COMBINE analysis of nuclear receptor-DNA binding specificity: Comparison of two sets of data

To identify the major determinants of the DNA binding specificity of nuclear transcription factors, the Comparative Binding Energy (COMBINE) analysis has been performed for two datasets. In Tomic et al.,l COMBINE QSAR models were derived for a set of 320 complexes of DNA and glucocorticoid receptor mutants. Here, we derive COMBINE QSAR models for a set of 32 complexes. This set differs from the larger one in two aspects. The complexes have additional mutation sites in the DNA binding domain and, instead of just activity measurements, both activity and binding affinity measurements are available. Models of better predictive ability were obtained with the smaller, but experimentally better characterized, dataset. The parameters important for determining binding specificity are nevertheless similar for both datasets: the electrostatic interaction energies between the mutated nucleotides and mutated residue(s) as well as some charged amino acid residues (Arg-447, Arg-470, Arg-477), and the solvation free energies of the mutated base(s). However, the relative importance of these parameters is different in the two datasets

COMBINE Analysis of Nuclear Receptor-DNA Binding Specificity: Comparison of Two Sets of Data

To identify the major determinants of the DNA binding specificity of nuclear transcription factors, the Comparative Binding Energy (COMBINE) analysis has been performed for two datasets. In Tomić et al.,1 COMBINE QSAR models were derived for a set of 320 complexes of DNA and glucocorticoid receptor mutants. Here, we derive COMBINE QSAR models for a set of 32 complexes. This set differs from the larger one in two aspects. The complexes have additional mutation sites in the DNA binding domain and, instead of just activity measurements, both activity and binding affinity measurements are available. Models of better predictive ability were obtained with the smaller, but experimentally better characterized, dataset. The parameters important for determining binding specificity are nevertheless similar for both datasets: the electrostatic interaction energies between the mutated nucleotides and mutated residue(s) as well as some charged amino acid residues (Arg-447, Arg-470, Arg-477), and the solvation free energies of the mutated base(s). However, the relative importance of these parameters is different in the two datasets

Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times

Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization

Inhibitor Specificity via Protein Dynamics Insights from the Design of Antibacterial Agents Targeted Against Thymidylate Synthase

AbstractStructure-based drug design of species-specific inhibitors generally exploits structural differences in proteins from different organisms. Here, we demonstrate how achieving specificity can be aided by targeting differences in the dynamics of proteins. Thymidylate synthase (TS) is a good target for anticancer agents and a potential target for antibacterial agents. Most inhibitors are folate-analogs that bind at the folate binding site and are not species specific. In contrast, α156 is not a folate-analog and is specific for bacterial TS; it has been shown crystallographically to bind in a nonconserved binding site. Docking calculations and crystal structure-based estimation of the essential dynamics of TSs from five different species show that differences in the dynamics of TSs make the active site more accessible to α156 in the prokaryotic than in the eukaryotic TSs and thereby enhance the specificity of α156

The Shape of Protein Crowders is a Major Determinant of Protein Diffusion

AbstractAs a model for understanding how molecular crowding influences diffusion and transport of proteins in cellular environments, we combined experimental and theoretical approaches to study the diffusion of proteins in highly concentrated protein solutions. Bovine serum albumin and γ-Globulin were chosen as molecular crowders and as tracers. These two proteins are representatives of the main types of plasma protein and have different shapes and sizes. Solutions consisting of one or both proteins were studied. The self-diffusion coefficients of the fluorescently labeled tracer proteins were measured by means of fluorescence correlation spectroscopy at a total protein concentration of up to 400 g/L. γ-Globulin is found to have a stronger influence as a crowder on the tracer self-diffusion coefficient than Bovine serum albumin. Brownian dynamics simulations show that the excluded volume and the shape of the crowding protein have a significantly stronger influence on translational and rotational diffusion coefficients, as well as transient oligomerization, than hydrodynamic or direct interactions. Anomalous subdiffusion, which is not observed at the experimental fluorescence correlation spectroscopy timescales (>100 μs), appears only at very short timescales (<1 μs) in the simulations due to steric effects of the proteins. We envision that the combined experimental and computational approach employed here can be developed to unravel the different biophysical contributions to protein motion and interaction in cellular environments by systematically varying protein properties such as molecular weight, size, shape, and electrostatic interactions

Modeling and simulation of protein-surface interactions: Achievements and challenges

AbstractUnderstanding protein–inorganic surface interactions is central to the rational design of new tools in biomaterial sciences, nanobiotechnology and nanomedicine. Although a significant amount of experimental research on protein adsorption onto solid substrates has been reported, many aspects of the recognition and interaction mechanisms of biomolecules and inorganic surfaces are still unclear. Theoretical modeling and simulations provide complementary approaches for experimental studies, and they have been applied for exploring protein–surface binding mechanisms, the determinants of binding specificity towards different surfaces, as well as the thermodynamics and kinetics of adsorption. Although the general computational approaches employed to study the dynamics of proteins and materials are similar, the models and force-fields (FFs) used for describing the physical properties and interactions of material surfaces and biological molecules differ. In particular, FF and water models designed for use in biomolecular simulations are often not directly transferable to surface simulations and vice versa. The adsorption events span a wide range of time- and length-scales that vary from nanoseconds to days, and from nanometers to micrometers, respectively, rendering the use of multi-scale approaches unavoidable. Further, changes in the atomic structure of material surfaces that can lead to surface reconstruction, and in the structure of proteins that can result in complete denaturation of the adsorbed molecules, can create many intermediate structural and energetic states that complicate sampling. In this review, we address the challenges posed to theoretical and computational methods in achieving accurate descriptions of the physical, chemical and mechanical properties of protein-surface systems. In this context, we discuss the applicability of different modeling and simulation techniques ranging from quantum mechanics through all-atom molecular mechanics to coarse-grained approaches. We examine uses of different sampling methods, as well as free energy calculations. Furthermore, we review computational studies of protein–surface interactions and discuss the successes and limitations of current approaches.</jats:p

Diffusion and association processes in biological systems: theory, computation and experiment

Macromolecular diffusion plays a fundamental role in biological processes. Here, we give an overview of recent methodological advances and some of the challenges for understanding how molecular diffusional properties influence biological function that were highlighted at a recent workshop, BDBDB2, the second Biological Diffusion and Brownian Dynamics Brainstorm