Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Brajesh Kumar Rai

dblp:08/4827 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
protein structure prediction
0.122007
Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments · Bioinform. 2007
MMM: a sequence-to-structure alignment protocol · Bioinform. 2006
Bioinformatics and computational biology › structural bioinformatics › protein modeling
comparative protein structure modeling
0.112007
Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments · Bioinform. 2007
Bioinformatics and computational biology › protein structure prediction › template-based modeling
homology modeling
0.112006
MMM: a sequence-to-structure alignment protocol · Bioinform. 2006
Bioinformatics and computational biology › structural bioinformatics
sequence-structure alignment
0.012007
Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments · Bioinform. 2007

Methods — techniques the papers use, named apart from their topics

template combination · 0.1iterative clustering · 0.1profile-to-profile alignment · 0.1alignment optimization · 0.1
YearPublicationVenuePosition
2007 Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments
abstract
MOTIVATION: Two major bottlenecks in advancing comparative protein structure modeling are the efficient combination of multiple template structures and the generation of a correct input target-template alignment. RESULTS: A novel method, Multiple Mapping Method with Multiple Templates (M4T) is introduced that implements an algorithm to automatically select and combine Multiple Template structures (MT) and an alignment optimization protocol (Multiple Mapping Method, MMM). The MT module of M4T selects and combines multiple template structures through an iterative clustering approach that takes into account the 'unique' contribution of each template, their sequence similarity among themselves and to the target sequence, and their experimental resolution. MMM is a sequence-to-structure alignment method that optimally combines alternatively aligned regions according to their fit in the structural environment of the template structure. The resulting M4T alignment is used as input to a comparative modeling module. The performance of M4T has been benchmarked on CASP6 comparative modeling target sequences and on a larger independent test set, and showed favorable performance to current state of the art methods.
Narcis Fernandez-Fuentes, Brajesh Kumar Rai, Carlos J. Madrid-Aliste, J. Eduardo Fajardo, András Fiser
Bioinform.2
2006 MMM: a sequence-to-structure alignment protocol
abstract
MOTIVATION: Accurate alignment of a target sequence to a template structure continues to be a bottleneck in producing good quality comparative protein structure models. RESULTS: Multiple Mapping Method (MMM) is a comparative protein structure modeling server with an emphasis on a novel alignment optimization protocol. MMM takes inputs from five profile-to-profile based alignment methods. The alternatively aligned regions from the input alignment set are combined according to their fit in the structural environment of the template structure. The resulting, optimally spliced MMM alignment is used as input to an automated comparative modeling module to produce a full atom model. AVAILABILITY: The MMM server is freely accessible at http://www.fiserlab.org/servers/mmm
Brajesh Kumar Rai, Carlos J. Madrid-Aliste, J. Eduardo Fajardo, András Fiser
Bioinform.1