| Title |
MaxMod: a hidden Markov model based novel interface to MODELLER for improved prediction of protein 3D models
|
|---|---|
| Published in |
Journal of Molecular Modeling, January 2015
|
| DOI | 10.1007/s00894-014-2563-3 |
| Pubmed ID | |
| Authors |
Bikram K. Parida, Prasanna K. Panda, Namrata Misra, Barada K. Mishra |
| Abstract |
Modeling the three-dimensional (3D) structures of proteins assumes great significance because of its manifold applications in biomolecular research. Toward this goal, we present MaxMod, a graphical user interface (GUI) of the MODELLER program that combines profile hidden Markov model (profile HMM) method with Clustal Omega program to significantly improve the selection of homologous templates and target-template alignment for construction of accurate 3D protein models. MaxMod distinguishes itself from other existing GUIs of MODELLER software by implementing effortless modeling of proteins using templates that bear modified residues. Additionally, it provides various features such as loop optimization, express modeling (a feature where protein model can be generated directly from its sequence, without any further user intervention) and automatic update of PDB database, thus enhancing the user-friendly control of computational tasks. We find that HMM-based MaxMod performs better than other modeling packages in terms of execution time and model quality. MaxMod is freely available as a downloadable standalone tool for academic and non-commercial purpose at http://www.immt.res.in/maxmod/ . |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 3 | 16% |
| Professor | 2 | 11% |
| Student > Ph. D. Student | 2 | 11% |
| Student > Bachelor | 1 | 5% |
| Student > Master | 1 | 5% |
| Other | 2 | 11% |
| Unknown | 8 | 42% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 11% |
| Computer Science | 2 | 11% |
| Agricultural and Biological Sciences | 1 | 5% |
| Chemistry | 1 | 5% |
| Materials Science | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 11 | 58% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 September 2017.
All research outputs
#7,208,880
of 22,785,242 outputs
Outputs from Journal of Molecular Modeling
#173
of 813 outputs
Outputs of similar age
#101,753
of 353,099 outputs
Outputs of similar age from Journal of Molecular Modeling
#1
of 16 outputs
Altmetric has tracked 22,785,242 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 813 research outputs from this source. They receive a mean Attention Score of 2.7. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 353,099 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.