Monte Carlo study of cooperativity in homopolypeptides

©1992 American Institute of PhysicsThe electronic version of this article is the complete one and can be found online at: http://link.aip.org/link/?JCPSA6/97/9412/1DOI:10.1063/1.463317A discretized model of globular proteins is employed in a Monte Carlo study of the helix-coil transition of polyalanine and the collapse transition of polyvaline. The present lattice realization permits real protein crystal structures to be represented at the level of 1 A resolution. Furthermore, the Monte Carlo dynamic scheme is capable of moving elements of assembled secondary and supersecondary structure. The potentials of mean force for the interactions are constructed from the statistics of a set of high resolution x-ray structures of nonhomologous proteins. The cooperativity of formation of ordered structures is found to be larger when the major contributions to the conformational energy of the low temperature states come from hydrogen bonds and short range conformational propensities. The secondary structure seen in the folded state is the result of an interplay between the short and long range interactions. Compactness itself, driven by long range, nonspecific interactions, seems to be insufficient to generate any appreciable secondary structure. A detailed examination of the dynamics of highly helical model proteins demonstrates that all elements of secondary structure are mobile in the present algorithm, and thus the folding pathways do not depend on the use of a lattice approximation. Possible applications of the present model to the prediction of protein 3D structures are briefly discussed

Monte Carlo dynamics of diamond-lattice multichain systems

©1986 American Institute of Physics. The electronic version of this article is the complete one and can be found online at: http://link.aip.org/link/?APCPCS/137/241/1DOI:10.1063/1.35530Presented at the 1985 La Jolla Workshop on Polymer Flow Interaction.We present preliminary results of Monte Carlo studies on the dynamics of multichain diamond-lattice systems at considerably greater densities than those done previously. Chain dynamics were simulated by a random sequence of three or four bond kink motions. The single bead autocorrelation function exhibits "slow" mode relaxation behavior with a g(t)∝ tβ. There is a smooth crossover from Rouse-like dynamics, β=1/2, at low density to smaller values of β at higher density and β=0 at the glass transition density (φG≅0.92). The simulation provides a self-diffusion coefficient D ∝ n-2, with n the number of beads, in agreement with experiment. A phenomenological model, different from the widely accepted reptation picture, is proposed

EFICAz²: enzyme function inference by a combined approach enhanced by machine learning

©2009 Arakaki et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/10/107doi:10.1186/1471-2105-10-107Background: We previously developed EFICAz, an enzyme function inference approach that combines predictions from non-completely overlapping component methods. Two of the four components in the original EFICAz are based on the detection of functionally discriminating residues (FDRs). FDRs distinguish between member of an enzyme family that are homofunctional (classified under the EC number of interest) or heterofunctional (annotated with another EC number or lacking enzymatic activity). Each of the two FDR-based components is associated to one of two specific kinds of enzyme families. EFICAz exhibits high precision performance, except when the maximal test to training sequence identity (MTTSI) is lower than 30%. To improve EFICAz's performance in this regime, we: i) increased the number of predictive components and ii) took advantage of consensual information from the different components to make the final EC number assignment. Results: We have developed two new EFICAz components, analogs to the two FDR-based components, where the discrimination between homo and heterofunctional members is based on the evaluation, via Support Vector Machine models, of all the aligned positions between the query sequence and the multiple sequence alignments associated to the enzyme families. Benchmark results indicate that: i) the new SVM-based components outperform their FDR-based counterparts, and ii) both SVM-based and FDR-based components generate unique predictions. We developed classification tree models to optimally combine the results from the six EFICAz components into a final EC number prediction. The new implementation of our approach, EFICAz², exhibits a highly improved prediction precision at MTTSI < 30% compared to the original EFICAz, with only a slight decrease in prediction recall. A comparative analysis of enzyme function annotation of the human proteome by EFICAz² and KEGG shows that: i) when both sources make EC number assignments for the same protein sequence, the assignments tend to be consistent and ii) EFICAz² generates considerably more unique assignments than KEGG. Conclusion: Performance benchmarks and the comparison with KEGG demonstrate that EFICAz² is a powerful and precise tool for enzyme function annotation, with multiple applications in genome analysis and metabolic pathway reconstruction. The EFICAz² web service is available at: http://cssb.biology.gatech.edu/skolnick/webservice/EFICAz2/index.htm

Effect of double bonds on the dynamics of hydrocarbon chains

©1992 American Institute of PhysicsThe electronic version of this article is the complete one and can be found online at: http://link.aip.org/link/?JCPSA6/97/1240/1DOI:10.1063/1.463250Brownian dynamics simulations of isolated 18-carbon chains have been performed, both for saturated and unsaturated hydrocarbons. The effect of one or several (nonconjugated) double bonds on the properties of the chains is discussed in terms of both equilibrium and dynamic properties. The introduction of a cis double bond increases the relaxation rates of the unsaturated chain with respect to the saturated alkane. On the other hand, coupling effects in the torsional transitions around a trans double bond make the dynamics of this unsaturated chain very similar to the saturated one. Based on these results, the parameters and moves of a dynamic Monte Carlo algorithm are tuned to reproduce the observed behavior, providing an efficient method for the study of more complicated systems

A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation

The detection of ligand-binding sites is often the starting point for protein function identification and drug discovery. Because of inaccuracies in predicted protein structures, extant binding pocketdetection methods are limited to experimentally solved structures. Here, FINDSITE, a method for ligand-binding site prediction and functional annotation based on binding-site similarity across groups of weakly homologous template structures identified from threading, is described. For crystal structures, considering a cutoff distance of 4 Å as the hit criterion, the success rate is 70.9% for identifying the best of top five predicted ligand-binding sites with a ranking accuracy of 76.0%. Both high prediction accuracy and ability to correctly rank identified binding sites are sustained when approximate protein models (\u3c35% sequence identity to the closest template structure) are used, showing a 67.3% success rate with 75.5% ranking accuracy. In practice, FINDSITE tolerates structural inaccuracies in protein models up to a rmsd from the crystal structure of 8-10 Å. This is because analysis of weakly homologous protein models reveals that about half have a rmsd from the native binding site \u3c2 Å. Furthermore, the chemical properties of template-bound ligands can be used to select ligand templates associated with the binding site. In most cases, FINDSITE can accurately assign a molecular function to the protein model. © 2007 by The National Academy of Sciences of the USA

TM-align: a protein structure alignment algorithm based on the TM-score

We have developed TM-align, a new algorithm to identify the best structural alignment between protein pairs that combines the TM-score rotation matrix and Dynamic Programming (DP). The algorithm is ∼4 times faster than CE and 20 times faster than DALI and SAL. On average, the resulting structure alignments have higher accuracy and coverage than those provided by these most often-used methods. TM-align is applied to an all-against-all structure comparison of 10 515 representative protein chains from the Protein Data Bank (PDB) with a sequence identity cutoff <95%: 1996 distinct folds are found when a TM-score threshold of 0.5 is used. We also use TM-align to match the models predicted by TASSER for solved non-homologous proteins in PDB. For both folded and misfolded models, TM-align can almost always find close structural analogs, with an average root mean square deviation, RMSD, of 3 Å and 87% alignment coverage. Nevertheless, there exists a significant correlation between the correctness of the predicted structure and the structural similarity of the model to the other proteins in the PDB. This correlation could be used to assist in model selection in blind protein structure predictions. The TM-align program is freely downloadable at