Blinded predictions of standard binding free energies: lessons learned from the SAMPL6 challenge

<p>In the context of the SAMPL6 challenges, series of blinded predictions of standard binding free energies were made with the SOMD software for a dataset of 27 host-guest systems featuring two octa-acids hosts (<i>OA </i>and <i>TEMOA) </i>and a cucuribituril ring (<i>CB</i>8)<i> </i>host. Three different models were used, <i>ModelA </i>computes the free energy of binding based on a double annihilation technique; <i>ModelB</i> additionally takes into account long-range dispersion and standard state corrections; <i>ModelC</i> additionally introduces an empirical correction term derived from a regression analysis of SAMPL5 predictions previously made with SOMD. The performance of each model was evaluated with two different setups; <i>buffer </i>explicitly matches the ionic strength from the binding assays, whereas <i>no-buffer</i> merely neutralizes the host-guest net charge with counter-ions. <i>ModelC/no-buffer</i> shows the lowest mean-unsigned error for the overall dataset (MUE 1.29 < 1.39 < 1.50 kcal mol^-1, 95% CI), while explicit modelling of the buffer improves significantly results for the CB8 host only. Correlation with experimental data ranges from excellent for the host TEMOA (R² 0.91 < 0.94 < 0.96), to poor for <i>CB8 </i>(R² 0.04 < 0.12 < 0.23). Further investigations indicate a pronounced dependence of the binding free energies on the modelled ionic strength, and variable reproducibility of the binding free energies between different simulation packages. </p

Effect of set up protocols on the accuracy of alchemical free energy calculation over a set of ACK1 inhibitors

Hit-to-lead virtual screening frequently relies on a cascade of computational methods that starts with rapid calculations applied to a large number of compounds and ends with more expensive computations restricted to a subset of compounds that passed initial filters. This work focuses on set up protocols for alchemical free energy (AFE) scoring in the context of a Docking–MM/PBSA–AFE cascade. A dataset of 15 congeneric inhibitors of the ACK1 protein was used to evaluate the performance of AFE set up protocols that varied in the steps taken to prepare input files (using previously docked and best scored poses, manual selection of poses, manual placement of binding site water molecules). The main finding is that use of knowledge derived from X-ray structures to model binding modes, together with the manual placement of a bridging water molecule, improves the R2 from 0.45 ± 0.06 to 0.76 ± 0.02 and decreases the mean unsigned error from 2.11 ± 0.08 to 1.24 ± 0.04 kcal mol-1. By contrast a brute force automated protocol that increased the sampling time ten-fold lead to little improvements in accuracy. Besides, it is shown that for the present dataset hysteresis can be used to flag poses that need further attention even without prior knowledge of experimental binding affinitiesPeer ReviewedPostprint (published version

Concurrent and Adaptive Extreme Scale Binding Free Energy Calculations

The efficacy of drug treatments depends on how tightly small molecules bind to their target proteins. The rapid and accurate quantification of the strength of these interactions (as measured by binding affinity) is a grand challenge of computational chemistry, surmounting which could revolutionize drug design and provide the platform for patient-specific medicine. Recent evidence suggests that molecular dynamics (MD) can achieve useful predictive accuracy (< 1 kcal/mol). For this predictive accuracy to impact clinical decision making, binding free energy computational campaigns must provide results rapidly and without loss of accuracy. This demands advances in algorithms, scalable software systems, and efficient utilization of supercomputing resources. We introduce a framework called HTBAC, designed to support accurate and scalable drug binding affinity calculations, while marshaling large simulation campaigns. We show that HTBAC supports the specification and execution of free-energy protocols at scale. This paper makes three main contributions: (1) shows the importance of adaptive execution for ensemble-based free energy protocols to improve binding affinity accuracy; (2) presents and characterizes HTBAC -- a software system that enables the scalable and adaptive execution of binding affinity protocols at scale; and (3) for a widely used free-energy protocol (TIES), shows improvements in the accuracy of simulations for a fixed amount of resource, or reduced resource consumption for a fixed accuracy as a consequence of adaptive execution

Molecular dynamics based methods for the computation of standard binding free energies and binding selectivity of inhibitors of proteins of pharmaceutical interest

The field of Computer Aided Drug Design (CADD) has experienced substantial developments over the last few decades thanks to a rapid growth incomputing power. In particular, Molecular Dynamics (MD) simulations and associated techniques have earned increased attention within the pharmaceutical sector thanks to their rising accuracy and diminishing cost. However, there are still limitations in the usage of these methods, due to thedifficulty of sampling the rugged energy landscapes of protein-ligand complexes. The main theme of this work is to address the sampling problem of MD methods for predicting the binding free energies of different biomolecular complexes. This work starts using MD simulations as a sampling technique for a relative free energy calculation protocol using the Sire Open Molecular Dynamics (SOMD) software. This protocol was then integrated in a ligand design workflow to optimize the binding selectivity of cyclophilin (Cyps) inhibitors. Cyps are proteins known to play a vital role in various diseases, such as cancer, Alzheimer and viral infections. Most Cyp inhibitors to date,however, are cyclic peptides that have potency in the nanomolar range but produce severe side effects, are complex to synthesize and display complex pharmacokinetic profiles. Thus, there is a need for new selective smallmolecules targeting specific Cyps isoforms, in order to gain new insightsfor the inhibition of these therapeutically vital proteins. The computational workflow was able to suggest auspicious designs that they will be synthesized and characterized using biophysical techniques from Alison Hulme’s lab. Following, MD simulation methods were employed for the more challenging task of predicting the absolute free energies of binding of protein-ligand complexes. For this purpose, an Alchemical Free Energy (AFE) protocol was generated and its efficiency was evaluated in the Statistical Assessment of Modelling of Proteins and Ligands (SAMPL6) challenge. SAMPL challenges involve a series of blinded predictions of standard binding freeenergies for toy host-guest molecules. The results obtained from our protocol were ranked among the top submissions in terms of accuracy and correlation with experimental data. Encouraged by these results, we wanted to compare the efficiency of the AFE protocol versus a Markov State Modelling (MSM) protocol for the calculation of the standard binding free energy of a ligand to the intrinsically disordered protein c-Myc. The oncoprotein c-Myc is overexpressed in over 70% of human cancers and its inhibition has been considered the holygrail in cancer therapy. Due to its structural elasticity it is difficult to perform structure-based drug design methods for the discovery of novel compounds. The results showed that MSM can describe accurately the binding process of the ligand to the oncoprotein c-Myc, but the binding free energies were similar with the ones of the AFE protocol. Finally, an adaptive sampling protocol was established for the computation of the standard binding free energy and binding selectivity of lead-like ligands for the flexible protein MDM2. MDM2 is a vital protein that acts as an inhibitory mechanism of the transcription factor p53. p53 plays animportant role in the regulation of cellular processes and suppression of tumor development. For this reason, it is important to develop methods for the discovery of novel ligands that could inhibit the MDM2-p53 interaction through binding to the MDM2 protein. The results of the adaptive sampling study were encouraging as the protocol was able to predict binding selectivity trends for the MDM2-ligand complexes approximately six times faster than the original AFE protocol