Improvement of side-chain modeling in proteins with the self-consistent mean field theory method based on an analysis of the factors influencing prediction

Mendes, Jorge M.; Soares, Cláudio M.; Carrondo, Maria Arménia

doi:10.1002/(sici)1097-0282(199908)50:2<111::aid-bip1>3.0.co;2-n

Cited by 44 publications

(26 citation statements)

References 37 publications

(75 reference statements)

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“…A number of recent studies suggest that further improvements may still be possible. Mendes et al [114] found, for example, that the use of an intrinsic torsional potential can improve prediction accuracy. Lovell et al [115] recently reported a novel rotamer library in which internal clashes between side chain and backbone are removed.…”

Section: Side-chain Predictionmentioning

confidence: 99%

Advances in Homology Protein Structure Modeling

Xiang

2006

CPPS

427

243

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Homology modeling plays a central role in determining protein structure in the structural genomics project. The importance of homology modeling has been steadily increasing because of the large gap that exists between the overwhelming number of available protein sequences and experimentally solved protein structures, and also, more importantly, because of the increasing reliability and accuracy of the method. In fact, a protein sequence with over 30% identity to a known structure can often be predicted with an accuracy equivalent to a low-resolution X-ray structure. The recent advances in homology modeling, especially in detecting distant homologues, aligning sequences with template structures, modeling of loops and side chains, as well as detecting errors in a model, have contributed to reliable prediction of protein structure, which was not possible even several years ago. The ongoing efforts in solving protein structures, which can be time-consuming and often difficult, will continue to spur the development of a host of new computational methods that can fill in the gap and further contribute to understanding the relationship between protein structure and function.

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Section: Side-chain Predictionmentioning

confidence: 99%

Advances in Homology Protein Structure Modeling

Xiang

2006

CPPS

427

243

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“…In the modeled C-terminal domain, given that the binding of arabinose depends heavily on side chain conformations, we decided to use a better method to predict them with more accuracy. For that method we used side chain prediction methods developed previously (22,24), tailored specifically for structures obtained using comparative modeling (and therefore containing some backbone conformation errors) (23). These methods use a backbone-dependent rotamer library, and in this case we used the "Direct" strategy (see reference 23 for details).…”

Section: Vol 188 2006 Characterization Of the B Subtilis Regulatormentioning

confidence: 99%

Functional Domains of theBacillus subtilisTranscription Factor AraR and Identification of Amino Acids Important for Nucleoprotein Complex Assembly and Effector Binding

et al. 2006

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The Bacillus subtilis AraR transcription factor represses at least 13 genes required for the extracellular degradation of arabinose-containing polysaccharides, transport of arabinose, arabinose oligomers, xylose, and galactose, intracellular degradation of arabinose oligomers, and further catabolism of this sugar. AraR exhibits a chimeric organization comprising a small N-terminal DNA-binding domain that contains a winged helix-turn-helix motif similar to that seen with the GntR family and a larger C-terminal domain homologous to that of the LacI/GalR family. Here, a model for AraR was derived based on the known crystal structures of the FadR and PurR regulators from Escherichia coli. We have used random mutagenesis, deletion, and construction of chimeric LexA-AraR fusion proteins to map the functional domains of AraR required for DNA binding, dimerization, and effector binding. Moreover, predictions for the functional role of specific residues were tested by site-directed mutagenesis. In vivo analysis identified particular amino acids required for dimer assembly, formation of the nucleoprotein complex, and composition of the sugar-binding cleft. This work presents a structural framework for the function of AraR and provides insight into the mechanistic mode of action of this modular repressor.The transcription factor AraR controls the expression of several Bacillus subtilis genes encoding enzymes and permeases involved in the degradation of arabinose-containing polysaccharides, uptake of L-arabinose (and possibly arabinose oligomers), xylose, and galactose, and further intracellular catabolism of arabinose and arabinose oligomers. The arabinose (ara) regulon comprises at least 13 genes (Fig. 1), the araAB DLMNPQ-abfA operon (43), the divergently arranged araE/ araR genes (42, 45) located in distinct regions of the B. subtilis chromosome, and the genes abnA and xsa positioned immediately upstream and 23 kb downstream from the operon, respectively (57). The first three genes of the arabinose metabolic operon, araA, araB, and araD, encode the enzymes required for the intracellular conversion of L-arabinose into D-xylulose 5-phosphate, which is further catabolized through the pentose phosphate pathway (41). The function of araL and araM is unknown, and genes araNPQ encode components of an ABC-type transporter most likely involved in the uptake of arabinose oligomers (43, 45). The product of the araE gene is a permease, the main transporter of arabinose into the cell (45), that is also responsible for the uptake of xylose and galactose (14). The araR gene encodes the regulatory protein of the system (27). The last gene of the metabolic operon, abfA, and the xsa gene most probably encode arabinofuranosidases involved in the intracellular degradation of arabinose oligomers (36). The abnA gene encodes an extracellular endo-arabinanase that degrades the arabinose homoglycan arabinan (16).The pathways of L-arabinose utilization in B. subtilis and Escherichia coli are identical, and the catabolic enzymes are functionally ...

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“…These authors developed also an optimization protocol called "Multiple Run Random Initialization" (MRRI) in which the rotamer probabilities are initialized to random values in each run to avoid finding only local minima [87]. This new protocol was compared with the MFA of Vasquez, and the authors concluded that the conformational free energy minimum reached by this protocol probably corresponds to the global minimum, whereas the free energy minimum reached with their original protocol or the one by Vasquez more likely corresponds to a local minimum [87].…”

Section: Strategies For Conformational Searchmentioning

confidence: 99%

“…A systematic study on the influence of each of these terms on the correctness of the predictions was made by Mendes' group in their above mentioned study on the influence of several factors involved in prediction of side chains [87]. In addition to the search for an optimized SCMF protocol, these authors also deeply analyse the influence of the potential energy function.…”

Section: Potential Energy Functionmentioning

confidence: 99%

“…It is easy to understand that all these factors are much more difficult to predict, and therefore, there is a need for more specialized programs to solve the side chain structure determination problem. Iterative minimization van der Waals, torsion term SCCOMP [33] http://ignmtest.ccbb.pitt.edu/cgi-bin/sccomp/sccomp1.cgi Dunbrack and Cohen [29], modified as in [87] Iterative During the time, several approaches have been developed to predict side chain conformations, and some of them (especially those that are freely available at least for academic users) are quite popular and widely used by the scientific community ( Table 1). The aim of this review is to summarize the main features of such programs and discuss the principal solutions adopted by the different approaches.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Conformation of Side Chains in Proteins: Approaches, Problems and Possible Developments

Marabotti

2008

Current Chemical Biology

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Simulation of the correct side chain conformations of amino acid residues is an intriguing issue not only for computational biology, but also for practical outcomes in biotechnology and medicine. This is also a main challenge for molecular simulations, since even in the homology modelling strategy (which uses templates to predict the unknown structure of a protein), the conformation of a side chain generally cannot be easily deduced from the structure of the template. It is important also for applications such as molecular design and docking. Moreover, the correct simulation of the effects of amino acid mutations occurring in many genetic diseases may help in understanding the molecular mechanisms that underlie those pathologies.This review aims to summarize the different strategies developed to predict side chain conformations in proteins. In the current review, differences in approaches are discussed and problems are analyzed, as well as parameters and criteria to compare and evaluate performances of the programs.

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Improvement of side-chain modeling in proteins with the self-consistent mean field theory method based on an analysis of the factors influencing prediction

Cited by 44 publications

References 37 publications

Advances in Homology Protein Structure Modeling

Advances in Homology Protein Structure Modeling

Functional Domains of theBacillus subtilisTranscription Factor AraR and Identification of Amino Acids Important for Nucleoprotein Complex Assembly and Effector Binding

Modeling the Conformation of Side Chains in Proteins: Approaches, Problems and Possible Developments

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