An all atom energy based computational protocol for predicting binding affinities of protein–ligand complexes

Jain, Tarun; Jayaram, B.

doi:10.1016/j.febslet.2005.10.031

Cited by 71 publications

(67 citation statements)

References 70 publications

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“…[57] BAPPL has been validated on a heterogeneous dataset of 161 complexes yielding a r ¼ 0.92 correlation for the predicted binding free energies against experimental binding affinities. [43] Despite its computational simplicity, the method is able to provide reasonable estimates for binding free energies of proteinligand complexes and its implementation as web server offers a fast protocol to explore protein-ligand interactions. DG bind values in Table 4 (1) and (2), labeled hereafter ' 'MD-sim' ' and ' 'MD-dock,' ' respectively, structures picked from frames corresponding to the middle time of MD runs (1) were also taken (labeled ' 'MD-frame' ').…”

Section: Estimates Of Binding Energies For Nsltps-lipid Complexesmentioning

confidence: 99%

“…[46] After investigating the sensitivity to force field choice of the results on a heterogeneous dataset of 161 complexes, an empirical scoring function consisting of 25 independent variables (electrostatic, van der Waals, loss of conformational entropy and atom types for hydrophobicity) was developed. [43,44] NsLTP-LPC complexes are omitted because BAPPL failed to give binding energies for all of them (note that this lipid is the only positively charged ligand and all nsLTPs have net positive charge).…”

Section: Estimates Of Binding Free Energiesmentioning

confidence: 99%

“…It must be mentioned that neither experimental binding affinity data exist in the BindingDB (www.bindingdb.org) database [54] nor, to the best of our knowledge, any other binding affinities had been reported before for nsLTP-lipid complexes. We obtained estimates of DG bind with the BAPPL (Binding Affinity Prediction of Protein-Ligand) approach, [43,44] a computationally fast procedure for predicting binding affinities of nonmetal protein ligand complexes. This method is based upon an all- ) and p-values of observed solvation free energy gain upon formation of the interface (P int ) for nsLTP-lipid complexes.…”

Section: Estimates Of Binding Energies For Nsltps-lipid Complexesmentioning

confidence: 99%

“…Binding free energies DG bind were estimated with the all-atom energy based empirical scoring function [43,44] implemented in the Binding Affinity Prediction of Protein-Ligand, BAPPL server (www.scfbio-iitd.res.in/software/drugdesign/bappl.jsp) using ' 'Method 2' ' mode calculation, i.e., the net charge on the ligand is specified in input and the server assigns the parameters needed. For the ligand, BAPPL obtains partial atomic charges from AM1-BCC calculations [30] and assigns van der Waals parameters from GAFF force field.…”

Section: Estimates Of Binding Free Energiesmentioning

confidence: 99%

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Computational study of ligand binding in lipid transfer proteins: Structures, interfaces, and free energies of protein‐lipid complexes

et al. 2012

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Plant nonspecific lipid transfer proteins (nsLTPs) bind a wide variety of lipids, which allows them to perform disparate functions. Recent reports on their multifunctionality in plant growth processes have posed new questions on the versatile binding abilities of these proteins. The lack of binding specificity has been customarily explained in qualitative terms on the basis of a supposed structural flexibility and nonspecificity of hydrophobic protein-ligand interactions. We present here a computational study of protein-ligand complexes formed between five nsLTPs and seven lipids bound in two different ways in every receptor protein. After optimizing geometries in molecular dynamics calculations, we computed PoissonBoltzmann electrostatic potentials, solvation energies, properties of the protein-ligand interfaces, and estimates of binding free energies of the resulting complexes. Our results provide the first quantitative information on the ligand abilities of nsLTPs, shed new light into protein-lipid interactions, and reveal new features which supplement commonly held assumptions on their lack of binding specificity.

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Section: Estimates Of Binding Energies For Nsltps-lipid Complexesmentioning

confidence: 99%

Section: Estimates Of Binding Free Energiesmentioning

confidence: 99%

Section: Estimates Of Binding Energies For Nsltps-lipid Complexesmentioning

confidence: 99%

Section: Estimates Of Binding Free Energiesmentioning

confidence: 99%

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Computational study of ligand binding in lipid transfer proteins: Structures, interfaces, and free energies of protein‐lipid complexes

et al. 2012

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“…Low P values are indicative of a specific interface. The predicted binding free energy for MUT-A and MUT-A03 was calculated using the BAPPL server (33) and PDB2PQR for the ligand net charge (34). The net charges for MUT-A and MUT-A03 were found to be 0 and -1 respectively.…”

Section: Biochemical and Antiretroviral Activities Of Mut-amentioning

confidence: 99%

Structure-function analyses unravel distinct effects of allosteric inhibitors of HIV-1 integrase on viral maturation and integration

Bonnard

Rouzic

Eiler

et al. 2018

Journal of Biological Chemistry

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Recently, a new class of HIV-1 integrase (IN) inhibitors with a dual mode of action, called IN-LEDGF/p75 allosteric inhibitors (INLAIs), was described. Designed to interfere with the IN-LEDGF/p75 interaction during viral integration, unexpectedly, their major impact was on virus maturation. This activity has been linked to induction of aberrant IN multimerization, while inhibition of the IN-LEDGF/p75 interaction accounts for weaker antiretroviral effect at integration. Since these dual activities result from INLAI binding to IN at a single binding site, we expected that these activities co-evolved together, driven by the affinity for IN. Using an original INLAI, MUT-A, and its activity on an Ala-125 (A125) IN variant, we found that these two activities on A125 IN can be fully dissociated: MUT-A-induced IN multimerization and the formation of eccentric condensates in viral particles, that are responsible for inhibition of virus maturation, were lost, while inhibition of the IN-LEDGF/p75 interaction and consequently integration, was fully retained. Hence the mere binding of INLAI to A125 IN is insufficient to promote the conformational changes of IN required for aberrant multimerization. By analyzing the Xray structures of MUT-A bound to the IN catalytic core domain (CCD) with or without the A125 polymorphism, we discovered that the loss of IN multimerization is due to stabilization of the A125 IN variant CCD dimer, highlighting the importance of the CCD dimerization energy for IN multimerization. Our study reveals that affinity for the LEDGF/p75-binding pocket is not sufficient to induce INLAI-dependent IN multimerization and the associated inhibition of viral maturation.The integrase (IN) protein of Human Immunodeficiency Virus type 1 (HIV-1) catalyzes the stable insertion of the viral cDNA genome into the host cell chromatin, a step of the viral life cycle that is required for efficient viral gene expression. Integration occurs via a two-step reaction where IN initially cleaves after a conserved CA dinucleotide at the 3' end of the viral cDNA genome to free a 3'-OH group (3' processing), which is next used to carry out a nucleophilic attack on cellular chromosomal DNA (strand transfer).IN is one of the preferred targets for the development of antiretroviral (ARV) drugs. However, given the high genetic variability of HIV-1, IN mutations conferring cross-resistance to the first generation INSTIs, RAL and EVG, were described in patients receiving INSTI-containing regimens (2). The second generation INSTI DTG has a higher genetic barrier and conserves good ARV activity against a number of RAL-and EVGresistant strains. Recent reports showed that Bictegravir, a second generation INSTI still in development from Gilead Sciences, has a resistance profile similar to DTG (3). Nevertheless, DTG and Bictegravir are sensitive to the most detrimental INSTI resistant mutations albeit at lower levels than first generation INSTIs (4). Therefore, the development of small molecule inhibitors impairing IN functions with dis...

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Docking, Scoring, and Virtual Screening in Drug Discovery

Spyrakis

Sarkar

Kellogg

2021

Burger's Medicinal Chemistry and Drug Discovery

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Since its introduction about four decades ago, docking and scoring are now the very heart of structure‐based drug design. The past 10–20 years have seen a plethora of docking and scoring tools successfully integrated into drug discovery pipelines. Interestingly, while artificial intelligence is now receiving significant attention in the computational modeling arena, docking and scoring have long utilized these techniques. This comprehensive summary highlights some of the most significant achievements of docking and scoring, reviews the current status, provides descriptions of many of the tools available, comments on some of the outstanding challenges facing the paradigm, and offers perspectives and advice on best practices for users. While significant development is still needed in docking of flexible molecules and accurate Gibb's free energy predictions, docking and scoring are very useful when handled by experienced practitioners, but less so if treated as a “black box.” Lastly, we present a hypothetical case so beginners may appreciate the nuances of setting up a docking study. Focus in docking is now shifting toward parallel applications, i.e. protein–protein, protein–oligosaccharide, protein–DNA, or protein–RNA docking and polypharmacology. In summary, this article is intended to elucidate the nuances of the subject, while providing guidelines for practical implementation of effective workflows in drug discovery and structural biology.

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An all atom energy based computational protocol for predicting binding affinities of protein–ligand complexes

Cited by 71 publications

References 70 publications

Computational study of ligand binding in lipid transfer proteins: Structures, interfaces, and free energies of protein‐lipid complexes

Computational study of ligand binding in lipid transfer proteins: Structures, interfaces, and free energies of protein‐lipid complexes

Structure-function analyses unravel distinct effects of allosteric inhibitors of HIV-1 integrase on viral maturation and integration

Docking, Scoring, and Virtual Screening in Drug Discovery

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