A citation-based method for searching scientific literature

Carlos H M Rodrigues, Douglas E V Pires, David B Ascher. Protein Sci 2021
Times Cited: 69







List of co-cited articles
394 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


mCSM: predicting the effects of mutations in proteins using graph-based signatures.
Douglas E V Pires, David B Ascher, Tom L Blundell. Bioinformatics 2014
488
46


DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability.
Carlos Hm Rodrigues, Douglas Ev Pires, David B Ascher. Nucleic Acids Res 2018
417
34

mCSM-PPI2: predicting the effects of mutations on protein-protein interactions.
Carlos H M Rodrigues, Yoochan Myung, Douglas E V Pires, David B Ascher. Nucleic Acids Res 2019
127
30

Highly accurate protein structure prediction with AlphaFold.
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko,[...]. Nature 2021
27

SDM: a server for predicting effects of mutations on protein stability.
Arun Prasad Pandurangan, Bernardo Ochoa-Montaño, David B Ascher, Tom L Blundell. Nucleic Acids Res 2017
248
23


mCSM-membrane: predicting the effects of mutations on transmembrane proteins.
Douglas E V Pires, Carlos H M Rodrigues, David B Ascher. Nucleic Acids Res 2020
32
40

I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure.
Emidio Capriotti, Piero Fariselli, Rita Casadio. Nucleic Acids Res 2005
988
17

mCSM-NA: predicting the effects of mutations on protein-nucleic acids interactions.
Douglas E V Pires, David B Ascher. Nucleic Acids Res 2017
69
17

mmCSM-AB: guiding rational antibody engineering through multiple point mutations.
Yoochan Myung, Douglas E V Pires, David B Ascher. Nucleic Acids Res 2020
22
54

The Protein Data Bank.
H M Berman, J Westbrook, Z Feng, G Gilliland, T N Bhat, H Weissig, I N Shindyalov, P E Bourne. Nucleic Acids Res 2000
17

mCSM-AB2: guiding rational antibody design using graph-based signatures.
Yoochan Myung, Carlos H M Rodrigues, David B Ascher, Douglas E V Pires. Bioinformatics 2020
24
45


The FoldX web server: an online force field.
Joost Schymkowitz, Jesper Borg, Francois Stricher, Robby Nys, Frederic Rousseau, Luis Serrano. Nucleic Acids Res 2005
15

CSM-lig: a web server for assessing and comparing protein-small molecule affinities.
Douglas E V Pires, David B Ascher. Nucleic Acids Res 2016
76
14


Arpeggio: A Web Server for Calculating and Visualising Interatomic Interactions in Protein Structures.
Harry C Jubb, Alicia P Higueruelo, Bernardo Ochoa-Montaño, Will R Pitt, David B Ascher, Tom L Blundell. J Mol Biol 2017
193
13



ThermoMutDB: a thermodynamic database for missense mutations.
Joicymara S Xavier, Thanh-Binh Nguyen, Malancha Karmarkar, Stephanie Portelli, Pâmela M Rezende, João P L Velloso, David B Ascher, Douglas E V Pires. Nucleic Acids Res 2021
21
42


The mutational constraint spectrum quantified from variation in 141,456 humans.
Konrad J Karczewski, Laurent C Francioli, Grace Tiao, Beryl B Cummings, Jessica Alföldi, Qingbo Wang, Ryan L Collins, Kristen M Laricchia, Andrea Ganna, Daniel P Birnbaum,[...]. Nature 2020
13


dendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction.
Lisa M Kaminskas, Douglas E V Pires, David B Ascher. Sci Rep 2019
24
33

Understanding molecular consequences of putative drug resistant mutations in Mycobacterium tuberculosis.
Stephanie Portelli, Jody E Phelan, David B Ascher, Taane G Clark, Nicholas Furnham. Sci Rep 2018
40
20

DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks.
Huali Cao, Jingxue Wang, Liping He, Yifei Qi, John Z Zhang. J Chem Inf Model 2019
68
11

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.
Stephanie Portelli, Yoochan Myung, Nicholas Furnham, Sundeep Chaitanya Vedithi, Douglas E V Pires, David B Ascher. Sci Rep 2020
14
57

MAESTROweb: a web server for structure-based protein stability prediction.
Josef Laimer, Julia Hiebl-Flach, Daniel Lengauer, Peter Lackner. Bioinformatics 2016
69
11

Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine.
Tiziana Sanavia, Giovanni Birolo, Ludovica Montanucci, Paola Turina, Emidio Capriotti, Piero Fariselli. Comput Struct Biotechnol J 2020
41
19

MAESTRO--multi agent stability prediction upon point mutations.
Josef Laimer, Heidi Hofer, Marko Fritz, Stefan Wegenkittl, Peter Lackner. BMC Bioinformatics 2015
132
11

DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations.
Ludovica Montanucci, Emidio Capriotti, Yotam Frank, Nir Ben-Tal, Piero Fariselli. BMC Bioinformatics 2019
30
23



Exploring the structural distribution of genetic variation in SARS-CoV-2 with the COVID-3D online resource.
Stephanie Portelli, Moshe Olshansky, Carlos H M Rodrigues, Elston N D'Souza, Yoochan Myung, Michael Silk, Azadeh Alavi, Douglas E V Pires, David B Ascher. Nat Genet 2020
33
21

Systematic Investigation of the Data Set Dependency of Protein Stability Predictors.
Octav Caldararu, Rukmankesh Mehra, Tom L Blundell, Kasper P Kepp. J Chem Inf Model 2020
20
35

Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated?
Sirawit Ittisoponpisan, Suhail A Islam, Tarun Khanna, Eman Alhuzimi, Alessia David, Michael J E Sternberg. J Mol Biol 2019
164
10

Comparative Protein Structure Modeling Using MODELLER.
Benjamin Webb, Andrej Sali. Curr Protoc Bioinformatics 2016
10

SDM--a server for predicting effects of mutations on protein stability and malfunction.
Catherine L Worth, Robert Preissner, Tom L Blundell. Nucleic Acids Res 2011
309
10

Quantification of biases in predictions of protein stability changes upon mutations.
Fabrizio Pucci, Katrien V Bernaerts, Jean Marc Kwasigroch, Marianne Rooman. Bioinformatics 2018
61
11

PremPS: Predicting the impact of missense mutations on protein stability.
Yuting Chen, Haoyu Lu, Ning Zhang, Zefeng Zhu, Shuqin Wang, Minghui Li. PLoS Comput Biol 2020
46
15

Basic local alignment search tool.
S F Altschul, W Gish, W Miller, E W Myers, D J Lipman. J Mol Biol 1990
10

SIFT: Predicting amino acid changes that affect protein function.
Pauline C Ng, Steven Henikoff. Nucleic Acids Res 2003
8

Structural Implications of Mutations Conferring Rifampin Resistance in Mycobacterium leprae.
Sundeep Chaitanya Vedithi, Sony Malhotra, Madhusmita Das, Sheela Daniel, Nanda Kishore, Anuja George, Shantha Arumugam, Lakshmi Rajan, Mannam Ebenezer, David B Ascher,[...]. Sci Rep 2018
32
18

Structure guided prediction of Pyrazinamide resistance mutations in pncA.
Malancha Karmakar, Carlos H M Rodrigues, Kristy Horan, Justin T Denholm, David B Ascher. Sci Rep 2020
32
18

Empirical ways to identify novel Bedaquiline resistance mutations in AtpE.
Malancha Karmakar, Carlos H M Rodrigues, Kathryn E Holt, Sarah J Dunstan, Justin Denholm, David B Ascher. PLoS One 2019
29
20

Germline Mutations in the CDKN2B Tumor Suppressor Gene Predispose to Renal Cell Carcinoma.
Mariam Jafri, Naomi C Wake, David B Ascher, Douglas E V Pires, Dean Gentle, Mark R Morris, Eleanor Rattenberry, Michael A Simpson, Richard C Trembath, Astrid Weber,[...]. Cancer Discov 2015
71
8

Bio3d: an R package for the comparative analysis of protein structures.
Barry J Grant, Ana P C Rodrigues, Karim M ElSawy, J Andrew McCammon, Leo S D Caves. Bioinformatics 2006
955
8

A method and server for predicting damaging missense mutations.
Ivan A Adzhubei, Steffen Schmidt, Leonid Peshkin, Vasily E Ramensky, Anna Gerasimova, Peer Bork, Alexey S Kondrashov, Shamil R Sunyaev. Nat Methods 2010
8

Potent hepatitis C inhibitors bind directly to NS5A and reduce its affinity for RNA.
David B Ascher, Jerome Wielens, Tracy L Nero, Larissa Doughty, Craig J Morton, Michael W Parker. Sci Rep 2014
83
8


Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.