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NCBI: db=pubmed; Term=(Vakser, Ilya[Author]) OR ((Miao, Yinglong[Author]) AND Kansas) OR (Deeds, Eric[Author]) OR (Ray, Christian[Author]) OR (Slusky, Joanna[Author]) OR (Ray JC[Author] AND Kansas)
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Ensemble Docking in Drug Discovery.

Tue, 04/03/2018 - 05:04

Ensemble Docking in Drug Discovery.

Biophys J. 2018 Mar 29;:

Authors: Amaro RE, Baudry J, Chodera J, Demir Ö, McCammon JA, Miao Y, Smith JC

Abstract
Ensemble docking corresponds to the generation of an "ensemble" of drug target conformations in computational structure-based drug discovery, often obtained by using molecular dynamics simulation, that is used in docking candidate ligands. This approach is now well established in the field of early-stage drug discovery. This review gives a historical account of the development of ensemble docking and discusses some pertinent methodological advances in conformational sampling.

PMID: 29606412 [PubMed - as supplied by publisher]

Mechanism of the G-protein mimetic nanobody binding to a muscarinic G-protein-coupled receptor.

Thu, 03/08/2018 - 10:28
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Mechanism of the G-protein mimetic nanobody binding to a muscarinic G-protein-coupled receptor.

Proc Natl Acad Sci U S A. 2018 Mar 05;:

Authors: Miao Y, McCammon JA

Abstract
Protein-protein binding is key in cellular signaling processes. Molecular dynamics (MD) simulations of protein-protein binding, however, are challenging due to limited timescales. In particular, binding of the medically important G-protein-coupled receptors (GPCRs) with intracellular signaling proteins has not been simulated with MD to date. Here, we report a successful simulation of the binding of a G-protein mimetic nanobody to the M2 muscarinic GPCR using the robust Gaussian accelerated MD (GaMD) method. Through long-timescale GaMD simulations over 4,500 ns, the nanobody was observed to bind the receptor intracellular G-protein-coupling site, with a minimum rmsd of 2.48 Å in the nanobody core domain compared with the X-ray structure. Binding of the nanobody allosterically closed the orthosteric ligand-binding pocket, being consistent with the recent experimental finding. In the absence of nanobody binding, the receptor orthosteric pocket sampled open and fully open conformations. The GaMD simulations revealed two low-energy intermediate states during nanobody binding to the M2 receptor. The flexible receptor intracellular loops contribute remarkable electrostatic, polar, and hydrophobic residue interactions in recognition and binding of the nanobody. These simulations provided important insights into the mechanism of GPCR-nanobody binding and demonstrated the applicability of GaMD in modeling dynamic protein-protein interactions.

PMID: 29507218 [PubMed - as supplied by publisher]

Ligand Binding Pathways and Conformational Transitions of the HIV Protease.

Thu, 03/08/2018 - 10:28
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Ligand Binding Pathways and Conformational Transitions of the HIV Protease.

Biochemistry. 2018 Mar 06;57(9):1533-1541

Authors: Miao Y, Huang YM, Walker RC, McCammon JA, Chang CA

Abstract
It is important to determine the binding pathways and mechanisms of ligand molecules to target proteins to effectively design therapeutic drugs. Molecular dynamics (MD) is a promising computational tool that allows us to simulate protein-drug binding at an atomistic level. However, the gap between the time scales of current simulations and those of many drug binding processes has limited the usage of conventional MD, which has been reflected in studies of the HIV protease. Here, we have applied a robust enhanced simulation method, Gaussian accelerated molecular dynamics (GaMD), to sample binding pathways of the XK263 ligand and associated protein conformational changes in the HIV protease. During two of 10 independent GaMD simulations performed over 500-2500 ns, the ligand was observed to successfully bind to the protein active site. Although GaMD-derived free energy profiles were not fully converged because of insufficient sampling of the complex system, the simulations still allowed us to identify relatively low-energy intermediate conformational states during binding of the ligand to the HIV protease. Relative to the X-ray crystal structure, the XK263 ligand reached a minimum root-mean-square deviation (RMSD) of 2.26 Å during 2.5 μs of GaMD simulation. In comparison, the ligand RMSD reached a minimum of only ∼5.73 Å during an earlier 14 μs conventional MD simulation. This work highlights the enhanced sampling power of the GaMD approach and demonstrates its wide applicability to studies of drug-receptor interactions for the HIV protease and by extension many other target proteins.

PMID: 29394043 [PubMed - in process]

Natural language processing in text mining for structural modeling of protein complexes.

Wed, 03/07/2018 - 05:06
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Natural language processing in text mining for structural modeling of protein complexes.

BMC Bioinformatics. 2018 Mar 05;19(1):84

Authors: Badal VD, Kundrotas PJ, Vakser IA

Abstract
BACKGROUND: Structural modeling of protein-protein interactions produces a large number of putative configurations of the protein complexes. Identification of the near-native models among them is a serious challenge. Publicly available results of biomedical research may provide constraints on the binding mode, which can be essential for the docking. Our text-mining (TM) tool, which extracts binding site residues from the PubMed abstracts, was successfully applied to protein docking (Badal et al., PLoS Comput Biol, 2015; 11: e1004630). Still, many extracted residues were not relevant to the docking.
RESULTS: We present an extension of the TM tool, which utilizes natural language processing (NLP) for analyzing the context of the residue occurrence. The procedure was tested using generic and specialized dictionaries. The results showed that the keyword dictionaries designed for identification of protein interactions are not adequate for the TM prediction of the binding mode. However, our dictionary designed to distinguish keywords relevant to the protein binding sites led to considerable improvement in the TM performance. We investigated the utility of several methods of context analysis, based on dissection of the sentence parse trees. The machine learning-based NLP filtered the pool of the mined residues significantly more efficiently than the rule-based NLP. Constraints generated by NLP were tested in docking of unbound proteins from the DOCKGROUND X-ray benchmark set 4. The output of the global low-resolution docking scan was post-processed, separately, by constraints from the basic TM, constraints re-ranked by NLP, and the reference constraints. The quality of a match was assessed by the interface root-mean-square deviation. The results showed significant improvement of the docking output when using the constraints generated by the advanced TM with NLP.
CONCLUSIONS: The basic TM procedure for extracting protein-protein binding site residues from the PubMed abstracts was significantly advanced by the deep parsing (NLP techniques for contextual analysis) in purging of the initial pool of the extracted residues. Benchmarking showed a substantial increase of the docking success rate based on the constraints generated by the advanced TM with NLP.

PMID: 29506465 [PubMed - in process]

Modeling CAPRI targets 110-120 by template-based and free docking using contact potential and combined scoring function.

Thu, 03/01/2018 - 13:05
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Modeling CAPRI targets 110-120 by template-based and free docking using contact potential and combined scoring function.

Proteins. 2018 Mar;86 Suppl 1:302-310

Authors: Kundrotas PJ, Anishchenko I, Badal VD, Das M, Dauzhenka T, Vakser IA

Abstract
The paper presents analysis of our template-based and free docking predictions in the joint CASP12/CAPRI37 round. A new scoring function for template-based docking was developed, benchmarked on the Dockground resource, and applied to the targets. The results showed that the function successfully discriminates the incorrect docking predictions. In correctly predicted targets, the scoring function was complemented by other considerations, such as consistency of the oligomeric states among templates, similarity of the biological functions, biological interface relevance, etc. The scoring function still does not distinguish well biological from crystal packing interfaces, and needs further development for the docking of bundles of α-helices. In the case of the trimeric targets, sequence-based methods did not find common templates, despite similarity of the structures, suggesting complementary use of structure- and sequence-based alignments in comparative docking. The results showed that if a good docking template is found, an accurate model of the interface can be built even from largely inaccurate models of individual subunits. Free docking however is very sensitive to the quality of the individual models. However, our newly developed contact potential detected approximate locations of the binding sites.

PMID: 28905425 [PubMed - in process]

Dockground: A comprehensive data resource for modeling of protein complexes.

Thu, 03/01/2018 - 13:05
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Dockground: A comprehensive data resource for modeling of protein complexes.

Protein Sci. 2018 Jan;27(1):172-181

Authors: Kundrotas PJ, Anishchenko I, Dauzhenka T, Kotthoff I, Mnevets D, Copeland MM, Vakser IA

Abstract
Characterization of life processes at the molecular level requires structural details of protein interactions. The number of experimentally determined structures of protein-protein complexes accounts only for a fraction of known protein interactions. This gap in structural description of the interactome has to be bridged by modeling. An essential part of the development of structural modeling/docking techniques for protein interactions is databases of protein-protein complexes. They are necessary for studying protein interfaces, providing a knowledge base for docking algorithms, and developing intermolecular potentials, search procedures, and scoring functions. Development of protein-protein docking techniques requires thorough benchmarking of different parts of the docking protocols on carefully curated sets of protein-protein complexes. We present a comprehensive description of the Dockground resource (http://dockground.compbio.ku.edu) for structural modeling of protein interactions, including previously unpublished unbound docking benchmark set 4, and the X-ray docking decoy set 2. The resource offers a variety of interconnected datasets of protein-protein complexes and other data for the development and testing of different aspects of protein docking methodologies. Based on protein-protein complexes extracted from the PDB biounit files, Dockground offers sets of X-ray unbound, simulated unbound, model, and docking decoy structures. All datasets are freely available for download, as a whole or selecting specific structures, through a user-friendly interface on one integrated website.

PMID: 28891124 [PubMed - indexed for MEDLINE]

Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets.

Thu, 03/01/2018 - 13:05
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Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets.

J Pharm Sci. 2017 Nov;106(11):3270-3279

Authors: Nariya MK, Kim JH, Xiong J, Kleindl PA, Hewarathna A, Fisher AC, Joshi SB, Schöneich C, Forrest ML, Middaugh CR, Volkin DB, Deeds EJ

Abstract
There is growing interest in generating physicochemical and biological analytical data sets to compare complex mixture drugs, for example, products from different manufacturers. In this work, we compare various crofelemer samples prepared from a single lot by filtration with varying molecular weight cutoffs combined with incubation for different times at different temperatures. The 2 preceding articles describe experimental data sets generated from analytical characterization of fractionated and degraded crofelemer samples. In this work, we use data mining techniques such as principal component analysis and mutual information scores to help visualize the data and determine discriminatory regions within these large data sets. The mutual information score identifies chemical signatures that differentiate crofelemer samples. These signatures, in many cases, would likely be missed by traditional data analysis tools. We also found that supervised learning classifiers robustly discriminate samples with around 99% classification accuracy, indicating that mathematical models of these physicochemical data sets are capable of identifying even subtle differences in crofelemer samples. Data mining and machine learning techniques can thus identify fingerprint-type attributes of complex mixture drugs that may be used for comparative characterization of products.

PMID: 28743607 [PubMed - in process]

The Botanical Drug Substance Crofelemer as a Model System for Comparative Characterization of Complex Mixture Drugs.

Thu, 03/01/2018 - 13:05
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The Botanical Drug Substance Crofelemer as a Model System for Comparative Characterization of Complex Mixture Drugs.

J Pharm Sci. 2017 Nov;106(11):3242-3256

Authors: Kleindl PA, Xiong J, Hewarathna A, Mozziconacci O, Nariya MK, Fisher AC, Deeds EJ, Joshi SB, Middaugh CR, Schöneich C, Volkin DB, Forrest ML

Abstract
Crofelemer is a botanical polymeric proanthocyanidin that inhibits chloride channel activity and is used clinically for treating HIV-associated secretory diarrhea. Crofelemer lots may exhibit significant physicochemical variation due to the natural source of the raw material. A variety of physical, chemical, and biological assays were used to identify potential critical quality attributes (CQAs) of crofelemer, which may be useful in characterizing differently sourced and processed drug products. Crofelemer drug substance was extracted from tablets of one commercial drug product lot, fractionated, and subjected to accelerated thermal degradation studies to produce derivative lots with variations in chemical and physical composition potentially representative of manufacturing and raw material variation. Liquid chromatography, UV absorbance spectroscopy, mass spectrometry, and nuclear magnetic resonance analysis revealed substantial changes in the composition of derivative lots. A chloride channel inhibition cell-based bioassay suggested that substantial changes in crofelemer composition did not necessarily result in major changes to bioactivity. In 2 companion papers, machine learning and data mining approaches were applied to the analytical and biological data sets presented herein, along with chemical stability data sets derived from forced degradation studies, to develop an integrated mathematical model that can identify CQAs which are most relevant in distinguishing between different populations of crofelemer.

PMID: 28743606 [PubMed - in process]

Crosstalk and the evolvability of intracellular communication.

Thu, 03/01/2018 - 13:05
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Crosstalk and the evolvability of intracellular communication.

Nat Commun. 2017 Jul 10;8:16009

Authors: Rowland MA, Greenbaum JM, Deeds EJ

Abstract
Metazoan signalling networks are complex, with extensive crosstalk between pathways. It is unclear what pressures drove the evolution of this architecture. We explore the hypothesis that crosstalk allows different cell types, each expressing a specific subset of signalling proteins, to activate different outputs when faced with the same inputs, responding differently to the same environment. We find that the pressure to generate diversity leads to the evolution of networks with extensive crosstalk. Using available data, we find that human tissues exhibit higher levels of diversity between cell types than networks with random expression patterns or networks with no crosstalk. We also find that crosstalk and differential expression can influence drug activity: no protein has the same impact on two tissues when inhibited. In addition to providing a possible explanation for the evolution of crosstalk, our work indicates that consideration of cellular context will likely be crucial for targeting signalling networks.

PMID: 28691706 [PubMed - in process]

Chemical Stability of the Botanical Drug Substance Crofelemer: A Model System for Comparative Characterization of Complex Mixture Drugs.

Thu, 03/01/2018 - 13:05
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Chemical Stability of the Botanical Drug Substance Crofelemer: A Model System for Comparative Characterization of Complex Mixture Drugs.

J Pharm Sci. 2017 Nov;106(11):3257-3269

Authors: Hewarathna A, Mozziconacci O, Nariya MK, Kleindl PA, Xiong J, Fisher AC, Joshi SB, Middaugh CR, Forrest ML, Volkin DB, Deeds EJ, Schöneich C

Abstract
As the second of a 3-part series of articles in this issue concerning the development of a mathematical model for comparative characterization of complex mixture drugs using crofelemer (CF) as a model compound, this work focuses on the evaluation of the chemical stability profile of CF. CF is a biopolymer containing a mixture of proanthocyanidin oligomers which are primarily composed of gallocatechin with a small contribution from catechin. CF extracted from drug product was subjected to molecular weight-based fractionation and thiolysis. Temperature stress and metal-catalyzed oxidation were selected for accelerated and forced degradation studies. Stressed CF samples were size fractionated, thiolyzed, and analyzed with a combination of negative-ion electrospray ionization mass spectrometry (ESI-MS) and reversed-phase-HPLC with UV absorption and fluorescence detection. We further analyzed the chemical stability data sets for various CF samples generated from reversed-phase-HPLC-UV and ESI-MS using data-mining and machine learning approaches. In particular, calculations based on mutual information of over 800,000 data points in the ESI-MS analytical data set revealed specific CF cleavage and degradation products that were differentially generated under specific storage/degradation conditions, which were not initially identified using traditional analysis of the ESI-MS results.

PMID: 28688843 [PubMed - in process]

Dynamical predictors of an imminent phenotypic switch in bacteria.

Thu, 03/01/2018 - 13:05
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Dynamical predictors of an imminent phenotypic switch in bacteria.

Phys Biol. 2017 Jun 29;14(4):045007

Authors: Wang H, Ray JCJ

Abstract
Single cells can stochastically switch across thresholds imposed by regulatory networks. Such thresholds can act as a tipping point, drastically changing global phenotypic states. In ecology and economics, imminent transitions across such tipping points can be predicted using dynamical early warning indicators. A typical example is 'flickering' of a fast variable, predicting a longer-lasting switch from a low to a high state or vice versa. Considering the different timescales between metabolite and protein fluctuations in bacteria, we hypothesized that metabolic early warning indicators predict imminent transitions across a network threshold caused by enzyme saturation. We used stochastic simulations to determine if flickering predicts phenotypic transitions, accounting for a variety of molecular physiological parameters, including enzyme affinity, burstiness of enzyme gene expression, homeostatic feedback, and rates of metabolic precursor influx. In most cases, we found that metabolic flickering rates are robustly peaked near the enzyme saturation threshold. The degree of fluctuation was amplified by product inhibition of the enzyme. We conclude that sensitivity to flickering in fast variables may be a possible natural or synthetic strategy to prepare physiological states for an imminent transition.

PMID: 28597843 [PubMed - in process]

Fundamental trade-offs between information flow in single cells and cellular populations.

Thu, 03/01/2018 - 13:05
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Fundamental trade-offs between information flow in single cells and cellular populations.

Proc Natl Acad Sci U S A. 2017 May 30;114(22):5755-5760

Authors: Suderman R, Bachman JA, Smith A, Sorger PK, Deeds EJ

Abstract
Signal transduction networks allow eukaryotic cells to make decisions based on information about intracellular state and the environment. Biochemical noise significantly diminishes the fidelity of signaling: networks examined to date seem to transmit less than 1 bit of information. It is unclear how networks that control critical cell-fate decisions (e.g., cell division and apoptosis) can function with such low levels of information transfer. Here, we use theory, experiments, and numerical analysis to demonstrate an inherent trade-off between the information transferred in individual cells and the information available to control population-level responses. Noise in receptor-mediated apoptosis reduces information transfer to approximately 1 bit at the single-cell level but allows 3-4 bits of information to be transmitted at the population level. For processes such as eukaryotic chemotaxis, in which single cells are the functional unit, we find high levels of information transmission at a single-cell level. Thus, low levels of information transfer are unlikely to represent a physical limit. Instead, we propose that signaling networks exploit noise at the single-cell level to increase population-level information transfer, allowing extracellular ligands, whose levels are also subject to noise, to incrementally regulate phenotypic changes. This is particularly critical for discrete changes in fate (e.g., life vs. death) for which the key variable is the fraction of cells engaged. Our findings provide a framework for rationalizing the high levels of noise in metazoan signaling networks and have implications for the development of drugs that target these networks in the treatment of cancer and other diseases.

PMID: 28500273 [PubMed - in process]

Outer membrane protein design.

Thu, 03/01/2018 - 13:05
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Outer membrane protein design.

Curr Opin Struct Biol. 2017 08;45:45-52

Authors: Slusky JS

Abstract
Membrane proteins are the gateway to the cell. These proteins are also a control center of the cell, as information from the outside is passed through membrane proteins as signals to the cellular machinery. The design of membrane proteins seeks to harness the power of these gateways and signal carriers. This review will focus on the design of the membrane proteins that are in the outer membrane, a membrane which only exists for gram negative bacteria, mitochondria, and chloroplasts. Unlike other membrane proteins, outer membrane proteins are uniquely shaped as β-barrels. Herein, I describe most known examples of membrane β-barrel design to date, focusing particularly on categorizing designs as: Firstly, structural deconstruction; secondly, structural changes; thirdly, chemical function design; and finally, the creation of new folds.

PMID: 27894013 [PubMed - in process]

Modeling complexes of modeled proteins.

Thu, 03/01/2018 - 13:05
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Modeling complexes of modeled proteins.

Proteins. 2017 Mar;85(3):470-478

Authors: Anishchenko I, Kundrotas PJ, Vakser IA

Abstract
Structural characterization of proteins is essential for understanding life processes at the molecular level. However, only a fraction of known proteins have experimentally determined structures. This fraction is even smaller for protein-protein complexes. Thus, structural modeling of protein-protein interactions (docking) primarily has to rely on modeled structures of the individual proteins, which typically are less accurate than the experimentally determined ones. Such "double" modeling is the Grand Challenge of structural reconstruction of the interactome. Yet it remains so far largely untested in a systematic way. We present a comprehensive validation of template-based and free docking on a set of 165 complexes, where each protein model has six levels of structural accuracy, from 1 to 6 Å Cα RMSD. Many template-based docking predictions fall into acceptable quality category, according to the CAPRI criteria, even for highly inaccurate proteins (5-6 Å RMSD), although the number of such models (and, consequently, the docking success rate) drops significantly for models with RMSD > 4 Å. The results show that the existing docking methodologies can be successfully applied to protein models with a broad range of structural accuracy, and the template-based docking is much less sensitive to inaccuracies of protein models than the free docking. Proteins 2017; 85:470-478. © 2016 Wiley Periodicals, Inc.

PMID: 27701777 [PubMed - indexed for MEDLINE]

Bilayer Properties of Lipid A from Various Gram-Negative Bacteria.

Thu, 03/01/2018 - 13:05
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Bilayer Properties of Lipid A from Various Gram-Negative Bacteria.

Biophys J. 2016 Oct 18;111(8):1750-1760

Authors: Kim S, Patel DS, Park S, Slusky J, Klauda JB, Widmalm G, Im W

Abstract
Lipid A is the lipid anchor of a lipopolysaccharide in the outer leaflet of the outer membrane of Gram-negative bacteria. In general, lipid A consists of two phosphorylated N-acetyl glucosamine and several acyl chains that are directly linked to the two sugars. Depending on the bacterial species and environments, the acyl chain number and length vary, and lipid A can be chemically modified with phosphoethanolamine, aminoarabinose, or glycine residues, which are key to bacterial pathogenesis. In this work, homogeneous lipid bilayers of 21 distinct lipid A types from 12 bacterial species are modeled and simulated to investigate the differences and similarities of their membrane properties. In addition, different neutralizing ion types (Ca2+, K+, and Na+) are considered to examine the ion's influence on the membrane properties. The trajectory analysis shows that (1) the area per lipid is mostly correlated to the acyl chain number, and the area per lipid increases as a function of the acyl chain number; (2) the hydrophobic thickness is mainly determined by the average acyl chain length with slight dependence on the acyl chain number, and the hydrophobic thickness generally increases with the average acyl chain length; (3) a good correlation is observed among the area per lipid, hydrophobic thickness, and acyl chain order; and (4) although the influence of neutralizing ion types on the area per lipid and hydrophobic thickness is minimal, Ca2+ stays longer on the membrane surface than K+ or Na+, consequently leading to lower lateral diffusion and a higher compressibility modulus, which agrees well with available experiments.

PMID: 27760361 [PubMed - indexed for MEDLINE]

Structural quality of unrefined models in protein docking.

Thu, 03/01/2018 - 13:05
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Structural quality of unrefined models in protein docking.

Proteins. 2017 Jan;85(1):39-45

Authors: Anishchenko I, Kundrotas PJ, Vakser IA

Abstract
Structural characterization of protein-protein interactions is essential for understanding life processes at the molecular level. However, only a fraction of protein interactions have experimentally resolved structures. Thus, reliable computational methods for structural modeling of protein interactions (protein docking) are important for generating such structures and understanding the principles of protein recognition. Template-based docking techniques that utilize structural similarity between target protein-protein interaction and cocrystallized protein-protein complexes (templates) are gaining popularity due to generally higher reliability than that of the template-free docking. However, the template-based approach lacks explicit penalties for intermolecular penetration, as opposed to the typical free docking where such penalty is inherent due to the shape complementarity paradigm. Thus, template-based docking models are commonly assumed to require special treatment to remove large structural penetrations. In this study, we compared clashes in the template-based and free docking of the same proteins, with crystallographically determined and modeled structures. The results show that for the less accurate protein models, free docking produces fewer clashes than the template-based approach. However, contrary to the common expectation, in acceptable and better quality docking models of unbound crystallographically determined proteins, the clashes in the template-based docking are comparable to those in the free docking, due to the overall higher quality of the template-based docking predictions. This suggests that the free docking refinement protocols can in principle be applied to the template-based docking predictions as well. Proteins 2016; 85:39-45. © 2016 Wiley Periodicals, Inc.

PMID: 27756103 [PubMed - indexed for MEDLINE]

Template-Based Modeling of Protein-RNA Interactions.

Thu, 03/01/2018 - 13:05
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Template-Based Modeling of Protein-RNA Interactions.

PLoS Comput Biol. 2016 09;12(9):e1005120

Authors: Zheng J, Kundrotas PJ, Vakser IA, Liu S

Abstract
Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes.

PMID: 27662342 [PubMed - indexed for MEDLINE]

Challenges in structural approaches to cell modeling.

Thu, 03/01/2018 - 13:05
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Challenges in structural approaches to cell modeling.

J Mol Biol. 2016 07 31;428(15):2943-64

Authors: Im W, Liang J, Olson A, Zhou HX, Vajda S, Vakser IA

Abstract
Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field.

PMID: 27255863 [PubMed - indexed for MEDLINE]


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