Vienna-PTM - establishment of a server extending simulation capabilities of proteins by post-translational modifications

Post-translationale Modifikationen (PTMs) wurden während der letzten Jahrzehnte ausgiebig mit verschiedensten Methoden erforscht. Sie sind Bestandteil einer Vielzahl wichtiger Zellprozesse, einschließlich verschiedener Signalweiterleitungs-, Regulations- und Lokalisierungsvorgänge. PTMs ermöglichen die vorwiegend reversible Veränderung von Aminosäuren, sind oft abhängig voneinander und bilden unter Umständen ganze Netzwerke. Ihre Wirkung auf die physikalisch-chemischen Eigenschaften betroffener Reste ist oft signifikant und kann sogar Parameter des gesamten Proteins beeinflussen. Im Hinblick auf ihre hohe biologische Relevanz werden in silico Simulationen von PTMs jedoch nach wie vor stark vernachlässigt. Es scheint deshalb an der Zeit, PTMs in klassischen, mechanischen Kraftfeldsimulationen zu berücksichtigen. Die vorliegende Arbeit präsentiert Vienna-PTM, ein Server, der sowohl die Möglichkeiten für die Einführung von post-translationalen Modifikationen in PDB Dateien, als auch die notwendigen Parameter für die modifizierten Aminosäuren bietet. Dadurch wird die Bandbreite der zur Verfügung stehenden Proteinbausteine von 20 auf über 200 erweitert, wobei sowohl die häufigsten Modifikationen wie Phosphorylierungen, Acetylierungen und Methylierungen als auch weniger genutzte unterstützt werden. Alle Modifikationen sind für die GROMOS Kraftfelder ffG45a3 und ffG54a7 verfügbar.Post-translational modifications (PTMs) of proteins have, over the last decades, been extensively investigated from a number of different aspects. They are involved in a manifold of critical processes in the cell, including signaling, regulation and localization control. PTMs make fast and predominantly reversible amino acid alteration possible, are often inter-dependent and build sometimes networks on their own. The impact on the physical-chemical properties of affected residues is often significant and potentially affecting the overall properties of the whole protein. However, it in silico simulations of PTMs have been strongly neglected, especially considering their biological relevance. With increasing numbers of observed types and occurrences of PTMs, it seems therefore timely and important to include them in classical mechanical force fields used for biomolecular simulations. This work presents Vienna-PTM, a server designed to provide both a workflow for introducing post-translational modifications in protein PDB files as well as parameters for these modified amino acids. Thereby, the arsenal of possible building blocks is enriched from 20 to over 200 distinct residues, including common modifications such as phosphorylation, acetylation and methylation as well as a number of less widely used ones. All modifications are available for GROMOS force fields ffG45a3 and ffG54a7

Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES

Using generative deep learning models and reinforcement learning together can effectively generate new molecules with desired properties. By employing a multi-objective scoring function, thousands of high-scoring molecules can be generated, making this approach useful for drug discovery and material science. However, the application of these methods can be hindered by computationally expensive or time-consuming scoring procedures, particularly when a large number of function calls are required as feedback in the reinforcement learning optimization. Here, we propose the use of double-loop reinforcement learning with simplified molecular line entry system (SMILES) augmentation to improve the efficiency and speed of the optimization. By adding an inner loop that augments the generated SMILES strings to non-canonical SMILES for use in additional reinforcement learning rounds, we can both reuse the scoring calculations on the molecular level, thereby speeding up the learning process, as well as offer additional protection against mode collapse. We find that employing between 5 and 10 augmentation repetitions is optimal for the scoring functions tested and is further associated with an increased diversity in the generated compounds, improved reproducibility of the sampling runs and the generation of molecules of higher similarity to known ligands.Comment: 25 pages and 18 Figures. Supplementary material include

Icolos: a workflow manager for structure-based post-processing of de novo generated small molecules

A Summary: We present Icolos, a workflow manager written in Python as a tool for automating complex structure-based workflows for drug design. Icolos can be used as a standalone tool, for example in virtual screening campaigns, or can be used in conjunction with deep learning-based molecular generation facilitated for example by REINVENT, a previously published molecular de novo design package. In this publication, we focus on the internal structure and general capabilities of Icolos, using molecular docking experiments as an illustrative example

Link-INVENT: generative linker design with reinforcement learning

In this work, we present Link-INVENT as an extension to the existing de novo molecular design platform REINVENT. We provide illustrative examples on how Link-INVENT can be applied to fragment linking, scaffold hopping, and PROTAC design case studies where the desirable molecules should satisfy a combination of different criteria. With the help of reinforcement learning, the agent used by Link-INVENT learns to generate favourable linkers connecting molecular subunits that satisfy diverse objectives, facilitating practical application of the model for real-world drug discovery projects. We also introduce a range of linker-specific objectives in the Scoring Function of REINVENT. The code is freely available at https://github.com/MolecularAI/Reinvent

DockStream: a docking wrapper to enhance de novo molecular design

Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream

From amino acids to proteins: on the parametrization and automation of molecular simulation

Molekulardynamiksimulationen haben sich über die letzten Jahrzehnte zu einem weit verbreiteten und zuverlässigen Werkzeug entwickelt. Der Schlüssel dazu war sowohl die ständige Zunahme der Leistungsfähigkeit von Computern, als auch die Genauigkeit der ständig verbesserten Computermodelle. Das erste Ziel dieser Arbeit ist die weitere Verbesserung dieser Modelle durch die Reparametrisierung von Phosphaten und der Rückgratdihedrale von Aminosäuren in GROMOS Kraftfeldern. Phosphate sind nicht nur wichtig im Rückgrat von Nukleinsäuren und in Lipiden, sondern stellen auch eine der bedeutendsten Gruppen von post-translationalen Modifikationen in Proteinen dar, während die erwähnten Torsionspotentiale in jeder Peptid- oder Proteinsimulation verwendet werden und damit zu den am Häufigsten verwendeten Parametern zählen. Das zweite Ziel war die Herstellung einer Verbindung zwischen der experimentellen und theoretischen Analyse der Bindungseigenschaften von Antikörpern. Die hierfür entwickelte Methode erlaubt eine qualitative Vorhersage des Bindungsverhaltens durch Simulationen, ohne jedoch auf die schwierige Berechnung der freien Energie dieses Prozesses angewiesen zu sein. Um diese grossen Projekte standardisiert und automatisiert durchführen zu können, war die Entwicklung eines Prozessmanagers für automatisierte Simulationen und Analysen naheliegend: PROMETHEUS erlaubt die Definition und einfache Wiederverwendbarkeit einer Prozesslogik für eine beliebige Anzahl von Unterprozessen eines Projektes. Im Zusammenhang damit steht die Entwicklung eines Pakets in R, MDplot, das die Generierung und Bereitstellung von (publikationstauglichen) Graphiken vereinfacht und ein direktes Laden der Analysedaten einer Simulation ermöglicht. Diese Arbeit versucht, die Genauigkeit und Anwendbarkeit von Molekulardynamiksimulationen zu verbessern, indem geeignete Softwareanwendungen und Kraftfeldparameter zur Verfügung gestellt werden.Molecular dynamics simulations have evolved into a broadly used and reliable tool in recent decades. The key to this progress are both increasingly powerful computers and the higher accuracy of our computational models, which are constantly improved. This thesis' first goal is to support the latter developments by working on a better description of phosphate moieties and the amino acid backbone torsions in the context of GROMOS parameter sets. Phosphates are pivotal in the nucleic acids' backbone, lipids and as a major group of protein post-translational modifications. The backbone torsions on the other hand are utilized in every simulation using peptides and proteins and therefore of high importance. The second aim was to link simulations and experiments of antibodies by the establishment of an in-silico binding prediction approach. Experimentally verified, it allows to get a qualitative forecast on the antibodies' binding behaviour from simulations without the hassle to calculate full binding free energies. This type of large projects made an automated workflow, simulating multiple candidates automatically, highly advantageous. We have therefore implemented the molecular dynamics workflow manager PROMETHEUS. It allows to define a workflow logic for a distinct project and to subsequently perform the execution completely automated an arbitrary number of times for all subjobs part of the project. Furthermore, in order to readily see the results afterwards and to provide the user with publishable figures, the R package MDplot has been developed, which is able to parse and plot the simulation data directly. This work tries to push the boundaries of molecular dynamics simulations in terms of accuracy and applicability further, by provision of appropriate computational tools and force field parameterseingereicht von Mag. Christian MargreitterZusammenfassung in deutscher SpracheUniversität für Bodenkultur Wien, Dissertation, 2016OeBB(VLID)193109

Optimization of Protein Backbone Dihedral Angles by Means of Hamiltonian Reweighting

Molecular dynamics simulations depend critically on the accuracy of the underlying force fields in properly representing biomolecules. Hence, it is crucial to validate the force-field parameter sets in this respect. In the context of the GROMOS force field, this is usually achieved by comparing simulation data to experimental observables for small molecules. In this study, we develop new amino acid backbone dihedral angle potential energy parameters based on the widely used 54A7 parameter set by matching to experimental <i>J</i> values and secondary structure propensity scales. In order to find the most appropriate backbone parameters, close to 100 000 different combinations of parameters have been screened. However, since the sheer number of combinations considered prohibits actual molecular dynamics simulations for each of them, we instead predicted the values for every combination using Hamiltonian reweighting. While the original 54A7 parameter set fails to reproduce the experimental data, we are able to provide parameters that match significantly better. However, to ensure applicability in the context of larger peptides and full proteins, further studies have to be undertaken

REINVENT 2.0 – an AI Tool for De Novo Drug Design

With this application note we aim to offer the community a production-ready tool for de novo design. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. By releasing the code we are aiming to facilitate the research on using generative methods on drug discovery problems and to promote the collaborative efforts in this area so that it can be used as an interaction point for future scientific collaborations