Modeling literature digest – it’s back.. | Macromolecular Modeling Blog ™

Nov
07
2009

Modeling literature digest – it’s back..

After a long break, the molecular modeling literature digest is back with ~50 recent publication which we believe would be of interest. As usual you are most welcome to add relevant publications or suggest specific papers for us to focus on.

Computational design of second-site suppressor mutations at protein-protein interfaces from Proteins: Structure, Function, and Bioinformatics by Deanne W. Sammond, Ziad M. Eletr, Carrie Purbeck, Brian Kuhlman
Computational exploration of the network of sequence flow between protein structures from Proteins: Structure, Function, and Bioinformatics by Baoqiang Cao, Ron Elber
Molecular dynamics of leucine and dopamine transporter proteins in a model cell membrane lipid bilayer from Proteins: Structure, Function, and Bioinformatics by Patrick C. Gedeon, Martín Indarte, Christopher K. Surratt, Jeffry D. Madura
Multi-scale characterization of the energy landscape of proteins with application to the C3D/Efb-C complex from Proteins: Structure, Function, and Bioinformatics by Nurit Haspel, Brian V. Geisbrecht, John Lambris, Lydia Kavraki
A novel approach to segregate and identify functional loop regions in protein structures using their Ramachandran maps from Proteins: Structure, Function, and Bioinformatics by Mattaparthi Venkata Satish Kumar, Rajaram Swaminathan
Open and closed conformations reveal induced fit movements in butyrate kinase 2 activation from Proteins: Structure, Function, and Bioinformatics by Jiasheng Diao, Yunglin D. Ma, Miriam S. Hasson
A novel and efficient tool for locating and characterizing protein cavities and binding sites from Proteins: Structure, Function, and Bioinformatics by Ashutosh Tripathi, Glen E. Kellogg
A binding free energy decomposition approach for accurate calculations of the fidelity of DNA polymerases from Proteins: Structure, Function, and Bioinformatics by Robert Rucker, Peter Oelschlaeger, Arieh Warshel
Design of multispecific protein sequences using probabilistic graphical modeling from Proteins: Structure, Function, and Bioinformatics by Menachem Fromer, Chen Yanover, Michal Linial
Quantifying the evolutionary divergence of protein structures: The role of function change and function conservation from Proteins: Structure, Function, and Bioinformatics by Alberto Pascual-García, David Abia, Raúl Méndez, Gonzalo S. Nido, Ugo Bastolla
Protein flexibility from discrete molecular dynamics simulations using quasi-physical potentials from Proteins: Structure, Function, and Bioinformatics by Agustí Emperador, Tim Meyer, Modesto Orozco
Modeling the functional consequences of single residue replacements in bacteriophage f1 gene V protein from Protein Engineering Design and Selection – current issue by Masso, M., Mathe, E., Parvez, N., Hijazi, K., Vaisman, I. I.
Intrinsic Structural Disorder Confers Cellular Viability on Oncogenic Fusion Proteins from PLoS Computational Biology: New Articles by Hedi Hegyi et al.
The Role of Medical Structural Genomics in Discovering New Drugs for Infectious Diseases from PLoS Computational Biology: New Articles by Wesley C. Van Voorhis et al.
An Atlas of the Thioredoxin Fold Class Reveals the Complexity of Function-Enabling Adaptations from PLoS Computational Biology: New Articles by Holly J. Atkinson et al.
Perturbation-Response Scanning Reveals Ligand Entry-Exit Mechanisms of Ferric Binding Protein from PLoS Computational Biology: New Articles by Canan Atilgan et al.
High-Performance Drug Discovery: Computational Screening by Combining Docking and Molecular Dynamics Simulations from PLoS Computational Biology: New Articles by Noriaki Okimoto et al.
Structure-Based Predictive Models for Allosteric Hot Spots from PLoS Computational Biology: New Articles by Omar N. A. Demerdash et al.
VENN, a tool for titrating sequence conservation onto protein structures from Nucleic Acids Research – current issue by Vyas, J., Gryk, M. R., Schiller, M. R.
Crystal structure of human selenocysteine tRNA from Nucleic Acids Research – current issue by Itoh, Y., Chiba, S., Sekine, S.-i., Yokoyama, S.
Partially-supervised protein subclass discovery with simultaneous annotation of functional residues from BMC Structural Biology – Latest articles by Alexander Schliep
Machine learning integration for predicting the effect of single amino acid substitutions on protein stability from BMC Structural Biology – Latest articles by Turkan Haliloglu
RNA folding on the 3D triangular lattice from BMC Bioinformatics – Latest articles by Minghui Jiang
(PS)2-v2: template-based protein structure prediction server from BMC Bioinformatics – Latest articles by Jinn-Moon Yang
Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods from BMC Bioinformatics – Latest articles by David Jones
A structure filter for the Eukaryotic Linear Motif Resource from BMC Bioinformatics – Latest articles by Manuela Helmer-Citterich
Exploiting structural and topological information to improve prediction of RNA-protein binding sites from BMC Bioinformatics – Latest articles by Zheng Yuan
Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks from BMC Bioinformatics – Latest articles by Rasmus Fonseca
Clustering of protein domains for functional and evolutionary studies from BMC Bioinformatics – Latest articles by John Cullum
Understanding hydrogen-bond patterns in proteins using network motifs from Bioinformatics – current issue by Rahat, O., Alon, U., Levy, Y., Schreiber, G.
Algorithms for optimal protein structure alignment from Bioinformatics – current issue by Poleksic, A.
3D-SURFER: software for high-throughput protein surface comparison and analysis from Bioinformatics – current issue by La, D., Esquivel-Rodriguez, J., Venkatraman, V., Li, B., Sael, L., Ueng, S., Ahrendt, S., Kihara, D.
Enhancement of beta-sheet assembly by cooperative hydrogen bonds potential from Bioinformatics – current issue by Levy-Moonshine, A., Amir, E.-a. D., Keasar, C.
MISTRAL: a tool for energy-based multiple structural alignment of proteins from Bioinformatics – current issue by Micheletti, C., Orland, H.
PiSQRD: a web server for decomposing proteins into quasi-rigid dynamical domains from Bioinformatics – current issue by Aleksiev, T., Potestio, R., Pontiggia, F., Cozzini, S., Micheletti, C.
ANCHOR: web server for predicting protein binding regions in disordered proteins from Bioinformatics – current issue by Dosztanyi, Z., Meszaros, B., Simon, I.
Solution and crystal molecular dynamics simulation study of m4-cyanovirin-N mutants complexed with di-mannose. from Biophys J. by Vorontsov II, Miyashita O.
Protein stabilization and the hofmeister effect: the role of hydrophobic solvation from Biophys J. by Tadeo X, López-Méndez B, Castaño D, Trigueros T, Millet O.
Exploitation of binding energy for catalysis and design from Nature by Thyme SB, Jarjour J, Takeuchi R, Havranek JJ, Ashworth J, Scharenberg AM, Stoddard BL, Baker D.
How do transcription factors select specific binding sites in the genome? from NSMB by Yongping Pan, Chung-Jung Tsai, Buyong Ma and Ruth Nussino
Defining coarse-grained representations of large biomolecules and biomolecular complexes from elastic network models. from Biophys J. by Zhang Z, Pfaendtner J, Grafmüller A, Voth GA.
Conformation Dependence of Backbone Geometry in Proteins from STRUCTURE by Donald S. Berkholz, Maxim V. Shapovalov, Roland L. Dunbrack, P. Andrew Karplus.
OMP Peptides Activate the DegS Stress-Sensor Protease by a Relief of Inhibition Mechanism from STRUCTURE by Jungsan Sohn, Robert A. Grant, Robert T. Sauer.
Rational Design and biophysical characterization of Thioredoxin-based aptamers: Insights into peptide grafting from JMB by Christopher J., Brown , Shubhra Ghosh, Dastidar , Hai Yun, See , David W., Coomber , Miguel, Ortiz-Lombardía , Chandra Verma and David P. Lane
Evolution of Protein Binding Modes in Homooligomers from JMB by Judith E., Dayhoff , Benjamin A., Shoemaker , Stephen H., Bryant , Anna R., Panchenko
Exploring linkage dependence of polyubiquitin conformations using molecular modeling from JMB by David, Fushman , Olivier, Walker
All-atom Monte Carlo approach to protein-peptide binding from JMB by Iskra, Staneva , Stefan, Wallin
Exploring conformational modes of macromolecular assemblies by multiparticle cryo-EM from Curr. op. in Struct. Biol. by Christian MT, Spahn , Pawel A, Penczek
Stability effects of mutations and protein evolvability from Curr. op. in Struct. Biol. by Nobuhiko, Tokuriki , Dan S, Tawfik

Be Sociable, Share!

Written by Nir London in: Title Madness | Tags: Literature digest

Literature Digest

Enjoyed this Post ?

Subscribe in a reader. Follow us on twitter.

http://cowsandmilk.net David Hall

On Andrew Karplus’s paper, I would find any thoughts of what Rosetta people thought of Figure 9 and whether it would mean using his library is worthwhile during minimization. He indicates that the main improvements came from modeling of loops, particularly around residue 60, where the 2 chains in the crystal show a larger difference than Karplus’s model does to chain a.

Since loops are so dynamic, I find most modeling software has put a lot of effort into ignoring them and getting good results no matter what the conformation of the loop. So, I question the need for this if it only improves loops. But having the loops in the right place never hurts. And computationally, you can decide to turn it on only in non-loop regions and avoid the table look ups if you declare a region structured (although that declaration might be as expensive as the table look up, both looking at the dihedrals).

So, if you guys want to talk about that paper, it would be wonderful. Modeling backbones is something I’ve tried hard to avoid beyond just telling CHARMM to minimize a docked structure, so I really don’t know what I’m talking about. Also, his Figure 9A looks nice, but I’m used to Rosetta papers where I see a whole bunch of graphs like that, and there are always really beautiful cases, but also some crappy ones. Only having one graph didn’t leave me with much belief. I’m not sure why similar figures for 1r6j didn’t make the supplemental information.
http://departments.agri.huji.ac.il/biochemfoodsci722/teachers/niv_masha/index.htm Masha Niv

Hey,
great to have your literature digest back
If you find PiSQRD of interest, I would like to point out our BlockMaster method,recently published in:
J Phys Chem A. 2009 Jul 2;113(26):7528-34
“BlockMaster: partitioning protein kinase structures using normal-mode analysis.”
Shudler M, Niv MY.
Notably, BlockMaster does not force all atoms into rigid blocs, but rather identifies some rigid blocks and leaves other parts of the protein fully flexible.
We don’t have a server yet (but then again PiSQRD died when we tried it..)
http://departments.agri.huji.ac.il/biochemfoodsci722/teachers/niv_masha/index.htm Masha Niv

correction – tried PiSQRD again – it worked
http://dberkholz.wordpress.com/ Donnie Berkholz

I’m glad you enjoyed our paper “Conformation Dependence of Backbone Geometry in Proteins” and think it’s worth recommending to others!
http://dberkholz.wordpress.com/ Donnie Berkholz

David: I agree that putting a lot of effort into finding a single “perfect” conformation for loops that are truly dynamic makes little sense. The key distinction is discriminating between dynamic (disordered) and static (well-ordered) loops before spending a lot of time coming up with a single conformation that only makes sense for the latter. This could be as easy as passing the sequence through a consensus-based disorder prediction.

You’re correct that Fig. 9A is only one example. That’s why in the preceding section of the paper, we also refolded 100+ atomic-resolution structures using conformation-dependent geometry or EH geometry. Conformation-dependent geometry improved the normalized median RMSDs from native structures by nearly 0.5 A. Of course this is medians we’re talking about, and there is a distribution in reality, where some structures improve very little and others improve far more.
http://www.rosettadesigngroup.com Nir London

@David, regarding figure 9, I personally conducted the same experiment several times (using native bond length and angles instead of ideal ones) and indeed it improves both the modeling and the score – I believe this is due to Rosetta’s sensitivity to clashes.
@ Donnie – on this point, I think the real test would be to use the CDL values and compare them to modeling with either native or ideal. Since this would take some implementation in the code I suggest you use PyRosetta to instantiate a model with the CDL values – output it and trick Rosetta to read it as native for the modeling. I wonder if that would improve modeling over ideal values… (hope it will : )

Donnie has graciously agreed to post a summary of this interesting and important work in our blog, as soon as it comes out I’ll be happy to engage in an open debate about it. I believe improving the naive approach to bond length and angles would be key to several modeling tasks!
http://www.rosettadesigngroup.com Nir London

@Masha – would you also pick up the glove and post about BlockMaster ?