Screening a peptidyl database for potential ligands to proteins with side-chain flexibility

The three key challenges addressed in our development of SPECITOPE , a tool for screening large structural databases for potential ligands to a protein, are to eliminate infeasible candidates early in the search, incorporate ligand and protein side-chain flexibility upon docking, and provide an appropriate rank for potential new ligands. The protein ligand-binding site is modeled by a shell of surface atoms and by hydrogen-bonding template points for the ligand to match, conferring specificity to the interaction. SPECITOPE combinatorially matches all hydrogen-bond donors and acceptors of the screened molecules to the template points. By eliminating molecules that cannot match distance or hydrogen-bond constraints, the transformation of potential docking candidates into the ligand-binding site and the shape and hydrophobic complementarity evaluations are only required for a small subset of the database. SPECITOPE screens 140,000 peptide fragments in about an hour and has identified and docked known inhibitors and potential new ligands to the free structures of four distinct targets: a serine protease, a DNA repair enzyme, an aspartic proteinase, and a glycosyltransferase. For all four, protein side-chain rotations were critical for successful docking, emphasizing the importance of inducible complementarity for accurately modeling ligand interactions. SPECITOPE has a range of potential applications for understanding and engineering protein recognition, from inhibitor and linker design to protein docking and macromolecular assembly. Proteins 33:74–87, 1998. © 1998 Wiley-Liss, Inc.     

Comparative docking of dual conformations in human fatty acid synthase thioesterase domain reveals potential binding cavity for virtual screening of ligands

Human fatty acid synthase (hFASN), a homo dimeric lipogenic enzyme with seven catalytic domains, is an important clinical target in cancer, metabolic syndrome and infections. Here, molecular modelling and docking methods were implemented to examine the inter-molecular interactions of thioesterase (TE) domain in hFASN with its physiological substrate, and to identify potential chemical inhibitors. TE catalyses the hydrolysis of thioester bond between palmitate and the 4’ phosphopantetheine of acyl carrier protein, releasing 16-carbon palmitate. The crystal structure of hFASN TE in two inhibitory conformations (A and B) were geometry-optimized and used for molecular docking with palmitate, orlistat (a known FASN inhibitor) and virtual screening against compounds from National Cancer Institute (NCI) database. Relatively, low binding affinity was observed during the complex formation of palmitate with A (?.164 kcal/mol) and B (?.332 kcal/mol) forms of TE, when compared with orlistat-docked TE (A form: ?5.872 kcal/mol and B form: ?5.484 kcal/mol), clearly indicating that the native inhibited conformation (crystal structure) was unfavourable for substrate binding. We used these orlistat dual binding modes as positive controls for prioritizing the ligands during virtual screening. From 2, 31,617 molecules in the NCI database, 916 high-scoring compounds (hit ligands) were obtained for A-form and 4582 for B-form of the TE-domain, which were then ranked according to glide docking score, XP H bond score, absorption, distribution, metabolism and excretion and binding free energy (Prime/MM-GBSA). Consequently, two top scoring ligands (NSC: 319661 and NSC: 153166) emerged as promising drug candidates that may be tested in FASN-over-expressing diseases.     

Targeting imidazoline site on monoamine oxidase B through molecular docking simulations

Monoamine oxidase (MAO) is an enzyme of major importance in neurochemistry, because it catalyzes the inactivation pathway for the catecholamine neurotransmitters, noradrenaline, adrenaline and dopamine. In the last decade it was demonstrated that imidazoline derivatives were able to inhibit MAO activity. Furthermore, crystallographic studies identified the imidazoline-binding domain on monoamine oxidase B (MAO-B), which opens the possibility of molecular docking studies focused on this binding site. The goal of the present study is to identify new potential inhibitors for MAO-B. In addition, we are also interested in establishing a fast and reliable computation methodology to pave the way for future molecular docking simulations focused on the imidazoline-binding site of this enzyme. We used the program 'molegro virtual docker' (MVD) in all simulations described here. All results indicate that simplex evolution algorithm is able to succesfully simulate the protein-ligand interactions for MAO-B. In addition, a scoring function implemented in the program MVD presents high correlation coefficient with experimental activity of MAO-B inhibitors. Taken together, our results identified a new family of potential MAO-B inhibitors and mapped important residues for intermolecular interactions between this enzyme and ligands.     

Structural insight into Mycobacterium tuberculosis maltosyl transferase inhibitors: pharmacophore-based virtual screening,docking, and molecular dynamics simulations

Pharmacophore-based virtual screening, subsequent docking, and molecular dynamics (MD) simulations have been done to identify potential inhibitors of maltosyl transferase of Mycobacterium tuberculosis (mtb GlgE). Ligand and structure-based pharmacophore models representing its primary binding site (pbs) and unique secondary binding site 2 (sbs2), respectively, were constructed based on the three dimensional structure of mtb GlgE. These pharmacophore models were further used for screening of ZINC and antituberculosis compounds database (ATD). Virtually screened molecules satisfying Lipinski’s rule of five were then analyzed using docking studies and have identified 23 molecules with better binding affinity than its natural substrate, maltose. Four top scoring ligands from ZINC and ATD that either binds to pbs or sbs2 have been subjected to 10 ns each MD simulations and binding free energy calculations. Results of these studies have confirmed stable protein ligand binding. Results reported in the article are likely to be helpful in antitubercular therapeutic development research.     

Shape based virtual screening and molecular docking towards designing novel pancreatic lipase inhibitors

Increase in obesity rates and obesity associated health issues became one of the greatest health concerns in the present world population. With alarming increase in obese percentage there is a need to design new drugs related to the obesity targets. Among the various targets linked to obesity, pancreatic lipase was one of the promising targets for obesity treatment. Using the in silico methods like structure based virtual screening, QikProp, docking studies and binding energy calculations three molecules namely zinc85531017, zinc95919096 and zinc33963788 from the natural database were reported as the potential inhibitors for the pancreatic lipase. Among them zinc95919096 presented all the interactions matching to both standard and crystal ligand and hence it can be further proceeded to drug discovery process.     

Probing molecular docking in a charged model binding site

A model binding site was used to investigate charge-charge interactions in molecular docking. This simple site, a small (180A(3)) engineered cavity in cyctochrome c peroxidase (CCP), is negatively charged and completely buried from solvent, allowing us to explore the balance between electrostatic energy and ligand desolvation energy in a system where many of the common approximations in docking do not apply. A database with about 5300 molecules was docked into this cavity. Retrospective testing with known ligands and decoys showed that overall the balance between electrostatic interaction and desolvation energy was captured. More interesting were prospective docking scre"ens that looked for novel ligands, especially those that might reveal problems with the docking and energy methods. Based on screens of the 5300 compound database, both high-scoring and low-scoring molecules were acquired and tested for binding. Out of 16 new, high-scoring compounds tested, 15 were observed to bind. All of these were small heterocyclic cations. Binding constants were measured for a few of these, they ranged between 20microM and 60microM. Crystal structures were determined for ten of these ligands in complex with the protein. The observed ligand geometry corresponded closely to that predicted by docking. Several low-scoring alkyl amino cations were also tested and found to bind. The low docking score of these molecules owed to the relatively high charge density of the charged amino group and the corresponding high desolvation penalty. When the complex structures of those ligands were determined, a bound water molecule was observed interacting with the amino group and a backbone carbonyl group of the cavity. This water molecule mitigates the desolvation penalty and improves the interaction energy relative to that of the "naked" site used in the docking screen. Finally, six low-scoring neutral molecules were also tested, with a view to looking for false negative predictions. Whereas most of these did not bind, two did (phenol and 3-fluorocatechol). Crystal structures for these two ligands in complex with the cavity site suggest reasons for their binding. That these neutral molecules do, in fact bind, contradicts previous results in this site and, along with the alkyl amines, provides instructive false negatives that help identify weaknesses in our scoring functions. Several improvements of these are considered.     

Complex-type-dependent scoring functions in protein-protein docking

A major challenge in the field of protein-protein docking is to discriminate between the many wrong and few near-native conformations, i.e. scoring. Here, we introduce combinatorial complex-type-dependent scoring functions for different types of protein-protein complexes, protease/inhibitor, antibody/antigen, enzyme/inhibitor and others. The scoring functions incorporate both physical and knowledge-based potentials, i.e. atomic contact energy (ACE), the residue pair potential (RP), electrostatic and van der Waals' interactions. For different type complexes, the weights of the scoring functions were optimized by the multiple linear regression method, in which only top 300 structures with ligand root mean square deviation (L_RMSD) less than 20 A from the bound (co-crystallized) docking of 57 complexes were used to construct a training set. We employed the bound docking studies to examine the quality of the scoring function, and also extend to the unbound (separately crystallized) docking studies and extra 8 protein-protein complexes. In bound docking of the 57 cases, the first hits of protease/inhibitor cases are all ranked in the top 5. For the cases of antibody/antigen, enzyme/inhibitor and others, there are 17/19, 5/6 and 13/15 cases with the first hits ranked in the top 10, respectively. In unbound docking studies, the first hits of 9/17 protease/inhibitor, 6/19 antibody/antigen, 1/6 enzyme/inhibitor and 6/15 others' complexes are ranked in the top 10. Additionally, for the extra 8 cases, the first hits of the two protease/inhibitor cases are ranked in the top for the bound and unbound test. For the two enzyme/inhibitor cases, the first hits are ranked 1st for bound test, and the 119th and 17th for the unbound test. For the others, the ranks of the first hits are the 1st for the bound test and the 12th for the 1WQ1 unbound test. To some extent, the results validated our divide-and-conquer strategy in the docking study, which might hopefully shed light on the prediction of protein-protein interactions.     

3D structure generation,virtual screening and docking of human Ras-associated binding (Rab3A) protein involved in tumourigenesis

Rab3A is expressed predominantly in brain and synaptic vesicles. Rab3A is involved specifically in tethering and docking of synaptic vesicles prior to fusion which is a critical step in regulated release of neurotransmitters. The precise function of Rab3A is still not known. However, up-regulation of Rab3A has been reported in malignant neuroendocrine and breast cancer cells. In the present study, the structure of Rab3A protein was generated using MODELLER 9v8 software. The modeled protein structure was validated and subjected to molecular docking analyses. Docking with GTP was carried out on the binding site of Rab3A using GOLD software. The Rab3A-GTP complex has best GOLD fitness value of 77.73. Ligplot shows hydrogen bondings (S16, S17, V18, G19, K20, T21, S22, S31, T33, A35, S38, T39 and G65) and hydrophobic interacting residues (F25, F32, P34, F36, V37, D62 and A64) with the GTP ligands in the binding site of Rab3A protein. Here, the ligand molecules of NCI diversity set II from the ZINC database against the active site of the Rab3A protein were screened. For this purpose, the incremental construction algorithm of GLIDE and the genetic algorithm of GOLD were used. Docking results were analyzed for top ranking compounds using a consensus scoring function of X-Score to calculate the binding affinity and Ligplot was used to measure protein–ligand interactions. Five compounds which possess good inhibitory activity and may act as potential high affinity inhibitors against Rab3A active site were identified. The top ranking molecule (ZINC13152284) has a Glide score of ?6.65 kcal/mol, X-Score of ?3.02 kcal/mol and GOLD score of 64.54 with 03 hydrogen bonds and 09 hydrophobic contacts. This compound is thus a good starting point for further development of strong inhibitors.     

Virtual screening against highly charged active sites: identifying substrates of alpha-beta barrel enzymes

We have developed a virtual ligand screening method designed to help assign enzymatic function for alpha-beta barrel proteins. We dock a library of approximately 19,000 known metabolites against the active site and attempt to identify the relevant substrate based on predicted relative binding free energies. These energies are computed using a physics-based energy function based on an all-atom force field (OPLS-AA) and a generalized Born implicit solvent model. We evaluate the ability of this method to identify the known substrates of several members of the enolase superfamily of enzymes, including both holo and apo structures (11 total). The active sites of these enzymes contain numerous charged groups (lysines, carboxylates, histidines, and one or more metal ions) and thus provide a challenge for most docking scoring functions, which treat electrostatics and solvation in a highly approximate manner. Using the physics-based scoring procedure, the known substrate is ranked within the top 6% of the database in all cases, and in 8 of 11 cases, it is ranked within the top 1%. Moreover, the top-ranked ligands are strongly enriched in compounds with high chemical similarity to the substrate (e.g., different substitution patterns on a similar scaffold). These results suggest that our method can be used, in conjunction with other information including genomic context and known metabolic pathways, to suggest possible substrates or classes of substrates for experimental testing. More broadly, the physics-based scoring method performs well on highly charged binding sites and is likely to be useful in inhibitor docking against polar binding sites as well. The method is fast (<1 min per ligand), due largely to an efficient minimization algorithm based on the truncated Newton method, and thus, it can be applied to thousands of ligands within a few hours on a small Linux cluster.     

Identification of abelson tyrosine kinase inhibitors as potential therapeutics for Alzheimer’s disease using multiple e-pharmacophore modeling and molecular dynamics

Efforts to combat Alzheimer’s disease are focused predominantly on inhibiting the activity of the enzyme(s) that have been identified to be responsible for the production of the amyloid-forming peptide. However, the inherent complexity associated with the network of pathways leading to the disease may involve additional targets for designing effective therapies. Recent experimental findings have identified abelson tyrosine kinase, a non-receptor kinase as a new target for Alzheimer’s. In this work, we employed energy optimized multiple pharmacophore modeling strategy from multiple c-Abl structures bound with ligands in the inactive ATP binding conformation (DFG-out). Virtual screening followed by docking of molecules from ChemBridge resulted in the identification of 10 best scoring molecules. MD simulations of the top three complexes revealed that Compound A, C are the most stable complexes with the most persistent protein–ligand interactions consistent with the calculated binding affinities for the top three compounds. Given the implied role of c-Abl not only in AD but in Parkinson’s disease, the identified compounds may serve as leads for effective neurotherapeutics.     

A computational approach to finding novel targets for existing drugs

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.     

Molecular dynamics and docking studies on cardiac troponin C

Cardiac troponin C (cTnC) is the Ca2? dependent switch for contraction in heart muscle making it a potential target for drug research in the therapy of heart failure. Calcium binding on Troponin C (TnC) triggers a series of conformational changes exposing a hydrophobic pocket in the N-domain of TnC (cNTnC), which leads to force generation. Mutations and acidic pH have been related to altering the sensitivity of TnC affecting the efficiency of the heart. Bepridil, identified as a calcium sensitizer to TnC, has been experimentally found to bind to the N-domain pocket of TnC but with negative cooperativity. Screening and de novo design were carried out using LUDI and AUTOLUDI programs in this work to identify and design potential ligands that can bind to the hydrophobic pocket of TnC. Two docking centers and multiple searching radii including 5 ?, 5.5 ?, 6 ?, 6.5 ?, 7.0 ? and 7.5 ? were used in LUDI to screen the ZINC database. Based on the LUDI docking results, 8 molecules were identified from the database with good potential to bind into the binding pocket and they were used as template molecules to generate a series of new molecules by AUTOLUDI design. Out of all the newly-designed molecules, 14 new ligands were recognized to be potential ligands that can bind and fit well into the binding pocket. These molecules can be used as starting molecules to develop TnC ligands. The binding stability and binding affinity of these molecules to the protein was further analyzed by molecular dynamics simulations. The results show that the binding energies, interactions and complex stabilities of 6 ligands are comparable to or better than bepridil.     

Molecular docking of inhibitors into monoamine oxidase B

Monoamine oxidase B (MAO-B) functions in the deamination of monoamines, including dopamine and norepinephrine. The search for MAO-B inhibitors increased following the discovery that the enzyme may be responsible for generating neurotoxins from various endogenous or exogenous compounds. Computational screening methods aid in the search for new inhibitors, but validation studies for specific software packages and receptors are necessary for effective application of these methods. In this study, DOCK 6.0.0 was used to dock a series of inhibitors to MAO-B. Included were studies of re-docking ligands into MAO-B crystal structures, after which a set of 30 compounds with known inhibition constants for MAO-B were docked, including 15 strong inhibitors and 15 weak inhibitors. Good agreement was observed between the top experimental inhibitors and the top ranked docking results, and key interactions between the ligands and receptor were identified.     

Identification of new potential Mycobacterium tuberculosis shikimate kinase inhibitors through molecular docking simulations

Tuberculosis (TB) is the major cause of human mortality from a curable infectious disease, attacking mainly in developing countries. Among targets identified in Mycobacterium tuberculosis genome, enzymes of the shikimate pathway deserve special attention, since they are essential to the survival of the microorganism and absent in mammals. The object of our study is shikimate kinase (SK), the fifth enzyme of this pathway. We applied virtual screening methods in order to identify new potential inhibitors for this enzyme. In this work we employed MOLDOCK program in all molecular docking simulations. Accuracy of enzyme-ligand docking was validated on a set of 12 SK-ligand complexes for which crystallographic structures were available, generating root-mean square deviations below 2.0 Å. Application of this protocol against a commercially available database allowed identification of new molecules with potential to become drugs against TB. Besides, we have identified the binding cavity residues that are essential to intermolecular interactions of this enzyme.

     

Docking molecules by families to increase the diversity of hits in database screens: computational strategy and experimental evaluation

Molecular docking programs screen chemical databases for novel ligands that fit protein binding sites. When one compound fits the site well, close analogs typically do the same. Therefore, many of the compounds that are found in such screens resemble one another. This reduces the variety and novelty of the compounds suggested. In an attempt to increase the diversity of docking hit lists, the Available Chemicals Directory was grouped into families of related structures. All members of every family were docked and scored, but only the best scoring molecule of a high-ranking family was allowed in the hit list. The identity and scores of the other members of these families were recorded as annotations to the best family member, but they were not independently ranked. This family-based docking method was compared with molecule-by-molecule docking in screens against the structures of thymidylate synthase, dihydrofolate reductase (DHFR), and the cavity site of the mutant T4 lysozyme Leu99 --> Ala (L99A). In each case, the diversity of the hit list increased, and more families of known ligands were found. To investigate whether the newly identified hits were sensible, we tested representative examples experimentally for binding to L99A and DHFR. Of the six compounds tested against L99A, five bound to the internal cavity. Of the seven compounds tested against DHFR, six inhibited the enzyme with apparent K(i) values between 0.26 and 100 microM. The segregation of potential ligands into families of related molecules is a simple technique to increase the diversity of candidates suggested by database screens. The general approach should be applicable to most docking methods. Proteins 2001;42:279-293.     

Identification of selective MMP-9 inhibitors through multiple e-pharmacophore,ligand-based pharmacophore,molecular docking,and density functional theory approaches

Matrix metalloproteinase-9 (MMP-9) is a significant target for the development of drugs for the treatment of arthritis, CNS disorders, and cancer metastasis. The structure-based and ligand-based methods were used for the virtual screening (VS) of database compounds to obtain potent and selective MMP-9 inhibitors. Experimentally known MMP-9 inhibitors were used to grow up ligand-based three pharmacophore models utilizing Schrodinger suite. The X-ray crystallographic structures of MMP-9 with different inhibitors were used to develop five energy-optimized structure-based (e-pharmacophore) models. All developed pharmacophores were validated and applied to screen the Zinc database. Pharmacophore matched compounds were subjected to molecular docking to retrieve hits with novel scaffolds. The molecules with diverse structures, high docking scores and low binding energies for various crystal structures of MMP-9, were selected as final hits. The Induced fit docking (IFD) analysis provided significant information about the driving of inhibitor to approve a suitable bioactive conformational position in the active site of protein. Since charge transfer reaction occurs during receptor–ligand interaction, therefore, electronic features of hits (ligands) are interesting parameters to explain the binding interactions. Density functional theory (DFT) at B3LYP/6-31G* level was utilized to explore electronic features of hits. The docking study of hits using AutoDock was helpful to establish the binding interactions. The study illustrates that the combined pharmacophore approach is advantageous to identify diverse hits which have better binding affinity to the active site of the enzyme for all possible bioactive conformations. The approach used in the study is worthy to design drugs for other targets.     

Identification of specific features of inhibition of PKCβII and its potential lead by shape-based virtual screening and molecular docking studies

Protein kinase C βII (PKCβII) is preferentially expressed during hyperglycemic state resulting in diabetic complications, particularly, diabetic cardiomyopathy. An effective inhibition of PKCβII is a potential option to directly treat the diabetic cardiomyopathy; however, till date no efficient drug targeting PKCβII is available and all the reported PKCβII ligands are maleimide derivatives. The purpose of the present work is to study the importance of the maleimide moiety in PKCβII inhibition and the effects that follow after replacing the maleimide with similar moiety on PKCβII inhibition. For this, an in-house database of maleimide analogues was prepared and shape based screening of commercial databases viz. Specs2009, NCI2003 was performed, followed by filtration using virtual filters. The binding features of reported PKCβII inhibitors, and high scoring hits were analyzed with the help of molecular docking studies. The features identified from the above studies were used for the rational design of new PKCβII inhibitors. The molecular dynamics simulation and ligand-receptor binding affinity studies of the designed molecules has been reported. The toxicity of all the shortlisted and designed molecules was predicted.     

Structural insight into the inhibition of acetylcholinesterase by 2,3,4, 5-tetrahydro-1, 5-benzothiazepines

Benzothiazepines 1-3 inhibited acetylcholinesterase (AChE; EC 3.1.1.7) enzyme in a concentration-dependent fashion with IC(50) values of 1.0 +/- 0.002, 1.2 +/- 0.005 and 1.3 +/- 0.001 microM, respectively. By using linear-regression equations, Lineweaver-Burk, Dixon plots and their secondary replots were constructed which indicated that compounds 1-3 are non-competitive inhibitors of AChE with K(i) values of 0.8 +/- 0.04, 1.1 +/- 0.002, and 1.5 +/- 0.001 microM, respectively. Molecular docking studies revealed that all the compounds are completely buried inside the aromatic gorge of AChE, extending deep into the gorge of AChE. A comparison of the docking results of compounds 1-3 displayed that these compounds generally adopt the same binding mode in the active site of AChE. The superposition of the docked structures demonstrated that the non-flexible benzothiazepine always penetrate into the aromatic gorge through the six-membered ring A, which allowed the ligands to interact simultaneously with more than one subsites of the active center of AChE. The higher AChE inhibitory potential of compounds 1-3 was found to be the cumulative effect of hydrophobic contacts and pi-pi interactions between the ligands and AChE. The relatively high affinity of benzothiazepine 1 with AChE was found to be due to additional hydrogen bond in benzothiazepine 1-AChE complex. The results indicated that substitution of halogen and methyl groups by hydrogen at aromatic ring of the benzothiazepine decreased the affinity of these molecules towards enzyme that may be due to the polar non-polar repulsions of these moieties with the amino acid residues in the active site of AChE. The observed binding modes of benzothiazepines 1-3 in the active site of AChE explain the affinities of benzothiazepines and provide a rational basis for the structure-based drug design of benzothiazepines with improved pharmacological properties.     

On scaffold hopping: Challenges in the discovery of sulfated small molecules as mimetics of glycosaminoglycans

The design of sulfated, small, nonsaccharide molecules as modulators of proteins is still in its infancy as standard drug discovery tools such as library of diverse sulfated molecules and in silico docking and scoring protocol have not been firmly established. Databases, such as ZINC, contain too few sulfate-containing nonsaccharide molecules, which severely limits the identification of new hits. Lack of a generally applicable protocol for scaffold hopping limits the development of sulfated small molecules as synthetic mimetics of the highly sulfated glycosaminoglycans. We explored a sequential ligand-based (LBVS) and structure-based virtual screening (SBVS) approach starting from our initial discovery of monosulfated benzofurans to discover alternative scaffolds as allosteric modulators of thrombin, a key coagulation enzyme. Screening the ZINC database containing nearly 1 million nonsulfated small molecules using a pharmacophore developed from the parent sulfated benzofurans followed by a genetic algorithm-based dual-filter docking and scoring screening identified a group of 10 promising hits, of which three top-scoring hits were synthesized. Each was found to selectively inhibit human alpha-thrombin suggesting the possibility of this approach for scaffold hopping. Michaelis–Menten kinetics showed allosteric inhibition mechanism for the best molecule and human plasma studies confirmed good anticoagulation potential as expected. Our simple sequential LBVS and SBVS approach is likely to be useful as a general strategy for identification of sulfated small molecules hits as modulators of glycosaminoglycan–protein interactions.