A receptor dependent-4D QSAR approach to predict the activity of mutated enzymes.

Screening and selection tools to obtain focused libraries play a key role in successfully engineering enzymes of desired qualities. The quality of screening depends on efficient assays; however, a focused library generated with a priori information plays a major role in effectively identifying the right enzyme. As a proof of concept, for the first time, receptor dependent - 4D Quantitative Structure Activity Relationship (RD-4D-QSAR) has been implemented to predict kinetic properties of an enzyme. The novelty of this study is that the mutated enzymes also form a part of the training data set. The mutations were modeled in a serine protease and molecular dynamics simulations were conducted to derive enzyme-substrate (E-S) conformations. The E-S conformations were enclosed in a high resolution grid consisting of 156,250 grid points that stores interaction energies to generate QSAR models to predict the enzyme activity. The QSAR predictions showed similar results as reported in the kinetic studies with >80% specificity and >50% sensitivity revealing that the top ranked models unambiguously differentiated enzymes with high and low activity. The interaction energy descriptors of the best QSAR model were used to identify residues responsible for enzymatic activity and substrate specificity.

NLScore: a novel quantitative algorithm based on 3 dimensional structural determinants to predict the probability of nuclear localization in proteins containing classical nuclear localization signals.

The presence of a nuclear localization signal (NLS) in proteins can be inferred by the presence of a stretch of basic amino acids (KRKK). These NLSs are termed classical NLS (cNLS). However, only a fraction of proteins containing the cNLS pattern are transported into the nucleus by binding to importin α. Hence, there must exist, additional structural determinants that guide the appropriate interaction between putative NLSs containing cargo and importin α. Using 52 protein structures containing cNLS obtained from RCSB PDB, we assembled a training set and a validation set such that both sets were comprised of a combination of proteins with proven nuclear localization and ones that were non-nuclear. We modeled the interface between cargoes containing cNLS and importin α. We conducted rigid body docking and produced induced-fit modes by allowing both side chain and the backbone to be flexible. The output of these studies and additional determinants such as energy of interaction, atomic contacts, hydrophilic interaction, cationic interaction, and penetration of the cargo protein were used to derive a 26 parameter quantitative structure activity relationship based regression equation. This was further optimized by a step-wise backward elimination approach to derive a 15 parameter score. This NLScore was not only able to correctly classify confirmed nuclear and non-nuclear localized proteins but it was able to perform better than currently implemented algorithms like NucPred, Euk-mPLoc 2.0, cNls Mapper, and NLStradamus. Leave-one-out cross validation (LOOCV) showed that NLScore correctly predicted 78.6% and 81.6% of non-nuclear and nuclear proteins respectively. Graphical abstract NLScore: a novel quantitative algorithm based on 3 dimensional structural determinants to predict the probability of nuclear localization in proteins.

Docking, molecular dynamics and QM/MM studies to delineate the mode of binding of CucurbitacinE to F-actin.

CucurbitacinE (CurE) has been known to bind covalently to F-actin and inhibit depolymerization. However, the mode of binding of CurE to F-actin and the consequent changes in the F-actin dynamics have not been studied. Through quantum mechanical/molecular mechanical (QM/MM) and density function theory (DFT) simulations after the molecular dynamics (MD) simulations of the docked complex of F-actin and CurE, a detailed transition state (TS) model for the Michael reaction is proposed. The TS model shows nucleophilic attack of the sulphur of Cys257 at the ß-carbon of Michael Acceptor of CurE producing an enol intermediate that forms a covalent bond with CurE. The MD results show a clear difference between the structure of the F-actin in free form and F-actin complexed with CurE. CurE affects the conformation of the nucleotide binding pocket increasing the binding affinity between F-actin and ADP, which in turn could affect the nucleotide exchange. CurE binding also limits the correlated displacement of the relatively flexible domain 1 of F-actin causing the protein to retain a flat structure and to transform into a stable "tense" state. This structural transition could inhibit depolymerization of F-actin. In conclusion, CurE allosterically modulates ADP and stabilizes F-actin structure, thereby affecting nucleotide exchange and depolymerization of F-actin.

Mutations in Drosophila myosin rod cause defects in myofibril assembly.

The roles of myosin during muscle contraction are well studied, but how different domains of this protein are involved in myofibril assembly in vivo is far less understood. The indirect flight muscles (IFMs) of Drosophila melanogaster provide a good model for understanding muscle development and function in vivo. We show that two missense mutations in the rod region of the myosin heavy-chain gene, Mhc, give rise to IFM defects and abnormal myofibrils. These defects likely result from thick filament abnormalities that manifest during early sarcomere development or later by hypercontraction. The thick filament defects are accompanied by marked reduction in accumulation of flightin, a myosin binding protein, and its phosphorylated forms, which are required to stabilise thick filaments. We investigated with purified rod fragments whether the mutations affect the coiled-coil structure, rod aggregate size or rod stability. No significant changes in these parameters were detected, except for rod thermodynamic stability in one mutation. Molecular dynamics simulations suggest that these mutations may produce localised rod instabilities. We conclude that the aberrant myofibrils are a result of thick filament defects, but that these in vivo effects cannot be detected in vitro using the biophysical techniques employed. The in vivo investigation of these mutant phenotypes in IFM development and function provides a useful platform for studying myosin rod and thick filament formation generically, with application to the aetiology of human myosin rod myopathies.