In our recent efforts to map protein surfaces using mixed-solvent molecular dynamics (MixMD), 1 we were able to successfully capture active sites and allosteric sites within the top-four most occupied hotspots. In this study, we describe our approach for estimating the thermodynamic profile of the binding sites identified by MixMD. First, we establish a framework for calculating free energies from MixMD simulations, and we compare our approach to alternative methods. Second, we present a means to obtain a relative ranking of the binding sites by their configurational entropy. The theoretical maximum and minimum free energy and entropy values achievable under such a framework along with the limitations of the techniques are discussed. Using this approach, the free energy and relative entropy ranking of the top-four MixMD binding sites were computed and analyzed across our allosteric protein targets:
The mitogen-activated protein kinases (MAPKs) govern various cellular programs and crucial intermediate pathways in signaling. Microtubule affinity-regulating kinase 4 (MARK4) is a part of the kinase family recognized for actively...
Sequence to structure of proteins is an unsolved problem. A possible coarse grained resolution to this entails specification of all the torsional (Φ, Ψ) angles along the backbone of the polypeptide chain. The Ramachandran map quite elegantly depicts the allowed conformational (Φ, Ψ) space of proteins which is still very large for the purposes of accurate structure generation. We have divided the allowed (Φ, Ψ) space in Ramachandran maps into 27 distinct conformations sufficient to regenerate a structure to within 5 Å from the native, at least for small proteins, thus reducing the structure prediction problem to a specification of an alphanumeric string, i.e., the amino acid sequence together with one of the 27 conformations preferred by each amino acid residue. This still theoretically results in 27(n) conformations for a protein comprising "n" amino acids. We then investigated the spatial correlations at the two-residue (dipeptide) and three-residue (tripeptide) levels in what may be described as higher order Ramachandran maps, with the premise that the allowed conformational space starts to shrink as we introduce neighborhood effects. We found, for instance, for a tripeptide which potentially can exist in any of the 27(3) "allowed" conformations, three-fourths of these conformations are redundant to the 95% confidence level, suggesting sequence context dependent preferred conformations. We then created a look-up table of preferred conformations at the tripeptide level and correlated them with energetically favorable conformations. We found in particular that Boltzmann probabilities calculated from van der Waals energies for each conformation of tripeptides correlate well with the observed populations in the structural database (the average correlation coefficient is ∼0.8). An alpha-numeric string and hence the tertiary structure can be generated for any sequence from the look-up table within minutes on a single processor and to a higher level of accuracy if secondary structure can be specified. We tested the methodology on 100 small proteins, and in 90% of the cases, a structure within 5 Å is recovered. We thus believe that the method presented here provides the missing link between Ramachandran maps and tertiary structures of proteins. A Web server to convert a tertiary structure to an alphanumeric string and to predict the tertiary structure from the sequence of a protein using the above methodology is created and made freely accessible at http://www.scfbio-iitd.res.in/software/proteomics/rm2ts.jsp.
Iron deposition in the central nervous system (CNS) is one of the causes of neurodegenerative diseases. Human transferrin (hTf) acts as an iron carrier present in the blood plasma, preventing it from contributing to redox reactions. Plant compounds and their derivatives are frequently being used in preventing or delaying Alzheimer's disease (AD). Thymoquinone (TQ), a natural product has gained popularity because of its broad therapeutic applications. TQ is one of the significant phytoconstituent of Nigella sativa. The binding of TQ to hTf was determined by spectroscopic methods and isothermal titration calorimetry. We have observed that TQ strongly binds to hTf with a binding constant (K) of 0.22 × 106 M−1 and forming a stable complex. In addition, isothermal titration calorimetry revealed the spontaneous binding of TQ with hTf. Molecular docking analysis showed key residues of the hTf that were involved in the binding to TQ. We further performed a 250 ns molecular dynamics simulation which deciphered the dynamics and stability of the hTf‐TQ complex. Structure analysis suggested that the binding of TQ doesn't cause any significant alterations in the hTf structure during the course of simulation and a stable complex is formed. Altogether, we have elucidated the mechanism of binding of TQ with hTf, which can be further implicated in the development of a novel strategy for AD therapy.
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