Interactive Jupyter Notebooks in combination with Conda environments can be used to generate FAIR (Findable, Accessible, Interoperable and Reusable/Reproducible) biomolecular simulation workflows. The interactive programming code accompanied by documentation and the possibility to inspect intermediate results with versatile graphical charts and data visualization is very helpful, especially in iterative processes, where parameters might be adjusted to a particular system of interest. This work presents a collection of FAIR notebooks covering various areas of the biomolecular simulation field, such as molecular dynamics (MD), protein-ligand docking, molecular checking/modeling, molecular interactions, and free energy perturbations. Workflows can be launched with myBinder or easily installed in a local system. The collection of notebooks aims to provide a compilation of demonstration workflows, and it is continuously updated and expanded with examples using new methodologies and tools.

Developing complex biomolecular workflows is not always straightforward. It requires tedious developments to enable the interoperability between the different biomolecular simulation and analysis tools. Moreover, the need to execute the pipelines on distributed systems increases the complexity of these developments. To address these issues, we propose a methodology to simplify the implementation of these workflows on HPC infrastructures. It combines a library, the BioExcel Building Blocks (BioBBs), that allows scientists to implement biomolecular pipelines as Python scripts, and the PyCOMPSs programming framework which allows to easily convert Python scripts into task-based parallel workflows executed in distributed computing systems such as HPC clusters, clouds, containerized platforms, etc. Using this methodology, we have implemented a set of computational molecular workflows and we have performed several experiments to validate its portability, scalability, reliability and malleability.

MOTIVATION: The BioExcel Building Blocks (BioBB) library offers a broad collection of wrappers on top of common biomolecular simulation and bioinformatics tools. The possibility to access the library remotely and programmatically increases its usability, allowing individual and sporadic executions and enabling remote workflows. RESULTS: BioBB REST API extends and complements the BioBB library offering programmatic access to the collection of biomolecular simulation tools included in the BioExcel Building Blocks library. Molecular Dynamics setup, docking, structure modeling, free energy simulations and flexibility analyses are examples of functionalities included in the endpoints collection. All functionalities are accessible through standard REST API calls, voiding the need for tool installation. AVAILABILITY AND IMPLEMENTATION: All the information related to the BioBB REST API endpoints is accessible from https://mmb.irbbarcelona.org/biobb-api/. Links to extended documentation, including OpenAPI endpoints specification and examples, Read-The-Docs documentation and a complete workflow tutorial can be found in the Supplementary Table S1. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: High-quality dynamic visuals are needed at all levels of science communication, from the conference hall to the classroom. As scientific journals embrace new article formats, many key concepts-particularly, in structural biology-are also more easily conveyed as videos than still frames. Notwithstanding, the design and rendering of a complex molecular movie remain an arduous task. Here, we introduce Molywood, a robust and intuitive tool that builds on the capabilities of Visual Molecular Dynamics (VMD) to automate all stages of movie rendering. RESULTS: Molywood is a Python-based script that uses an integrated workflow to give maximal flexibility in movie design. It implements the basic concepts of actions, layers, grids and concurrency and requires no programming experience to run. AVAILABILITY AND IMPLEMENTATION: The script is freely available on GitLab (gitlab.com/KomBioMol/molywood) and PyPI (through pip), and features an extended documentation, tutorial and gallery hosted on mmb.irbbarcelona.org/molywood.

MOTIVATION: A new algorithm to trace conformational transitions in proteins is presented. The method uses discrete molecular dynamics as engine to sample protein conformational space. A multiple minima Go-like potential energy function is used in combination with several enhancing sampling strategies, such as metadynamics, Maxwell Demon molecular dynamics and essential dynamics. The method, which shows an unprecedented computational efficiency, is able to trace a wide range of known experimental transitions. Contrary to simpler methods our strategy does not introduce distortions in the chemical structure of the protein and is able to reproduce well complex non-linear conformational transitions. The method, called GOdMD, can easily introduce additional restraints to the transition (presence of ligand, known intermediate, known maintained contacts, …) and is freely distributed to the community through the Spanish National Bioinformatics Institute (http://mmb.irbbarcelona.org/GOdMD). AVAILABILITY: Freely available on the web at http://mmb.irbbarcelona.org/GOdMD.

SUMMARY: MDWeb and MDMoby constitute a web-based platform to help access to molecular dynamics (MD) in the standard and high-throughput regime. The platform provides tools to prepare systems from PDB structures mimicking the procedures followed by human experts. It provides inputs and can send simulations for three of the most popular MD packages (Amber, NAMD and Gromacs). Tools for analysis of trajectories, either provided by the user or retrieved from our MoDEL database (http://mmb.pcb.ub.es/MoDEL) are also incorporated. The platform has two ways of access, a set of web-services based on the BioMoby framework (MDMoby), programmatically accessible and a web portal (MDWeb). AVAILABILITY: http://mmb.irbbarcelona.org/MDWeb; additional information and methodology details can be found at the web site ( http://mmb.irbbarcelona.org/MDWeb/help.php)

Abstract Summary: FlexServ is a web-based tool for the analysis of protein flexibility. The server incorporates powerful protocols for the coarse-grained determination of protein dynamics using different versions of Normal Mode Analysis (NMA), Brownian dynamics (BD) and Discrete Dynamics (DMD). It can also analyze user provided trajectories. The server allows a complete analysis of flexibility using a large variety of metrics, including basic geometrical analysis, B-factors, essential dynamics, stiffness analysis, collectivity measures, Lindemann's indexes, residue correlation, chain-correlations, dynamic domain determination, hinge point detections, etc. Data is presented through a web interface as plain text, 2D and 3D graphics. Availability: http://mmb.pcb.ub.es/FlexServ; http://www.inab.org Contact: [email protected] or [email protected] Supplementary information: Additional information and methodology details can be found at http://mmb.pcb.ub.es/FlexServ/help.

BACKGROUND: The study of the functional role of alternative splice isoforms of a gene is a very active area of research in biology. The difficulty of the experimental approach (in particular, in its high-throughput version) leaves ample room for the development of bioinformatics tools that can provide a useful first picture of the problem. Among the possible approaches, one of the simplest is to follow classical protein function annotation protocols and annotate target alternative splice events with the information available from conserved events in other species. However, the application of this protocol requires a procedure capable of recognising such events. Here we present a simple but accurate method developed for this purpose. RESULTS: We have developed a method for identifying homologous, or equivalent, alternative splicing events, based on the combined use of neural networks and sequence searches. The procedure comprises four steps: (i) BLAST search for homologues of the two isoforms defining the target alternative splicing event; (ii) construction of all possible candidate events; (iii) scoring of the latter with a series of neural networks; and (iv) filtering of the results. When tested in a set of 473 manually annotated pairs of homologous events, our method showed a good performance, with an accuracy of 0.99, a precision of 0.98 and a sensitivity of 0.93. When no candidates were available, the specificity of our method varied between 0.81 and 0.91. CONCLUSION: The method described in this article allows the identification of homologous alternative splicing events, with a good success rate, indicating that such method could be used for the development of functional annotation of alternative splice isoforms.