Complete protein–protein kinetics by molecular dynamics

From Stefan Doerr

Proteins are essential in the regulation of most of the processes in a cell. They do so by interacting with other molecules, including other proteins.
Protein-protein interactions have been the subject of study for a long time using both experimental and computational methods, however they have eluded the atomic-level analysis and accuracy that unbiased molecular dynamics simulations can provide.

While protein-ligand interactions have been simulated already in many studies including our own (,, these interactions are relatively simple compared to two proteins interacting. Due to the size of proteins and their potential interaction sites, protein-protein interactions happen on timescales that used to be out of our computational reach until now.

In this study, Frank Noe’s team in collaboration with our group has been able to produce simulations that investigate both the binding and unbinding processes of two proteins called Barnase/Barstar. This was only possible through the use of GPU based ACEMD simulations ( and adaptive sampling methods such as those implemented in the HTMD software ( and the analysis of powerful Markov model methods.

Thus we were able to obtain an atomic level description of interaction states, equilibrium populations and kinetics of the binding process of the two proteins. Additionally, investigations on the effect of mutations on this process were reported. All results were validated with reference to experimental studies previously performed.

This study is of great interest as it demonstrates the possibility of protein-protein investigation using MD and opens the gates to the investigation of other very important protein-protein interaction processes.

Plattner N. et al. Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. Nat. Chem. (2017).

Franck ChevalierComplete protein–protein kinetics by molecular dynamics
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A Decade of GPUs based MD

By Gerard Martinez-Rosell

ACEMD was introduced in the field of Molecular Dynamics (MD) back in 2008. Its release, together with other MD engines, revolutionized the simulation throughput by leveraging the intrinsic parallelism present in a specific computer processor termed GPU (Graphic Processor Unit). Using a cluster of GPUs or a distributed network like GPUGRID one is able to reach performances of HPC-class computers using commodity software, yielding a significant (x10) reduction in the Euro/simulated time cost. Furthermore, the evolution of GPUs performance, which is mainly driven by a market demanding more graphic processing power (e.g. in videogames), has doubled approximately every 3 years and with it, since 2008, ACEMD performance has increased 2.1x. To illustrate this fact, we have compiled the maximum simulated time per year published by prof. Gianni de Fabritiis using ACEMD (which yields the oldest historic data) and we extrapolate that the MD field will reach the second aggregated time timescale by 2022.


It is really interesting to envision how things will have changed by then and how the increased performance will help to overcome current challenges. For instance, two of the present issues in MD are polarizability and fix protonation. These limitations are a model simplification necessary to reduce the computational cost that could be overcome in exchange of more computational power. One could easily picture that, in a context of computer power abundance, polarizable force fields and constant-pH simulations will become much more popular and the main choices to address the study of systems especially sensitive to those effects such as certain ligand-protein binding complexes or pH-sensitive proteins. Furthermore, access to increased computational power will probably motivate the community to tackle currently impossible approaches such as mesoscale simulations (e.g. whole protein cascade pathway, full virus, etc.).

Analysis and Perspective

We have analyzed a large number of MD studies carried out since the release of ACEMD engine in 2008. One important lesson we can learn from these studies is that, while an initial phase of proof of concept and technique validation is necessary to evolve and mature MD simulations as any other technique, the field is getting closer to the inflexion point where both accuracy (i.e. force field improvement) and precision (i.e. higher throughput) together with the introduction of tools that decrease the learning curve and entry-point to the technology (e.g. HTMD software package) will allow MD to become a mainstream technique routinely used in drug discovery. This inflexion point will have several practical implications, including a shift of the current paradigm to a new one in which MD is not only used in retrospective and validation studies but into prospective searches where MD leverages its full predictive potential decipher the mechanism of unknown biological processes and bring new drugs into the market


M. Harvey, G. Giupponi and G. De Fabritiis, ACEMD: Accelerated molecular dynamics simulations in the microseconds timescale, J. Chem. Theory and Comput. 5, 1632 (2009)
G Martinez-Rosell, T Giorgino, MJ Harvey, G de Fabritiis, Drug Discovery and Molecular Dynamics: Methods, Applications and Perspective Beyond the Second Timescale in Curr Top Med Chem. 2017 Apr 14.
S. Doerr, MJ. Harvey, F. Noé and G. De Fabritiis. HTMD: High-Throughput Molecular Dynamics for Molecular Discovery. J. Chem. Theory Comput. (2016) 12 (4), pp 1845-1852

Franck ChevalierA Decade of GPUs based MD
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Helical unwinding and side-chain unlocking unravel the outward open conformation of the melibiose transporter

By Alex Perálvarez-Marín


In Wang et al. we have studied and described an intermediate step of the molecular mechanism of sugar transport thanks to the possibility to run several MD simulation replicates in the 100 ns range using a single GPU-based desktop running ACEMD. Our solvated membrane protein system embedded in the lipid bilayer consisted of ca. 100,000 atoms, and the combination between ACEMD and a single GTX-780 helped us to understand discrete events happening between two metastable intermediates. The combination of MD simulations and experimental data was key to achieve our goal to understand the conformational changes to allow the entrance of the substrates within the transporter. For the full story, refer to Wang et al Sci Rep 2016(

For the full story, refer to Wang et al Sci Rep 2016(

Franck ChevalierHelical unwinding and side-chain unlocking unravel the outward open conformation of the melibiose transporter
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Acellera, celebrating the 10th Anniversary

What has been achieved in Molecular Dynamics and Acellera contribution.

Acellera celebrates its 10th anniversary. A decade focusing on molecular dynamics (MD) and dedicated to improve this technique as a strong partner of biophysical approaches. Objectively, molecular dynamics is the only technique allowing to study conformational changes and interaction at atomistic level, to quantify binding energies, binding kinetics for any kind of molecules since a force field has been defined for these. It represents an adequate combination with NMR, Crystallography, ITC, Surface plasmon resonance, Thermal shift that are already widely used in Drug Discovery.

What makes MD so interesting now?

In 10 years, we have seen how calculations done on cluster of CPU can now be done now on GPU cluster or in the Cloud. The first GPU offered already a large improvement on yield (ns/day) obtained. Where ns was the goal, nowadays it is a simple unit. In 2009, we ran microsecond, last year, 2 milliseconds (cumulated time) were obtained when performing a fragment screening (Ferruz et al, J. Chem. Inf. Model. 2015). If this rhythm is followed within the next 6 years, a second will probably be attained and maybe required for high index journal publication. Basically, we can simulate long process from side chain flip to domain motion and binding of fast Kon molecules for example.

The speed of GPU was undeniable in the achievement of long MD but they also generate higher amount of data to analyze. Where we needed Hard disk to store Gigabytes of data, we can count in terabyte…generated weekly. Storage is maybe the next challenge.

So it is possible to simulate the binding of benzamidine to Trypsin from scratch (Buch et al, PNAS 2011), to perform fragment screening, to study conformational change of antibodies, to analyze multibody cofactor and substrate molecular recognition…This has been possible not only thanks to the GPU development but also thanks to a huge work that contributed to innovative approach to sample the conformational space and energy of the system (Doerr et al, J. Chem. Theory Comput. 2014) and to the correct analysis of the trajectories.

What can be expected now from MD in Drug Discovery?

As solvation/desolvation processes are critical parameters for interaction studies, all atoms simulations represent an adequate method. During the simulations, at each step, the forces on each atom are computed and the atomic position and velocity are updated according to Newton’s laws of motion. This means millions of calculations magnified by the number of atoms composing your system. A 25000 atoms system can be simulated at 500 ns/day. If we take 100 GPU, after 20 days we have a millisecond of calculation which may be enough for micro molar affinity ligand. When studying larger system, the time needed to sample the interaction increase and the throughput we can expect decreases.

A category of molecules enters this affinity range and offers advantage when calculating compound parameters: fragment. MD is well suited for fragment interaction and low to medium throughput screening. Brute throughput will depend on the resources assigned, but in a single analysis binding kinetics, energy and pathway can be determined. The accuracy of low energy pose description reaches crystallography resolution.

What can be improved?

Improvements are needed in the timescale MD can reach, in reproducing experimental binding and throughput, meaning the number of molecules we can test. At the end of the day, we want to explore the chemical space and we are still far from scratching its surface. So, Speed of calculations, sampling and analysis are the main parameters where we can still gain time and open the way to build and test molecules newly designed and not yet synthesized.
Blue Waters, Anton, MDGRAPE are synonyms of (impressive) fast calculations. A more affordable solution has been developed at Acellera: the workstation Metrocubo equipped with 4 GPU; it allows to simulate a 2 microsecond/day (based on DHFR benchmark).

Sampling approaches are also being optimized (metadynamics, replica exchange, umbrella sampling, accelerated MD). At Acellera, we use Adaptive Sampling with ACEMD. Finally, analysis protocols have been largely improved and is still an active field of development.

In the last 10 years, Acellera developed ACEMD, enhancing the execution of MD simulations. These can now be run on GPU (workstation like Metrocubo or cluster) or in the cloud. For this purpose, we developed AceCloud, a tool allowing to run calculations on AWS. Finally, on top of all, a new interface providing a unified environment for molecular discovery has been designed for both computational and medicinal chemistry groups: HTMD. This tool allows encoding best practices, hiding complexity, from start to end: PDB to free energy calculations, to solve biological problem.

Fragment screening, GPCR ligand binding, protein folding, nucleic acids complex interaction are only few of the topics studied successfully by high throughput molecular dynamics and we hope its use will be generalized within the next decade.



Franck ChevalierAcellera, celebrating the 10th Anniversary
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Multibody cofactor and substrate molecular recognition

By Franck Chevalier


Molecular recognition is rarely a two-body protein-ligand problem, as it often involves the dynamic interplay of multiple molecules that together control the binding process. Myo-inositol monophosphatase (IMPase), a drug target for bipolar disorder, depends on 3 Mg2+ ions as cofactor for its catalytic activity. Although the crystallographic pose of the pre-catalytic complex is well characterized, the binding process by which substrate, cofactor and protein cooperate is essentially unknown. Here, we have characterized cofactor and substrate cooperative binding by means of large-scale molecular dynamics. Our study showed the first and second Mg2+ ions identify the binding pocket with fast kinetics whereas the third ion presents a much higher energy barrier. Substrate binding can occur in cooperation with cofactor, or alone to a binary or ternary cofactor-IMPase complex, although the last scenario occurs several orders of magnitude faster. Our atomic description of the three-body mechanism offers a particularly challenging example of pathway reconstruction, and may prove particularly useful in realistic contexts where water, ions, cofactors or other entities cooperate and modulate the binding process.

In all in-silico binding analyses, full kinetic and thermodynamic data were obtained by performing free-ligand binding, all-atom molecular dynamics simulations with the ACEMD molecular dynamics software and anlyzed with HTMD.

We have fully characterized substrate and cofactor binding prior to the catalytic event.
We have also provided an atomic-level description of substrate and cofactor cooperation and binding.
Large-scale HTMD has been used and been able to recapitulate the binding events of Mg ions and natural substrate at IMPase, and we identified structures close to the X-ray solutions.
N Ferruz, G Tresadern, A Pineda-Lucena, G De Fabritiis, Multibody cofactor and substrate molecular recognition in the myo-inositol monophosphatase enzyme, Scientific Reports 2016

Franck ChevalierMultibody cofactor and substrate molecular recognition
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By Franck Chevalier

Introduction on kinetics

Quest for new drug discovery usually involves a clear understanding of ligand binding to its receptor. Free binding energy helps to rank compounds and allows to select those based on their target affinity. Drug discovery programs focused on optimization of target affinity as a proxy of in-vivo efficacy. Within the last years, researchers have increasingly become interested in measuring and understanding drugs’ binding kinetics. Namely, the time in which drug and its target associate and dissociate.

Until recently, few methods offered the possibility to determine kinetics parameters and almost none at atomistic resolution. Amazing recent emergence of computational methods provided powerful tools in measuring and understanding binding event. It offers interesting and accurate methods to determine qualitative (binding mode) and quantitative (binding energy, Kon, Koff) parameters in a single atomistic approach.

Determination of these parameters with HTMD has been reviewed in previous posts:
such as this one . Recent works that highlight the importance of binding, molecular recognition and molecular determinants for rational optimization have been reviewed here.

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Acellera, Spring & Summer’16 conferences

By Franck Chevalier

Meet Acellera during Spring & Summer’16 Conferences.

Fragment Based Drug Discovery (FBDD), Markov state model, free energy methods, drug discovery by high throughput molecular dynamics (HTMD); these are few of the topics developed during the events we will join within the next weeks.

Learn more about ACEMD, HTMD and our last released Metrocubo workstation and server.

Free Energy Methods in Drug Design: Targeting Cancer, May 16th-18th, 2016, Boston, MA, USA

Workshop on Free Energy Methods in Drug Design: Targeting Cancer

Kinetics and Markov State Models in Drug Design, May 19th-20th, 2016, Boston, MA, USA

Workshop on Kinetics and Markov State Models in Drug Design

Both workshops focus on features integrated in our interface software for Medicinal Chemistry and Computational Chemistry groups. HTMD provides a user friendly environment to perform efficient and accurate trajectories analysis.  Download it and follow current development and applications.


Development of novel therapies through fragment based drug discovery, May 24th, Houston TX, USA

Novel Therapies through FBDD

Developing the Synergy between Biophysics and Medicinal Chemistry to Deliver Better Drugs, June 7th-10th, 2016 Strasbourg, France

Novalix 2016

ACEMD and HTMD provides a complete and fast solution package specially designed to run and analyze molecular dynamics simulations. Succesfull applications for FBDD and drug discovery have been reported and published. Here is a list of selected publications.

Theory and Simulation Across Scales in Molecular Science, July 24th-29th, Girona, Spain

Gordon Conference


Feel free to contact us at

Franck ChevalierAcellera, Spring & Summer’16 conferences
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GPCR Unbiased lipid-like ligand binding with ACEMD

By Franck Chevalier


The binding process through the membrane bilayer of lipid-like ligands to a GPCR protein target is an important but poorly explored recognition process at the atomic level. In this work ,the binding of the lipid inhibitor ML056 to the sphingosine-1-phosphate receptor 1 (S1P1R) has been successfully reported using unbiased molecular dynamics simulations with an aggregate sampling of over 800 μs. The binding pathway is a multi-stage process consisting of the ligand diffusing in the bilayer leaflet to contact a “membrane vestibule” at the top of TM 7, subsequently moving from this lipid-facing vestibule to the orthosteric binding cavity through a channel formed by TMs 1 and 7 and the N-terminal of the receptor. Unfolding of the GPCR N-terminal alpha-helix increases the volume of the channel upon ligand entry, helping to reach the crystallographic pose that also corresponds to the predicted favorable pose. The relaxation timescales of the binding process show that the binding of the ligand to the “membrane vestibule” is the rate-limiting step in the multi microseconds timescale. We comment on the significance and parallels of the binding process in the context of other binding studies.

Current challenges

Both ACEMD and HTMD software have permitted to perform the preparation, simulation and analysis of trajectories and elucidate the binding pathway along with critical atomic interactions and conformational changes. Similar to other studies of ligand binding to GPCRs4 , there is a barrier to binding that occurs far away from the final binding pose. This barrier is formed by the interaction between the zwitterionic head group of the ligand and R2927.34 and E2947.36 in the “membrane vestibule” and the desolvation necessary for the ligand to enter the channel.

A final barrier is due to rearrangements of the water molecules in the binding cavity and of dihedral angles in the head and tail of the ligand such that all these favorable interactions can be formed.

Along the binding process, the widening of the cavity by the flexible N-terminus and the desolvation of the ligand head group appear as general trends.



In this work,  the binding of a lipid-like ligand from the membrane bilayer directly to the orthosteric binding site of a GPCR using unbiased MD simulations has been shown. The ML056 inhibitor, studied here, binds S1P1R through a multi-step process that finally leads to the crystallographic binding pose.



Nathaniel Stanley, Leonardo Pardo & Gianni De Fabritiis, The pathway of ligand entry from the membrane bilayer to a lipid G protein-coupled receptor (GPCR), Scientific Reports 2016

Franck ChevalierGPCR Unbiased lipid-like ligand binding with ACEMD
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HTMD: Molecular discovery simplified

By Stefan Doerr


Molecular dynamics simulations (MD) can provide an atomic level resolution of biological processes at very high temporal resolution, but it comes with its own set of limitations; the most pronounced ones being the accuracy of the forcefields and the time sampling limitations.

Yet, we believe that there are further important problems: the data analysis and the reproducibility of experiments. In the last few years, specialized hardware, high-throughput methods and advanced sampling techniques have come to significantly improve molecular dynamics, allowing them to reach aggregate simulation times of multiple milliseconds. Forcefields have also dramatically improved. This increase of simulation accuracy and data has led to the necessity of a more standardized methodology for preparing, executing and handling thousands of individual trajectories.

Recently we have been working on provide an environment for molecular discovery which simplifies every step of molecular modelling and simulations. We call this environment high-throughput molecular dynamics (HTMD) and the final paper is now published in JCTC. A short intro is provided in this blog.


Current challenges

Investigating biological processes using MD usually requires the processing of large amounts of data and files, using various tools and adapting to peculiarities of many different software packages developed over several decades. With all these fragile set of tools, it is hard to follow the steps of a workflow that lead from the original PDB to the results, even for the scientist who wrote the workflow. Secondly, it is hard to extend the functionality of the tools because of such diversity of languages and the absence of a common programming environment where to introduce new extensions.

Our vision

HTMD is our vision of a unified platform, a programmable workspace for simulation based molecular discovery. We name it HTMD (high-throughput molecular dynamics) to indicate the fact that it allows the handling of thousands of simulations and multiple systems in a controlled manner. HTMD extends the Python programming language with functions and classes to handle molecular systems at different levels while abstracting implementation details and best-practice knowledge. Python is a scripting language which enjoys widespread usage in the scientific community and thus provides an ideal platform on which to develop and distribute HTMD. HTMD’s functionalities span from molecular structure manipulation to visualization, to preparing and executing molecular simulations on different compute resources and data analysis, including the use of Markov state models (MSMs) to identify slow events, kinetic rates, affinities and pathways.


Stefan Doerr, Matthew J. Harvey, Frank Noé, and Gianni De Fabritiis, HTMD: High-throughput molecular dynamics for molecular discovery, in press JCTC 2016

Franck ChevalierHTMD: Molecular discovery simplified
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Large molecular dynamics simulations as a tool to understand experimental polyethylene phase transitions

By Javier Ramos, Juan F. Vega and Javier Martínez-Salazar

Research activity in the field of macromolecular science requires the use of a variety of techniques. This is due to the intrinsic characteristics of polymeric materials, which reveal interesting physical phenomena in the length scale from sub-nanometer up to microns or, alternatively, in the time scale from picoseconds to years. Covering these wide length and time scales demands the use of powerful experimental techniques, with the combination of complementary computational tools in order to effectively resolve the processes of interest. At this respect major advances in computational power, such as the use of GPU processors, and methodologies can nowadays help to face classical problems, and to establish a shared knowledge between theorists and experimentalists.

The polyethylene phase transitions

The glass transition and the crystallization processes in polymers still remain under debate. Among all synthetic polymeric materials, polyethylene (PE) represents a model system in polymer physics. The advantage of this polymer is the simplicity of its chemical structure, and the extensive set of experimental data available to validate the simulations. The objective of this contribution is to examine fundamental physical processes in a model of entangled polyethylene (C192) using two different force fields, the so-called PYS and TraPPe-UA. We mainly focus our attention on the ability of each model to simulate complex phenomena as glass transition and especially crystallization process.

Computational modeling as a tool to understand polymer physics

Most of the minimizations and molecular dynamics (MD) simulations were performed with GROMACS 4.6.x package installed in Metrocubo Graphical Processor Units (GPU) workstations bought to Acellera. The glass transition and crystallization processes of polymer chains can be studied by cooling an amorphous system at a constant cooling rate. If the cooling is sufficiently slow, early stages of nucleation and crystallization can be studied. On the contrary, at high cooling rates a glass state is produced. Non-isothermal simulations using several cooling rates from an amorphous system in the range of 2,000 (tsim.~0.1 ns) and 0.05 K ns-1 (tsim.~4-5 μs) were performed.

The glass transition temperature (Tg) and the crystallization process

The equilibrated PE system was cooled down at different finite cooling rates, G = 1 to 2,000 K×ns-1 (high enough to prevent polymer crystallization). From these simulations one can calculate the apparent Tg versus cooling rate (Figure 1). Thus, estimate values of Tg0 of 187.0 K and 214.1 K for TraPPe-UA and PYS FFs, respectively are obtained. Experimentally, it has been usual to obtain the Tg by extrapolation to an amorphous PE free of constraints provoked by the presence of crystals, i.e. that obtained by Gordon-Taylor equation. The use of this equation gives rise to a value of Tg0 = 185-195 K. which is very close to that obtained from the TraPPE-UA system.

MD simulations at low cooling rates (G = 0.05 to 1 K·ns-1) allow one to study the crystallization process (Figure 2). A sudden drop of the specific volume and a single peak of the specific heat clearly indicate a phase transition at a crystallization temperature, Tc, during cooling. A clear segregation process (semicrystalline state) has been observed at the early stages of crystallization. Thus, one can observe two different layers, one ordered, and the other one amorphous. The final structure clearly recalls the model experimentally proposed by Ungar and coworkers for quenched long-alkanes of similar molecular length.

Towards a better understanding of the phase transitions in polymers by using large molecular dynamics simulations

We have performed simulations to capture the complex behavior of glass transition and crystallization processes at different cooling rates. This requires large simulation boxes (at the nanometer scale) and long time simulations (at the microsecond scale) well-suited to be performed in GPU processors. We can draw the following general conclusions:

  • The apparent glass transition and its dependence with the cooling rate are well described by TraPPe-UA force field. In fact the extrapolated value at experimental conditions is close to that obtained for totally amorphous PE without crystalline constraints (Gordon-Taylor equation)
  • For the first time the TraPPe-UA force field is employed to simulate the homogeneous early stages of the crystallization process of an entangled n-alkane. Basic experimental facts are correctly described by the simulations, primarily (i) the initial fold length expected for these high supercoolings, and (ii) the segregation of the systems in alternating ordered and disordered layers.

Reference and further reading:

  • Javier Ramos, Juan F Vega, Javier Martínez-Salazar, “Molecular Dynamics Simulations for the Description of Experimental Molecular Conformation, Melt Dynamics, and Phase Transitions in Polyethylene”, 2015, Macromolecules, 48(14), 5016-5027.
  • Sara Sanmartín, Javier Ramos, Juan Francisco Vega, Javier Martínez-Salazar, “Strong influence of branching on the early stage of nucleation and crystal formation of fast cooled ultralong n-alkanes as revealed by computer simulation”, 2014, European Polymer Journal, 50, 190-199
  • Binder, K.; Baschnagel, J.; Paul, W. “Glass transition of polymer melts: test of theoretical concepts by computer simulation”, 2003, Progress in Polymer Science, 28(1), 115-172
  • Goran Ungar and Xiang-bing Zeng, “Learning Polymer Crystallization with the Aid of Linear, Branched and Cyclic Model Compounds”, 2001, Chem. Reviews, 4157–4188
gianniLarge molecular dynamics simulations as a tool to understand experimental polyethylene phase transitions
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