Field Of Structure-based Drug Design Research Articles

Generation of 3D molecules in pockets via a language model

Generative models for molecules based on sequential line notation (for example, the simplified molecular-input line-entry system) or graph representation have attracted an increasing interest in the field of structure-based drug design, but they struggle to capture important three-dimensional (3D) spatial interactions and often produce undesirable molecular structures. To address these challenges, we introduce Lingo3DMol, a pocket-based 3D molecule generation method that combines language models and geometric deep learning technology. A new molecular representation, the fragment-based simplified molecular-input line-entry system with local and global coordinates, was developed to assist the model in learning molecular topologies and atomic spatial positions. Additionally, we trained a separate non-covalent interaction predictor to provide essential binding pattern information for the generative model. Lingo3DMol can efficiently traverse drug-like chemical spaces, preventing the formation of unusual structures. The Directory of Useful Decoys-Enhanced dataset was used for evaluation. Lingo3DMol outperformed state-of-the-art methods in terms of drug likeness, synthetic accessibility, pocket binding mode and molecule generation speed.

DOX_BDW: Incorporating Solvation and Desolvation Effects of Cavity Water into Nonfitting Protein-Ligand Binding Affinity Prediction.

Accurate prediction of the protein-ligand binding affinity (PLBA) with an affordable cost is one of the ultimate goals in the field of structure-based drug design (SBDD), as well as a great challenge in the computational and theoretical chemistry. Herein, we have systematically addressed the complicated solvation and desolvation effects on the PLBA brought by the difference of the explicit water in the protein cavity before and after ligands bind to the protein-binding site. Based on the new solvation model, a nonfitting method at the first-principles level for the PLBA prediction was developed by taking the bridging and displaced water (BDW) molecules into account simultaneously. The newly developed method, DOX_BDW, was validated against a total of 765 noncovalent and covalent protein-ligand binding pairs, including the CASF2016 core set, Cov_2022 covalent binding testing set, and six testing sets for the hit and lead compound optimization (HLO) simulation. In all of the testing sets, the DOX_BDW method was able to produce PLBA predictions that were strongly correlated with the corresponding experimental data (R = 0.66-0.85). The overall performance of DOX_BDW is better than the current empirical scoring functions that are heavily parameterized. DOX_BDW is particularly outstanding for the covalent binding situation, implying the need for considering an electronic structure in covalent drug design. Furthermore, the method is especially recommended to be used in the HLO scenario of SBDD, where hundreds of similar derivatives need to be screened and refined. The computational cost of DOX_BDW is affordable, and its accuracy is remarkable.

Applications of machine learning in computer-aided drug discovery.

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

Overcoming Challenging Substituent Perturbations with Multisite λ-Dynamics: A Case Study Targeting β-Secretase 1.

Alchemical free energy calculations have made a dramatic impact upon the field of structure-based drug design by allowing functional group modifications to be explored computationally prior to experimental synthesis and assay evaluation, thereby informing and directing synthetic strategies. In furthering the advancement of this area, a series of 21 β-secretase 1 (BACE1) inhibitors developed by Janssen Pharmaceuticals were examined to evaluate the ability to explore large substituent perturbations, some of which contain scaffold modifications, with multisite λ-dynamics (MSλD), an innovative alchemical free energy framework. Our findings indicate that MSλD is able to efficiently explore all structurally diverse ligand end-states simultaneously within a single MD simulation with a high degree of precision and with reduced computational costs compared to the widely used approach TI/MBAR. Furthermore, computational predictions were shown to be accurate to within 0.5-0.8 kcal/mol when CM1A partial atomic charges were combined with CHARMM or OPLS-AA-based force fields, demonstrating that MSλD is force field independent and a viable alternative to FEP or TI approaches for drug design.

Replacement of Protein Binding-Site Waters Contributes to Favorable Halogen Bond Interactions.

Halogen bond interaction between a protein electronegative atom and a ligand halogen atom is increasingly attracting attention in the field of structure-based drug design. Nevertheless, gaps in understanding make it desirable to better examine the role of forces governing the formation of favorable halogen bond interactions, and the development of effective and efficient computational approaches to "design in" favorable halogen bond interactions in lead optimization process are warranted. Here, we analyzed the binding-site water properties of crystal structures with characterized halogen bond interactions between ligand halogen atoms and protein backbone carbonyl groups and, thus, found that halogen atoms involved in halogen bond interactions frequently replace calculated binding-site waters upon ligand binding. Moreover, we observed that the preferential directionality of halogen bond interactions aligns well with the orientations of these replaced waters, and these replaced waters exhibited differential energetic characteristics as compared to waters that are displaced by halogen atoms that do not form halogen bond interactions. Our discovery that replacement of calculated binding-site waters contributes to the formation of favorable halogen bond interactions suggests a practical approach for rational drug design utilizing halogen bond interactions with protein backbone carbonyl groups.

Effect of β-cyclodextrin-EGCG complexion against aggregated a-synuclein through density functional theory and discrete molecular dynamics

Misfolding and aggregation of a-synuclein (AS) protein are considered to be causative factors for Parkinson's disease. Herein, we describe molecular dynamics simulations of aggregated AS with βC-EGCG to better characterize the detailed conformational effects on their inhibitory action. Our results indicate that the binding of βC-EGCG disrupt the β-sheet of aggregated AS structure and cause impairment of intermolecular interactions. Furthermore, the free energy landscape portrayed the effect of βC-EGCG directly impedes the formation of aggregated intermediate conformers in AS. Hence, our study could aid in the field of structure-based drug design against the AS disorder.

Molecular binding response of naringin and naringenin to H46R mutant SOD1 protein in combating protein aggregation using density functional theory and discrete molecular dynamics

Amyotrophic lateral sclerosis (ALS) is a calamitous neurodegenerative disorder characterized by denervation of upper and lower motor neurons. Numerous hypotheses suggest that toxic protein misfolding and aggregation cause ALS, similar to that of other neurodegenerative diseases, such as Alzheimers and Parkinsons. Protruding causes of familial ALS are mutations in the gene encoding Cu/Zn superoxide dismutase-1 (SOD1), which decrease protein stability and endorse protein aggregation. Thus, the interference concerning aggregate formation and destabilization in SOD1 is considered to be an impending therapeutic strategy. In this work, we utilized computational chemistry methods to initially study the effect of substitution mutation, His46Arg on SOD1 protein. Further, we described the interaction of two naturally occurring polyphenol compounds, naringin and naringenin with mutant SOD1 that is regarded to hinder the protein aggregation. Subsequently, the use of quantum chemical and molecular mechanics calculations speculated that naringin had a strong binding affinity with mutant SOD1 and impeded the formation of toxic aggregates than that of naringenin. Ultimately, we could conjecture that ingesting of polyphenol-rich foods in ALS patients may be regarded to improvise their living. Moreover, the findings from our study could pave a way in the field of structure-based drug design in developing potential anti-aggregation inhibitors against incurable ALS, affecting the human population.

The potential use of single-particle electron microscopy as a tool for structure-based inhibitor design.

Recent developments in electron microscopy (EM) have led to a step change in our ability to solve the structures of previously intractable systems, especially membrane proteins and large protein complexes. This has provided new opportunities in the field of structure-based drug design, with a number of high-profile publications resolving the binding sites of small molecules and peptide inhibitors. There are a number of advantages of EM over the more traditional X-ray crystallographic approach, such as resolving different conformational states and permitting the dynamics of a system to be better resolved when not constrained by a crystal lattice. There are still significant challenges to be overcome using an EM approach, not least the speed of structure determination, difficulties with low-occupancy ligands and the modest resolution that is available. However, with the anticipated developments in the field of EM, the potential of EM to become a key tool for structure-based drug design, often complementing X-ray and NMR studies, seems promising.

Balancing target flexibility and target denaturation in computational fragment‐based inhibitor discovery

Accounting for target flexibility and selecting "hot spots" most likely to be able to bind an inhibitor continue to be challenges in the field of structure-based drug design, especially in the case of protein-protein interactions. Computational fragment-based approaches using molecular dynamics (MD) simulations are a promising emerging technology having the potential to address both of these challenges. However, the optimal MD conditions permitting sufficient target flexibility while also avoiding fragment-induced target denaturation remain ambiguous. Using one such technology (Site Identification by Ligand Competitive Saturation, SILCS), conditions were identified to either prevent denaturation or identify and exclude trajectories in which subtle but important denaturation was occurring. The target system used was the well-characterized protein cytokine IL-2, which is involved in a protein-protein interface and, in its unliganded crystallographic form, lacks surface pockets that can serve as small-molecule binding sites. Nonetheless, small-molecule inhibitors have previously been discovered that bind to two "cryptic" binding sites that emerge only in the presence of ligand binding, highlighting the important role of IL-2 flexibility. Using the above conditions, SILCS with hydrophobic fragments was able to identify both sites based on favorable fragment binding while avoiding IL-2 denaturation. An important additional finding was that acetonitrile, a water-miscible fragment, fails to identify either site yet can induce target denaturation, highlighting the importance of fragment choice.

Molecular Recognition in the Case of Flexible Targets

A protein's flexibility is well recognized to underlie its capacity to engage in critical functions, such as signal transduction, biomolecular transport and biochemical reactivity. Molecular recognition is also tightly linked to the dynamics of the binding partners, yet protein flexibility has largely been ignored by the growing field of structure-based drug design (SBDD). In combination with experimentally determined structures, a number of computational methods have been proposed to model protein movements, which may be important for small molecule binding. Such techniques have the ability to expose new binding site conformations, which may in turn recognize and lead to the discovery of more potent and selective drugs through molecular docking. In this article, we discuss various methods and focus on the Relaxed Complex Scheme (RCS), which uses Molecular Dynamics (MD) simulations to model full protein flexibility and enhance virtual screening programmes. We review practical applications of the RCS and use a recent study of the HIV-1 reverse transcriptase to illustrate the various phases of the scheme. We also discuss some encouraging developments, aimed at addressing current weaknesses of the RCS.

Method To Assess Packing Quality of Transmembrane α-Helices in Proteins. 1. Parametrization Using Structural Data

Integral membrane proteins (MPs) are pharmaceutical targets of exceptional importance. Modern methods of three-dimensional protein structure determination often fail to supply the fast growing field of structure-based drug design with the requested MPs' structures. That is why computational modeling techniques gain a special importance for these objects. Among the principal difficulties limiting application of these methods is the low quality of the MPs' models built in silico. In this series of two papers we present a computational approach to the assessment of the packing "quality" of transmembrane (TM) alpha-helical domains in proteins. The method is based on the concept of protein environment classes, whereby each amino acid residue is described in terms of its environment polarity and accessibility to the membrane. In the first paper we analyze a nonredundant set of 26 TM alpha-helical domains and compute the residues' propensities to five predefined classes of membrane-protein environments. Here we evaluate the proposed approach only by various test sets, cross-validation protocols and ability of the method to delimit the crystal structure of visual rhodopsin, and a number of its erroneous theoretical models. More advanced validation of the method is given in the second article of this series. We assume that the developed "membrane score" method will be helpful in optimizing computer models of TM domains of MPs, especially G-protein coupled receptors.