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Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease.

ABSTRACT: Molecular docking is a powerful tool used in drug discovery and structural biology for predicting the structures of ligand-receptor complexes. However, the accuracy of docking calculations can be limited by factors such as the neglect of protein reorganization in the scoring function; as a result, ligand screening can produce a high rate of false positive hits. Although absolute binding free energy methods still have difficulty in accurately rank-ordering binders, we believe that they can be fruitfully employed to distinguish binders from nonbinders and reduce the false positive rate. Here we study a set of ligands that dock favorably to a newly discovered, potentially allosteric site on the flap of HIV-1 protease. Fragment binding to this site stabilizes a closed form of protease, which could be exploited for the design of allosteric inhibitors. Twenty-three top-ranked protein-ligand complexes from AutoDock were subject to the free energy screening using two methods, the recently developed binding energy analysis method (BEDAM) and the standard double decoupling method (DDM). Free energy calculations correctly identified most of the false positives (?83%) and recovered all the confirmed binders. The results show a gap averaging ?3.7 kcal/mol, separating the binders and the false positives. We present a formula that decomposes the binding free energy into contributions from the receptor conformational macrostates, which provides insights into the roles of different binding modes. Our binding free energy component analysis further suggests that improving the treatment for the desolvation penalty associated with the unfulfilled polar groups could reduce the rate of false positive hits in docking. The current study demonstrates that the combination of docking with free energy methods can be very useful for more accurate ligand screening against valuable drug targets.

SUBMITTER: Deng N

PROVIDER: S-EPMC4306491 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease.

Deng Nanjie N Forli Stefano S He Peng P Perryman Alex A Wickstrom Lauren L Vijayan R S K RS Tiefenbrunn Theresa T Stout David D Gallicchio Emilio E Olson Arthur J AJ Levy Ronald M RM

The journal of physical chemistry. B 20140917 3

Molecular docking is a powerful tool used in drug discovery and structural biology for predicting the structures of ligand-receptor complexes. However, the accuracy of docking calculations can be limited by factors such as the neglect of protein reorganization in the scoring function; as a result, ligand screening can produce a high rate of false positive hits. Although absolute binding free energy methods still have difficulty in accurately rank-ordering binders, we believe that they can be fru ...[more]

PMID: 25189630

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Project description:BackgroundA large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation.ResultsFor all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results.ConclusionsOf the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.

Dataset Information

Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease.

Publications

Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease.

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