Navigating ligand–protein binding free energy landscapes: universality and diversity of protein folding and molecular recognition mechanisms

Energy Landscape of Pentapeptides in a Higher-Order (ϕ,ψ) Conformational Subspace

The potential energy landscape of pentapeptides was mapped in a collective coordinate principal conformational subspace derived from principal component analysis of a nonredundant representative set of protein structures from the PDB. Three pentapeptide sequences that are known to be distinct in terms of their secondary structure characteristics, (Ala)5, (Gly)5, and Val.Asn.Thr.Phe.Val, were considered. Partitioning the landscapes into different energy valleys allowed for calculation of the relative propensities of the peptide secondary structures in a statistical mechanical framework. The distribution of the observed conformations of pentapeptide data showed good correspondence to the topology of the energy landscape of the (Ala)5 sequence where, in accord with reported trends, the α-helix showed a predominant propensity at 298 K. The topography of the landscapes indicates that the stabilization of the α-helix in the (Ala)5 sequence is enthalpic in nature while entropic factors are important for stabilization of the β-sheet in the Val.Asn.Thr.Phe.Val sequence. The results indicate that local interactions within small pentapeptide segments can lead to conformational preference of one secondary structure over the other where account of conformational entropy is important in order to reveal such preference. The method, therefore, can provide critical structural information for ab initio protein folding methods.

In silico profiling of tyrosine kinases binding specificity and drug resistance using Monte Carlo simulations with the ensembles of protein kinase crystal structures

The molecular basis of the tyrosine kinases binding specificity and drug resistance against cancer drugs Imatinib and Dasatinib is elucidated using Monte Carlo simulations of the inhibitor-receptor binding with the ensembles of protein kinase crystal structures. In silico proteomics analysis unravels mechanisms by which structural plasticity of the tyrosine kinases is linked with the conformational preferences of Imatinib and Dasatinib in achieving effective drug binding with a distinct spectrum of the tyrosine kinome. The differences in the inhibitor sensitivities to the ABL kinase mutants are rationalized based on variations in the binding free energy profiles with the conformational states of the ABL kinase. While Imatinib binding is highly sensitive to the activation state of the enzyme, the computed binding profile of Dasatinib is remarkably tolerant to the conformational state of ABL. A comparative analysis of the inhibitor binding profiles with the clinically important ABL kinase mutants has revealed an excellent agreement with the biochemical and proteomics data. We have found that conformational adaptability of the kinase inhibitors to structurally different conformational states of the tyrosine kinases may have pharmacological relevance in acquiring a specific array of potent activities and regulating a scope of the inhibitor resistance mutations. This study outlines a useful approach for understanding and predicting the molecular basis of the inhibitor sensitivity against potential kinase targets and drug resistance.

Computational proteomics of biomolecular interactions in the sequence and structure space of the tyrosine kinome: Deciphering the molecular basis of the kinase inhibitors selectivity

Understanding and predicting the molecular basis of protein kinases specificity against existing therapeutic agents remains highly challenging and deciphering this complexity presents an important problem in discovery and development of effective cancer drugs. We explore a recently introduced computational approach for in silico profiling of the tyrosine kinases binding specificity with a class of the pyrido-[2,3-d]pyrimidine kinase inhibitors. Computational proteomics analysis of the ligand-protein interactions using parallel simulated tempering with an ensemble of the tyrosine kinases crystal structures reveals an important molecular determinant of the kinase specificity. The pyrido-[2,3-d]pyrimidine inhibitors are capable of dynamically interacting with both active and inactive forms of the tyrosine kinases, accommodating structurally different kinase conformations with a similar binding affinity. Conformational tolerance of the protein tyrosine kinases binding with the pyrido[2,3-d]pyrimidine inhibitors provides the molecular basis for the broad spectrum of potent activities and agrees with the experimental inhibition profiles. The analysis of the pyrido[2,3-d]pyrimidine sensitivities against a number of clinically relevant ABL kinase mutants suggests an important role of conformational adaptability of multitargeted kinase inhibitors in developing drug resistance mechanisms. The presented computational approach may be useful in complementing proteomics technologies to characterize activity signatures of small molecules against a large number of potential kinase targets.

Computational analysis of ligand binding dynamics at the intermolecular hot spots with the aid of simulated tempering and binding free energy calculations

Equilibrium binding dynamics is studied for a panel of benzimidazole-containing compounds at the remodeled interface between human growth hormone (hGH) and the extracellular domain of its receptor (hGHbp), engineered by mutating to glycine hot spot residues T175 from the hormone and W104 from the receptor. Binding energetics is predicted in a good agreement with the experimental data for a panel of these small molecules that complement the engineered defect and restore the binding affinity of the wild-type hGH-hGHbp complex. The results of simulated tempering ligand dynamics at the protein-protein interface reveals a diversity of ligand binding modes that is consistent with the structural orientation of the benzimidazole ring which closely mimics the position of the mutated W104 hot spot residue in the wild-type hGH-hGHbp complex. This structural positioning of the benzimidazole core motif is shown to be a critical feature of the low-energy ligand conformations binding in the engineered cavity. The binding free energy analysis provides a plausible rationale behind the experimental dissociation constants and suggests a key role of ligand-protein van der Waals interactions in restoring binding affinity.

Monte Carlo simulations of the peptide recognition at the consensus binding site of the constant fragment of human immunoglobulin G: the energy landscape analysis of a hot spot at the intermolecular interface

Monte Carlo simulations of molecular recognition at the consensus binding site of the constant fragment (Fc) of human immunoglobulin G (Ig) protein have been performed to analyze structural and thermodynamic aspects of binding for the 13-residue cyclic peptide DCAWHLGELVWCT. The energy landscape analysis of a hot spot at the intermolecular interface using alanine scanning and equilibrium-simulated tempering dynamics with the simplified, knowledge-based energy function has enabled the role of the protein hot spot residues in providing the thermodynamic stability of the native structure to be determined. We have found that hydrophobic interactions between the peptide and the Met-252, Ile-253, His-433, and His-435 protein residues are critical to guarantee the thermodynamic stability of the crystallographic binding mode of the complex. Binding free energy calculations, using a molecular mechanics force field and a solvation energy model, combined with alanine scanning have been conducted to determine the energetic contribution of the protein hot spot residues in binding affinity. The conserved Asn-434, Ser-254, and Tyr-436 protein residues contribute significantly to the binding affinity of the peptide-protein complex, serving as an energetic hot spot at the intermolecular interface. The results suggest that evolutionary conserved hot spot protein residues at the intermolecular interface may be partitioned in fulfilling thermodynamic stability of the native binding mode and contributing to the binding affinity of the complex.

Complexity and simplicity of ligand-macromolecule interactions: the energy landscape perspective

The energy landscape approach has contributed to recent progress in understanding the complexity and simplicity of ligand-macromolecule interactions. Significant advances in computational structure prediction of ligand-protein complexes have been made using approaches that include the effects of protein flexibility and incorporate a hierarchy of energy functions. The results suggest that the complexity of structure prediction in molecular recognition may be determined by low-resolution properties of the underlying binding energy landscapes and by the nature of the energy funnels near the native structures of the complexes.

Hierarchy of simulation models in predicting structure and energetics of the Src SH2 domain binding to tyrosyl phosphopeptides

Structure and energetics of the Src Src Homology 2 (SH2) domain binding with the recognition phosphopeptide pYEEI and its mutants are studied by a hierarchical computational approach. The proposed structure prediction strategy includes equilibrium sampling of the peptide conformational space by simulated tempering dynamics with the simplified, knowledge-based energy function, followed by structural clustering of the resulting conformations and binding free energy evaluation of a single representative from each cluster, a cluster center. This protocol is robust in rapid screening of low-energy conformations and recovers the crystal structure of the pYEEI peptide. Thermodynamics of the peptide-SH2 domain binding is analyzed by computing the average energy contributions over conformations from the clusters, structurally similar to the predicted peptide bound structure. Using this approach, the binding thermodynamics for a panel of studied peptides is predicted in a better agreement with the experiment than previously suggested models. However, the overall correlation between computed and experimental binding affinity remains rather modest. The results of this study show that small differences in binding free energies between the Ala and Gly mutants of the pYEEI peptide are considerably more difficult to predict than the structure of the bound peptides, indicating that accurate computational prediction of binding affinities still remains a major methodological and technical challenge.