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<searchterm>pdbid/1HIV</searchterm>
<article>
<journal-meta>
<journal-id journal-id-type="nlm-ta">BMC Struct Biol</journal-id><journal-title>BMC Structural Biology</journal-title>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.1186/1472-6807-3-2</article-id>
<article-id pub-id-type="pmid">12675950</article-id>
<article-id pub-id-type="pmcid">154089</article-id>
<article-id pub-id-type="uid">54411</article-id>
<article-title><title-group>Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations</title-group></article-title>
<contrib-group><contrib contrib-type="author">Ekachai Jenwitheesuk, Ram Samudrala
</contrib></contrib-group>
<pub-date><day>01</day><month>04</month><year>2003</year></pub-date>
<volume>3</volume>
<issue></issue>
<fpage>2</fpage>
<abstract><![CDATA[BackgroundThe accurate prediction of enzyme-substrate interaction energies is one of the major challenges in computational biology. This study describes the improvement of protein-ligand binding energy prediction by incorporating protein flexibility through the use of molecular dynamics (MD) simulations.ResultsDocking experiments were undertaken using the program AutoDock for twenty-five HIV-1 protease-inhibitor complexes determined by x-ray crystallography. Protein-rigid docking without any dynamics produced a low correlation of 0.38 between the experimental and calculated binding energies. Correlations improved significantly for all time scales of MD simulations of the receptor-ligand complex. The highest correlation coefficient of 0.87 between the experimental and calculated energies was obtained after 0.1 picoseconds of dynamics simulation.ConclusionOur results indicate that relaxation of protein complexes by MD simulation is useful and necessary to obtain binding energies that are representative of the experimentally determined values.]]></abstract>
</article-meta>
</article>
<article>
<journal-meta>
<journal-id journal-id-type="nlm-ta">PLoS ONE</journal-id><journal-title>PLoS ONE</journal-title>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.1371/journal.pone.0000421</article-id>
<article-id pub-id-type="pmid">17502913</article-id>
<article-id pub-id-type="pmcid">1855080</article-id>
<article-id pub-id-type="uid">65442</article-id>
<article-title><title-group>Efficient Identification of Critical Residues Based Only on Protein Structure by Network Analysis</title-group></article-title>
<contrib-group><contrib contrib-type="author">Michael P. Cusack, Boris Thibert, Dale E. Bredesen, Gabriel del Rio
</contrib></contrib-group>
<pub-date><day>09</day><month>05</month><year>2007</year></pub-date>
<volume>2</volume>
<issue>5</issue>
<fpage>e421</fpage>
<abstract><![CDATA[Despite the increasing number of published protein structures, and the fact that each protein's function relies on its three-dimensional structure, there is limited access to automatic programs used for the identification of critical residues from the protein structure, compared with those based on protein sequence. Here we present a new algorithm based on network analysis applied exclusively on protein structures to identify critical residues. Our results show that this method identifies critical residues for protein function with high reliability and improves automatic sequence-based approaches and previous network-based approaches. The reliability of the method depends on the conformational diversity screened for the protein of interest. We have designed a web site to give access to this software at http://bis.ifc.unam.mx/jamming/. In summary, a new method is presented that relates critical residues for protein function with the most traversed residues in networks derived from protein structures. A unique feature of the method is the inclusion of the conformational diversity of proteins in the prediction, thus reproducing a basic feature of the structure/function relationship of proteins.]]></abstract>
</article-meta>
</article>
<article>
<journal-meta>
<journal-id journal-id-type="nlm-ta">BMC Bioinformatics</journal-id><journal-title>BMC Bioinformatics</journal-title>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.1186/1471-2105-6-213</article-id>
<article-id pub-id-type="pmid">16124876</article-id>
<article-id pub-id-type="pmcid">1208857</article-id>
<article-id pub-id-type="uid">65527</article-id>
<article-title><title-group>Improved prediction of critical residues for protein function based on network and phylogenetic analyses</title-group></article-title>
<contrib-group><contrib contrib-type="author">Boris Thibert, Dale E Bredesen, Gabriel del Rio
</contrib></contrib-group>
<pub-date><day>26</day><month>08</month><year>2005</year></pub-date>
<volume>6</volume>
<issue></issue>
<fpage>213</fpage>
<abstract><![CDATA[BackgroundPhylogenetic approaches are commonly used to predict which amino acid residues are critical to the function of a given protein. However, such approaches display inherent limitations, such as the requirement for identification of multiple homologues of the protein under consideration. Therefore, complementary or alternative approaches for the prediction of critical residues would be desirable. Network analyses have been used in the modelling of many complex biological systems, but only very recently have they been used to predict critical residues from a protein's three-dimensional structure. Here we compare a couple of phylogenetic approaches to several different network-based methods for the prediction of critical residues, and show that a combination of one phylogenetic method and one network-based method is superior to other methods previously employed.ResultsWe associate a network with each member of a set of proteins for which the three-dimensional structure is known and the critical residues have been previously determined experimentally. We show that several network-based centrality measurements (connectivity, 2-connectivity, closeness centrality, betweenness and cluster coefficient) accurately detect residues critical for the protein's function. Phylogenetic approaches render predictions as reliable as the network-based measurements, although, interestingly, the two general approaches tend to predict different sets of critical residues. Hence we propose a hybrid method that is composed of one network-based calculation &#x02013; the closeness centrality &#x02013; and one phylogenetic approach &#x02013; the Conseq server. This hybrid approach predicts critical residues more accurately than the other methods tested here.ConclusionWe show that network analysis can be used to improve the prediction of amino acids critical for protein function, when utilized in combination with phylogenetic approaches. It is proposed that such improvement is due to the complementary nature of these approaches: network-based methods tend to predict as critical those residues that are highly connected and internal (i.e., non-surface), although some surface residues are indeed identified as critical by network analyses; whereas residues chosen by phylogenetic approaches display a lower overall probability of being surface inaccessible.]]></abstract>
</article-meta>
</article>
<article>
<journal-meta>
<journal-id journal-id-type="nlm-ta">PLoS Comput Biol</journal-id><journal-title>PLoS Computational Biology</journal-title>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.1371/journal.pcbi.0020090</article-id>
<article-id pub-id-type="pmid">16839194</article-id>
<article-id pub-id-type="pmcid">1500818</article-id>
<article-id pub-id-type="uid">76541</article-id>
<article-title><title-group>Wiggle&#x02014;Predicting Functionally Flexible Regions from Primary Sequence</title-group></article-title>
<contrib-group><contrib contrib-type="author">Jenny Gu, Michael Gribskov, Philip E Bourne
</contrib></contrib-group>
<pub-date><day>00</day><month>00</month><year>2006</year></pub-date>
<volume>2</volume>
<issue>7</issue>
<fpage>e90</fpage>
<abstract><![CDATA[The Wiggle series are support vector machine&#x02013;based predictors that identify regions of functional flexibility using only protein sequence information. Functionally flexible regions are defined as regions that can adopt different conformational states and are assumed to be necessary for bioactivity. Many advances have been made in understanding the relationship between protein sequence and structure. This work contributes to those efforts by making strides to understand the relationship between protein sequence and flexibility. A coarse-grained protein dynamic modeling approach was used to generate the dataset required for support vector machine training. We define our regions of interest based on the participation of residues in correlated large-scale fluctuations. Even with this structure-based approach to computationally define regions of functional flexibility, predictors successfully extract sequence-flexibility relationships that have been experimentally confirmed to be functionally important. Thus, a sequence-based tool to identify flexible regions important for protein function has been created. The ability to identify functional flexibility using a sequence based approach complements structure-based definitions and will be especially useful for the large majority of proteins with unknown structures. The methodology offers promise to identify structural genomics targets amenable to crystallization and the possibility to engineer more flexible or rigid regions within proteins to modify their bioactivity.]]></abstract>
</article-meta>
</article>
<article>
<journal-meta>
<journal-id journal-id-type="nlm-ta">PLoS Comput Biol</journal-id><journal-title>PLoS Computational Biology</journal-title>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.1371/journal.pcbi.1000009</article-id>
<article-id pub-id-type="pmid"></article-id>
<article-id pub-id-type="pmcid">2265533</article-id>
<article-id pub-id-type="uid">82496</article-id>
<article-title><title-group>Computer-Based Screening of Functional Conformers of Proteins</title-group></article-title>
<contrib-group><contrib contrib-type="author">Héctor Marlosti Montiel Molina, César Millán-Pacheco, Nina Pastor, Gabriel del Rio
</contrib></contrib-group>
<pub-date><day>29</day><month>02</month><year>2008</year></pub-date>
<volume>4</volume>
<issue>2</issue>
<fpage>e1000009</fpage>
<abstract><![CDATA[A long-standing goal in biology is to establish the link between function, structure, and dynamics of proteins. Considering that protein function at the molecular level is understood by the ability of proteins to bind to other molecules, the limited structural data of proteins in association with other bio-molecules represents a major hurdle to understanding protein function at the structural level. Recent reports show that protein function can be linked to protein structure and dynamics through network centrality analysis, suggesting that the structures of proteins bound to natural ligands may be inferred computationally. In the present work, a new method is described to discriminate protein conformations relevant to the specific recognition of a ligand. The method relies on a scoring system that matches critical residues with central residues in different structures of a given protein. Central residues are the most traversed residues with the same frequency in networks derived from protein structures. We tested our method in a set of 24 different proteins and more than 260,000 structures of these in the absence of a ligand or bound to it. To illustrate the usefulness of our method in the study of the structure/dynamics/function relationship of proteins, we analyzed mutants of the yeast TATA-binding protein with impaired DNA binding. Our results indicate that critical residues for an interaction are preferentially found as central residues of protein structures in complex with a ligand. Thus, our scoring system effectively distinguishes protein conformations relevant to the function of interest.]]></abstract>
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