K
Kevin Karplus
Researcher at University of California, Santa Cruz
Publications - 67
Citations - 21578
Kevin Karplus is an academic researcher from University of California, Santa Cruz. The author has contributed to research in topics: Protein structure prediction & Hidden Markov model. The author has an hindex of 37, co-authored 66 publications receiving 18819 citations. Previous affiliations of Kevin Karplus include Cornell University & Stanford University.
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Journal ArticleDOI
Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega
Fabian Sievers,Andreas Wilm,David Dineen,Toby J. Gibson,Kevin Karplus,Weizhong Li,Rodrigo Lopez,Hamish McWilliam,Michael Remmert,Johannes Söding,Julie D. Thompson,Desmond G. Higgins +11 more
TL;DR: A new program called Clustal Omega is described, which can align virtually any number of protein sequences quickly and that delivers accurate alignments, and which outperforms other packages in terms of execution time and quality.
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Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure.
TL;DR: A new procedure is described for detecting and correcting those errors that arise at the model-building stage of the procedure and a good procedure for creating HMMs for sequences of proteins of known structure are determined.
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Hidden Markov models for detecting remote protein homologies.
TL;DR: A new hidden Markov model method (SAM-T98) for finding remote homologs of protein sequences is described and evaluated, which is optimized to recognize superfamilies, and would require parameter adjustment to be used to find family or fold relationships.
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Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8.
Elmar Krieger,Keehyoung Joo,Jinwoo Lee,Jooyoung Lee,Srivatsan Raman,James Thompson,Michael D. Tyka,David Baker,Kevin Karplus +8 more
TL;DR: In this article, the authors describe four approaches that address the last mile of the protein folding problem and have performed well during CASP8, yielding physically realistic models: YASARA, which runs molecular dynamics simulations of models in explicit solvent, using a new partly knowledge-based all atom force field derived from Amber.
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Automated forward and reverse ratcheting of DNA in a nanopore at 5-A precision
Gerald M. Cherf,Kate R. Lieberman,Hytham Rashid,Christopher Evan Lam,Kevin Karplus,Mark Akeson +5 more
TL;DR: Forward and reverse ratcheting of DNA templates through the α-hemolysin nanopore controlled by phi29 DNA polymerase without the need for active voltage control facilitates multiple reads of individual strands and is transferable to other nanopore devices for implementation of DNA sequence analysis.