The Drug Design Data Resource (D3R) consortium organises blinded challenges to address the latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy ...calculations. Within the context of the second D3R Grand Challenge several blinded binding free energies predictions were made for two congeneric series of Farsenoid X Receptor (FXR) inhibitors with a semi-automated alchemical free energy calculation workflow featuring FESetup and SOMD software tools. Reasonable performance was observed in retrospective analyses of literature datasets. Nevertheless, blinded predictions on the full D3R datasets were poor due to difficulties encountered with the ranking of compounds that vary in their net-charge. Performance increased for predictions that were restricted to subsets of compounds carrying the same net-charge. Disclosure of X-ray crystallography derived binding modes maintained or improved the correlation with experiment in a subsequent rounds of predictions. The best performing protocols on D3R set1 and set2 were comparable or superior to predictions made on the basis of analysis of literature structure activity relationships (SAR)s only, and comparable or slightly inferior, to the best submissions from other groups.
In the context of the SAMPL5 challenge water-cyclohexane distribution coefficients for 53 drug-like molecules were predicted. Four different models based on molecular dynamics free energy ...calculations were tested. All models initially assumed only one chemical state present in aqueous or organic phases.
Model A
is based on results from an alchemical annihilation scheme;
model B
adds a long range correction for the Lennard Jones potentials to
model A
;
model C
adds charging free energy corrections;
model D
applies the charging correction from
model C
to ionizable species only.
Model A
and
B
perform better in terms of mean-unsigned error (
MUE
=
6.79
<
6.87
<
6.95
log
D units − 95 % confidence interval) and determination coefficient
(
R
2
=
0.26
<
0.27
<
0.28
)
, while charging corrections lead to poorer results with
model D
(
MUE
=
12.8
<
12.63
<
12.98
and
R
2
=
0.16
<
0.17
<
0.18
). Because overall errors were large, a retrospective analysis that allowed co-existence of ionisable and neutral species of a molecule in aqueous phase was investigated. This considerably reduced systematic errors (
MUE
=
1.87
<
1.97
<
2.07
and
R
2
=
0.35
<
0.40
<
0.45
). Overall accurate
log
D
predictions for drug-like molecules that may adopt multiple tautomers and charge states proved difficult, indicating a need for methodological advances to enable satisfactory treatment by explicit-solvent molecular simulations.
Active learning (AL) has become a powerful tool in computational drug discovery, enabling the identification of top binders from vast molecular libraries. To design a robust AL protocol, it is ...important to understand the influence of AL parameters, as well as the features of the data sets on the outcomes. We use four affinity data sets for different targets (TYK2, USP7, D2R, Mpro) to systematically evaluate the performance of machine learning models Gaussian process (GP) model and Chemprop model, sample selection protocols, and the batch size based on metrics describing the overall predictive power of the model (R2, Spearman rank, root-mean-square error) as well as the accurate identification of top 2%/5% binders (Recall, F1 score). Both models have a comparable Recall of top binders on large data sets, but the GP model surpasses the Chemprop model when training data are sparse. A larger initial batch size, especially on diverse data sets, increased the Recall of both models as well as overall correlation metrics. However, for subsequent cycles, smaller batch sizes of 20 or 30 compounds proved to be desirable. Furthermore, adding artificial Gaussian noise to the data up to a certain threshold still allowed the model to identify clusters with top-scoring compounds. However, excessive noise (<1σ) did impact the model’s predictive and exploitative capabilities.
The mechanisms by which a protein's 3D structure can be determined based on its amino acid sequence have long been one of the key mysteries of biophysics. Often simplistic models, such as those ...derived from geometric constraints, capture bulk real-world 3D protein-protein properties well. One approach is using protein contact maps (PCMs) to better understand proteins' properties. In this study, we explore the emergent behaviour of contact maps for different geometrically constrained models and compare them to real-world protein systems. Specifically, we derive an analytical approximation for the distribution of amino acid distances, denoted as
(
), using a mean-field approach based on a geometric constraint model. This approximation is then validated for amino acid distance distributions generated from a 2D and 3D version of the geometrically constrained random interaction model. For real protein data, we show how the analytical approximation can be used to fit amino acid distance distributions of protein chain lengths of
≈ 100,
≈ 200, and
≈ 300 generated from two different methods of evaluating a PCM, a simple cutoff based method and a shadow map based method. We present evidence that geometric constraints are sufficient to model the amino acid distance distributions of protein chains in bulk and amino acid sequences only play a secondary role, regardless of the definition of the PCM.
β-coronavirus (CoVs) alone has been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number ...of vaccines are available, alternative strategies targeting essential viral components are required as a backup against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensable role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand–protein interactions. Herein, we provide a comprehensive comparison of all nonredundant ligand-binding sites available for SARS-CoV2, SARS-CoV, and MERS-CoV Mpro. Extensive adaptive sampling has been used to investigate structural conservation of ligand-binding sites using Markov state models (MSMs) and compare conformational dynamics employing convolutional variational auto-encoder-based deep learning. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across β-CoV homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor.
Active control of a turbulent boundary layer has been experimentally investigated with a view to reducing the skin-friction drag and gaining some insight into the mechanism that leads to drag ...reduction. A spanwise-aligned array of piezo-ceramic actuators was employed to generate a transverse travelling wave along the wall surface, with a specified phase shift between adjacent actuators. Local skin-friction drag exhibits a strong dependence on control parameters, including the wavelength, amplitude and frequency of the oscillation. A maximum drag reduction of 50 % has been achieved at 17 wall units downstream of the actuators. The near-wall flow structure under control, measured using smoke–wire flow visualization, hot-wire and particle image velocimetry techniques, is compared with that without control. The data have been carefully analysed using techniques such as streak detection, power spectra and conditional averaging based on the variable-interval time-average detection. All the results point to a pronounced change in the organization of the perturbed boundary layer. It is proposed that the actuation-induced wave generates a layer of highly regularized streamwise vortices, which acts as a barrier between the large-scale coherent structures and the wall, thus interfering with the turbulence production cycle and contributing partially to the drag reduction. Associated with the generation of regularized vortices is a significant increase, in the near-wall region, of the mean energy dissipation rate, as inferred from a substantial decrease in the Taylor microscale. This increase also contributes to the drag reduction. The scaling of the drag reduction is also examined empirically, providing valuable insight into the active control of drag reduction.
We investigate how the molecular mechanism of monomer addition to a growing amyloid fibril of the transthyretin TTR 105–115 peptide is affected by pH. Using Markov state models to extract equilibrium ...and dynamical information from extensive all atom simulations allowed us to characterize both productive pathways in monomer addition as well as several off-pathway trapped states. We found that multiple pathways result in successful addition. All productive pathways are driven by the central hydrophobic residues in the peptide. Furthermore, we show that the slowest transitions in the system involve trapped configurations, that is, long-lived metastable states. These traps dominate the rate of fibril growth. Changing the pH essentially reweights the system, leading to clear differences in the relative importance of both productive paths and traps, yet retains the core mechanism.
Acquired resistance (AR) to programmed cell death protein 1/programmed death-ligand 1 PD-(L)1 blockade is frequent in non-small-cell lung cancer (NSCLC), occurring in a majority of initial ...responders. Patients with AR may have unique properties of persistent antitumor immunity that could be re-harnessed by investigational immunotherapies. The absence of a consistent clinical definition of AR to PD-(L)1 blockade and lack of uniform criteria for ensuing enrollment in clinical trials remains a major barrier to progress; such clinical definitions have advanced biologic and therapeutic discovery. We examine the considerations and potential controversies in developing a patient-level definition of AR in NSCLC treated with PD-(L)1 blockade. Taking into account the specifics of NSCLC biology and corresponding treatment strategies, we propose a practical, clinical definition of AR to PD-(L)1 blockade for use in clinical reports and prospective clinical trials. Patients should meet the following criteria: received treatment that includes PD-(L)1 blockade; experienced objective response on PD-(L)1 blockade (inclusion of a subset of stable disease will require future investigation); have progressive disease occurring within 6 months of last anti-PD-(L)1 antibody treatment or rechallenge with anti-PD-(L)1 antibody in patients not exposed to anti-PD-(L)1 in 6 months.
•In NSCLC, acquired resistance to immunotherapy is common and poorly understood.•A uniform clinical definition is imperative to further characterize patients and develop a rational approach to overcoming acquired resistance.•The proposed definition seeks to unify language for future reports and clinical trials in NSCLC.•We also highlight specific areas of uncertainty in classification of acquired resistance that require urgent attention and could lead to further refinements in the future.
This article describes a proposal for the new diagnosis of chronic primary pain (CPP) in ICD-11. Chronic primary pain is chosen when pain has persisted for more than 3 months and is associated with ...significant emotional distress and/or functional disability, and the pain is not better accounted for by another condition. As with all pain, the article assumes a biopsychosocial framework for understanding CPP, which means all subtypes of the diagnosis are considered to be multifactorial in nature, with biological, psychological, and social factors contributing to each. Unlike the perspectives found in DSM-5 and ICD-10, the diagnosis of CPP is considered to be appropriate independently of identified biological or psychological contributors, unless another diagnosis would better account for the presenting symptoms. Such other diagnoses are called "chronic secondary pain" where pain may at least initially be conceived as a symptom secondary to an underlying disease. The goal here is to create a classification that is useful in both primary care and specialized pain management settings for the development of individualized management plans, and to assist both clinicians and researchers by providing a more accurate description of each diagnostic category.
In the framework of the 2015 D3R inaugural grand challenge, blind binding pose and affinity predictions were performed for a set of 180 ligands of the Heat Shock Protein HSP90-α protein, a relevant ...cancer target. Spectral clustering was used to rapidly identify alternative binding site conformations in publicly available crystallographic HSP90-α structures. Subsequently, multiple docking and scoring protocols employing the software Autodock Vina and rDock were applied to predict binding modes and rank order ligands. Alchemical free energy calculations were performed with the software FESetup and Sire/OpenMM to predict binding affinities for three congeneric series subsets. Some of the protocols used here were ranked among the top submissions according to most of the evaluation metrics. Docking performance was excellent, but the scoring results were disappointing. A critical assessment of the results is reported, as well as suggestions for future similar competitions.
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