Publications by authors named "Daria B Kokh"

24 Publications

  • Page 1 of 1

Brownian Dynamics Simulations of Proteins in the Presence of Surfaces: Long-Range Electrostatics and Mean-Field Hydrodynamics.

J Chem Theory Comput 2021 Jun 30;17(6):3510-3524. Epub 2021 Mar 30.

Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany.

Simulations of macromolecular diffusion and adsorption in confined environments can offer valuable mechanistic insights into numerous biophysical processes. In order to model solutes at atomic detail on relevant time scales, Brownian dynamics simulations can be carried out with the approximation of rigid body solutes moving through a continuum solvent. This allows the precomputation of interaction potential grids for the solutes, thereby allowing the computationally efficient calculation of forces. However, hydrodynamic and long-range electrostatic interactions cannot be fully treated with grid-based approaches alone. Here, we develop a treatment of both hydrodynamic and electrostatic interactions to include the presence of surfaces by modeling grid-based and long-range interactions. We describe its application to simulate the self-association and many-molecule adsorption of the well-characterized protein hen egg-white lysozyme to mica-like and silica-like surfaces. We find that the computational model can recover a number of experimental observables of the adsorption process and provide insights into their determinants. The computational model is implemented in the Simulation of Diffusional Association (SDA) software package.
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http://dx.doi.org/10.1021/acs.jctc.0c01312DOI Listing
June 2021

Structure-kinetic relationship reveals the mechanism of selectivity of FAK inhibitors over PYK2.

Cell Chem Biol 2021 May 25;28(5):686-698.e7. Epub 2021 Jan 25.

Structural Genomics Consortium, Goethe University Frankfurt, Buchmann Institute for Molecular Life Sciences, Max-von-Laue-Straße 15, 60438 Frankfurt am Main, Germany; Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Buchmann Institute for Molecular Life Sciences, Max-von-Laue-Straße 9, 60438 Frankfurt am Main, Germany; German Cancer network DKTK and Frankfurt Cancer Institute (FCI), Goethe University Frankfurt, Frankfurt am Main, Germany. Electronic address:

There is increasing evidence of a significant correlation between prolonged drug-target residence time and increased drug efficacy. Here, we report a structural rationale for kinetic selectivity between two closely related kinases: focal adhesion kinase (FAK) and proline-rich tyrosine kinase 2 (PYK2). We found that slowly dissociating FAK inhibitors induce helical structure at the DFG motif of FAK but not PYK2. Binding kinetic data, high-resolution structures and mutagenesis data support the role of hydrophobic interactions of inhibitors with the DFG-helical region, providing a structural rationale for slow dissociation rates from FAK and kinetic selectivity over PYK2. Our experimental data correlate well with computed relative residence times from molecular simulations, supporting a feasible strategy for rationally optimizing ligand residence times. We suggest that the interplay between the protein structural mobility and ligand-induced effects is a key regulator of the kinetic selectivity of inhibitors of FAK versus PYK2.
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http://dx.doi.org/10.1016/j.chembiol.2021.01.003DOI Listing
May 2021

A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories.

J Chem Phys 2020 Sep;153(12):125102

Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany.

The dissociation of ligands from proteins and other biomacromolecules occurs over a wide range of timescales. For most pharmaceutically relevant inhibitors, these timescales are far beyond those that are accessible by conventional molecular dynamics (MD) simulation. Consequently, to explore ligand egress mechanisms and compute dissociation rates, it is necessary to enhance the sampling of ligand unbinding. Random Acceleration MD (RAMD) is a simple method to enhance ligand egress from a macromolecular binding site, which enables the exploration of ligand egress routes without prior knowledge of the reaction coordinates. Furthermore, the τRAMD procedure can be used to compute the relative residence times of ligands. When combined with a machine-learning analysis of protein-ligand interaction fingerprints (IFPs), molecular features that affect ligand unbinding kinetics can be identified. Here, we describe the implementation of RAMD in GROMACS 2020, which provides significantly improved computational performance, with scaling to large molecular systems. For the automated analysis of RAMD results, we developed MD-IFP, a set of tools for the generation of IFPs along unbinding trajectories and for their use in the exploration of ligand dynamics. We demonstrate that the analysis of ligand dissociation trajectories by mapping them onto the IFP space enables the characterization of ligand dissociation routes and metastable states. The combined implementation of RAMD and MD-IFP provides a computationally efficient and freely available workflow that can be applied to hundreds of compounds in a reasonable computational time and will facilitate the use of τRAMD in drug design.
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http://dx.doi.org/10.1063/5.0019088DOI Listing
September 2020

Recent progress in molecular simulation methods for drug binding kinetics.

Curr Opin Struct Biol 2020 10 6;64:126-133. Epub 2020 Aug 6.

Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany; Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, Germany. Electronic address:

Due to the contribution of drug-target binding kinetics to drug efficacy, there is a high level of interest in developing methods to predict drug-target binding kinetic parameters. During the review period, a wide range of enhanced sampling molecular dynamics simulation-based methods has been developed for computing drug-target binding kinetics and studying binding and unbinding mechanisms. Here, we assess the performance of these methods considering two benchmark systems in detail: mutant T4 lysozyme-ligand complexes and a large set of N-HSP90-inhibitor complexes. The results indicate that some of the simulation methods can already be usefully applied in drug discovery or lead optimization programs but that further studies on more high-quality experimental benchmark datasets are necessary to improve and validate computational methods.
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http://dx.doi.org/10.1016/j.sbi.2020.06.022DOI Listing
October 2020

Druggability Assessment in TRAPP Using Machine Learning Approaches.

J Chem Inf Model 2020 03 11;60(3):1685-1699. Epub 2020 Mar 11.

Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany.

Accurate protein druggability predictions are important for the selection of drug targets in the early stages of drug discovery. Because of the flexible nature of proteins, the druggability of a binding pocket may vary due to conformational changes. We have therefore developed two statistical models, a logistic regression model (TRAPP-LR) and a convolutional neural network model (TRAPP-CNN), for predicting druggability and how it varies with changes in the spatial and physicochemical properties of a binding pocket. These models are integrated into TRAnsient Pockets in Proteins (TRAPP), a tool for the analysis of binding pocket variations along a protein motion trajectory. The models, which were trained on publicly available and self-augmented datasets, show equivalent or superior performance to existing methods on test sets of protein crystal structures and have sufficient sensitivity to identify potentially druggable protein conformations in trajectories from molecular dynamics simulations. Visualization of the evidence for the decisions of the models in TRAPP facilitates identification of the factors affecting the druggability of protein binding pockets.
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http://dx.doi.org/10.1021/acs.jcim.9b01185DOI Listing
March 2020

Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times.

Front Mol Biosci 2019 24;6:36. Epub 2019 May 24.

Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.

Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.
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http://dx.doi.org/10.3389/fmolb.2019.00036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543870PMC
May 2019

Estimation of Drug-Target Residence Times by τ-Random Acceleration Molecular Dynamics Simulations.

J Chem Theory Comput 2018 Jul 4;14(7):3859-3869. Epub 2018 Jun 4.

Molecular and Cellular Modeling Group , Heidelberg Institute for Theoretical Studies , Heidelberg 69118 , Germany.

Drug-target residence time (τ), one of the main determinants of drug efficacy, remains highly challenging to predict computationally and, therefore, is usually not considered in the early stages of drug design. Here, we present an efficient computational method, τ-random acceleration molecular dynamics (τRAMD), for the ranking of drug candidates by their residence time and obtaining insights into ligand-target dissociation mechanisms. We assessed τRAMD on a data set of 70 diverse drug-like ligands of the N-terminal domain of HSP90α, a pharmaceutically important target with a highly flexible binding site, obtaining computed relative residence times with an accuracy of about 2.3τ for 78% of the compounds and less than 2.0τ within congeneric series. Analysis of dissociation trajectories reveals features that affect ligand unbinding rates, including transient polar interactions and steric hindrance. These results suggest that τRAMD will be widely applicable as a computationally efficient aid to improving drug residence times during lead optimization.
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http://dx.doi.org/10.1021/acs.jctc.8b00230DOI Listing
July 2018

New approaches for computing ligand-receptor binding kinetics.

Curr Opin Struct Biol 2018 04 11;49:1-10. Epub 2017 Nov 11.

Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany; Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany. Electronic address:

The recent and growing evidence that the efficacy of a drug can be correlated to target binding kinetics has seeded the development of a multitude of novel methods aimed at computing rate constants for receptor-ligand binding processes, as well as gaining an understanding of the binding and unbinding pathways and the determinants of structure-kinetic relationships. These new approaches include various types of enhanced sampling molecular dynamics simulations and the combination of energy-based models with chemometric analysis. We assess these approaches in the light of the varying levels of complexity of protein-ligand binding processes.
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http://dx.doi.org/10.1016/j.sbi.2017.10.001DOI Listing
April 2018

TRAPP webserver: predicting protein binding site flexibility and detecting transient binding pockets.

Nucleic Acids Res 2017 07;45(W1):W325-W330

Molecular and Cellular Modeling group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Baden-Württemberg 69118, Germany.

The TRAnsient Pockets in Proteins (TRAPP) webserver provides an automated workflow that allows users to explore the dynamics of a protein binding site and to detect pockets or sub-pockets that may transiently open due to protein internal motion. These transient or cryptic sub-pockets may be of interest in the design and optimization of small molecular inhibitors for a protein target of interest. The TRAPP workflow consists of the following three modules: (i) TRAPP structure- generation of an ensemble of structures using one or more of four possible molecular simulation methods; (ii) TRAPP analysis-superposition and clustering of the binding site conformations either in an ensemble of structures generated in step (i) or in PDB structures or trajectories uploaded by the user; and (iii) TRAPP pocket-detection, analysis, and visualization of the binding pocket dynamics and characteristics, such as volume, solvent-exposed area or properties of surrounding residues. A standard sequence conservation score per residue or a differential score per residue, for comparing on- and off-targets, can be calculated and displayed on the binding pocket for an uploaded multiple sequence alignment file, and known protein sequence annotations can be displayed simultaneously. The TRAPP webserver is freely available at http://trapp.h-its.org.
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http://dx.doi.org/10.1093/nar/gkx277DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5570179PMC
July 2017

Kinetics for Drug Discovery: an industry-driven effort to target drug residence time.

Drug Discov Today 2017 06 13;22(6):896-911. Epub 2017 Apr 13.

Department of Pharmaceutical Chemistry, University of Vienna, UZA 2, Althanstrasse 14, 1090 Vienna, Austria. Electronic address:

A considerable number of approved drugs show non-equilibrium binding characteristics, emphasizing the potential role of drug residence times for in vivo efficacy. Therefore, a detailed understanding of the kinetics of association and dissociation of a target-ligand complex might provide crucial insight into the molecular mechanism-of-action of a compound. This deeper understanding will help to improve decision making in drug discovery, thus leading to a better selection of interesting compounds to be profiled further. In this review, we highlight the contributions of the Kinetics for Drug Discovery (K4DD) Consortium, which targets major open questions related to binding kinetics in an industry-driven public-private partnership.
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http://dx.doi.org/10.1016/j.drudis.2017.02.002DOI Listing
June 2017

Perturbation Approaches for Exploring Protein Binding Site Flexibility to Predict Transient Binding Pockets.

J Chem Theory Comput 2016 Aug 25;12(8):4100-13. Epub 2016 Jul 25.

Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies , 69118 Heidelberg, Germany.

Simulations of the long-time scale motions of a ligand binding pocket in a protein may open up new perspectives for the design of compounds with steric or chemical properties differing from those of known binders. However, slow motions of proteins are difficult to access using standard molecular dynamics (MD) simulations and are thus usually neglected in computational drug design. Here, we introduce two nonequilibrium MD approaches to identify conformational changes of a binding site and detect transient pockets associated with these motions. The methods proposed are based on the rotamerically induced perturbation (RIP) MD approach, which employs perturbation of side-chain torsional motion for initiating large-scale protein movement. The first approach, Langevin-RIP (L-RIP), entails a series of short Langevin MD simulations, each starting with perturbation of one of the side-chains lining the binding site of interest. L-RIP provides extensive sampling of conformational changes of the binding site. In less than 1 ns of MD simulation with L-RIP, we observed distortions of the α-helix in the ATP binding site of HSP90 and flipping of the DFG loop in Src kinase. In the second approach, RIPlig, a perturbation is applied to a pseudoligand placed in different parts of a binding pocket, which enables flexible regions of the binding site to be identified in a small number of 10 ps MD simulations. The methods were evaluated for four test proteins displaying different types and degrees of binding site flexibility. Both methods reveal all transient pocket regions in less than a total of 10 ns of simulations, even though many of these regions remained closed in 100 ns conventional MD. The proposed methods provide computationally efficient tools to explore binding site flexibility and can aid in the functional characterization of protein pockets, and the identification of transient pockets for ligand design.
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http://dx.doi.org/10.1021/acs.jctc.6b00101DOI Listing
August 2016

Protein Binding Pocket Dynamics.

Acc Chem Res 2016 05 25;49(5):809-15. Epub 2016 Apr 25.

Heidelberg Institute for Theoretical Studies (HITS) , Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany.

The dynamics of protein binding pockets are crucial for their interaction specificity. Structural flexibility allows proteins to adapt to their individual molecular binding partners and facilitates the binding process. This implies the necessity to consider protein internal motion in determining and predicting binding properties and in designing new binders. Although accounting for protein dynamics presents a challenge for computational approaches, it expands the structural and physicochemical space for compound design and thus offers the prospect of improved binding specificity and selectivity. A cavity on the surface or in the interior of a protein that possesses suitable properties for binding a ligand is usually referred to as a binding pocket. The set of amino acid residues around a binding pocket determines its physicochemical characteristics and, together with its shape and location in a protein, defines its functionality. Residues outside the binding site can also have a long-range effect on the properties of the binding pocket. Cavities with similar functionalities are often conserved across protein families. For example, enzyme active sites are usually concave surfaces that present amino acid residues in a suitable configuration for binding low molecular weight compounds. Macromolecular binding pockets, on the other hand, are located on the protein surface and are often shallower. The mobility of proteins allows the opening, closing, and adaptation of binding pockets to regulate binding processes and specific protein functionalities. For example, channels and tunnels can exist permanently or transiently to transport compounds to and from a binding site. The influence of protein flexibility on binding pockets can vary from small changes to an already existent pocket to the formation of a completely new pocket. Here, we review recent developments in computational methods to detect and define binding pockets and to study pocket dynamics. We introduce five different classes of protein pocket dynamics: (1) appearance/disappearance of a subpocket in an existing pocket; (2) appearance/disappearance of an adjacent pocket on the protein surface in the direct vicinity of an already existing pocket; (3) pocket breathing, which may be caused by side-chain fluctuations or backbone or interdomain vibrational motion; (4) opening/closing of a channel or tunnel, connecting a pocket inside the protein with solvent, including lid motion; and (5) the appearance/disappearance of an allosteric pocket at a site on a protein distinct from an already existing pocket with binding of a ligand to the allosteric binding site affecting the original pocket. We suggest that the class of pocket dynamics, as well as the type and extent of protein motion affecting the binding pocket, should be factors considered in choosing the most appropriate computational approach to study a given binding pocket. Furthermore, we examine the relationship between pocket dynamics classes and induced fit, conformational selection, and gating models of ligand binding on binding kinetics and thermodynamics. We discuss the implications of protein binding pocket dynamics for drug design and conclude with potential future directions for computational analysis of protein binding pocket dynamics.
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http://dx.doi.org/10.1021/acs.accounts.5b00516DOI Listing
May 2016

Modeling and simulation of protein-surface interactions: achievements and challenges.

Q Rev Biophys 2016 ;49:e4

Heidelberg Institute for Theoretical Studies (HITS),Schloss-Wolfsbrunnenweg 35,69118 Heidelberg,Germany.

Understanding protein-inorganic surface interactions is central to the rational design of new tools in biomaterial sciences, nanobiotechnology and nanomedicine. Although a significant amount of experimental research on protein adsorption onto solid substrates has been reported, many aspects of the recognition and interaction mechanisms of biomolecules and inorganic surfaces are still unclear. Theoretical modeling and simulations provide complementary approaches for experimental studies, and they have been applied for exploring protein-surface binding mechanisms, the determinants of binding specificity towards different surfaces, as well as the thermodynamics and kinetics of adsorption. Although the general computational approaches employed to study the dynamics of proteins and materials are similar, the models and force-fields (FFs) used for describing the physical properties and interactions of material surfaces and biological molecules differ. In particular, FF and water models designed for use in biomolecular simulations are often not directly transferable to surface simulations and vice versa. The adsorption events span a wide range of time- and length-scales that vary from nanoseconds to days, and from nanometers to micrometers, respectively, rendering the use of multi-scale approaches unavoidable. Further, changes in the atomic structure of material surfaces that can lead to surface reconstruction, and in the structure of proteins that can result in complete denaturation of the adsorbed molecules, can create many intermediate structural and energetic states that complicate sampling. In this review, we address the challenges posed to theoretical and computational methods in achieving accurate descriptions of the physical, chemical and mechanical properties of protein-surface systems. In this context, we discuss the applicability of different modeling and simulation techniques ranging from quantum mechanics through all-atom molecular mechanics to coarse-grained approaches. We examine uses of different sampling methods, as well as free energy calculations. Furthermore, we review computational studies of protein-surface interactions and discuss the successes and limitations of current approaches.
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http://dx.doi.org/10.1017/S0033583515000256DOI Listing
October 2016

When the Label Matters: Adsorption of Labeled and Unlabeled Proteins on Charged Surfaces.

Nano Lett 2015 Nov 28;15(11):7508-13. Epub 2015 Oct 28.

Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies , 69117 Heidelberg, Germany.

Fluorescent labels are often attached to proteins to monitor binding and adsorption processes. Docking simulations for native hen egg white lysozyme (HEWL) and HEWL labeled with fluorescein isothiocyanate show that these adsorb differently on charged surfaces. Attachment of even a small label can significantly change the interaction properties of a protein. Thus, the results of experiments with fluorescently labeled proteins should be interpreted by modeling the structures and computing the interaction properties of both labeled and unlabeled species.
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http://dx.doi.org/10.1021/acs.nanolett.5b03168DOI Listing
November 2015

SDA 7: A modular and parallel implementation of the simulation of diffusional association software.

J Comput Chem 2015 Aug 29;36(21):1631-45. Epub 2015 Jun 29.

Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, 69118, Heidelberg, Germany.

The simulation of diffusional association (SDA) Brownian dynamics software package has been widely used in the study of biomacromolecular association. Initially developed to calculate bimolecular protein-protein association rate constants, it has since been extended to study electron transfer rates, to predict the structures of biomacromolecular complexes, to investigate the adsorption of proteins to inorganic surfaces, and to simulate the dynamics of large systems containing many biomacromolecular solutes, allowing the study of concentration-dependent effects. These extensions have led to a number of divergent versions of the software. In this article, we report the development of the latest version of the software (SDA 7). This release was developed to consolidate the existing codes into a single framework, while improving the parallelization of the code to better exploit modern multicore shared memory computer architectures. It is built using a modular object-oriented programming scheme, to allow for easy maintenance and extension of the software, and includes new features, such as adding flexible solute representations. We discuss a number of application examples, which describe some of the methods available in the release, and provide benchmarking data to demonstrate the parallel performance.
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http://dx.doi.org/10.1002/jcc.23971DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4755232PMC
August 2015

TRAPP: a tool for analysis of transient binding pockets in proteins.

J Chem Inf Model 2013 May 16;53(5):1235-52. Epub 2013 May 16.

Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.

We present TRAPP (TRAnsient Pockets in Proteins), a new automated software platform for tracking, analysis, and visualization of binding pocket variations along a protein motion trajectory or within an ensemble of protein structures that may encompass conformational changes ranging from local side chain fluctuations to global backbone motions. TRAPP performs accurate grid-based calculations of the shape and physicochemical characteristics of a binding pocket for each structure and detects the conserved and transient regions of the pocket in an ensemble of protein conformations. It also provides tools for tracing the opening of a particular subpocket and residues that contribute to the binding site. TRAPP thus enables an assessment of the druggability of a disease-related target protein taking its flexibility into account.
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http://dx.doi.org/10.1021/ci4000294DOI Listing
May 2013

Docking of ubiquitin to gold nanoparticles.

ACS Nano 2012 Nov 10;6(11):9863-78. Epub 2012 Oct 10.

Center S3, CNR Institute Nanoscience, Via Campi 213/A, 41125 Modena, Italy.

Protein-nanoparticle associations have important applications in nanoscience and nanotechnology such as targeted drug delivery and theranostics. However, the mechanisms by which proteins recognize nanoparticles and the determinants of specificity are still poorly understood at the microscopic level. Gold is a promising material in nanoparticles for nanobiotechnology applications because of the ease of its functionalization and its tunable optical properties. Ubiquitin is a small, cysteine-free protein (ubiquitous in eukaryotes) whose binding to gold nanoparticles has been characterized recently by nuclear magnetic resonance (NMR). To reveal the molecular basis of these protein-nanoparticle interactions, we performed simulations at multiple levels (ab initio quantum mechanics, classical molecular dynamics and Brownian dynamics) and compared the results with experimental data (circular dichroism and NMR). The results provide a model of the ensemble of structures constituting the ubiquitin-gold surface complex, and insights into the driving forces for the binding of ubiquitin to gold nanoparticles, the role of nanoparticle surfactants (citrate) in the association process, and the origin of the perturbations in the NMR chemical shifts.
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http://dx.doi.org/10.1021/nn303444bDOI Listing
November 2012

A quantitative, real-time assessment of binding of peptides and proteins to gold surfaces.

Chemistry 2011 Jan 1;17(4):1327-36. Epub 2010 Dec 1.

Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel.

Interactions of peptides and proteins with inorganic surfaces are important to both natural and artificial systems; however, a detailed understanding of such interactions is lacking. In this study, we applied new approaches to quantitatively measure the binding of amino acids and proteins to gold surfaces. Real-time surface plasmon resonance (SPR) measurements showed that TEM1-β-lactamase inhibitor protein (BLIP) interacts only weakly with Au nanoparticles (NPs). However, fusion of three histidine residues to BLIP (3H-BLIP) resulted in a significant increase in the binding to the Au NPs, which further increased when the histidine tail was extended to six histidines (6H-BLIP). Further increasing the number of His residues had no effect on the binding. A parallel study using continuous (111)-textured Au surfaces and single-crystalline, (111)-oriented, Au islands by ellipsometry, FTIR, and localized surface plasmon resonance (LSPR) spectroscopy further confirmed the results, validating the broad applicability of Au NPs as model surfaces. Evaluating the binding of all other natural amino acid homotripeptides fused to BLIP (except Cys and Pro) showed that aromatic and positively-charged residues bind preferentially to Au with respect to small aliphatic and negatively charged residues, and that the rate of association is related to the potency of binding. The binding of all fusions was irreversible. These findings were substantiated by SPR measurements of synthesized, free, soluble tripeptides using Au-NP-modified SPR chips. Here, however, the binding was reversible allowing for determination of binding affinities that correlate with the binding potencies of the related BLIP fusions. Competition assays performed between 3H-BLIP and the histidine tripeptide (3 His) suggest that Au binding residues promote the adsorption of proteins on the surface, and by this facilitate the irreversible interaction of the polypeptide chain with Au. The binding of amino acids to Au was simulated by using a continuum solvent model, showing agreement with the experimental values. These results, together with the observed binding potencies and kinetics of the BLIP fusions and free peptides, suggest a binding mechanism that is markedly different from biological protein-protein interactions.
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http://dx.doi.org/10.1002/chem.201001781DOI Listing
January 2011

ProMetCS: An Atomistic Force Field for Modeling Protein-Metal Surface Interactions in a Continuum Aqueous Solvent.

J Chem Theory Comput 2010 May 16;6(5):1753-68. Epub 2010 Apr 16.

Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Schloss-Wolfsbrunnenweg 35, D-69118 Heidelberg, Germany, INFM-CNR National Research Center on nanoStructures and BioSystems at Surface (S3), Modena, Italy, Centre for Systems Biology, School of Biosciences, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom, and Ludwig Maximilians University, Munich, German.

In order to study protein-inorganic surface association processes, we have developed a physics-based energy model, the ProMetCS model, which describes protein-surface interactions at the atomistic level while treating the solvent as a continuum. Here, we present an approach to modeling the interaction of a protein with an atomically flat Au(111) surface in an aqueous solvent. Protein-gold interactions are modeled as the sum of van der Waals, weak chemisorption, and electrostatic interactions, as well as the change in free energy due to partial desolvation of the protein and the metal surface upon association. This desolvation energy includes the effects of water-protein, water-surface, and water-water interactions and has been parametrized using molecular dynamics (MD) simulations of water molecules and a test atom at a gold-water interface. The proposed procedure for computing the energy terms is mostly grid-based and is therefore efficient for application to long-time simulations of protein binding processes. The approach was tested for capped amino acid residues whose potentials of mean force for binding to a gold surface were computed and compared with those obtained previously in MD simulations with water treated explicitly. Calculations show good quantitative agreement with the results from MD simulations for all but one amino acid (Trp), as well as correspondence with available experimental data on the adhesion properties of amino acids.
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http://dx.doi.org/10.1021/ct100086jDOI Listing
May 2010

Photodissociation of CH3Cl, C2H5Cl, and C6H5Cl on the Ag(111) surface: ab initio embedded cluster and configuration interaction study.

J Chem Phys 2010 Feb;132(7):074707

Theoretische Chemie, Fachbereich C, Bergische Universität Wuppertal, Gaussstr. 20, D-Wuppertal 42097, Germany.

We report a comparative study of the photoinduced C-Cl bond cleavage in three Rd-Cl molecules (Rd=CH(3), C(2)H(5), and C(6)H(5) radicals) on the Ag(111) surface. The ground, lowest excited states as well as anion states of adsorbed molecules have been computed at their equilibrium geometry and along the C-Cl dissociation pathway using the ab initio embedded cluster and multireference configuration interaction methods. The anion state can be formed by photoinduced electron transfer from the substrate to an adsorbate and is strongly bound to the surface in contrast with the electronic states of the adsorbate itself, which are only weakly perturbed by the silver surface. The excitation energy of the anion state lies lower in the Franck-Condon region than that of the lowest singlet excited state for all adsorbates and correlates directly with the dissociation products: adsorbed chlorine atom and the gas phase or adsorbed radical for Rd=CH(3), C(2)H(5), and C(6)H(5), respectively. The computed redshift of the photodissociation spectrum for the substrate-mediated photodissociation process relative to the corresponding gas-phase reaction is approximately 2 eV for CH(3)Cl and C(2)H(5)Cl, and approximately 1 eV for C(6)H(5)Cl, which result is in good agreement with experimental data.
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http://dx.doi.org/10.1063/1.3322289DOI Listing
February 2010

Flexible side chain models improve enrichment rates in in silico screening.

J Med Chem 2008 Oct 5;51(19):5919-31. Epub 2008 Sep 5.

Fachbereich C-Mathematik and Naturwissenschaften, Bergische Universität Wuppertal, Wuppertal, Germany.

While modern docking methods often predict accurate binding modes, affinity calculations remain challenging and enrichment rates of in silico screening methods unsatisfactory. Inadequate treatment of induced fit effects is one major shortcoming of existing in silico screening methods. Here we investigate enrichment rates of rigid-, soft- and flexible-receptor models for 12 diverse receptors using libraries containing up to 13000 molecules. For the rigid-receptor model, we observed high enrichment (EF1 > 20) only for four target proteins. A soft-receptor model showed improved docking rates at the expense of reduced enrichment rates. A flexible side-chain model with flexible dihedral angles for up to 12 amino acids (3-8 flexible side chains) increased both binding propensity and enrichment rates: EF1 values increased by approximately 35% on average with respect to rigid docking. We find on average 4 known ligands in the top 10 molecules in the rank-ordered databases for the receptors investigated.
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http://dx.doi.org/10.1021/jm800217kDOI Listing
October 2008

Theoretical study of the CH2 + O photodissociation of formaldehyde adsorbed on the Ag(111) surface.

J Phys Chem B 2005 Sep;109(38):18070-80

Fachbereich C-Mathematik und Naturwissenschaften, Bergische Universität Wuppertal, Gaussstrasse 20, D-42119 Wuppertal, Germany.

Configuration interaction calculations of the ground and excited states of the H2CO molecule adsorbed on the Ag(111) surface have been carried out to study the photoinduced dissociation process leading to polymerization of formaldehyde. The metal-adsorbate system has been described by the embedded cluster and multireference configuration interaction methods. The pi electron-attachment H2CO- and n-pi* internally excited H2CO* states have been considered as possible intermediates. The calculations have shown that H2CO* is only very weakly bound on Ag(111), and thus that the dissociation of adsorbed formaldehyde due to internal excitation is unlikely. By contrast, the H2CO- anion is strongly bound to Ag(111) and gains additional vibrational energy along the C-O stretch coordinate via Franck-Condon excitation from the neutral molecule. Computed energy variations of adsorbed H2CO and H2CO- at different key geometries along the pathway for C-O bond cleavage make evident, however, that complete dissociation is very difficult to attain on the potential energy surface of either of these states. Instead, reneutralization of the vibrationally excited anion by electron transfer back to the substrate is the most promising means of breaking the C-O bond, with subsequent formation of the coadsorbed O and CH2 fragments. Furthermore, it has been demonstrated that the most stable state for both dissociation fragments on Ag(111) is a closed-shell singlet, with binding energies relative to the gas-phase products of approximately 3.2 and approximately 1.3 eV for O and CH2, respectively. Further details of the reaction mechanism for the photoinduced C-O bond cleavage of H2CO on the Ag(111) surface are also given.
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http://dx.doi.org/10.1021/jp051728aDOI Listing
September 2005

HI photofragmentation revisited. Comment on "Probing excited electronic states using vibrationally mediated photolysis: Application to hydrogen iodide".

J Phys Chem A 2005 Apr;109(13):3094-6

Bergische Universität Wuppertal, Fachbereich C-Theoretische Chemie, Gaussstrasse 20, D-42097 Wuppertal, Germany.

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http://dx.doi.org/10.1021/jp044146mDOI Listing
April 2005

Theoretical study of the UV photodissociation of Cl2: potentials, transition moments, extinction coefficients, and Cl*/Cl branching ratio.

J Chem Phys 2004 Jun;120(24):11549-56

Bergische Universitat Wuppertal, Fachbereich C-Theoretische Chemie, Gaussstrasse 20, D-42097 Wuppertal, Germany.

Potential energy curves for the X (1)Sigma(g) (+) ground state and Omega=0(u) (+), 1(u) valence states and dipole moments for the 0(u) (+), 1(u)-X transitions are obtained in an ab initio configuration interaction study of Cl(2) including spin-orbit coupling. In contrast to common assumptions, it is found that the B (3)Pi(0(+)u)-X transition moment strongly depends on internuclear distance, which has an important influence on the Cl(2) photodissociation. Computed energy curves and transition moments are employed to calculate the A, B, C<--X extinction coefficients, the total spectrum for the first absorption band, and the Cl(*)((2)P(1/2))/Cl((2)P(3/2)) branching ratio as a function of excitation wavelength. The calculated data are shown to be in good agreement with available experimental results.
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http://dx.doi.org/10.1063/1.1753554DOI Listing
June 2004