Method for Computing Protein Binding Affinity

A Monte Carlo method is given to compute the binding affinity of a ligand to a protein. The method involves extending configuration space by a discrete variable indicating whether the ligand is bound to the protein and a special Monte Carlo move which allows transitions between the unbound and bound states. Provided that an accurate protein structure is given, that the protein-ligand binding site is known, and that an accurate chemical force field together with a continuum solvation model is used, this method provides a quantitative estimate of the free energy of binding.Comment: RevTex, 10 pages with 5 figures. Explanatory figure adde

Binding of Small-Molecule Ligands to Proteins: “What You See” Is Not Always “What You Get”

We review insights from computational studies of affinities of ligands binding to proteins. The power of structural biology is in translating knowledge of protein structures into insights about their forces, binding, and mechanisms. However, the complementary power of computer modeling is in showing “the rest of the story” (i.e., how motions and ensembles and alternative conformers and the entropies and forces that cannot be seen in single molecular structures also contribute to binding affinities). Upon binding to a protein, a ligand can bind in multiple orientations; the protein or ligand can be deformed by the binding event; waters, ions, or cofactors can have unexpected involvement; and conformational or solvation entropies can sometimes play large and otherwise unpredictable roles. Computer modeling is helping to elucidate these factors

Molecular dynamics simulations of HIV-1 protease complexed with saquinavir

Inhibition of the Human Immunode�ficiency virus type-1 (HIV-1) protease enzyme blocks HIV-1 replication. Protease inhibitor drugs have successfully been used as a therapy for HIV-infected individuals to reduce their viral loads and slow the progression to Acquired Immune Defi�ciency Syndrome (AIDS). However, mutations readily and rapidly accrue in the protease gene resulting in a reduced sensitivity of the protein to the inhibitor. In this thesis, molecular dynamics simulations (MDS) were run on HIV proteases complexed with the protease inhibitor saquinavir, and the strength of affinity calculated through MMPBSA and normal mode analysis. We show in this thesis that at least 13 residues can be computationally mutated in the proteases sequence without adversely aff�ecting its structure or dynamics, and can still replicate the change in binding affinity to saquinavir caused by said mutations. Using 6 protease genotypes with an ordered decrease in saquinavir sensitivity we use MDS to calculate drug binding affinity. Our results show that single 10ns simulations of the systems resulted in good concurrence for the wild-type (WT) system, but an overall strong anti-correlation to biochemically derived results. Extension of the WT and multi-drug resistant (MDR) systems to 50ns yielded no improvement in the correlation to experimental. However, expansion of these systems to a 10-repetition ensemble MDS considerably improved the MDR binding affinity compared to the biochemical result. Principle components analysis on the simulations revealed that a much greater confi�gurational sampling was achieved through ensemble MD than simulation extension. These data suggest a possible mechanism for saquinavir resistance in the MDR system, where a transitioning to a lower binding-affinity configuration than WT occurs. Furthermore, we show that ensembles of 1ns in length sample a significant proportion of the con�figurations adopted over 10ns, and generate sufficiently similar binding affinities

Molecular dynamics simulation of drug resistance in HIV-1 protease and reverse transcriptase

The emergence of drug resistant strains of HIV represents a major challenge in the treatment of patients who contract the virus. We investigate the use of classical molecular dynamics to give quantitative and qualitative molecular insight into the causes of resistance in the two main drug targets in HIV, protease and reverse transcriptase. We initially establish a simulation and free energy analysis protocol for the study of resistance in protease. Focusing on the binding of the inhibitor lopinavir to a series of six mutants with increasing resistance we demonstrate that ensemble simulations exhibit significantly enhanced thermodynamic sampling over single long simulations. We achieve accurate and converged relative binding free energies, reproducible to within 0.5 kcal mol^-1. The experimentally derived ranking of the systems is reproduced with a correlation coefficient of 0.89 and a mean relative deviation from experiment of 0.9 kcal mol^-1. Our protocol is then applied to investigate a patient derived viral sequence for which contradictory resistance assessments for lopinavir were obtained from existing clinical decision support systems (CDSS). Mutations at only three locations (L10I, A71I/V and L90M) in uenced the ranking. Free energies were computed for HXB2 wildtype sequences incorporating each mutation individually and all possible combinations, along with the full patient sequence. Only in the case of the patient sequence was any resistance observed. This observation suggests an explanation for the discordance found using the CDSS. The effects on drug binding of the mutations at positions 10, 71 and 90 appear to be highly dependent on the background mutations present in the remainder of the sequence. In preparation for the extension of our simulation and free energy protocol to reverse transcriptase the impact of binding both natural DNA substrates and two non nucleoside reverse transcriptase inhibitor (NNRTI) class drugs on the dynamics of reverse transcriptase are investigated. Free energies of both inhibitors (efavirenz and neviripine) are determined which are seen to be independent of the subdomain motions of the protein observed during simulation. Preliminary calculations of the free energies for a set of NNRTI resistant mutants bound to efavirenz are also presented

An in-silico study: Investigating small molecule modulators of bio-molecular interactions

Small molecule inhibitors are commonly used to target protein targets that assist in the spread of diseases such as AIDS, cancer and deadly forms of influenza. Despite drug companies spending millions on R&D, the number of drugs that pass clinical trials is limited due to difficulties in engineering optimal non-covalent interactions. As many protein targets have the ability to rapidly evolve resistance, there is an urgent need for methods that rapidly identify effective new compounds. The thermodynamic driving force behind most biochemical reactions is known as the Gibbs free energy and it contains opposing dynamic and structural components that are known as the entropy (ΔS°) and enthalpy (ΔH°) respectively. ΔG° = ΔH° - TΔS°. Traditionally, drug design focussed on complementing the shape of an inhibitor to the binding cavity to optimise ΔG° favourability. However, this approach neglects the entropic contribution and phenomena such as Entropy-Enthalpy Compensation (EEC) often result in favourable bonding interactions not improving ΔG°, due to entropic unfavorability. Similarly, attempts to optimise inhibitor entropy can also have unpredictable results. Experimental methods such as ITC report on global thermodynamics, but have difficulties identifying the underlying molecular rationale for measured values. However, computational techniques do not suffer from the same limitations. MUP-I can promiscuously bind panels of hydrophobic ligands that possess incremental structural differences. Thus, small perturbations to the system can be studied through various in silico approaches. This work analyses the trends exhibited across these panels by examining the dynamic component via the calculation of per-unit entropies of protein, ligand and solvent. Two new methods were developed to assess the translational and rotational contributions to TΔS°, and a protocol created to study ligand internalisation. Synthesising this information with structural data obtained from spatial data on the binding cavity, intermolecular contacts and H-bond analysis allowed detailed molecular rationale for the global thermodynamic signatures to be derived