount of research dedicated to decoding how metallic species bind to proteins, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal-binding templates. These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parameterization from a statistical analysis. Then, the successful benchmark of BioMetAll on a set of more than 90 metal-binding X-ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems whose structures (either experimental or theoretical) are not optimal for metal-binding sites. We report here its application on three different challenging cases: (i) the modulation of metal-binding sites during conformational transition in human serum albumin, (ii) the identification of possible routes of metal migration in hemocyanins, and (iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding. BioMetAll is an open-source application available at https://github.com/insilichem/biometall.},
author = {S{\'{a}}nchez-Aparicio, Jos{\'{e}}-Emilio and Tiessler-Sala, Laura and Velasco-Carneros, Lorea and Rold{\'{a}}n-Mart{\'{i}}n, Lorena and Sciortino, Giuseppe and Mar{\'{e}}chal, Jean-Didier},
doi = {10.1021/acs.jcim.0c00827},
file = {:C$\backslash$:/Users/makro/Documents/Mendeley Desktop/S{\'{a}}nchez-Aparicio et al. - 2021 - BioMetAll Identifying Metal-Binding Sites in Proteins from Backbone Preorganization.pdf:pdf},
issn = {1549-9596},
journal = {Journal of Chemical Information and Modeling},
month = {jan},
number = {1},
pages = {311--323},
pmid = {33337144},
title = {{BioMetAll: Identifying Metal-Binding Sites in Proteins from Backbone Preorganization}},
url = {https://pubs.acs.org/doi/10.1021/acs.jcim.0c00827},
volume = {61},
year = {2021}
}
@article{Alderighi1999,
abstract = {Hyperquad simulation and speciation (HySS) is a computer program written for the Windows operating system on personal computers which provides (a) a system for simulating titration curves and (b) a system for providing speciation diagrams. The calculations relate to equilibria in solution and also include the possibility of formation of a partially soluble precipitate. There are no restrictions as to the number of reagents that may be present or the number of complexes that may be formed. {\textcopyright} 1999 Elsevier Science S.A. All rights reserved.},
author = {Alderighi, Lucia and Gans, Peter and Ienco, Andrea and Peters, Daniel and Sabatini, Antonio and Vacca, Alberto},
doi = {10.1016/S0010-8545(98)00260-4},
file = {:C$\backslash$:/Users/makro/Documents/Mendeley Desktop/Alderighi et al. - 1999 - Hyperquad simulation and speciation (HySS) a utility program for the investigation of equilibria involving sol.pdf:pdf},
issn = {00108545},
journal = {Coordination Chemistry Reviews},
keywords = {Precipitates,Solution equilibria,Speciation},
month = {apr},
number = {1},
pages = {311--318},
title = {{Hyperquad simulation and speciation (HySS): a utility program for the investigation of equilibria involving soluble and partially soluble species}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0010854598002604},
volume = {184},
year = {1999}
}
@article{Sathyaseelan2023,
abstract = {Zinc finger (ZnF) is an important class of nucleic acid and protein recognition domain, wherein, zinc ion is the inorganic co-factor that forms a tetrahedral geometry with the cysteine and/or histidine residues. ZnF domains take up diverse architectures with different ZnF motifs and have a wide range of biological functions. Nonetheless, predicting the ZnF motif(s) from the sequence is quite challenging. To this end, 74 unique ZnF sequence patterns are collected from the literature and classified into 32 different classes. Since the shorter length of ZnF sequence patterns leads to inaccurate predictions, ZnF domain Pfam HMM profiles defined under 6 ZnF Pfam clans (215 HMM profiles) and a few undefined Pfam clans (74 HMM profiles) are used for the prediction. A web server, namely, ZnF-Prot (https://project.iith.ac.in/znprot/) is developed which can predict the presence of 31 ZnF domains in a protein/proteome sequence of any organism. The use of ZnF sequence patterns and Pfam HMM profiles resulted in an accurate prediction of 610 test cases (taken randomly from 249 organisms) considered here. Additionally, the application of ZnF-Prot is demonstrated by considering Arabidopsis thaliana, Homo sapiens, Saccharomyces cerevisiae, Caenorhabditis elegans and Ciona intestinalis proteomes as test cases, wherein, 87--96{\%} of the predicted ZnF motifs are cross-validated.},
author = {Sathyaseelan, Chakkarai and Patro, L Ponoop Prasad and Rathinavelan, Thenmalarchelvi},
doi = {10.1016/j.patcog.2022.109134},
file = {:C$\backslash$:/Users/makro/Documents/Mendeley Desktop/Sathyaseelan, Patro, Rathinavelan - 2023 - Sequence patterns and HMM profiles to predict proteome wide zinc finger motifs.pdf:pdf},
issn = {00313203},
journal = {Pattern Recognition},
keywords = {Pfam HMM profile,Zinc finger classification,Zinc finger motif,Zinc finger prediction,Zinc finger proteome},
month = {mar},
pages = {109134},
title = {{Sequence patterns and HMM profiles to predict proteome wide zinc finger motifs}},
url = {https://www.sciencedirect.com/science/article/pii/S0031320322006148 https://linkinghub.elsevier.com/retrieve/pii/S0031320322006148},
volume = {135},
year = {2023}
}
@article{Lu2022,
abstract = {MIB2 (metal ion-binding) attempts to overcome the limitation of structure-based prediction approaches, with many proteins lacking a solved structure. MIB2 also offers more accurate prediction performance and more metal ion types.MIB2 utilizes both the (PS)2 method and the AlphaFold Protein Structure Database to acquire predicted structures to perform metal ion docking and predict binding residues. MIB2 offers marked improvements over MIB by collecting more MIB residue templates and using the metal ion type-specific scoring function. It offers a total of 18 types of metal ions for binding site predictions.Freely available on the web at http://bioinfo.cmu.edu.tw/MIB2/.Supplementary data are available at Bioinformatics online.},
author = {Lu, Chih-Hao and Chen, Chih-Chieh and Yu, Chin-Sheng and Liu, Yen-Yi and Liu, Jia-Jun and Wei, Sung-Tai and Lin, Yu-Feng},
doi = {10.1093/bioinformatics/btac534},
editor = {Martelli, Pier Luigi},
issn = {1367-4803},
journal = {Bioinformatics},
month = {sep},
number = {18},
pages = {4428--4429},
title = {{MIB2: metal ion-binding site prediction and modeling server}},
url = {https://doi.org/10.1093/bioinformatics/btac534 https://academic.oup.com/bioinformatics/article/38/18/4428/6651835},
volume = {38},
year = {2022}
}
@article{Srivastava2018,
abstract = {Zinc is one the most abundant catalytic cofactor and also an important structural component of a large number of metallo-proteins. Hence prediction of zinc metal binding sites in proteins can be a significant step in annotation of molecular function of a large number of proteins. Majority of existing methods for zinc-binding site predictions are based on a data-set of proteins, which has been compiled nearly a decade ago. Hence there is a need to develop zinc-binding site prediction system using the current updated data to include recently added proteins. Herein, we propose a support vector machine-based method, named as ZincBinder, for prediction of zinc metal-binding site in a protein using sequence profile information. The predictor was trained using fivefold cross validation approach and achieved 85.37{\%} sensitivity with 86.20{\%} specificity during training. Benchmarking on an independent non-redundant data-set, which was not used during training, showed better performance of ZincBinder vis-{\`{a}}-vis existing methods. Executable versions, source code, sample datasets, and usage instructions are available at http://proteininformatics.org/mkumar/znbinder/.},
author = {Srivastava, Abhishikha and Kumar, Manish},
doi = {10.1080/07391102.2017.1417910},
file = {:C$\backslash$:/Users/makro/Documents/Mendeley Desktop/Srivastava, Kumar - 2018 - Prediction of zinc binding sites in proteins using sequence derived information.pdf:pdf},
issn = {0739-1102},
journal = {Journal of Biomolecular Structure and Dynamics},
keywords = {PSSM,fivefold cross-validation,machine learning,support vector machine,zinc metal binding site},
month = {dec},
number = {16},
pages = {4413--4423},
pmid = {29241411},
publisher = {Taylor {\&} Francis},
title = {{Prediction of zinc binding sites in proteins using sequence derived information}},
url = {http://doi.org/10.1080/07391102.2017.1417910 https://www.tandfonline.com/doi/full/10.1080/07391102.2017.1417910},
volume = {36},
year = {2018}
}
@article{Yang2013,
abstract = {Motivation: Identification of protein--ligand binding sites is critical to protein function annotation and drug discovery. However, there is no method that could generate optimal binding site prediction for different protein types. Combination of complementary predictions is probably the most reliable solution to the problem.Results: We develop two new methods, one based on binding-specific substructure comparison (TM-SITE) and another on sequence profile alignment (S-SITE), for complementary binding site predictions. The methods are tested on a set of 500 non-redundant proteins harboring 814 natural, drug-like and metal ion molecules. Starting from low-resolution protein structure predictions, the methods successfully recognize {\textgreater}51{\%} of binding residues with average Matthews correlation coefficient (MCC) significantly higher (with P-value {\textless}10--9 in student t-test) than other state-of-the-art methods, including COFACTOR, FINDSITE and ConCavity. When combining TM-SITE and S-SITE with other structure-based programs, a consensus approach (COACH) can increase MCC by 15{\%} over the best individual predictions. COACH was examined in the recent community-wide COMEO experiment and consistently ranked as the best method in last 22 individual datasets with the Area Under the Curve score 22.5{\%} higher than the second best method. These data demonstrate a new robust approach to protein--ligand binding site recognition, which is ready for genome-wide structure-based function annotations.Availability:http://zhanglab.ccmb.med.umich.edu/COACH/Contact:zhng@umich.eduSupplementary information:Supplementary data are available at Bioinformatics online.},
author = {Yang, Jianyi and Roy, Ambrish and Zhang, Yang},
doi = {10.1093/bioinformatics/btt447},
issn = {1367-4803},
journal = {Bioinformatics},
month = {oct},
number = {20},
pages = {2588--2595},
title = {{Protein--ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment}},
url = {https://doi.org/10.1093/bioinformatics/btt447},
volume = {29},
year = {2013}
}
@article{McGuffin2019,
abstract = {The IntFOLD server provides a unified resource for the automated prediction of: protein tertiary structures with built-in estimates of model accuracy (EMA), protein structural domain boundaries, natively unstructured or disordered regions in proteins, and protein--ligand interactions. The component methods have been independently evaluated via the successive blind CASP experiments and the continual CAMEO benchmarking project. The IntFOLD server has established its ranking as one of the best performing publicly available servers, based on independent official evaluation metrics. Here, we describe significant updates to the server back end, where we have focused on performance improvements in tertiary structure predictions, in terms of global 3D model quality and accuracy self-estimates (ASE), which we achieve using our newly improved ModFOLD7{\_}rank algorithm. We also report on various upgrades to the front end including: a streamlined submission process, enhanced visualization of models, new confidence scores for ranking, and links for accessing all annotated model data. Furthermore, we now include an option for users to submit selected models for further refinement via convenient push buttons. The IntFOLD server is freely available at: http://www.reading.ac.uk/bioinf/IntFOLD/.},
author = {McGuffin, Liam J and Adiyaman, Recep and Maghrabi, Ali H A and Shuid, Ahmad N and Brackenridge, Danielle A and Nealon, John O and Philomina, Limcy S},
doi = {10.1093/nar/gkz322},
issn = {0305-1048},
journal = {Nucleic Acids Research},
month = {jul},
number = {W1},
pages = {W408--W413},
title = {{IntFOLD: an integrated web resource for high performance protein structure and function prediction}},
url = {https://doi.org/10.1093/nar/gkz322},
volume = {47},
year = {2019}
}
@article{Yu2013,
abstract = {Accurately identifying the protein-ligand binding sites or pockets is of significant importance for both protein function analysis and drug design. Although much progress has been made, challenges remain, especially when the 3D structures of target proteins are not available or no homology templates can be found in the library, where the template-based methods are hard to be applied. In this paper, we report a new ligand-specific template-free predictor called TargetS for targeting protein-ligand binding sites from primary sequences. TargetS first predicts the binding residues along the sequence with ligand-specific strategy and then further identifies the binding sites from the predicted binding residues through a recursive spatial clustering algorithm. Protein evolutionary information, predicted protein secondary structure, and ligand-specific binding propensities of residues are combined to construct discriminative features; an improved AdaBoost classifier ensemble scheme based on random undersampling is proposed to deal with the serious imbalance problem between positive (binding) and negative (nonbinding) samples. Experimental results demonstrate that TargetS achieves high performances and outperforms many existing predictors. TargetS web server and data sets are freely available at: http://www.csbio.sjtu.edu.cn/bioinf/TargetS/for academic use. {\textcopyright} 2013 IEEE.},
author = {Yu, Dong-Jun and Hu, Jun and Yang, Jing and Shen, Hong-Bin and Tang, Jinhui and Yang, Jing-Yu},
doi = {10.1109/TCBB.2013.104},
file = {:C$\backslash$:/Users/makro/Documents/Mendeley Desktop/Yu et al. - 2013 - Designing Template-Free Predictor for Targeting Protein-Ligand Binding Sites with Classifier Ensemble and Spatial Clu.pdf:pdf},
issn = {1545-5963},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
keywords = {Protein-ligand binding sites,classifier ensemble,ligand-specific prediction model,spatial clustering,template-free},
month = {jul},
number = {4},
pages = {994--1008},
pmid = {24334392},
title = {{Designing Template-Free Predictor for Targeting Protein-Ligand Binding Sites with Classifier Ensemble and Spatial Clustering}},
url = {http://ieeexplore.ieee.org/document/6583160/},
volume = {10},
year = {2013}
}