Empirical Scoring Function Research Articles

HSADab: A comprehensive database for human serum albumin

Human Serum Albumin (HSA), the most abundant protein in human body fluids, plays a crucial role in the transportation, absorption, metabolism, distribution, and excretion of drugs, significantly influencing their therapeutic efficacy. Despite the importance of HSA as a drug target, the available data on its interactions with external agents, such as drug-like molecules and antibodies, are limited, posing challenges for molecular modeling investigations and the development of empirical scoring functions or machine learning predictors for this target. Furthermore, the reported entries in existing databases often contain major inconsistencies due to varied experiments and conditions, raising concerns about data quality. To address these issues, a pioneering database, HSADab, was established through an extensive review of >30,000 scientific publications published between 1987 and 2023. The database encompasses over 5000 affinity data points at multiple temperatures and >130 crystal structures, including both ligand-bound and apo forms. The current HSADab resource (www.hsadab.cn) serves as a reliable foundation for validating molecular simulation protocols, such as traditional virtual screening workflows using docking, end-point, and al-chemical free energy techniques. Additionally, it provides a valuable data source for the implementation of machine learning predictors, including plasma protein binding models and plasma protein-based drug design models.

Comprehensive analysis of protein-protein interactions (PPIs) with structure prediction program for breast cancer determination

Breast cancer is a prevalent disease that primarily affects women, with significant implications for public health. Early detection improves survival rates, and technological advancements can enhance early diagnosis. Protein structure prediction methods can provide valuable insights into the structure of proteins involved in breast cancer, including human epidermal growth factor receptor-2 (HER2). HER2 is a known protein receptor that plays a critical role in breast cancer development and is a target for therapy. Predicting the binding affinity between HER2 and potential ligands can help identify novel treatment options. This study aimed to predict the structure of HER2 using I-TASSER and determine potential ligands using empirical graph neural network-based scoring functions. Molecular docking simulations were performed to evaluate the binding conformation and stability of the HER2-ligand complexes. The results showed the potential ligands identified by I-TASSER: AEE, UUU, and Mg2+. Afterward, their binding affinity with HER2 was assessed, yielding AEE as the best binding with the lowest vina score.

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.

Enhancing Hit Discovery in Virtual Screening through Absolute Protein-Ligand Binding Free-Energy Calculations.

In the hit identification stage of drug discovery, a diverse chemical space needs to be explored to identify initial hits. Contrary to empirical scoring functions, absolute protein-ligand binding free-energy perturbation (ABFEP) provides a theoretically more rigorous and accurate description of protein-ligand binding thermodynamics and could, in principle, greatly improve the hit rates in virtual screening. In this work, we describe an implementation of an accurate and reliable ABFEP method in FEP+. We validated the ABFEP method on eight congeneric compound series binding to eight protein receptors including both neutral and charged ligands. For ligands with net charges, the alchemical ion approach is adopted to avoid artifacts in electrostatic potential energy calculations. The calculated binding free energies correlate with experimental results with a weighted average of R2 = 0.55 for the entire dataset. We also observe an overall root-mean-square error (RMSE) of 1.1 kcal/mol after shifting the zero-point of the simulation data to match the average experimental values. Through ABFEP calculations using apo versus holo protein structures, we demonstrated that the protein conformational and protonation state changes between the apo and holo proteins are the main physical factors contributing to the protein reorganization free energy manifested by the overestimation of raw ABFEP calculated binding free energies using the holo structures of the proteins. Furthermore, we performed ABFEP calculations in three virtual screening applications for hit enrichment. ABFEP greatly improves the hit rates as compared to docking scores or other methods like metadynamics. The good performance of ABFEP in rank ordering compounds demonstrated in this work confirms it as a useful tool to improve the hit rates in virtual screening, thus facilitating hit discovery.

Development of a polynomial scoring function P3-Score for improved scoring and ranking powers

Scoring functions are of great importance in fast evaluations of the protein–ligand binding affinity. To improve the scoring power and ranking power, some new features are constructed, and a new empirical scoring function (P3-Score) using 14 features was developed based on multivariate polynomial ridge regression and k-fold cross-validation on the training set. The scoring power and ranking power of P3-Score are compared with other 36 classical scoring functions on the test set in CASF-2016, the results indicate that P3-Score gives better scoring power (0.735) and ranking power (0.688) than the current empirical scoring functions. The multivariate polynomial ridge regression could be a promising method to improve the classical scoring function and prevent overfitting. However, in comparison with recently developed machine learning scoring functions, most ML scoring functions present better scoring performance than the classical scoring functions.

Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network.

Protein-ligand binding affinity prediction is an important task in structural bioinformatics for drug discovery and design. Although various scoring functions (SFs) have been proposed, it remains challenging to accurately evaluate the binding affinity of a protein-ligand complex with the known bound structure because of the potential preference of scoring system. In recent years, deep learning (DL) techniques have been applied to SFs without sophisticated feature engineering. Nevertheless, existing methods cannot model the differential contribution of atoms in various regions of proteins, and the relationship between atom properties and intermolecular distance is also not fully explored. We propose a novel empirical graph neural network for accurate protein-ligand binding affinity prediction (EGNA). Graphs of protein, ligand and their interactions are constructed based on different regions of each bound complex. Proteins and ligands are effectively represented by graph convolutional layers, enabling the EGNA to capture interaction patterns precisely by simulating empirical SFs. The contributions of different factors on binding affinity can thus be transparently investigated. EGNA is compared with the state-of-the-art machine learning-based SFs on two widely used benchmark data sets. The results demonstrate the superiority of EGNA and its good generalization capability.

A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios.

We release a new, high quality data set of 1162 PDE10A inhibitors with experimentally determined binding affinities together with 77 PDE10A X-ray co-crystal structures from a Roche legacy project. This data set is used to compare the performance of different 2D- and 3D-machine learning (ML) as well as empirical scoring functions for predicting binding affinities with high throughput. We simulate use cases that are relevant in the lead optimization phase of early drug discovery. ML methods perform well at interpolation, but poorly in extrapolation scenarios-which are most relevant to a real-world application. Moreover, we find that investing into the docking workflow for binding pose generation using multi-template docking is rewarded with an improved scoring performance. A combination of 2D-ML and 3D scoring using a modified piecewise linear potential shows best overall performance, combining information on the protein environment with learning from existing SAR data.

Structural bioinformatics analysis of SARS-CoV-2 variants reveals higher hACE2 receptor binding affinity for Omicron B.1.1.529 spike RBD compared to wild type reference

To date, more than 263 million people have been infected with SARS-CoV-2 during the COVID-19 pandemic. In many countries, the global spread occurred in multiple pandemic waves characterized by the emergence of new SARS-CoV-2 variants. Here we report a sequence and structural-bioinformatics analysis to estimate the effects of amino acid substitutions on the affinity of the SARS-CoV-2 spike receptor binding domain (RBD) to the human receptor hACE2. This is done through qualitative electrostatics and hydrophobicity analysis as well as molecular dynamics simulations used to develop a high-precision empirical scoring function (ESF) closely related to the linear interaction energy method and calibrated on a large set of experimental binding energies. For the latest variant of concern (VOC), B.1.1.529 Omicron, our Halo difference point cloud studies reveal the largest impact on the RBD binding interface compared to all other VOC. Moreover, according to our ESF model, Omicron achieves a much higher ACE2 binding affinity than the wild type and, in particular, the highest among all VOCs except Alpha and thus requires special attention and monitoring.

Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.

Protein-ligand scoring functions are widely used in structure-based drug design for fast evaluation of protein-ligand interactions, and it is of strong interest to develop scoring functions with machine-learning approaches. In this work, by expanding the training set, developing physically meaningful features, employing our recently developed linear empirical scoring function Lin_F9 (Yang, C. J. Chem. Inf. Model. 2021, 61, 4630-4644) as the baseline, and applying extreme gradient boosting (XGBoost) with Δ-machine learning, we have further improved the robustness and applicability of machine-learning scoring functions. Besides the top performances for scoring-ranking-screening power tests of the CASF-2016 benchmark, the new scoring function ΔLin_F9XGB also achieves superior scoring and ranking performances in different structure types that mimic real docking applications. The scoring powers of ΔLin_F9XGB for locally optimized poses, flexible redocked poses, and ensemble docked poses of the CASF-2016 core set achieve Pearson's correlation coefficient (R) values of 0.853, 0.839, and 0.813, respectively. In addition, the large-scale docking-based virtual screening test on the LIT-PCBA data set demonstrates the reliability and robustness of ΔLin_F9XGB in virtual screening application. The ΔLin_F9XGB scoring function and its code are freely available on the web at (https://yzhang.hpc.nyu.edu/Delta_LinF9_XGB).

AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking.

The protein-ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein-ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, π-stacking, π-cation, and metal-ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein-ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/xundrug/AA-Score-Tool.

Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term.

Scoring functions are important components in molecular docking for structure-based drug discovery. Traditional scoring functions, generally empirical- or force field-based, are robust and have proven to be useful for identifying hits and lead optimizations. Although multiple highly accurate deep learning- or machine learning-based scoring functions have been developed, their direct applications for docking and screening are limited. We describe a novel strategy to develop a reliable protein–ligand scoring function by augmenting the traditional scoring function Vina score using a correction term (OnionNet-SFCT). The correction term is developed based on an AdaBoost random forest model, utilizing multiple layers of contacts formed between protein residues and ligand atoms. In addition to the Vina score, the model considerably enhances the AutoDock Vina prediction abilities for docking and screening tasks based on different benchmarks (such as cross-docking dataset, CASF-2016, DUD-E and DUD-AD). Furthermore, our model could be combined with multiple docking applications to increase pose selection accuracies and screening abilities, indicating its wide usage for structure-based drug discoveries. Furthermore, in a reverse practice, the combined scoring strategy successfully identified multiple known receptors of a plant hormone. To summarize, the results show that the combination of data-driven model (OnionNet-SFCT) and empirical scoring function (Vina score) is a good scoring strategy that could be useful for structure-based drug discoveries and potentially target fishing in future.

Lin_F9: A Linear Empirical Scoring Function for Protein-Ligand Docking.

Molecular docking is one of the most widely used computational tools in structure-based drug design and is critically dependent on accuracy and robustness of the scoring function. In this work, we introduce a new scoring function Lin_F9, which is a linear combination of nine empirical terms, including a unified metal bond term to specifically describe metal-ligand interactions. Parameters in Lin_F9 are obtained with a multistage fitting protocol using explicit water-included structures. For the CASF-2016 benchmark test set, Lin_F9 achieves the top scoring power among all 34 classical scoring functions for both original crystal poses and locally optimized poses with Pearson correlation coefficients (R) of 0.680 and 0.687, respectively. Meanwhile, in comparison with Vina, Lin_F9 achieves consistently better scoring power and ranking power with various types of protein-ligand complex structures that mimic real docking applications, including end-to-end flexible docking for the CASF-2016 benchmark test set using a single or an ensemble of protein receptor structures, as well as for D3R Grand Challenge (GC4) test sets. Lin_F9 has been implemented in a fork of Smina as an optional built-in scoring function that can be used for docking applications as well as for further improvement of scoring functions and docking protocols. Lin_F9 is accessible through https://yzhang.hpc.nyu.edu/Lin_F9/.

Identification of potential inhibitory compounds of an essential nucleotide biosynthesis enzyme, 6‐Phosphogluconate Dehydrogenase in Plasmodium Vivax

The erythrocytic infection and onset of malaria is a result of the protozoan parasitism from the Plasmodium genus. In 2018, there were an estimated 228 million cases of malaria worldwide largely attributed to P. falciparum and P. vivax infections. Current chemotherapy efforts against the Plasmodium parasites target mitochondrial function (Malarone), the 30s ribosomal subunit (Vibramycin) and prevention of the parasite's abilities to degrade the host's hemoglobin (Chloroquine), however, rising mutations have decreased their therapeutic effects. The essentiality of 6-phosphogluconate dehydrogenase (6PGDH) is demonstrated in its oxidation of 6-phosphogluconate (6PG) to ribulose-5-phosphate and the reduction of nicotinamide adenine dinucleotide phosphate (NADP+) to provide precursors in nucleic acid synthesis. Here, the homologous 3D structure of 6PGDH from P. falciparum was used for molecular docking studies since there is not a published structure of P. vivax 6PGDH in the Protein Data Bank (PDB). The structures of 6PGDH from P. falciparum and P. vivax display 80.30% sequence identity. The apo form of Pf6PGDH (PDB ID: 6FQX) was aligned with the structures containing the 6PG substrate (PDB ID: 6FQZ) and NADP+ coenzyme (PDB ID: 6FQY) ligands in order to define the active site. The 6PGDH protein was virtually screened against 307,735 ligands from several diverse compound libraries using the molecular docking program Genetic Optimization Ligand Docking (GOLD) and ranked for their predicted binding fitness based on empirical scoring functions of the employed genetic algorithm. The highest binding compound, ZINC55224876, received a GOLD Fitness score of 91.1 and demonstrated strong hydrophobic interactions as quantified by the piecewise linear potential (PLP) sub-score. The ligands gathered to act as controls were obtained from studies conducted on Trypanosoma brucei 6PGDH that demonstrated a reduction in protein function to act as positive controls. The screening of the positive and negative control ligands against the Pf6PGDH structure culminated in GOLD Fitness scores of 74.76 for the highest to the lowest score of 41.75. The top 10% of the highest overall scoring library, the Zinc ChemBridge library (124,345 ligands) resulted in a range of GOLD Fitness scores from 91.1- 59.73, whereas the lowest overall scoring library, Fragment (3,963 ligands) received scores ranging from 59.84-20.88. The resulting top scoring ligands may further be used for future enzyme assays in the process of drug discovery.

New machine learning and physics-based scoring functions for drug discovery

Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein–ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein–protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br.

Double-Stranded DNA Matrix for Photosensitization Switching

Photosensitization, originated from the activation of triplet states, is the basis of many photodynamic applications, but often competes with a series of nonradiative processes. Herein, we communic...

Interaction Nature and Computational Methods for Halogen Bonding: A Perspective.

Halogen bonds are noncovalent interactions that have been widely used in many fields, including drug design, crystal engineering, and material sciences. A clear understanding of the nature of halogen bonding as well as the proper theoretical bonding description, especially the development of efficient and accurate computational chemical methods and their application in complex systems, is of great significance to promote the development of related fields. In this perspective, we reviewed the investigations of the nature of halogen bonding in recent years and discussed the development of quantum mechanical, molecular mechanical, and empirical scoring function methods in properly describing halogen-bonding interactions, as well as their achievements in corresponding areas. An evaluation on the performance of various quantum mechanical and semiempirical quantum mechanical methods in describing halogen bonds was also included, involving the DFT-D4 scheme and the recently reported xTB methods. We hope this perspective may be helpful, from the insights of computational tools and methods, in providing reference and enlightenment for the application of halogen bonds in fields like high-throughput virtual screening and rational drug design.

Expanding Mass Spectrometry Toolbox for RNA Modification Profiling

RNA modifications, by expanding the repertoire of RNA structure and interactions, play important roles in biology. At the cellular level, modifications have been linked to antibiotic resistance, RNA biogenesis, translational fidelity, and gene expression. Importantly, stoichiometry of many RNA modifications is responsive to cellular and environmental cues, and can vary across cell types and disease states. Here we present the experimental and bioinformatic tools for mass spectrometry (MS) based identification and quantitative profiling of RNA modifications in abundant RNAs. RNA modification analysis is based on nucleolytic cleavage of the purified RNA of interest into small 3-15 nt fragments that are chromatographically separated and sequenced via LC-MS/MS. Compared to the field of protein identification, analysis of the tandem MS spectra of RNA has been significantly lagging behind, due to more complex fragmentation patterns and high redundancy of RNA (4 nt vs. 20 aa in proteomics). To address the need we developed Pytheas, a bioinformatic software for annotation of RNA MS/MS spectra and assignment to the sequences present in the theoretical library. Pytheas is using an empirical scoring function that has been trained to successfully distinguish between the target and decoy sequences and is equipped with statistical tools for the automated analysis at the large scale. Pytheas performance has been tested using MS/MS data collected on different instruments and RNAs of different origin, including in-vitro transcribed RNA, rRNA, and tRNA. As an example, we will demonstrate the identification coverage of known ribosomal modifications in S. Cerevisiae. Additionally, 16S and 23S isolated from B. Subtilis will be used to show how Pytheas can be implemented for the discovery and sequence placement of previously unannotated methylated nucleosides and “mass-silent” pseudouridines using both stable-isotope labeling with 2H and 15N, and chemical-derivatization.

Cosolvent Analysis Toolkit (CAT): a robust hotspot identification platform for cosolvent simulations of proteins to expand the druggable proteome

Cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterization of allosteric and cryptic binding sites, which can be rendered “druggable” by small molecule ligands. Despite their conceptual simplicity and effectiveness, the analysis of cosolvent MD trajectories relies on pocket volume data, which requires a high level of manual investigation and may introduce a bias. In this work, we present CAT (Cosolvent Analysis Toolkit): an open-source, freely accessible analytical tool, suitable for automated analysis of cosolvent MD trajectories. CAT is compatible with commonly used molecular graphics software packages such as UCSF Chimera and VMD. Using a novel hybrid empirical force field scoring function, CAT accurately ranks the dynamic interactions between the macromolecular target and cosolvent molecules. To benchmark, CAT was used for three validated protein targets with allosteric and orthosteric binding sites, using five chemically distinct cosolvent molecules. For all systems, CAT has accurately identified all known sites. CAT can thus assist in computational studies aiming at identification of protein “hotspots” in a wide range of systems. As an easy-to-use computational tool, we expect that CAT will contribute to an increase in the size of the potentially ‘druggable’ human proteome.

An integrated computational approach to identify GC minor groove binders using various molecular docking scoring functions, dynamics simulations and binding free energy calculations

Understanding the DNA-ligand interaction mechanism is of utmost importance to design selective inhibitors targeting the GC- and AT-rich DNA. This forms a primary strategy to block the association of transcription factors to promoters and subsequently, reduce the expression of genes. We present here an integrated approach combining various docking scoring functions, selective ligand-based pharmacophore models, molecular dynamics simulations and binding free energy calculations to prioritize natural compounds specific to GC minor groove binding. The approach initially applies a selective ligand-based pharmacophore model built upon known GC minor groove binders to identify potential GC minor groove binders from natural compound repositories. These GC minor groove binders were then cross-examined with selective pharmacophore models (controls) based on AT-rich binders and GC intercalators to assess its unfitness. This approach involves the calculation of binding energies of known GC- and AT minor groove binders using three scoring functions without any constraint on groove specificity of GC- and AT-rich DNA. The evaluation of empirical scoring functions led to enumeration of a new parameter, the energy difference computed using Glide (sensitivity = 80%) to recognize GC-rich binders effectively. Molecular dynamics simulations and binding free energy calculations (MM/GBSA) constituted the final phase of this approach to analyze the interactions of natural molecules (hits) with GC-rich DNA comprehensively. Seven natural molecules were selected which exhibited fewer fluctuations in RMSD and RMSF profiles and better GC-rich DNA binding with low free energies of binding. These natural hits prioritized by this integrated approach can be tested in DNA binding assay.Communicated by Ramaswamy H. Sarma

Evaluating the performance of MM/PBSA for binding affinity prediction using class A GPCR crystal structures.

The recent expansion of GPCR crystal structures provides the opportunity to assess the performance of structure-based drug design methods for the GPCR superfamily. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA)-based methods are commonly used for binding affinity prediction, as they provide an intermediate compromise of speed and accuracy between the empirical scoring functions used in docking and more robust free energy perturbation methods. In this study, we systematically assessed the performance of MM/PBSA in predicting experimental binding free energies using twenty Class A GPCR crystal structures and 934 known ligands. Correlations between predicted and experimental binding free energies varied significantly between individual targets, ranging from r = - 0.334 in the inactive-state CB1 cannabinoid receptor to r = 0.781 in the active-state CB1 cannabinoid receptor, while average correlation across all twenty targets was relatively poor (r = 0.183). MM/PBSA provided better predictions of binding free energies compared to docking scores in eight out of the twenty GPCR targets while performing worse for four targets. MM/PBSA binding affinity predictions calculated using a single, energy minimized structure provided comparable predictions to sampling from molecular dynamics simulations and may be more efficient when computational cost becomes restrictive. Additionally, we observed that restricting MM/PBSA calculations to ligands with a high degree of structural similarity to the crystal structure ligands improved performance in several cases. In conclusion, while MM/PBSA remains a valuable tool for GPCR structure-based drug design, its performance in predicting the binding free energies of GPCR ligands remains highly system-specific as demonstrated in a subset of twenty Class A GPCRs, and validation of MM/PBSA-based methods for each individual case is recommended before prospective use.