The rise of molecular simulations in fragment-based drug design (FBDD): an overview

Accurate prediction of dynamic protein-ligand binding using P-score ranking

Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.

Rapid Protein-Ligand Affinity Determination by Photoinduced Hyperpolarized NMR

The binding affinity determination of protein-ligand complexes is a cornerstone of drug design. State-of-the-art techniques are limited by lengthy and expensive processes. Building upon our recently introduced novel screening method utilizing photochemically induced dynamic nuclear polarization (photo-CIDNP) NMR, we provide the methodological framework to determine binding affinities within 5-15 min using 0.1 mg of protein. The accuracy of our method is demonstrated for the affinity constants of peptides binding to a PDZ domain and fragment ligands binding to the protein PIN1. The method can also be extended to measure the affinity of nonphoto-CIDNP-polarizable ligands in competition binding experiments. Finally, we demonstrate a strong correlation between the ligand-reduced signals in photo-CIDNP-based NMR fragment screening and the well-established saturation transfer difference (STD) NMR. Thus, our methodology measures protein-ligand affinities in the micro- to millimolar range in only a few minutes and informs on the binding epitope in a single-scan experiment, opening new avenues for early stage drug discovery approaches.

Integrated Anchored Stapling and Hierarchical Dynamics: MSICDA-Driven CREBBP Bromodomain Inhibition

Despite recent success in the computational approaches of cyclic peptide design, current studies face challenges in modeling noncanonical amino acids and nonstandard cyclizations due to limited data. To address this challenge, we developed an integrated framework for the tailored design of stapled peptides (SPs) targeting the bromodomain of CREBBP (CREBBP-BrD). We introduce a powerful combination of anchored stapling and hierarchical molecular dynamics to design and optimize SPs by employing the MultiScale integrative conformational dynamics assessment (MSICDA) strategy, which involves an initial virtual screening of over 1.5 million SPs, followed by comprehensive simulations amounting to 154.54 μs across 5418 of instances. The MSICDA method provides a detailed and holistic stability view of peptide-protein interactions, systematically isolated optimized peptides and identified two leading candidates, DA#430 and DA#99409, characterized by their enhanced stability, optimized binding, and high affinity toward the CREBBP-BrD. In cell-free assays, DA#430 and DA#99409 exhibited 2- to 12-fold greater potency than inhibitor SGC-CBP30. Cell studies revealed higher peptide selectivity for cancerous versus normal cells over small molecules. DA#430 combined with (+)-JQ-1 showed promising synergistic effects. Our approach enables the identification of peptides with optimized binding, high affinity, and enhanced stability, leading to more precise and effective cyclic peptide design, thereby establishing MSICDA as a generalizable and transformative tool for uncovering novel targeted drug development in various therapeutic areas.

Recent developments on the synthesis of biologically active glycohybrids

The exploration of hybridization emerges as a potent tool in advancing drug discovery research, with a significant emphasis on carbohydrate-containing hybrid scaffolds. Evidence indicates that linking carbohydrate molecules to privileged bioactive scaffolds enhances the bioactivity of drug molecules. This synergy results in a diverse range of activities, making carbohydrate scaffolds pivotal for synthesizing compound libraries with significant functional and structural diversity. Beyond their synthesis utility, these scaffolds offer applications in screening bioactive molecules, presenting alternative avenues for drug development. This comprehensive review spanning 2015 to 2023 focuses on synthesized glycohybrid molecules, revealing their bioactivity in areas such as anti-microbial, anti-cancer, anti-diabetic, anti-inflammatory activities, enzyme inhibition and pesticides. Numerous novel glycohybrids surpass positive control drugs in biological activity. This focused study not only highlights the diverse bioactivities of glycohybrids but also underscores their promising role in innovative drug development strategies.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

A Novel NMR-Based Protocol to Screen Ultralow Molecular Weight Fragments

Fragment-based lead discovery has emerged as one of the most efficient screening strategies for finding hit molecules in drug discovery. Recently, a novel strategy based on a class of fragments characterized by an ultralow molecular weight (ULMW) has been proposed. These fragments bind to the target with a very low affinity, requiring reliable biophysical methods for detection. The most notable application of ULMW used a set of 81 fragments, named MiniFrags, and screened them by X-ray crystallography. We extended the utilization of this novel class of fragments to another gold standard technique for fragment-based screening: nuclear magnetic resonance (NMR). Here, we present a novel NMR protocol to detect and analyze such weak interactions in a challenging real-world scenario: a flexible target with a flat, water-exposed binding site. We identified a subset of 69 highly water-soluble MiniFrags that were screened against the antiapoptotic protein human Bfl-1.

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

r-BRICS - A Revised BRICS Module That Breaks Ring Structures and Carbon Chains

Molecular fragmentation has been frequently used for machine learning, molecular modeling, and drug discovery studies. However, the current molecular fragmentation tools often lead to large fragments that are useful to limited tasks. Specifically, long aliphatic chains, certain connected ring structures, fused rings, as well as various nitrogen-containing molecular entities often remain intact when using BRICS. With no known methods to solve this issue, we find that the fragments taken from BRICS are inflexible for tasks such as fragment-based machine learning, coarse-graining, and ligand-protein interaction assessment. In this work, a revised BRICS (r-BRICS) module is developed to allow more flexible fragmentation on a wider variety of molecules. It is shown that r-BRICS generates smaller fragments than BRICS, allowing localized fragment assessments. Furthermore, r-BRICS generates a fragment database with significantly more unique small fragments than BRICS, which is potentially useful for fragment-based drug discovery.

Emerging drug design strategies in anti-influenza drug discovery

Influenza is an acute respiratory infection caused by influenza viruses (IFV), According to the World Health Organization (WHO), seasonal IFV epidemics result in approximately 3-5 million cases of severe illness, leading to about half a million deaths worldwide, along with severe economic losses and social burdens. Unfortunately, frequent mutations in IFV lead to a certain lag in vaccine development as well as resistance to existing antiviral drugs. Therefore, it is of great importance to develop anti-IFV drugs with high efficiency against wild-type and resistant strains, needed in the fight against current and future outbreaks caused by different IFV strains. In this review, we summarize general strategies used for the discovery and development of antiviral agents targeting multiple IFV strains (including those resistant to available drugs). Structure-based drug design, mechanism-based drug design, multivalent interaction-based drug design and drug repurposing are amongst the most relevant strategies that provide a framework for the development of antiviral drugs targeting IFV.

Recent research of BTK inhibitors: Methods of structural design, pharmacological activities, manmade derivatives and structure-activity relationship

Protein kinases constitute the largest group within the kinase family, and mutations and translocations of protein kinases due to genetic alterations are intimately linked to the pathogenesis of numerous diseases. Bruton's tyrosine kinase (BTK) is a member of the protein kinases and plays a pivotal role in the development and function of B cells. BTK belongs to the tyrosine TEC family. The aberrant activation of BTK is closely associated with the pathogenesis of B-cell lymphoma. Consequently, BTK has always been a critical target for treating hematological malignancies. To date, two generations of small-molecule covalent irreversible BTK inhibitors have been employed to treat malignant B-cell tumors, and have exhibited clinical efficacy in hitherto refractory diseases. However, these drugs are covalent BTK inhibitors, which inevitably lead to drug resistance after prolonged use, resulting in poor tolerance in patients. The third-generation non-covalent BTK inhibitor Pirtobrutinib has obtained approval for marketing in the United States, thereby circumventing drug resistance caused by C481 mutation. Currently, enhancing safety and tolerance constitutes the primary issue in developing novel BTK inhibitors. This article systematically summarizes recently discovered covalent and non-covalent BTK inhibitors and classifies them according to their structures. This article also provides a detailed discussion of binding modes, structural features, pharmacological activities, advantages and limitations of typical compounds within each structure type, providing valuable references and insights for developing safer, more effective and more targeted BTK inhibitors in future studies.

Analyzing the Geometry and Dynamics of Viral Structures: A Review of Computational Approaches Based on Alpha Shape Theory, Normal Mode Analysis, and Poisson-Boltzmann Theories

The current SARS-CoV-2 pandemic highlights our fragility when we are exposed to emergent viruses either directly or through zoonotic diseases. Fortunately, our knowledge of the biology of those viruses is improving. In particular, we have more and more structural information on virions, i.e., the infective form of a virus that includes its genomic material and surrounding protective capsid, and on their gene products. It is important to have methods that enable the analyses of structural information on such large macromolecular systems. We review some of those methods in this paper. We focus on understanding the geometry of virions and viral structural proteins, their dynamics, and their energetics, with the ambition that this understanding can help design antiviral agents. We discuss those methods in light of the specificities of those structures, mainly that they are huge. We focus on three of our own methods based on the alpha shape theory for computing geometry, normal mode analyses to study dynamics, and modified Poisson-Boltzmann theories to study the organization of ions and co-solvent and solvent molecules around biomacromolecules. The corresponding software has computing times that are compatible with the use of regular desktop computers. We show examples of their applications on some outer shells and structural proteins of the West Nile Virus.

Advances of bioorthogonal coupling reactions in drug development

Currently, bioorthogonal coupling reactions have garnered considerable interest due to their high substrate selectivity and less restrictive reaction conditions. During recent decades, bioorthogonal coupling reactions have emerged as powerful tools in drug development. This review describes the current applications of bioorthogonal coupling reactions in compound library building mediated by the copper-catalyzed azide-alkyne cycloaddition (CuAAC) reaction and in situ click chemistry or conjunction with other techniques; druggability optimization with 1,2,3-triazole groups; and intracellular self-assembly platforms with ring tension reactions, which are presented from the viewpoint of drug development. There is a reasonable prospect that bioorthogonal coupling reactions will accelerate the screening of lead compounds, the designing strategies of small molecules and expand the variety of designed compounds, which will be a new trend in drug development in the future.

Applications and prospects of cryo-EM in drug discovery

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time- and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy (cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence (AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of medium-resolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Evaluating the use of absolute binding free energy in the fragment optimisation process

Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman's r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely N_wat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.

Implementing a Scoring Function Based on Interaction Fingerprint for Autogrow4: Protein Kinase CK1δ as a Case Study

In the last 20 years, fragment-based drug discovery (FBDD) has become a popular and consolidated approach within the drug discovery pipeline, due to its ability to bring several drug candidates to clinical trials, some of them even being approved and introduced to the market. A class of targets that have proven to be particularly suitable for this method is represented by kinases, as demonstrated by the approval of BRAF inhibitor vemurafenib. Within this wide and diverse set of proteins, protein kinase CK1δ is a particularly interesting target for the treatment of several widespread neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Computational methodologies, such as molecular docking, are already routinely and successfully applied in FBDD campaigns alongside experimental techniques, both in the hit-discovery and in the hit-optimization stage. Concerning this, the open-source software Autogrow, developed by the Durrant lab, is a semi-automated computational protocol that exploits a combination between a genetic algorithm and a molecular docking software for de novo drug design and lead optimization. In the current work, we present and discuss a modified version of the Autogrow code that implements a custom scoring function based on the similarity between the interaction fingerprint of investigated compounds and a crystal reference. To validate its performance, we performed both a de novo and a lead-optimization run (as described in the original publication), evaluating the ability of our fingerprint-based protocol to generate compounds similar to known CK1δ inhibitors based on both the predicted binding mode and the electrostatic and shape similarity in comparison with the standard Autogrow protocol.

Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design

Site Identification by Ligand Competitive Saturation (SILCS) is a molecular simulation approach that uses diverse small solutes in aqueous solution to obtain functional group affinity patterns of a protein or other macromolecule. This involves employing a combined Grand Canonical Monte Carlo (GCMC)-molecular dynamics (MD) method to sample the full 3D space of the protein, including deep binding pockets and interior cavities from which functional group free energy maps (FragMaps) are obtained. The information content in the maps, which include contributions from protein flexibilty and both protein and functional group desolvation contributions, can be used in many aspects of the drug discovery process. These include identification of novel ligand binding pockets, including allosteric sites, pharmacophore modeling, prediction of relative protein-ligand binding affinities for database screening and lead optimization efforts, evaluation of protein-protein interactions as well as in the formulation of biologics-based drugs including monoclonal antibodies. The present article summarizes the various tools developed in the context of the SILCS methodology and their utility in computer-aided drug design (CADD) applications, showing how the SILCS toolset can improve the drug-development process on a number of fronts with respect to both accuracy and throughput representing a new avenue of CADD applications.

Allosteric binding on nuclear receptors: Insights on screening of non-competitive endocrine-disrupting chemicals

Endocrine-disrupting chemicals (EDCs) can compete with endogenous hormones and bind to the orthosteric site of nuclear receptors (NRs), affecting normal endocrine system function and causing severe symptoms. Recently, a series of pharmaceuticals and personal care products (PPCPs) have been discovered to bind to the allosteric sites of NRs and induce similar effects. However, it remains unclear how diverse EDCs work in this new way. Therefore, we have systematically summarized the allosteric sites and underlying mechanisms based on existing studies, mainly regarding drugs belonging to the PPCP class. Advanced methods, classified as structural biology, biochemistry and computational simulation, together with their advantages and hurdles for allosteric site recognition and mechanism insight have also been described. Furthermore, we have highlighted two available strategies for virtual screening of numerous EDCs, relying on the structural features of allosteric sites and lead compounds, respectively. We aim to provide reliable theoretical and technical support for a broader view of various allosteric interactions between EDCs and NRs, and to drive high-throughput and accurate screening of potential EDCs with non-competitive effects.

Progress in Free Energy Perturbation: Options for Evolving Fragments

One of the remaining bottlenecks in fragment-based drug design (FBDD) is the initial exploration and optimization of the identified hit fragments. There is a growing interest in computational approaches that can guide these efforts by predicting the binding affinity of newly designed analogues. Among others, alchemical free energy (AFE) calculations promise high accuracy at a computational cost that allows their application during lead optimization campaigns. In this review, we discuss how AFE could have a strong impact in fragment evolution, and we raise awareness on the challenges that could be encountered applying this methodology in FBDD studies.

TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning

One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based de novo drug design, which we call TRIOMPHE (transcriptome-based inference and generation of molecules with desired phenotypes). We investigated the correlation between chemically induced transcriptome profiles (reflecting cellular responses to compound treatment) and genetically perturbed transcriptome profiles (reflecting cellular responses to gene knock-down or gene overexpression of target proteins) in terms of ligand-target interactions. Subsequently, we developed novel machine learning methods to generate the chemical structures of new molecules with desired transcriptome profiles in the framework of a variational autoencoder. The use of desired transcriptome profiles enables the automatic design of molecules that are likely to have bioactivities for target proteins of interest. We showed that our methods can generate chemically valid molecules that are likely to have biological activities on 10 target proteins; moreover, they can outperform previous methods that had the same objective. Our omics-based structure generator is expected to be useful for the de novo design of drugs for a variety of target proteins.

In silico modeling of the interaction between TEX19 and LIRE1, and analysis of TEX19 gene missense SNPs

BACKGROUND

Testis expressed 19 (TEX19) is a specific human stem cell gene identified as cancer-testis antigen (CTA), which emerged as a potential therapeutic drug target. TEX19.1, a mouse paralog of human TEX19, can interact with LINE-1 retrotransposable element ORF1 protein (LIRE1) and subsequently restrict mobilization of LINE-1 elements in the genome.

AIM

This study aimed to predict the interaction of TEX19 with LIRE1 and analyze TEX19 missense polymorphisms. TEX19 model was generated using I-TASSER and the interaction between TEX19 and LIRE1 was studied using the HADDOCK software.

METHODS

The stability of the docking formed complex was studied through the molecular dynamic simulation using GROMACS. Missense SNPs (n=102) of TEX19 were screened for their potential effects on protein structure and function using different software.

RESULTS

Outcomes of this study revealed amino acids that potentially stabilize the predicted interaction interface between TEX19 and LIRE1. Of these SNPs, 37 were predicted to play a probably damaging role for the protein, three of them (F35S, P61R, and E55L) located at the binding site of LIRE1 and could disturb this binding affinity.

CONCLUSION

This information can be verified by further in vitro and in vivo experimentations and could be exploited for potential therapeutic targets.

Rh-Catalyzed cascade C-H activation/C-C cleavage/cyclization of carboxylic acids with cyclopropanols

Merging both C-H and C-C activation in a tandem process is a marked challenge. A novel Rh(iii)-catalyzed C-H activation/ring opening C-C cleavage/cyclization of carboxylic acids with cyclopropanols was developed for the synthesis of 3-substituted phthalides and α,β-butenolides. This reaction displays excellent functional group tolerance with respect to both carboxylic acids and cyclopropanols and features relatively mild conditions. Remarkably, the utility of this method was highlighted by the rapid construction of bioactive compounds bearing a 3-substituted phthalide framework via late-stage functionalization.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

Simultaneous Identification of Multiple Binding Sites in Proteins: A Statistical Mechanics Approach

We present an extension of the Poisson-Boltzmann model in which the solute of interest is immersed in an assembly of self-orienting Langevin water dipoles, anions, cations, and hydrophobic molecules, all of variable densities. Interactions between charges are controlled by electrostatics, while hydrophobic interactions are modeled with a Yukawa potential. We impose steric constraints by assuming that the system is represented on a cubic lattice. We also assume incompressibility; i.e., all sites of the lattice are occupied. This model, which we refer to as the Hydrophobic Dipolar Poisson-Boltzmann Langevin (HDPBL) model, leads to a system of two equations whose solutions give the water dipole, salt, and hydrophobic molecule densities, all of them in the presence of the others in a self-consistent way. We use those to study the organization of the ions, cosolvent, and solvent molecules around proteins. In particular, peaks of densities are expected to reveal, simultaneously, the presence of compatible binding sites of different kinds on a protein. We have tested and validated the ability of HDPBL to detect pockets in proteins that bind to hydrophobic ligands, polar ligands, and charged small probes as well as to characterize the binding sites of lipids for membrane proteins.

Modulating Target Protein Biology Through the Re-mapping of Conformational Distributions Using Small Molecules

Over the last 10 years considerable progress has been made in the application of small molecules to modulating protein-protein interactions (PPIs), and the navigation from "undruggable" to a host of candidate molecules in clinical trials has been well-charted in recent, comprehensive reviews. Structure-based design has played an important role in this scientific journey, with three dimensional structures guiding medicinal chemistry efforts. However, the importance of two additional dimensions: movement and time is only now being realised, as increasing computing power, closely aligned with wet lab validation, is applied to the challenge. Protein dynamics are fundamental to biology and disease, and application to PPI drug discovery has massively widened the scope for new chemical entities to influence function from allosteric, and previously unreported, sites. In this forward-looking perspective we highlight exciting, new opportunities for small molecules to modulate disease biology, by adjusting the frequency profile of natural conformational sampling, through the stabilisation of clinically desired conformers of target proteins.

Using NMR Spectroscopy in the Fragment-Based Drug Discovery of Small-Molecule Anticancer Targeted Therapies

Against the challenge of providing personalized cancer care, the development of targeted therapies stands as a promising approach. The discovery of these agents can benefit from fragment-based drug discovery (FBDD) methods that help guide ligand design and provide key structural information on the targets of interest. In particular, nuclear magnetic resonance spectroscopy is a promising biophysical tool in fragment discovery due to its detection capabilities and versatility. This review provides an overview of FBDD, describes the basis of NMR-based fragment screening, summarizes some exciting technical advances reported over the past decades, and closes with a discussion of selected case studies where this technique has been used as part of drug discovery campaigns to produce lead compounds towards the design of anti-cancer targeted therapies.