SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction

Binding-Site Purification of Actives (B-SPA) Enables Efficient Large-Scale Progression of Fragment Hits by Combining Multi-Step Array Synthesis With HT Crystallography

Fragment approaches are long-established in target-based ligand discovery, yet their full transformative potential lies dormant because progressing the initial weakly binding hits to potency remains a formidable challenge. The only credible progression paradigm involves multiple cycles of costly conventional design-make-test-analyse medicinal chemistry. We propose an alternative approach to fragment elaboration, namely performing large numbers of parallel and diverse automated multiple step reactions, and evaluating the binding of the crude reaction products by high-throughput protein X-ray crystallography. We show it is effective and low-cost to perform, in parallel, large numbers of non-uniform multi-step reactions, because, even without compound purification, crystallography provides a high-quality readout of binding. This can detect low-level binding of weakly active compounds, which the target binding site extracts directly from crude reaction mixtures. In this proof-of-concept study, we have expanded a fragment hit, from a crystal-based screen of the second bromodomain of pleckstrin homology domain-interacting protein (PHIP(2)), using array synthesis on low-cost robotics. We were able to implement 6 independent multi-step reaction routes of up to 5 steps, attempting the synthesis of 1876 diverse expansions, designs entirely driven by synthetic tractability. The expected product was present in 1108 (59%) crude reaction mixtures, detected by liquid chromatography mass spectrometry (LCMS). 22 individual products were resolved in the crystal structures of crude reaction mixtures added to crystals, providing an initial structure activity relationship map. 19 of these showed binding pose stability, while, through binding instability in the remaining 3 products, we could resolve a stereochemical preference for mixtures containing racemic compounds. One compound showed biochemical potency (IC50=34 μM) and affinity (Kd=50 μM) after resynthesis. This approach therefore lends itself to routine fragment progression, if coupled with algorithmically guided compound and reaction design and new formalisms for data analysis.

The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks

Computational tools for structure-based drug design (SBDD) are widely used in drug discovery and can provide valuable insights to advance projects in an efficient and cost-effective manner. However, despite the importance of SBDD to the field, the underlying methodologies and techniques have many limitations. In particular, binding pose and activity predictions (P-AP) are still not consistently reliable. We strongly believe that a limiting factor is the lack of a widely accepted and established community benchmarking process that independently assesses the performance and drives the development of methods, similar to the CASP benchmarking challenge for protein structure prediction. Here, we provide an overview of P-AP, unblinded benchmarking data sets, and blinded benchmarking initiatives (concluded and ongoing) and offer a perspective on learnings and the future of the field. To accelerate a breakthrough on the development of novel P-AP methods, it is necessary for the community to establish and support a long-term benchmark challenge that provides nonbiased training/test/validation sets, a systematic independent validation, and a forum for scientific discussions.

Thermal Titration Molecular Dynamics: The Revenge of the Fragments

During the last 20 years, the fragment-based drug discovery approach gained popularity in both industrial and academic settings due to its efficient exploration of the chemical space. This bottom-up approach relies on identifying high-efficiency small ligands (fragments) that bind to a target binding site and then rationally evolve them into mature druglike compounds. To achieve such a task, researchers rely on accurate information about the ligand binding mode, usually obtained through experimental techniques, such as X-ray crystallography or computer simulations. However, the physicochemical characteristics of fragments limit the accuracy and reliability of computational predictions of their binding mode. This article presents a new Thermal Titration Molecular Dynamics (TTMD) protocol, a recently developed enhanced sampling method for qualitatively estimating protein-ligand-binding stability, specifically tuned for the refinement of fragment docking results. The protocol has been applied to eight pharmaceutically relevant targets on 12 different test cases, including ligands with very low molecular weight and structural complexity (MiniFrag/FragLites). In more than 80% of cases, TTMD successfully identified the native fragment binding mode among a set of docking poses, outperforming docking alone and proving to be a useful tool to assist the fragment screening and optimization process.

Fragment-to-Lead Medicinal Chemistry Publications in 2022

This Perspective is the eighth in an annual series that summarizes successful fragment-to-lead (F2L) case studies published each year. A tabulated summary of relevant articles published in 2022 is provided, and features such as target class, screening methods, and ligand efficiency are discussed both for the 2022 examples and for the combined examples over the years 2015-2022. In addition, trends and new developments in the field are summarized. In 2022, 18 publications described successful fragment-to-lead studies, including the development of three clinical compounds (MTRX1719, MK-8189, and BI-823911).

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.

Selenium-Priming mediated growth and yield improvement of turnip under saline conditions

Salt toxicity is one of the foremost environmental stresses that declines nutrient uptake, photosynthetic activity and growth of plants resulting in a decrease in crop yield and quality. Seed priming has become an emergent strategy to alleviate abiotic stress and improve plant growth. During the current study, turnip seed priming with sodium selenite (Na2SeO3) was investigated for its ability to mitigate salt stress. Turnip (Brassica rapa L. var. Purple Top White Globe) seeds primed with 75, 100, and 125 μML-1 of Se were subjected to 200 mM salt stress under field conditions. Findings of the current field research demonstrated that salt toxicity declined seed germination, chlorophyll content, and gas exchange characteristics of B. rapa seedling. Whereas, Se-primed seeds showed higher germination rate and plant growth which may be attributed to the decreased level of hydrogen peroxide (H2O2) and malondialdehyde (MDA) decreased synthesis of proline (36%) and besides increased total chlorophyll (46%) in applied turnip plants. Higher expression levels of genes encoding antioxidative activities (CAT, POD, SO,D and APX) mitigated oxidative stress induced by the salt toxicity. Additionally, Se treatment decreased Na+ content and enhanced K+ content resulting in elevated K+/Na+ ratio in the treated plants. The in-silico assessment revealed the interactive superiority of Se with antioxidant enzymes including CAT, POD, SOD, and APX as compared to sodium chloride (NaCl). Computational study of enzymes-Se and enzymes-NaCl molecules also revealed the stress ameliorative potential of Se through the presence of more Ramachandran-favored regions (94%) and higher docking affinities of Se (-6.3). The in-silico studies through molecular docking of Na2SeO3, NaCl, and ROS synthesizing enzymes (receptors) including cytochrome P450 (CYP), lipoxygenase (LOX), and xanthine oxidase (XO), also confirmed the salt stress ameliorative potential of Se in B. rapa. The increased Ca, P, Mg, and Zn nutrients uptake nutrients uptake in 100 μML-1 Se primed seedlings helped to adjust the stomatal conductivity (35%) intercellular CO2 concentration (32%), and photosynthetic activity (41%) resulting in enhancement of the yield attributes. More number of seeds per plant (6%), increased turnip weight (115 gm) root length (17.24 cm), root diameter (12 cm) as well as turnip yield increased by (9%tons ha-1) were recorded for 100 μML-1 Se treatment under salinity stress. Findings of the current research judiciously advocate the potential of Se seed priming for salt stress alleviation and growth improvement in B. rapa.

Fighting Antimicrobial Resistance: Insights on How the Staphylococcus aureus NorA Efflux Pump Recognizes 2-Phenylquinoline Inhibitors by Supervised Molecular Dynamics (SuMD) and Molecular Docking Simulations

The superbug Staphylococcus aureus (S. aureus) exhibits several resistance mechanisms, including efflux pumps, that strongly contribute to antimicrobial resistance. In particular, the NorA efflux pump activity is associated with S. aureus resistance to fluoroquinolone antibiotics (e.g., ciprofloxacin) by promoting their active extrusion from cells. Thus, since efflux pump inhibitors (EPIs) are able to increase antibiotic concentrations in bacteria as well as restore their susceptibility to these agents, they represent a promising strategy to counteract bacterial resistance. Additionally, the very recent release of two NorA efflux pump cryo-electron microscopy (cryo-EM) structures in complex with synthetic antigen-binding fragments (Fabs) represents a real breakthrough in the study of S. aureus antibiotic resistance. In this scenario, supervised molecular dynamics (SuMD) and molecular docking experiments were combined to investigate for the first time the molecular mechanisms driving the interaction between NorA and efflux pump inhibitors (EPIs), with the ultimate goal of elucidating how the NorA efflux pump recognizes its inhibitors. The findings provide insights into the dynamic NorA-EPI intermolecular interactions and lay the groundwork for future drug discovery efforts aimed at the identification of novel molecules to fight antimicrobial resistance.

Predictions from First-Principles of Membrane Permeability to Small Molecules: How Useful Are They in Practice?

Predicting from first-principles the rate of passive permeation of small molecules across the biological membrane represents a promising strategy for screening lead compounds upstream in the drug-discovery and development pipeline. One popular avenue for the estimation of permeation rates rests on computer simulations in conjunction with the inhomogeneous solubility-diffusion model, which requires the determination of the free-energy change and position-dependent diffusivity of the substrate along the translocation pathway through the lipid bilayer. In this Perspective, we will clarify the physical meaning of the membrane permeability inferred from such computer simulations, and how theoretical predictions actually relate to what is commonly measured experimentally. We will also examine why these calculations remain both technically challenging and overly computationally expensive, which has hitherto precluded their routine use in nonacademic settings. We finally synopsize possible research directions to meet these challenges, increase the predictive power of physics-based rates of passive permeation, and, by ricochet, improve their practical usefulness.

MEDIATE - Molecular DockIng at homE: Turning collaborative simulations into therapeutic solutions

INTRODUCTION

Collaborative computing has attracted great interest in the possibility of joining the efforts of researchers worldwide. Its relevance has further increased during the pandemic crisis since it allows for the strengthening of scientific collaborations while avoiding physical interactions. Thus, the E4C consortium presents the MEDIATE initiative which invited researchers to contribute via their virtual screening simulations that will be combined with AI-based consensus approaches to provide robust and method-independent predictions. The best compounds will be tested, and the biological results will be shared with the scientific community.

AREAS COVERED

In this paper, the MEDIATE initiative is described. This shares compounds' libraries and protein structures prepared to perform standardized virtual screenings. Preliminary analyses are also reported which provide encouraging results emphasizing the MEDIATE initiative's capacity to identify active compounds.

EXPERT OPINION

Structure-based virtual screening is well-suited for collaborative projects provided that the participating researchers work on the same input file. Until now, such a strategy was rarely pursued and most initiatives in the field were organized as challenges. The MEDIATE platform is focused on SARS-CoV-2 targets but can be seen as a prototype which can be utilized to perform collaborative virtual screening campaigns in any therapeutic field by sharing the appropriate input files.

An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation

Force fields (FFs) for molecular simulation have been under development for more than half a century. As with any predictive model, rigorous testing and comparisons of models critically depends on the availability of standardized data sets and benchmarks. While such benchmarks are rather common in the fields of quantum chemistry, this is not the case for empirical FFs. That is, few benchmarks are reused to evaluate FFs, and development teams rather use their own training and test sets. Here we present an overview of currently available tests and benchmarks for computational chemistry, focusing on organic compounds, including halogens and common ions, as FFs for these are the most common ones. We argue that many of the benchmark data sets from quantum chemistry can in fact be reused for evaluating FFs, but new gas phase data is still needed for compounds containing phosphorus and sulfur in different valence states. In addition, more nonequilibrium interaction energies and forces, as well as molecular properties such as electrostatic potentials around compounds, would be beneficial. For the condensed phases there is a large body of experimental data available, and tools to utilize these data in an automated fashion are under development. If FF developers, as well as researchers in artificial intelligence, would adopt a number of these data sets, it would become easier to compare the relative strengths and weaknesses of different models and to, eventually, restore the balance in the force.

ChemFlow─From 2D Chemical Libraries to Protein-Ligand Binding Free Energies

The accurate prediction of protein-ligand binding affinities is a fundamental problem for the rational design of new drug entities. Current computational approaches are either too expensive or inaccurate to be effectively used in virtual high-throughput screening campaigns. In addition, the most sophisticated methods, e.g., those based on configurational sampling by molecular dynamics, require significant pre- and postprocessing to provide a final ranking, which hinders straightforward applications by nonexpert users. We present a novel computational platform named ChemFlow to bridge the gap between 2D chemical libraries and estimated protein-ligand binding affinities. The software is designed to prepare a library of compounds provided in SMILES or SDF format, dock them into the protein binding site, and rescore the poses by simplified free energy calculations. Using a data set of 626 protein-ligand complexes and GPU computing, we demonstrate that ChemFlow provides relative binding free energies with an RMSE < 2 kcal/mol at a rate of 1000 ligands per day on a midsize computer cluster. The software is publicly available at https://github.com/IFMlab/ChemFlow.

Investigating RNA-protein recognition mechanisms through supervised molecular dynamics (SuMD) simulations

Ribonucleic acid (RNA) plays a key regulatory role within the cell, cooperating with proteins to control the genome expression and several biological processes. Due to its characteristic structural features, this polymer can mold itself into different three-dimensional structures able to recognize target biomolecules with high affinity and specificity, thereby attracting the interest of drug developers and medicinal chemists. One successful example of the exploitation of RNA's structural and functional peculiarities is represented by aptamers, a class of therapeutic and diagnostic tools that can recognize and tightly bind several pharmaceutically relevant targets, ranging from small molecules to proteins, making use of the available structural and conformational freedom to maximize the complementarity with their interacting counterparts. In this scientific work, we present the first application of Supervised Molecular Dynamics (SuMD), an enhanced sampling Molecular Dynamics-based method for the study of receptor-ligand association processes in the nanoseconds timescale, to the study of recognition pathways between RNA aptamers and proteins, elucidating the main advantages and limitations of the technique while discussing its possible role in the rational design of RNA-based therapeutics.