Macromolecular Modeling Blog ™

Did we miss anything? Know of an interesting paper that just got out? Have you read any of these? Tell us in the comments.

Bi-Weekly Digest 22/02/09

Bi-Weekly Digest 31/01/09

Bi-Weekly Digest 08/01/09

Bi-Weekly Digest 27/11/08

Bi-Weekly Digest 08/11/08

Weekly Digest 12/10/08

Older:

Crystal structure of opsin in its G-protein-interacting conformation.
Scheerer P, Park JH, Hildebrand PW, Kim YJ, Krauss N, Choe HW, Hofmann KP, Ernst OP.

Assembling materials with DNA as the guide.
Aldaye FA, Palmer AL, Sleiman HF.

Proteins. 2008 Sep 24;
Improving NMR protein structure quality by Rosetta refinement: A molecular replacement study.
Ramelot TA, Raman S, Kuzin AP, Xiao R, Ma LC, Acton TB, Hunt JF, Montelione GT, Baker D, Kennedy MA.

A double S shape provides the structural basis for the extraordinary binding specificity of Dscam isoforms.

Sawaya MR, Wojtowicz WM, Andre I, Qian B, Wu W, Baker D, Eisenberg D, Zipursky SL.

Howard Hughes Medical Institute, UCLA-DOE Institute of Genomics and Proteomics, Los Angeles, CA 90095, USA.

Drosophila Dscam encodes a vast family of immunoglobulin (Ig)-containing proteins that exhibit isoform-specific homophilic binding. This diversity is essential for cell recognition events required for wiring the brain. Each isoform binds to itself but rarely to other isoforms. Specificity is determined by “matching” of three variable Ig domains within an approximately 220 kD ectodomain. Here, we present the structure of the homophilic binding region of Dscam, comprising the eight N-terminal Ig domains (Dscam(1-8)). Dscam(1-8) forms a symmetric homodimer of S-shaped molecules. This conformation, comprising two reverse turns, allows each pair of the three variable domains to “match” in an antiparallel fashion. Structural, genetic, and biochemical studies demonstrate that, in addition to variable domain “matching,” intramolecular interactions between constant domains promote homophilic binding. These studies provide insight into how “matching” at all three pairs of variable domains in Dscam mediates isoform-specific recognition.

Experimental and Computational Analyses of the Energetic Basis for Dual Recognition of Immunity Proteins by Colicin Endonucleases.

Keeble AH, Joachimiak LA, Mate MJ, Meenan N, Kirkpatrick N, Baker D, Kleanthous C.

Crystal structure of squid rhodopsin.

Murakami M, Kouyama T.

Modest stabilization by most hydrogen-bonded side-chain interactions in membrane proteins.

Joh NH, Min A, Faham S, Whitelegge JP, Yang D, Woods VL, Bowie JU.

Design of Protein-Ligand Binding Based on the Molecular-Mechanics Energy Model.

Boas FE, Harbury PB.

A Simple Model of Backbone Flexibility Improves Modeling of Side-chain Conformational Variability.

Friedland GD, Linares AJ, Smith CA, Kortemme T.

Backrub-Like Backbone Simulation Recapitulates Natural Protein Conformational Variability and Improves Mutant Side-Chain Prediction.

Smith CA, Kortemme T.

Modest membrane hydrogen bonds deliver rich results.

Grigoryan G, Degrado WF.

Accurate solution of multi-region continuum biomolecule electrostatic problems using the linearized Poisson-Boltzmann equation with curved boundary elements.

Altman MD, Bardhan JP, White JK, Tidor B.

Assembly reflects evolution of protein complexes.

Levy ED, Erba EB, Robinson CV, Teichmann SA.

Crystal structure of the ligand-free G-protein-coupled receptor opsin.

Park JH, Scheerer P, Hofmann KP, Choe HW, Ernst OP.

The RosettaDock server for local protein-protein docking.

Lyskov S, Gray JJ.

Structure of a beta(1)-adrenergic G-protein-coupled receptor.

Warne T, Serrano-Vega MJ, Baker JG, Moukhametzianov R, Edwards PC, Henderson R, Leslie AG, Tate CG, Schertler GF.

The interplay of functional tuning, drug resistance, and thermodynamic stability in the evolution of the m2 proton channel from the influenza a virus.

Stouffer AL, Ma C, Cristian L, Ohigashi Y, Lamb RA, Lear JD, Pinto LH, Degrado WF.

Protein-protein interactions in the membrane: sequence, structural, and biological motifs.

Moore DT, Berger BW, Degrado WF.

Structure of the Ebola virus glycoprotein bound to an antibody from a human survivor.

Lee JE, Fusco ML, Hessell AJ, Oswald WB, Burton DR, Saphire EO.

Conformer Selection and Induced Fit in Flexible Backbone Protein-Protein Docking Using Computational and NMR Ensembles.

Chaudhury S, Gray JJ.

Protein folding and design: from simple models to complex systems.

Regan L, Woolfson DN.

Synthetic biology through biomolecular design and engineering.

Channon K, Bromley EH, Woolfson DN.

Evaluating and optimizing computational protein design force fields using fixed composition-based negative design.

Alvizo O, Mayo SL.

MagicWand: A Single, Designed Peptide That Assembles to Stable, Ordered alpha-Helical Fibers.

Gribbon C, Channon KJ, Zhang W, Banwell EF, Bromley EH, Chaudhuri JB, Oreffo RO, Woolfson DN.

Bioinformatics:

• FlexStem: improving predictions of RNA secondary structures with pseudoknots by reducing the search space. Xiang Chen, Si-Min He, Dongbo Bu, Fa Zhang, Zhiyong Wang, Runsheng Chen, and Wen Gao.
• Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis. Majid Masso and Iosif I. Vaisman.
• MetalDetector: a web server for predicting metal-binding sites and disulfide bridges in proteins from sequence. Marco Lippi, Andrea Passerini, Marco Punta, Burkhard Rost, and Paolo Frasconi.
• HELIQUEST: a web server to screen sequences with specific {alpha}-helical properties. Romain Gautier, Dominique Douguet, Bruno Antonny, and Guillaume Drin.

Plos Computational Biology:

• Identification and Rational Redesign of Peptide Ligands to CRIP1, A Novel Biomarker for Cancers. Jihua Hao, Adrian W. R. Serohijos, Gail Newton, Gina Tassone, Zuncai Wang, Dennis C. Sgroi, Nikolay V. Dokholyan, James P. Basilion.
• A Feature-Based Approach to Modeling Protein–DNA Interactions. Eilon Sharon, Shai Lubliner, Eran Segal.
• Classifying RNA-Binding Proteins Based on Electrostatic Properties. Shula Shazman, Yael Mandel-Gutfreund.

• Principles of flexible protein-protein docking. Nelly Andrusier, Efrat Mashiach, Ruth Nussinov, Haim J. Wolfson.
• HingeMaster: Normal mode hinge prediction approach and integration of complementary predictors. Samuel C. Flores, Kevin S. Keating, Jay Painter, Faruck Morcos, Khang Nguyen, Ethan A. Merritt, Leslie A. Kuhn, Mark B. Gerstein.
• Constructing templates for protein structure prediction by simulation of protein folding pathways. Ilona Kifer, Ruth Nussinov, Haim J. Wolfson.
• Highly accurate method for ligand-binding site prediction in unbound state (apo) protein structures. Mizuki Morita, Shugo Nakamura, Kentaro Shimizu.
• A molecular dynamics approach to study the importance of solvent in protein interactions. Sergey Samsonov, Joan Teyra, M. Teresa Pisabarro.

Structure:

• Ab initio folding of proteins with all-atom discrete molecular dynamics. Ding F, Tsao D, Nie H, Dokholyan NV.
• Dynamic properties of a type II cadherin adhesive domain: implications for the mechanism of strand-swapping of classical cadherins. Miloushev VZ, Bahna F, Ciatto C, Ahlsen G, Honig B, Shapiro L, Palmer AG 3rd.
• Iterative assembly of helical proteins by optimal hydrophobic packing. Wu GA, Coutsias EA, Dill KA.

NAR:

• A protein-DNA docking benchmark. van Dijk M, Bonvin AM.

Nature:

• Assembly reflects evolution of protein complexes. Levy ED, Boeri Erba E, Robinson CV, Teichmann SA.

JMB:

• Backrub-like backbone simulation recapitulates natural protein conformational variability and improves mutant side-chain prediction. Smith CA, Kortemme T.
• A simple model of backbone flexibility improves modeling of side-chain conformational variability. Friedland GD, Linares AJ, Smith CA, Kortemme T.
• A dominant conformational role for amino acid diversity in minimalist protein-protein interfaces. Gilbreth RN, Esaki K, Koide A, Sidhu SS, Koide S.
• Statistical analysis of interface similarity in crystals of homologous proteins. Xu Q, Canutescu AA, Wang G, Shapovalov M, Obradovic Z, Dunbrack RL Jr.
• Architectures and functional coverage of protein-protein interfaces. Tuncbag N, Gursoy A, Guney E, Nussinov R, Keskin O.
• Changing the determinants of protein stability from covalent to non-covalent interactions by in vitro evolution: a structural and energetic analysis. Kather I, Jakob R, Dobbek H, Schmid FX.
• Conformer selection and induced fit in flexible backbone protein-protein docking using computational and NMR ensembles. Chaudhury S, Gray JJ.
• Force-based analysis of multidimensional energy landscapes: application of dynamic force spectroscopy and steered molecular dynamics simulations to an antibody fragment-peptide complex. Morfill J, Neumann J, Blank K, Steinbach U, Puchner EM, Gottschalk KE, Gaub HE.
• Rate of loop formation in peptides: a simulation study. Feige MJ, Paci E.
• Computational redesign of the SHV-1 beta-lactamase/beta-lactamase inhibitor protein interface. Reynolds KA, Hanes MS, Thomson JM, Antczak AJ, Berger JM, Bonomo RA, Kirsch JF, Handel TM.
• Identification of protein interaction partners and protein-protein interaction sites. Sacquin-Mora S, Carbone A, Lavery R.

Miscellaneous:

The pathogen protein EspF(U) hijacks actin polymerization using mimicry and multivalency.

Sallee NA, Rivera GM, Dueber JE, Vasilescu D, Mullins RD, Mayer BJ, Lim WA.
Graduate Program in Chemistry and Chemical Biology, University of California, San Francisco, 600 16th Street, San Francisco, California 94158, USA.

Enterohaemorrhagic Escherichia coli attaches to the intestine through actin pedestals that are formed when the bacterium injects its protein EspF(U) (also known as TccP) into host cells. EspF(U) potently activates the host WASP (Wiskott-Aldrich syndrome protein) family of actin-nucleating factors, which are normally activated by the GTPase CDC42, among other signalling molecules. Apart from its amino-terminal type III secretion signal, EspF(U) consists of five-and-a-half 47-amino-acid repeats. Here we show that a 17-residue motif within this EspF(U) repeat is sufficient for interaction with N-WASP (also known as WASL). Unlike most pathogen proteins that interface with the cytoskeletal machinery, this motif does not mimic natural upstream activators: instead of mimicking an activated state of CDC42, EspF(U) mimics an autoinhibitory element found within N-WASP. Thus, EspF(U) activates N-WASP by competitively disrupting the autoinhibited state. By mimicking an internal regulatory element and not the natural activator, EspF(U) selectively activates only a precise subset of CDC42-activated processes. Although one repeat is able to stimulate actin polymerization, we show that multiple-repeat fragments have notably increased potency. The activities of these EspF(U) fragments correlate with their ability to coordinate activation of at least two N-WASP proteins. Thus, this pathogen has used a simple autoinhibitory fragment as a component to build a highly effective actin polymerization machine.