Journal of Computer-Aided Molecular Design最新文献

The influence of polar, water-miscible organic solvents (POS) on protein structure, stability, and functional activity is a subject of significant interest and complexity. This study examines the effects of acetonitrile (ACN), a semipolar, aprotic solvent, on the solvation properties of blocked Ace-Gly-X-Gly-Nme tripeptides (where Ace and Nme stands for acetyl and N-methyl amide groups respectively and X is any amino acid) through extensive molecular dynamics simulations. Individual simulations were conducted for each peptide, encompassing five different ACN concentrations within the range of χ_ACN = 0.1–0.9. The preferential solvation parameter (Γ) calculated using the Kirkwood-Buff integral method was used for the assessment of peptide interactions with water/ACN. Additionally, weighted Voronoi tessellation was applied to obtain a three-way data set containing four time-averaged contact surface area types between peptide atoms and water/ACN atoms. A mathematical technique known as N-way Partial Least Squares (NPLS) was utilized to anticipate the preferential interactions between peptides and water/ACN from the contact surface areas. Furthermore, the temperature dependency of peptide-solvent interactions was investigated using a subset of 10 amino acids representing a range of hydrophobicities. MD simulations were conducted at five temperatures, spanning from 283 to 343 K, with subsequent analysis of data focusing on both preferential solvation and peptide-solvent contact surface areas. The results demonstrate the efficacy of utilizing contact surface areas between the peptide and solvent constituents for successfully predicting preferential interactions in water/ACN mixtures across various ACN concentrations and temperatures.

Human Hippo signaling pathway is an evolutionarily conserved regulator network that controls organ development and has been implicated in various cancers. Transcriptional enhanced associate domain-4 (TEAD4) is the final nuclear effector of Hippo pathway, which is activated by Yes-associated protein (YAP) through binding to two separated YAP regions of α1-helix and Ω-loop. Previous efforts have all been addressed on deriving peptide inhibitors from the YAP to target TEAD4. Instead, we herein attempted to rationally design a so-called ‘YAP helix^α1-trap’ based on the TEAD4 to target YAP by using dynamics simulation and energetics analysis as well as experimental assays at molecular and cellular levels. The trap represents a native double-stranded helical hairpin covering a specific YAP-binding site on TEAD4 surface, which is expected to form a three-helix bundle with the α1-helical region of YAP, thus competitively disrupting TEAD4–YAP interaction. The hairpin was further stapled by a disulfide bridge across its two helical arms. Circular dichroism characterized that the stapling can effectively constrain the trap into a native-like structured conformation in free state, thus largely minimizing the entropy penalty upon its binding to YAP. Affinity assays revealed that the stapling can considerably improve the trap binding potency to YAP α1-helix by up to 8.5-fold at molecular level, which also exhibited a good tumor-suppressing effect at cellular level if fused with TAT cell permeation sequence. In this respect, it is considered that the YAP helix^α1-trap-mediated blockade of Hippo pathway may be a new and promising therapeutic strategy against cancers.

Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large databases is possible with cloud-scale computing. However, rapid docking necessitates compromises in scoring, often leading to poor enrichment and an abundance of false positives in docking results. This work describes a new scoring function composed of two parts – a knowledge-based component that predicts the probability of a particular atom type being in a particular receptor environment, and a tunable weight matrix that converts the probability predictions into a dimensionless score suitable for virtual screening enrichment. This score, the FitScore, represents the compatibility between the ligand and the binding site and is capable of a high degree of enrichment across standardized docking test sets.

Antioxidants agents play an essential role in the food industry for improving the oxidative stability of food products. In the last years, the search for new natural antioxidants has increased due to the potential high toxicity of chemical additives. Therefore, the synthesis and evaluation of the antioxidant activity in peptides is a field of current research. In this study, we performed a Quantitative Structure Activity Relationship analysis (QSAR) of cysteine-containing 19 dipeptides and 19 tripeptides. The main objective is to bring information on the relationship between the structure of peptides and their antioxidant activity. For this purpose, 1D and 2D molecular descriptors were calculated using the PaDEL software, which provides information about the structure, shape, size, charge, polarity, solubility and other aspects of the compounds. Different QSAR model for di- and tripeptides were developed. The statistic parameters for di-peptides model (R²_train = 0.947 and R²_test = 0.804) and for tripeptide models (R²_train = 0.923 and R²_test = 0.847) indicate that the generated models have high predictive capacity. Then, the influence of the cysteine position was analyzed predicting the antioxidant activity for new di- and tripeptides, and comparing them with glutathione. In dipeptides, excepting SC, TC and VC, the activity increases when cysteine is at the N-terminal position. For tripeptides, we observed a notable increase in activity when cysteine is placed in the N-terminal position.

Nonadditivity (NA) in Structure-Activity and Structure-Property Relationship (SAR) data is a rare but very information rich phenomenon. It can indicate conformational flexibility, structural rearrangements, and errors in assay results and structural assignment. While purely ligand-based conformational causes of NA are rather well understood and mundane, other factors are less so and cause surprising NA that has a huge influence on SAR analysis and ML model performance. We here report a systematic analysis across a wide range of properties (20 on-target biological activities and 4 physicochemical ADME-related properties) to understand the frequency of various different phenomena that may lead to NA. A set of novel descriptors were developed to characterize double transformation cycles and identify trends in NA. Double transformation cycles were classified into “surprising” and “mundane” categories, with the majority being classed as mundane. We also examined commonalities among surprising cycles, finding LogP differences to have the most significant impact on NA. A distinct behavior of NA for on-target sets compared to ADME sets was observed. Finally, we show that machine learning models struggle with highly nonadditive data, indicating that a better understanding of NA is an important future research direction.

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand–protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein–ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein–ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein–ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure–activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

Adenosine deaminases acting on RNA (ADARs) are pivotal RNA-editing enzymes responsible for converting adenosine to inosine within double-stranded RNA (dsRNA). Dysregulation of ADAR1 editing activity, often arising from genetic mutations, has been linked to elevated interferon levels and the onset of autoinflammatory diseases. However, understanding the molecular underpinnings of this dysregulation is impeded by the lack of an experimentally determined structure for the ADAR1 deaminase domain. In this computational study, we utilized homology modeling and the AlphaFold2 to construct structural models of the ADAR1 deaminase domain in wild-type and two pathogenic variants, R892H and Y1112F, to decipher the structural impact on the reduced deaminase activity. Our findings illuminate the critical role of structural complementarity between the ADAR1 deaminase domain and dsRNA in enzyme-substrate recognition. That is, the relative position of E1008 and K1120 must be maintained so that they can insert into the minor and major grooves of the substrate dsRNA, respectively, facilitating the flipping-out of adenosine to be accommodated within a cavity surrounding E912. Both amino acid replacements studied, R892H at the orthosteric site and Y1112F at the allosteric site, alter K1120 position and ultimately hinder substrate RNA binding.

In this work, we present the frontend of GeoMine and showcase its application, focusing on the new features of its latest version. GeoMine is a search engine for ligand-bound and predicted empty binding sites in the Protein Data Bank. In addition to its basic text-based search functionalities, GeoMine offers a geometric query type for searching binding sites with a specific relative spatial arrangement of chemical features such as heavy atoms and intermolecular interactions. In contrast to a text search that requires simple and easy-to-formulate user input, a 3D input is more complex, and its specification can be challenging for users. GeoMine’s new version aims to address this issue from the graphical user interface perspective by introducing an additional visualization concept and a new query template type. In its latest version, GeoMine extends its query-building capabilities primarily through input formulation in 2D. The 2D editor is fully synchronized with GeoMine’s 3D editor and provides the same functionality. It enables template-free query generation and template-based query selection directly in 2D pose diagrams. In addition, the query generation with the 3D editor now supports predicted empty binding sites for AlphaFold structures as query templates. GeoMine is freely accessible on the ProteinsPlus web server (https://proteins.plus).

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki–Miyaura, Hiyama and Liebeskind–Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors.