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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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Gene ontology improves template selection in comparative protein docking.

Fri, 12/07/2018 - 10:05
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Gene ontology improves template selection in comparative protein docking.

Proteins. 2018 Dec 06;:

Authors: Hadarovich A, Anishchenko I, Tuzikov AV, Kundrotas PJ, Vakser IA

Abstract
Structural characterization of protein-protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure, and as a way to understand the principles of protein interaction. Rapidly evolving comparative docking approaches utilize target/template similarity metrics, which are often based on the protein structure. Although the structural similarity, generally, yields good performance, other characteristics of the interacting proteins (eg, function, biological process, localization, and such) may improve the prediction quality, especially in the case of weak target/template structural similarity. For the ranking of a pool of models for each target, we tested scoring functions that quantify similarity of Gene Ontology (GO) terms assigned to target and template proteins in three ontology domains - biological process, molecular function and cellular component (GO-score). The scoring functions were tested in docking of bound, unbound and modeled proteins. The results indicate that the combined structural and GO-terms functions improve the scoring, especially in the twilight zone of structural similarity, typical for protein models of limited accuracy. This article is protected by copyright. All rights reserved.

PMID: 30520123 [PubMed - as supplied by publisher]

Evolutionary pathways of repeat protein topology in bacterial outer membrane proteins.

Fri, 11/30/2018 - 10:17

Evolutionary pathways of repeat protein topology in bacterial outer membrane proteins.

Elife. 2018 Nov 29;7:

Authors: Franklin MW, Nepomnyachyi S, Feehan R, Ben-Tal N, Kolodny R, Slusky JS

Abstract
Outer membrane proteins (OMPs) are the proteins in the surface of Gram-negative bacteria. These proteins have diverse functions but a single topology: the β-barrel. Sequence analysis has suggested that this common fold is a β-hairpin repeat protein, and that amplification of the β-hairpin has resulted in 8-26-stranded barrels. Using an integrated approach that combines sequence and structural analyses we find events in which non-amplification diversification also increases barrel strand number. Our network-based analysis reveals strand-number evolutionary pathways, including one that progresses from a primordial 8-stranded barrel to 16-strands and further, to 18-strands. Among these are mechanisms of strand number accretion without domain duplication, like a loop-to-hairpin transition. These mechanisms illustrate perpetuation of repeat protein topology without genetic duplication, likely induced by the hydrophobic membrane. Finally, we find that the evolutionary trace is particularly prominent in the C-terminal half of OMPs, implicating this region in the nucleation of OMP folding.

PMID: 30489257 [PubMed - as supplied by publisher]

Intrinsic limits of information transmission in biochemical signalling motifs.

Sun, 11/18/2018 - 05:07
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Intrinsic limits of information transmission in biochemical signalling motifs.

Interface Focus. 2018 Dec 06;8(6):20180039

Authors: Suderman R, Deeds EJ

Abstract
All living things have evolved to sense changes in their environment in order to respond in adaptive ways. At the cellular level, these sensing systems generally involve receptor molecules at the cell surface, which detect changes outside the cell and relay those changes to the appropriate response elements downstream. With the advent of experimental technologies that can track signalling at the single-cell level, it has become clear that many signalling systems exhibit significant levels of 'noise,' manifesting as differential responses of otherwise identical cells to the same environment. This noise has a large impact on the capacity of cell signalling networks to transmit information from the environment. Application of information theory to experimental data has found that all systems studied to date encode less than 2.5 bits of information, with the majority transmitting significantly less than 1 bit. Given the growing interest in applying information theory to biological data, it is crucial to understand whether the low values observed to date represent some sort of intrinsic limit on information flow given the inherently stochastic nature of biochemical signalling events. In this work, we used a series of computational models to explore how much information a variety of common 'signalling motifs' can encode. We found that the majority of these motifs, which serve as the basic building blocks of cell signalling networks, can encode far more information (4-6 bits) than has ever been observed experimentally. In addition to providing a consistent framework for estimating information-theoretic quantities from experimental data, our findings suggest that the low levels of information flow observed so far in living system are not necessarily due to intrinsic limitations. Further experimental work will be needed to understand whether certain cell signalling systems actually can approach the intrinsic limits described here, and to understand the sources and purpose of the variation that reduces information flow in living cells.

PMID: 30443336 [PubMed]

Structural Basis for Binding of Allosteric Drug Leads in the Adenosine A1 Receptor.

Sun, 11/18/2018 - 05:07
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Structural Basis for Binding of Allosteric Drug Leads in the Adenosine A1 Receptor.

Sci Rep. 2018 Nov 15;8(1):16836

Authors: Miao Y, Bhattarai A, Nguyen ATN, Christopoulos A, May LT

Abstract
Despite intense interest in designing positive allosteric modulators (PAMs) as selective drugs of the adenosine A1 receptor (A1AR), structural binding modes of the receptor PAMs remain unknown. Using the first X-ray structure of the A1AR, we have performed all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) technique to determine binding modes of the A1AR allosteric drug leads. Two prototypical PAMs, PD81723 and VCP171, were selected. Each PAM was initially placed at least 20 Å away from the receptor. Extensive GaMD simulations using the AMBER and NAMD simulation packages at different acceleration levels captured spontaneous binding of PAMs to the A1AR. The simulations allowed us to identify low-energy binding modes of the PAMs at an allosteric site formed by the receptor extracellular loop 2 (ECL2), which are highly consistent with mutagenesis experimental data. Furthermore, the PAMs stabilized agonist binding in the receptor. In the absence of PAMs at the ECL2 allosteric site, the agonist sampled a significantly larger conformational space and even dissociated from the A1AR alone. In summary, the GaMD simulations elucidated structural binding modes of the PAMs and provided important insights into allostery in the A1AR, which will greatly facilitate the receptor structure-based drug design.

PMID: 30442899 [PubMed - in process]

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

Tue, 11/06/2018 - 08:10
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Natural language processing in text mining for structural modeling of protein complexes.

BMC Bioinformatics. 2018 03 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 - indexed for MEDLINE]

Gaussian accelerated molecular dynamics for elucidation of drug pathways.

Tue, 10/30/2018 - 05:34

Gaussian accelerated molecular dynamics for elucidation of drug pathways.

Expert Opin Drug Discov. 2018 Oct 29;:1-11

Authors: Bhattarai A, Miao Y

Abstract
INTRODUCTION: Understanding pathways and mechanisms of drug binding to receptors is important for rational drug design. Remarkable advances in supercomputing and methodological developments have opened a new era for application of computer simulations in predicting drug-receptor interactions at an atomistic level. Gaussian accelerated molecular dynamics (GaMD) is a computational enhanced sampling technique that works by adding a harmonic boost potential to reduce energy barriers. GaMD enables free energy calculations without the requirement of predefined collective variables. GaMD has proven useful in biomolecular simulations, in particular, the prediction of drug-receptor interactions. Areas covered: Herein, the authors review recent GaMD simulation studies that elucidated pathways of drug binding to proteins including the G-protein-coupled receptors and HIV protease. Expert opinion: GaMD is advantageous for enhanced simulations of, amongst many biological processes, drug binding to target receptors. Compared with conventional molecular dynamics, GaMD speeds up biomolecular simulations by orders of magnitude. GaMD enables routine drug binding simulations using personal computers with GPUs or common computing clusters. GaMD and, more broadly, enhanced sampling simulations are expected to dramatically increase our capabilities to determine the mechanisms of drug binding to a wide range of receptors in the near future. This will greatly facilitate computer-aided drug design.

PMID: 30371112 [PubMed - as supplied by publisher]

Identification of SLAC1 anion channel residues required for CO2/bicarbonate sensing and regulation of stomatal movements.

Fri, 10/12/2018 - 05:59
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Identification of SLAC1 anion channel residues required for CO2/bicarbonate sensing and regulation of stomatal movements.

Proc Natl Acad Sci U S A. 2018 Oct 09;:

Authors: Zhang J, Wang N, Miao Y, Hauser F, McCammon JA, Rappel WJ, Schroeder JI

Abstract
Increases in CO2 concentration in plant leaves due to respiration in the dark and the continuing atmospheric [CO2] rise cause closing of stomatal pores, thus affecting plant-water relations globally. However, the underlying CO2/bicarbonate (CO2/HCO3 -) sensing mechanisms remain unknown. [CO2] elevation in leaves triggers stomatal closure by anion efflux mediated via the SLAC1 anion channel localized in the plasma membrane of guard cells. Previous reconstitution analysis has suggested that intracellular bicarbonate ions might directly up-regulate SLAC1 channel activity. However, whether such a CO2/HCO3 - regulation of SLAC1 is relevant for CO2 control of stomatal movements in planta remains unknown. Here, we computationally probe for candidate bicarbonate-interacting sites within the SLAC1 anion channel via long-timescale Gaussian accelerated molecular dynamics (GaMD) simulations. Mutations of two putative bicarbonate-interacting residues, R256 and R321, impaired the enhancement of the SLAC1 anion channel activity by CO2/HCO3 - in Xenopus oocytes. Mutations of the neighboring charged amino acid K255 and residue R432 and the predicted gate residue F450 did not affect HCO3 - regulation of SLAC1. Notably, gas-exchange experiments with slac1-transformed plants expressing mutated SLAC1 proteins revealed that the SLAC1 residue R256 is required for CO2 regulation of stomatal movements in planta, but not for abscisic acid (ABA)-induced stomatal closing. Patch clamp analyses of guard cells show that activation of S-type anion channels by CO2/HCO3 -, but not by ABA, was impaired, indicating the relevance of R256 for CO2 signal transduction. Together, these analyses suggest that the SLAC1 anion channel is one of the physiologically relevant CO2/HCO3 - sensors in guard cells.

PMID: 30301791 [PubMed - as supplied by publisher]

A Computational Modeling Approach Predicts Interaction of the Antifungal Protein AFP from Aspergillus giganteus with Fungal Membranes via Its γ-Core Motif.

Fri, 10/05/2018 - 06:58
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A Computational Modeling Approach Predicts Interaction of the Antifungal Protein AFP from Aspergillus giganteus with Fungal Membranes via Its γ-Core Motif.

mSphere. 2018 Oct 03;3(5):

Authors: Utesch T, de Miguel Catalina A, Schattenberg C, Paege N, Schmieder P, Krause E, Miao Y, McCammon JA, Meyer V, Jung S, Mroginski MA

Abstract
Fungal pathogens kill more people per year globally than malaria or tuberculosis and threaten international food security through crop destruction. New sophisticated strategies to inhibit fungal growth are thus urgently needed. Among the potential candidate molecules that strongly inhibit fungal spore germination are small cationic, cysteine-stabilized proteins of the AFP family secreted by a group of filamentous Ascomycetes. Its founding member, AFP from Aspergillus giganteus, is of particular interest since it selectively inhibits the growth of filamentous fungi without affecting the viability of mammalian, plant, or bacterial cells. AFPs are also characterized by their high efficacy and stability. Thus, AFP can serve as a lead compound for the development of novel antifungals. Notably, all members of the AFP family comprise a γ-core motif which is conserved in all antimicrobial proteins from pro- and eukaryotes and known to interfere with the integrity of cytoplasmic plasma membranes. In this study, we used classical molecular dynamics simulations combined with wet laboratory experiments and nuclear magnetic resonance (NMR) spectroscopy to characterize the structure and dynamical behavior of AFP isomers in solution and their interaction with fungal model membranes. We demonstrate that the γ-core motif of structurally conserved AFP is the key for its membrane interaction, thus verifying for the first time that the conserved γ-core motif of antimicrobial proteins is directly involved in protein-membrane interactions. Furthermore, molecular dynamic simulations suggested that AFP does not destroy the fungal membrane by pore formation but covers its surface in a well-defined manner, using a multistep mechanism to destroy the membranes integrity.IMPORTANCE Fungal pathogens pose a serious danger to human welfare since they kill more people per year than malaria or tuberculosis and are responsible for crop losses worldwide. The treatment of fungal infections is becoming more complicated as fungi develop resistances against commonly used fungicides. Therefore, discovery and development of novel antifungal agents are of utmost importance.

PMID: 30282755 [PubMed - in process]

Computational Feasibility of an Exhaustive Search of Side-Chain Conformations in Protein-Protein Docking.

Wed, 09/19/2018 - 06:23
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Computational Feasibility of an Exhaustive Search of Side-Chain Conformations in Protein-Protein Docking.

J Comput Chem. 2018 Sep 18;:

Authors: Dauzhenka T, Kundrotas PJ, Vakser IA

Abstract
Protein-protein docking procedures typically perform the global scan of the proteins relative positions, followed by the local refinement of the putative matches. Because of the size of the search space, the global scan is usually implemented as rigid-body search, using computationally inexpensive intermolecular energy approximations. An adequate refinement has to take into account structural flexibility. Since the refinement performs conformational search of the interacting proteins, it is extremely computationally challenging, given the enormous amount of the internal degrees of freedom. Different approaches limit the search space by restricting the search to the side chains, rotameric states, coarse-grained structure representation, principal normal modes, and so on. Still, even with the approximations, the refinement presents an extreme computational challenge due to the very large number of the remaining degrees of freedom. Given the complexity of the search space, the advantage of the exhaustive search is obvious. The obstacle to such search is computational feasibility. However, the growing computational power of modern computers, especially due to the increasing utilization of Graphics Processing Unit (GPU) with large amount of specialized computing cores, extends the ranges of applicability of the brute-force search methods. This proof-of-concept study demonstrates computational feasibility of an exhaustive search of side-chain conformations in protein pocking. The procedure, implemented on the GPU architecture, was used to generate the optimal conformations in a large representative set of protein-protein complexes. © 2018 Wiley Periodicals, Inc.

PMID: 30226647 [PubMed - as supplied by publisher]

Acceleration of biomolecular kinetics in Gaussian accelerated molecular dynamics.

Fri, 08/24/2018 - 08:31
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Acceleration of biomolecular kinetics in Gaussian accelerated molecular dynamics.

J Chem Phys. 2018 Aug 21;149(7):072308

Authors: Miao Y

Abstract
Recent studies demonstrated that Gaussian accelerated molecular dynamics (GaMD) is a robust computational technique, which provides simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. However, the exact acceleration of biomolecular dynamics or speedup of kinetic rates in GaMD simulations and, more broadly, in enhanced sampling methods, remains a challenging task to be determined. Here, the GaMD acceleration is examined using alanine dipeptide in explicit solvent as a biomolecular model system. Relative to long conventional molecular dynamics simulation, GaMD simulations exhibited ∼36-67 times speedup for sampling of the backbone dihedral transitions. The acceleration depended on level of the GaMD boost potential. Furthermore, Kramers' rate theory was applied to estimate GaMD acceleration using simulation-derived diffusion coefficients, curvatures and barriers of free energy profiles. In most cases, the calculations also showed significant speedup of dihedral transitions in GaMD, although the GaMD acceleration factors tended to be underestimated by ∼3-96 fold. Because greater boost potential can be applied in GaMD simulations of systems with increased sizes, which potentially leads to higher acceleration, it is subject to future studies on accelerating the dynamics and recovering kinetic rates of larger biomolecules such as proteins and protein-protein/nucleic acid complexes.

PMID: 30134710 [PubMed - in process]

Lineage space and the propensity of bacterial cells to undergo growth transitions.

Thu, 08/23/2018 - 07:40
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Lineage space and the propensity of bacterial cells to undergo growth transitions.

PLoS Comput Biol. 2018 Aug 22;14(8):e1006380

Authors: Bandyopadhyay A, Wang H, Ray JCJ

Abstract
The molecular makeup of the offspring of a dividing cell gradually becomes phenotypically decorrelated from the parent cell by noise and regulatory mechanisms that amplify phenotypic heterogeneity. Such regulatory mechanisms form networks that contain thresholds between phenotypes. Populations of cells can be poised near the threshold so that a subset of the population probabilistically undergoes the phenotypic transition. We sought to characterize the diversity of bacterial populations around a growth-modulating threshold via analysis of the effect of non-genetic inheritance, similar to conditions that create antibiotic-tolerant persister cells and other examples of bet hedging. Using simulations and experimental lineage data in Escherichia coli, we present evidence that regulation of growth amplifies the dependence of growth arrest on cellular lineage, causing clusters of related cells undergo growth arrest in certain conditions. Our simulations predict that lineage correlations and the sensitivity of growth to changes in toxin levels coincide in a critical regime. Below the critical regime, the sizes of related growth arrested clusters are distributed exponentially, while in the critical regime clusters sizes are more likely to become large. Furthermore, phenotypic diversity can be nearly as high as possible near the critical regime, but for most parameter values it falls far below the theoretical limit. We conclude that lineage information is indispensable for understanding regulation of cellular growth.

PMID: 30133447 [PubMed - as supplied by publisher]

Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model.

Tue, 08/21/2018 - 05:28
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Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model.

Biophys J. 2018 Aug 08;:

Authors: Anishchenko I, Kundrotas PJ, Vakser IA

Abstract
The energy function is the key component of protein modeling methodology. This work presents a semianalytical approach to the development of contact potentials for protein structure modeling. Residue-residue and atom-atom contact energies were derived by maximizing the probability of observing native sequences in a nonredundant set of protein structures. The optimization task was formulated as an inverse statistical mechanics problem applied to the Potts model. Its solution by pseudolikelihood maximization provides consistent estimates of coupling constants at atomic and residue levels. The best performance was achieved when interacting atoms were grouped according to their physicochemical properties. For individual protein structures, the performance of the contact potentials in distinguishing near-native structures from the decoys is similar to the top-performing scoring functions. The potentials also yielded significant improvement in the protein docking success rates. The potentials recapitulated experimentally determined protein stability changes upon point mutations and protein-protein binding affinities. The approach offers a different perspective on knowledge-based potentials and may serve as the basis for their further development.

PMID: 30122295 [PubMed - as supplied by publisher]


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