Improvement of protein-compound docking scores by using amino-acid sequence similarities of proteins

The low accuracy of predicted docking scores is critical at in silico drug screening. In order to improve the accuracy of docking scores, we approximated the protein-compound binding free energy as a linear combination of the raw docking scores of a target compound with many different protein pockets. The coefficients of the linear combination were estimated by the similarities among proteins, simply by using the amino-acid sequence similarities or identities of the proteins. This method was applied to in silico screening of the active compounds of five target proteins, and it increased the hit ratio by approximately four to five times compared to that given only by the raw docking scores in every case. The hit ratio also became robust against differences of target proteins.     

An efficient in silico screening method based on the protein-compound affinity matrix and its application to the design of a focused library for cytochrome P450 (CYP) ligands

A new method has been developed to design a focused library based on available active compounds using protein-compound docking simulations. This method was applied to the design of a focused library for cytochrome P450 (CYP) ligands, not only to distinguish CYP ligands from other compounds but also to identify the putative ligands for a particular CYP. Principal component analysis (PCA) was applied to the protein-compound affinity matrix, which was obtained by thorough docking calculations between a large set of protein pockets and chemical compounds. Each compound was depicted as a point in the PCA space. Compounds that were close to the known active compounds were selected as candidate hit compounds. A machine-learning technique optimized the docking scores of the protein-compound affinity matrix to maximize the database enrichment of the known active compounds, providing an optimized focused library.     

Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes

We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely β(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.     

In silico discovery of beta-secretase inhibitors

Alzheimer's disease, the most common amyloid-associated disorder, accounts for the majority of the dementia diagnosed after the age of 60. The cleavage of the beta-amyloid precursor protein is initiated by beta-secretase (BACE-1), a membrane-bound aspartic protease, which has emerged as an important but difficult protein target. Here, an in silico screening approach consisting of fragment-based docking, ligand conformational search by a genetic algorithm, and evaluation of free energy of binding was used to identify low-molecular-weight inhibitors of BACE-1. More than 300,000 small molecules were docked and about 15,000 prioritized according to a linear interaction energy model with evaluation of solvation by continuum electrostatics. Eighty-eight compounds were tested in vitro, and 10 of them showed an IC(50) value lower than 100 microM in a BACE-1 enzymatic assay. Interestingly, the 10 active compounds shared a triazine scaffold. Moreover, four of them were active in an assay with mammalian cells (EC(50) < 20 microM), indicating that they are cell-permeable. Therefore, these triazine derivatives are very promising lead candidates for BACE-1 inhibition. The discoveries of this series and two other series of nonpeptidic BACE-1 inhibitors demonstrate the usefulness of our in silico high-throughput screening approach.     

Optimization of high throughput virtual screening by combining shape-matching and docking methods

Receptor flexibility is a critical issue in structure-based virtual screening methods. Although a multiple-receptor conformation docking is an efficient way to account for receptor flexibility, it is still too slow for large molecular libraries. It was reported that a fast ligand-centric, shape-based virtual screening was more consistent for hit enrichment than a typical single-receptor conformation docking. Thus, we designed a "distributed docking" method that improves virtual high throughput screening by combining a shape-matching method with a multiple-receptor conformation docking. Database compounds are classified in advance based on shape similarities to one of the crystal ligands complexed with the target protein. This classification enables us to pick the appropriate receptor conformation for a single-receptor conformation docking of a given compound, thereby avoiding time-consuming multiple docking. In particular, this approach utilizes cross-docking scores of known ligands to all available receptor structures in order to optimize the algorithm. The present virtual screening method was tested for reidentification of known PPARgamma and p38 MAP kinase active compounds. We demonstrate that this method improves the enrichment while maintaining the computation speed of a typical single-receptor conformation docking.     

Molecular modeling on pyruvate phosphate dikinase of Entamoeba histolytica and in silico virtual screening for novel inhibitors

Pyruvate phosphate dikinase (PPDK) is the key enzyme essential for the glycolytic pathway in most common and perilous parasite Entamoeba histolytica. Inhibiting the function of this enzyme could control the wide spread of intestinal infections caused by Entamoeba histolytica in humans. With this objective, we modeled the three dimensional structure of the PPDK protein. We used templates with 51% identity and 67% similarity to employ homology-modeling approach. Stereo chemical quality of protein structure was validated by protein structure validation program PROCHECK and VERIFY3D. Experimental proof available in literature along with the in silico studies indicated Lys21, Arg91, Asp323, Glu325 and Gln337 to be the probable active sites in the target protein. Virtual screening was carried out using the genetic docking algorithm GOLD and a consensus scoring function X-Score to substantiate the prediction. The small molecule libraries (ChemDivision database, Diversity dataset, Kinase inhibitor database) were used for screening process. Along with the high scoring results, the interaction studies provided promising ligands for future experimental screening to inhibit the function of PPDK in Entamoeba histolytica. Further, the phylogeny study was carried out to assess the possibility of using the proposed ligands as inhibitors in related pathogens.     

Ranking targets in structure-based virtual screening of three-dimensional protein libraries: methods and problems

Structure-based virtual screening is a promising tool to identify putative targets for a specific ligand. Instead of docking multiple ligands into a single protein cavity, a single ligand is docked in a collection of binding sites. In inverse screening, hits are in fact targets which have been prioritized within the pool of best ranked proteins. The target rate depends on specificity and promiscuity in protein-ligand interactions and, to a considerable extent, on the effectiveness of the scoring function, which still is the Achilles' heel of molecular docking. In the present retrospective study, virtual screening of the sc-PDB target library by GOLD docking was carried out for four compounds (biotin, 4-hydroxy-tamoxifen, 6-hydroxy-1,6-dihydropurine ribonucleoside, and methotrexate) of known sc-PDB targets and, several ranking protocols based on GOLD fitness score and topological molecular interaction fingerprint (IFP) comparison were evaluated. For the four investigated ligands, the fusion of GOLD fitness and two IFP scores allowed the recovery of most targets, including the rare proteins which are not readily suitable for statistical analysis, while significantly filtering out most false positive entries. The current survey suggests that selecting a small number of targets (<20) for experimental evaluation is achievable with a pure structure-based approach.     

Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets

Virtual screening by molecular docking has become a widely used approach to lead discovery in the pharmaceutical industry when a high-resolution structure of the biological target of interest is available. The performance of three widely used docking programs (Glide, GOLD, and DOCK) for virtual database screening is studied when they are applied to the same protein target and ligand set. Comparisons of the docking programs and scoring functions using a large and diverse data set of pharmaceutically interesting targets and active compounds are carried out. We focus on the problem of docking and scoring flexible compounds which are sterically capable of docking into a rigid conformation of the receptor. The Glide XP methodology is shown to consistently yield enrichments superior to the two alternative methods, while GOLD outperforms DOCK on average. The study also shows that docking into multiple receptor structures can decrease the docking error in screening a diverse set of active compounds.     

A comprehensive docking study on the selectivity of binding of aromatic compounds to proteins

Generally, computer-aided drug design is focused on screening of ligand molecules for a single protein target. The screening of several proteins for a ligand is a relatively new application of molecular docking. In the present study, complexes from the Brookhaven Protein Databank were used to investigate a docking approach of protein screening. Automated molecular docking calculations were applied to reproduce 44 protein-aromatic ligand complexes (31 different proteins and 39 different ligand molecules) of the databank. All ligands were docked to all different protein targets in altogether 12090 docking runs. Based on the results of the extensive docking simulations, two relative measures, the molecular interaction fingerprint (MIF) and the molecular affinity fingerprint (MAF), were introduced to describe the selectivity of aromatic ligands to different proteins. MIF and MAF patterns are in agreement with fragment and similarity considerations. Limitations and future extension of our approach are discussed.     

A Virtual Active Compound Produced from the Negative Image of a Ligand-binding Pocket, and its Application to in-silico Drug Screening

We developed a new structure-based in-silico screening method using a negative image of a ligand-binding pocket and a multi-protein–compound interaction matrix. Based on the structure of the ligand pocket of the target protein, we designed a negative image, which consists of virtual atoms whose radii are close to those of carbon atoms. The virtual atoms fit the pocket ideally and achieve an optimal Coulomb interaction. A protein–compound docking program calculates the protein–compound interaction matrix for many proteins and many compounds including the negative image, which can be treated as a virtual compound. With specific attention to a vector of docking scores for a single compound with many proteins, we selected a compound whose score vector was similar to that of the negative image as a candidate hit compound. This method was applied to representative target proteins and showed high database enrichment with a relatively quick procedure.     

Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications

ABSTRACT: BACKGROUND: Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis. RESULTS: We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system. As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins. CONCLUSIONS: This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.     

DEKOIS: demanding evaluation kits for objective in silico screening--a versatile tool for benchmarking docking programs and scoring functions

For widely applied in silico screening techniques success depends on the rational selection of an appropriate method. We herein present a fast, versatile, and robust method to construct demanding evaluation kits for objective in silico screening (DEKOIS). This automated process enables creating tailor-made decoy sets for any given sets of bioactives. It facilitates a target-dependent validation of docking algorithms and scoring functions helping to save time and resources. We have developed metrics for assessing and improving decoy set quality and employ them to investigate how decoy embedding affects docking. We demonstrate that screening performance is target-dependent and can be impaired by latent actives in the decoy set (LADS) or enhanced by poor decoy embedding. The presented method allows extending and complementing the collection of publicly available high quality decoy sets toward new target space. All present and future DEKOIS data sets will be made accessible at www.dekois.com.     

In silico chemical library screening and experimental validation of a novel 9-aminoacridine based lead-inhibitor of human S-adenosylmethionine decarboxylase

In silico chemical library screening (virtual screening) was used to identify a novel lead compound capable of inhibiting S-adenosylmethionine decarboxylase (AdoMetDC). AdoMetDC is intimately involved in the biosynthesis of polyamines, which are essential for tumor progression and are elevated in numerous types of tumors. Therefore, inhibition of this enzyme provides an attractive target for the discovery of novel anticancer drugs. We performed virtual screening using a computer model derived from the X-ray crystal structure of human AdoMetDC and the National Cancer Institute's Diversity Set (1990 compounds). Our docking study suggested several compounds that could serve as drug candidates since their docking modes and scores revealed potential inhibitory activity toward AdoMetDC. Experimental testing of the top-scoring compounds indicated that one of these compounds (NSC 354961) possesses an IC50 in the low micromolar range. A search of the entire NCI compound collection for compounds similar to NSC 354961 yielded two additional compounds that exhibited activity in the experimental assay but with significantly diminished potency relative to NSC 354961. In this report, we disclose the activity of NSC 354961 against AdoMetDC and its probable binding mode based on computational modeling. We also discuss the importance of virtual screening in the context of enzymes that are not readily amenable to high-throughput assays, thereby demonstrating the efficacy of virtual screening, combined with selective experimental testing, in identifying new potential drug candidates.     

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.     

In silico and in vitro identification of candidate SIRT1 activators from Indonesian medicinal plants compounds database

Sirtuin 1 (SIRT1) is a class III family of protein histone deacetylases involved in NAD⁺-dependent deacetylation reactions. It has been suggested that SIRT1 activators may have a protective role against type 2 diabetes, the aging process, and inflammation. This study aimed to explore and identify medicinal plant compounds from Indonesian Herbal Database (HerbalDB) that might potentially become a candidate for SIRT1 activators through a combination of in silico and in vitro methods. Two pharmacophore models were developed using co-crystalized ligands that allosterically bind with SIRT1 similar to the putative ligands used by SIRT1 activators. Then, these were used for the virtual screening of HerbalDB. The identified compounds were subjected to molecular docking and 50 ns molecular dynamics simulation. Molecular dynamics simulation was analyzed using MM-GB(PB)SA methods. The compounds identified by these methods were tested in an in vitro study using a SIRT-Glo™ luminescence assay. Virtual screening using structure-based pharmacophores predicted that mulberrin as the best candidate SIRT1 activator. Virtual screening using ligand-based pharmacophores predicted that gartanin, quinidine, and quinine to be the best candidates as SIRT1 activators. The molecular docking studies showed the important residues involved were Ile223 and Ile227 at the allosteric region. The MM-GB(PB)SA calculations confirmed that mulberrin, gartanin, quinidine, quinine showed activity at allosteric region and their EC₅₀ in vitro values are 2.10; 1.79; 1.71; 1.14 μM, respectively. Based on in silico and in vitro study results, mulberin, gartanin, quinidine, and quinine had good activity as SIRT1 activators.     

NMR and in silico screening

NMR-based screening and virtual, or in silico, screening can be highly complementary and synergistic. NMR-based screening is a rapid and reliable method for validating hits that come from in silico screens. In addition, ligand-binding data derived from NMR-based screens can focus and direct subsequent in silico screening. We will first give a short overview of existing NMR and in silico screening methods, discuss the drawbacks associated with each, and finally present applications that highlight the combination of the two technologies.     

SDOVS: A solvent dipole ordering‐based method for virtual screening

We previously reported that solvent dipole ordering (SDO) at the ligand binding site of a protein indicates an outline of the preferred shape and binding pose of the ligands. We suggested that SDO‐mimetic pseudo‐molecules that mimic the 3D shape of the SDO region could be used as molecular queries with a shape similarity matching method in virtual screening. In this work, a virtual screening method based on SDO, named SDOVS, was proposed. This method was applied to virtual screening of ligands for four typical drug target proteins and the performance compared with that of FRED (well‐known rigid docking method); the efficiency of SDOVS was demonstrated to be better than FRED. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

In silico carborane docking to proteins and potential drug targets

The presence of boron atoms has made carboranes, C(2)B(10)H(12), attractive candidates for boron neutron capture therapy. Because of their chemistry and possible conjugation with proteins, they can also be used to enhance interactions between pharmaceuticals and their targets and to increase the in vivo stability and bioavailability of compounds that are normally metabolized rapidly. Carboranes are isosteric to a rotating phenyl group, which they can substitute successfully in biologically active systems. A reverse ligand-protein docking approach was used in this work to identify binding proteins for carboranes. The screening was carried out on the drug target database PDTD that contains 1207 entries covering 841 known potential drug targets with structures taken from the Protein Data Bank. First, for validation, the protocol was applied to three crystal structures of proteins in which carborane derivatives are present. Then, the model was applied to systems for which the protein structure is available, but the binding site of carborane has not been reported. These systems were used for further validation of the protocol, while simultaneously providing new insight into the interactions between cage and protein. Finally, the screening was carried out on the database to reveal potential carborane binding targets of interest for biological and pharmacological activity. Carboranes are predicted to bind well to protease and metalloprotease enzymes. Other carborane pharmaceutical targets are also discussed, together with possible protein carriers.     

A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes

In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.     

Identification of SARS-CoV-2 Papain-like Protease (PLpro) Inhibitors Using Combined Computational Approach**

In the current pandemic, finding an effective drug to prevent or treat the infection is the highest priority. A rapid and safe approach to counteract COVID-19 is in silico drug repurposing. The SARS-CoV-2 PLpro promotes viral replication and modulates the host immune system, resulting in inhibition of the host antiviral innate immune response, and therefore is an attractive drug target. In this study, we used a combined in silico virtual screening for candidates for SARS-CoV-2 PLpro protease inhibitors. We used the Informational spectrum method applied for Small Molecules for searching the Drugbank database followed by molecular docking. After in silico screening of drug space, we identified 44 drugs as potential SARS-CoV-2 PLpro inhibitors that we propose for further experimental testing.