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BioExcel Building Blocks

BioExcel Building Blocks Workflows is a collection of biomolecular workflows to explore the flexibility and dynamics of macromolecules, including signal transduction proteins or molecules related to the Central Nervous System. Molecular dynamics setup for protein and protein-ligand complexes are examples of workflows available as Jupyter Notebooks. The workflows are built using the BioBB software library, developed in the framework of the BioExcel Centre of Excellence. BioBBis a collection of Python wrappers on top of popular biomolecular simulation tools, offering a layer of interoperability between the wrapped tools, which make them compatible and prepared to be directly interconnected to build complex biomolecular workflows.

Modelling and simulationMolecular and subcellular simulation


BioExcel-CV19 is a platform designed to provide web-access to atomistic-molecular dynamics trajectories for macromolecules involved in the COVID-19 disease. The BioExcel-CV19 web server interface presents simulated trajectories, with a set of quality control analyses, system information and interactive and graphical information on key structural and flexibility features. All the analyses integrated in the web portal are completely interactive. Whenever possible, a direct link from the analysis to the 3D representation is offered, using the NGL viewer tool. All data produced is available to download from an associated programmatic access API.

Modelling and simulationMolecular and subcellular simulation

Central Nervous System ligands

Central Nervous System (CNS) ligands is a platform designed to efficiently generate and parameterize bioactive conformers of ligands binding to neuronal proteins. CNS conformers are generated using a powerful multilevel strategy that combines a low-level (LL) method for sampling the conformational minima and high-level (HL) ab-initio calculations for estimating their relative stability.CNS database presents the results in a graphical user interface, displaying small molecule properties, analyses and generated 3D conformers. All data produced is available to download. CNS ligands provides important data for workflows for parameter generation and mechanistic studies of neuronal cascades using multi-scale molecular simulations in the Human Brain Project.

Modelling and simulationMolecular and subcellular simulation

CGMD Platform

The Coarse-grained Molecular dynamics(CGMD) platform is a publicly available web server for preparing and running coarse-grained molecular dynamics simulations using different force-fields. The input file is a protein structure. The user is guided through the preparation of the systems, either in a membrane or in solvent, and the running of short simulations following standard protocols.

Modelling and simulationMolecular and subcellular simulation

Distance Fluctuation (DF) Analysis

Distance-Fluctuation (DF) Analysis is a Python tool that performs analysis of the results of a MD simulation using the Distance Fluctuation matrices (DF), based on the Coordination Propensity (CP) hypothesis. Specifically, low CP values, corresponding to low pair-distance fluctuations, highlight groups of residues that move in a mechanically coordinated way. The script can analyze a MD trajectory and identify the coordinated motions between residues. It can then filter the output matrix based on the distance to identify long-range coordinated motions.

Modelling and simulationMolecular and subcellular simulation


MEDIUM is a Python tool that uses the DF matrices to extract global information on the protein. It works directly on DF images and uses a Convolutional Neural Network (CNN) machine learning approach to train the model in recognising specific patterns in the DF matrix capable of classifying the protein in a specific state in the presence of a ligand (e.g., HOLO or APO states). Then, the trained model can be used to classify the protein state in presence of different ligands, by testing new DF matrices arising from short simulations performed on the new system.

Modelling and simulationMolecular and subcellular simulation


MLCE stands for Matrix of Lowest Coupling Energies and they can be used to represent the region of the protein lowest dynamically coupled. This means that they are also the most prone to be involved in interaction with external partners.

Modelling and simulationMolecular and subcellular simulation

MMCG Webserver

The Hybrid Molecular Mechanics/Coarse-Grained (MM/CG) is a server that helps in the preparation and running of multiscale molecular dynamics simulations of G protein-coupled receptors (GPCR) in complex with their ligands. The Hybrid MM/CG Webserver requires the structure of a GPCR/ligand complex as input and then guides the user through the preparation and running of the simulation.

Modelling and simulationMolecular and subcellular simulation


MoDEL_CNS is a platform designed to provide web-access to atomistic molecular dynamics trajectories for relevant signal transduction proteins. MoDEL_CNS expands the Molecular Dynamics Extended Library (MoDEL) database of atomistic Molecular Dynamics trajectories with proteins involved in Central Nervous System (CNS) processes, including membrane proteins. MoDEL_CNS web server interface presents the resulting trajectories, analyses, and protein properties. All data produced by the project is available to download. MoDEL_CNS will contribute to the improvement of the understanding of neuronal signalling cascades by protein structure-based simulations, calculating molecular flexibility and dynamics, and guiding systems level modelling.

Modelling and simulationMolecular and subcellular simulation


PIPSA (Protein Interaction Property Similarity Analysis) is a method to compare proteins according to their interaction properties. PIPSA may assist in function assignment, and the estimation of binding properties and enzyme kinetic parameters. The PIPSA webserver, webPIPSA, computes protein electrostatic potentials and corresponding similarity indices for a user-defined set of proteins. The standalone code, multipipsa, which includes a python wrapper, provides further options, including running PIPSA on multiple sites on a protein and performing a comparative analysis of the binding properties of user-defined groups of proteins.

Modelling and simulationMolecular and subcellular simulation


SDA (Simulation of Diffusional Association) is a Brownian dynamics simulation software package for the simulation of the diffusion of biomacromolecules in aqueous solution. SDA can be used to compute bimolecular diffusional association rate constants and to predict the structures of diffusional encounter complexes. It can also be used to simulate dilute or concentrated protein solutions and to investigate the adsorption of proteins to solid surfaces. SDA7 is available for standalone use and a subset of the functionality is implemented in the webSDA webserver.

Modelling and simulationMolecular and subcellular simulation

SSB Toolkit

The SSB Toolkit is a Python library specifically designed for conducting simulations of mathematical models that represent the signal-transduction pathways of G-protein coupled receptors (GPCRs). This library consists of a set of systems biology simulation routines, enabling the investigation of pharmacodynamic models associated with GPCRs. It provides a means to explore how the structural characteristics of these receptors influence subcellular signaling dynamics.

Modelling and simulationMolecular and subcellular simulation

Subcellular model building and calibration tool set

The toolset includes interoperable modules for: model building, calibration (parameter estimation) and model analysis. All information needed to perform these tasks are stored in a structured, human- and machine-readable file format based on SBtab. This information includes: models, experimental calibration data and prior assumptions on parameter distributions. The toolset enables simulations of the same model in simulators with different characteristics, e.g. STEPS, NEURON, MATLAB’s Simbiology and R via automatic code generation.

Modelling and simulationMolecular and subcellular simulation

Subcellular Simulation Webapp

This tool allows import of SBML model files from the subcellular model building and calibration toolset workflow or other external sources. The tool allows users to setup and configure BioNetGen and STEPS simulations. Users can populate mesh models of spines and other neural structures, and run stochastic simulations of signalling pathways.

Modelling and simulationMolecular and subcellular simulation


τRAMD is a computationally efficient procedure that enables the computation of relative residence times (τ) or dissociation rates of protein-ligand complexes. It makes use of random acceleration molecular dynamics (RAMD) simulations to facilitate ligand egress in a short timescale and without imposing any bias regarding the ligand egress route. τRAMD is a powerful tool for ranking drug candidates according to their residence times and it can be used with the MD-IFP (Molecular Dynamics - Interaction Fingerprint) tool to investigate dissociation mechanisms and pathways. The combined use of τRAMD and MD-IFP may assist the design of new molecules or ligand optimization.

Modelling and simulationMolecular and subcellular simulation

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