Molecular docking using surface complementarity

A method is described to dock a ligand into a binding site in a protein on the basis of the complementarity of the inter-molecular atomic contacts. Docking is performed by maximization of a complementarity function that is dependent on atomic contact surface area and the chemical properties of the contacting atoms. The generality and simplicity of the complementarity function ensure that a wide range of chemical structures can be handled. The ligand and the protein are treated as rigid bodies, but displacement of a small number of residues lining the ligand binding site can be taken into account. The method can assist in the design of improved ligands by indicating what changes in complementarity may occur as a result of the substitution of an atom in the ligand. The capabilities of the method are demonstrated by application to 14 protein–ligand complexes of known crystal structure. © 1996 Wiley Liss, Inc.     

How long does it take to equilibrate the unfolded state of a protein?

How long does it take to equilibrate the unfolded state of a protein? The answer to this question has important implications for our understanding of why many small proteins fold with two state kinetics. When the equilibration within the unfolded state U is much faster than the folding, the folding kinetics will be two state even if there are many folding pathways with different barriers. Yet the mean first passage times (MFPTs) between different regions of the unfolded state can be much longer than the folding time. This seems to imply that the equilibration within U is much slower than the folding. In this communication we resolve this paradox. We present a formula for estimating the time to equilibrate the unfolded state of a protein. We also present a formula for the MFPT to any state within U, which is proportional to the average lifetime of that state divided by the state population. This relation is valid when the equilibration within U is very fast as compared with folding as it often is for small proteins. To illustrate the concepts, we apply the formulas to estimate the time to equilibrate the unfolded state of Trp-cage and MFPTs within the unfolded state based on a Markov State Model using an ultra-long 208 microsecond trajectory of the miniprotein to parameterize the model. The time to equilibrate the unfolded state of Trp-cage is ∼100 ns while the typical MFPTs within U are tens of microseconds or longer.     

Heterogeneity even at the speed limit of folding: large-scale molecular dynamics study of a fast-folding variant of the villin headpiece

We have performed molecular dynamics simulations on a set of nine unfolded conformations of the fastest-folding protein yet discovered, a variant of the villin headpiece subdomain (HP-35 NleNle). The simulations were generated using a new distributed computing method, yielding hundreds of trajectories each on a time scale comparable to the experimental folding time, despite the large (10,000 atom) size of the simulation system. This strategy eliminates the need to assume a two-state kinetic model or to build a Markov state model. The relaxation to the folded state at 300 K from the unfolded configurations (generated by simulation at 373 K) was monitored by a method intended to reflect the experimental observable (quenching of tryptophan by histidine). We also monitored the relaxation to the native state by directly comparing structural snapshots with the native state. The rate of relaxation to the native state and the number of resolvable kinetic time scales both depend upon starting structure. Moreover, starting structures with folding rates most similar to experiment show some native-like structure in the N-terminal helix (helix 1) and the phenylalanine residues constituting the hydrophobic core, suggesting that these elements may exist in the experimentally relevant unfolded state. Our large-scale simulation data reveal kinetic complexity not resolved in the experimental data. Based on these findings, we propose additional experiments to further probe the kinetics of villin folding.     

The highly dynamic oligomeric structure of bradavidin II is unique among avidin proteins

Bradavidin II is a biotin‐binding protein from Bradyrhizobium japonicum that resembles chicken avidin and bacterial streptavidin. A biophysical characterization was carried out using dynamic light scattering, native mass spectrometry, differential scanning calorimetry, and isothermal titration calorimetry combined with structural characterization using X‐ray crystallography. These observations revealed that bradavidin II differs from canonical homotetrameric avidin protein family members in its quaternary structure. In contrast with the other avidins, bradavidin II appears to have a dynamic (transient) oligomeric state in solution. It is monomeric at low protein concentrations but forms higher oligomeric assemblies at higher concentrations. The crystal structure of bradavidin II revealed an important role for Phe42 in shielding the bound ligand from surrounding water molecules, thus functionally replacing the L7,8 loop essential for tight ligand binding in avidin and streptavidin. This bradavidin II characterization opens new avenues for oligomerization‐independent biotin‐binding protein development.     

Evaluation of protein–ligand affinity prediction using steered molecular dynamics simulations

In computational drug design, ranking a series of compound analogs in a manner that is consistent with experimental affinities remains a challenge. In this study, we evaluated the prediction of protein–ligand binding affinities using steered molecular dynamics simulations. First, we investigated the appropriate conditions for accurate predictions in these simulations. A conic harmonic restraint was applied to the system for efficient sampling of work values on the ligand unbinding pathway. We found that pulling velocity significantly influenced affinity predictions, but that the number of collectable trajectories was less influential. We identified the appropriate pulling velocity and collectable trajectories for binding affinity predictions as 1.25 Å/ns and 100, respectively, and these parameters were used to evaluate three target proteins (FK506 binding protein, trypsin, and cyclin-dependent kinase 2). For these proteins using our parameters, the accuracy of affinity prediction was higher and more stable when Jarzynski’s equality was employed compared with the second-order cumulant expansion equation of Jarzynski’s equality. Our results showed that steered molecular dynamics simulations are effective for predicting the rank order of ligands; thus, they are a potential tool for compound selection in hit-to-lead and lead optimization processes.     

Arg149 is involved in switching the low affinity, open state of the binding protein AfProX into its high affinity, closed state

The substrate binding protein AfProX from the Archaeoglobus fulgidus ProU ATP binding cassette transporter is highly selective for the compatible solutes glycine betaine (GB) and proline betaine, which confer thermoprotection to this hyperthermophilic archaeon. A detailed mutational analysis of the substrate binding site revealed the contribution of individual amino acids for ligand binding. Replacement of Arg149 by an Ala residue displayed the largest impact on substrate binding. The structure of a mutant AfProX protein (substitution of Tyr111 with Ala) in complex with GB was solved in the open liganded conformation to gain further insight into ligand binding. In this crystal structure, GB is bound differently compared to the GB closed liganded structure of the wild-type AfProX protein. We found that a network of amino acid side chains communicates the presence of GB toward Arg149, which increases ligand affinity and induces domain closure of AfProX. These results were corroborated by molecular dynamics studies and support the view that Arg149 finalizes the high-affinity state of the AfProX substrate binding protein.