DEPTH: a web server to compute depth and predict small-molecule binding cavities in proteins

Depth measures the extent of atom/residue burial within a protein. It correlates with properties such as protein stability, hydrogen exchange rate, protein–protein interaction hot spots, post-translational modification sites and sequence variability. Our server, DEPTH, accurately computes depth and solvent-accessible surface area (SASA) values. We show that depth can be used to predict small molecule ligand binding cavities in proteins. Often, some of the residues lining a ligand binding cavity are both deep and solvent exposed. Using the depth-SASA pair values for a residue, its likelihood to form part of a small molecule binding cavity is estimated. The parameters of the method were calibrated over a training set of 900 high-resolution X-ray crystal structures of single-domain proteins bound to small molecules (molecular weight <1.5 KDa). The prediction accuracy of DEPTH is comparable to that of other geometry-based prediction methods including LIGSITE, SURFNET and Pocket-Finder (all with Matthew’s correlation coefficient of ∼0.4) over a testing set of 225 single and multi-chain protein structures. Users have the option of tuning several parameters to detect cavities of different sizes, for example, geometrically flat binding sites. The input to the server is a protein 3D structure in PDB format. The users have the option of tuning the values of four parameters associated with the computation of residue depth and the prediction of binding cavities. The computed depths, SASA and binding cavity predictions are displayed in 2D plots and mapped onto 3D representations of the protein structure using Jmol. Links are provided to download the outputs. Our server is useful for all structural analysis based on residue depth and SASA, such as guiding site-directed mutagenesis experiments and small molecule docking exercises, in the context of protein functional annotation and drug discovery

Characterization of physicochemical environments of proteins

Proteins are molecular machines in cells that perform a diverse set of essential biological functions. The functions of proteins are determined by its 3D structure. The struc- ture creates local microenvironments for protein atoms to interact with one another. A detailed understanding of these microenvironments would allow better characterization and engineering of protein functions. For example, this knowledge forms the basis of modern therapeutics innovations such as rational drug and vaccine design, and could have implications in other industries, including bioprocessing, biomimetics, biomaterials among others. This thesis presents my results on the characterization of physicochemical properties of microenvironment in proteins. To investigate the complex nature of protein microenvironment, the characterization effort can be broadly categorized into three in- terconnected topics, namely (i) residue depth (ii) hydrogen bonding, and (iii) multibody statistical potential. The first topic aims to quantify protein microenvironment using the biophysical pa- rameter of residue depth. Depth of an amino acid measures the degree of amino acid burial in proteins. I have shown that the energetics of proteins, the spatial distribution and chemical properties of amino acids are dependent on residue depth. To exemplify and utilize the results, I have designed several computational methods for protein en- gineering and functional characterization. First, a novel method to design temperature sensitive alleles of proteins was proposed by making point mutations of these residues. Next, I have used residue depth to identify small molecule ligand binding site on proteins by supplementing it with solvent accessibility and evolutionary information. Benchmarks have shown that the method has comparable or better than the best available methods, and could reveal unconventional sites unidentifiable with other methods. In addition, I have also shown that residue depth can be used in the estimation of protein cavity volume using a Monte Carlo sampling approach, and pK a of amino acid residues using a linear model. The second topic studies the physicochemical properties of hydrogen bonding in dif- ferent protein environments. I have performed statistical analysis on databases and clas- sified hydrogen bonds into different types, and characterized the geometrical preference and variations of the different types. By analyzing quantum simulation of the system, I have shown that the geometrical preference of main-chain hydrogen bond is due to elec- tron density arising from the planar nature of the peptide bond. I have also performed empirical simulations that strongly suggest the causal link between this geometrical pref- erence and secondary structure formation. Next, I have discovered that low-resolution protein models in databases are consistently missing hydrogen bonds. To ameliorate the models, I have designed a two-step refinement protocol. First, a simple algorithm was used to predict missing pairs of donor-acceptor to form hydrogen bonds based on their mutual preference and specificity. Second, Gaussian restraints were applied on the geometry distribution of the missing pairs, after which a standard modelling protocol can be implemented to refine the protein model. The refinement protocol was shown capable of re-introducing hydrogen bonds in the local environment as well as improving overall model quality. The refinement has functional implication on the protein chemical properties, as exemplified with the more accurate pK a prediction. The third topic is constructing an environmental dependent protein statistical po- tential Packpred. Here, I have explicitly defined protein microenvironments as a set of tightly packed amino acids, dubbed as ”residue cliques”. Employing Sippl’s formulation, the non-random occurrence of microenvironments is characterized. The non-random occurrence is indicative of the strength of interaction among amino acids, and can be interpreted as an energy potential. I have evaluated the capability of the potential in describing protein energetics on a large number of mutagenesis data. The benchmark has shown that, as compared to all other competing methods, Packpred has the best performance not only in binary classification of destabilizing mutants, but also correctly rank-ordering the degree of phenotypical change associated with different mutations. Lastly, I also present three biomolecular system modelling studies involving non- globular proteins. These system are (i) Cohesin ring protein with coiled-coil structure (ii) transmembrane transporters OCTN-1 and -2, (iii) interaction interface between onco- genic proteins VAV1 and EZH2. Modelling of these systems are challenging because con- ventional tools and framework of comparative modelling are not applicable. Instead, an integrative modelling approach was undertaken pertaining to individual systems. In all the modelling work I have proposed experimentally testable hypotheses to decipher the biological mechanism underlying the systems. In conclusion, in this thesis I have presented an extensive characterization of physic- ochemical environments of protein. The complex nature of the environment was elu- cidated by three interdependent topics of residue depth, hydrogen bonding and amino acid cliques. In addition to novel results, for every investigation I have also explored their biological utilities, and have built open-access tools for them. I hope that the work presented here would facilitate future research into protein structures and their functions.Doctor of Philosophy (SCE

New multibody statistical potential for protein models assessment.

Pair-wise amino acid residue-residue contact potentials are widely used to describe the accuracy of 3D protein structure models. These contact potentials (or statistical potentials) are however approximations as they consider all pair of residues as non-interacting/independent entities. Increased efforts have been made to obtain higher order statistical potentials to address these shortcomings. Here, we propose a new multibody statistical potential focusing on local environments created by the close packing of amino acid residues inside a protein. We name these local environment descriptors as 'cliques' and its corresponding statistical potential 'CLIQUE'. CLIQUE potential takes into consideration the interdependence of interactions of residues in a given neighborhood. Its utility would be to accurately recognize fundamental elements of protein structure, such as motifs and folds. A globular, non-redundant, single domain set of 1442 protein structures was used to construct the CLIQUE potential. Means of using this potential to construct an appropriate scoring function to distinguish between native and mis-folded proteins were then explored.Bachelor of Science in Biological Science

Packpred: predicting the functional effect of missense mutations

Predicting the functional consequences of single point mutations has relevance to protein function annotation and to clinical analysis/diagnosis. We developed and tested Packpred that makes use of a multi-body clique statistical potential in combination with a depth-dependent amino acid substitution matrix (FADHM) and positional Shannon entropy to predict the functional consequences of point mutations in proteins. Parameters were trained over a saturation mutagenesis data set of T4-lysozyme (1,966 mutations). The method was tested over another saturation mutagenesis data set (CcdB; 1,534 mutations) and the Missense3D data set (4,099 mutations). The performance of Packpred was compared against those of six other contemporary methods. With MCC values of 0.42, 0.47, and 0.36 on the training and testing data sets, respectively, Packpred outperforms all methods in all data sets, with the exception of marginally underperforming in comparison to FADHM in the CcdB data set. A meta server analysis was performed that chose best performing methods of wild-type amino acids and for wild-type mutant amino acid pairs. This led to an increase in the MCC value of 0.40 and 0.51 for the two meta predictors, respectively, on the Missense3D data set. We conjecture that it is possible to improve accuracy with better meta predictors as among the seven methods compared, at least one method or another is able to correctly predict ∼99% of the data.Published versionThis work was supported by a Wellcome Trust-DBT India Alliance Senior Fellowship

TSpred: a web server for the rational design of temperature-sensitive mutants

Temperature sensitive (Ts) mutants of proteins provide experimentalists with a powerful and reversible way of conditionally expressing genes. The technique has been widely used in determining the role of gene and gene products in several cellular processes. Traditionally, Ts mutants are generated by random mutagenesis and then selected though laborious large-scale screening. Our web server, TSpred (http://mspc.bii.a-star.edu.sg/TSpred/), now enables users to rationally design Ts mutants for their proteins of interest. TSpred uses hydrophobicity and hydrophobic moment, deduced from primary sequence and residue depth, inferred from 3D structures to predict/identify buried hydrophobic residues. Mutating these residues leads to the creation of Ts mutants. Our method has been experimentally validated in 36 positions in six different proteins. It is an attractive proposition for Ts mutant engineering as it proposes a small number of mutations and with high precision. The accompanying web server is simple and intuitive to use and can handle proteins and protein complexes of different sizes

Depth: a web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pK(a) of ionizable residues in proteins

Residue depth accurately measures burial and parameterizes local protein environment. Depth is the distance of any atom/residue to the closest bulk water. We consider the non-bulk waters to occupy cavities, whose volumes are determined using a Voronoi procedure. Our estimation of cavity sizes is statistically superior to estimates made by CASTp and VOIDOO, and on par with McVol over a data set of 40 cavities. Our calculated cavity volumes correlated best with the experimentally determined destabilization of 34 mutants from five proteins. Some of the cavities identified are capable of binding small molecule ligands. In this study, we have enhanced our depth-based predictions of binding sites by including evolutionary information. We have demonstrated that on a database (LigASite) of similar to 200 proteins, we perform on par with ConCavity and better than MetaPocket 2.0. Our predictions, while less sensitive, are more specific and precise. Finally, we use depth (and other features) to predict pK(a)s of GLU, ASP, LYS and HIS residues. Our results produce an average error of just <1 pH unit over 60 predictions. Our simple empirical method is statistically on par with two and superior to three other methods while inferior to only one. The DEPTH server (http://mspc.bii.a-star.edu.sg/depth/) is an ideal tool for rapid yet accurate structural analyses of protein structures

EZH2 promotes neoplastic transformation through VAV interaction-dependent extranuclear mechanisms

Recently, we reported that the histone methyltransferase, EZH2, controls leukocyte migration through interaction with the cytoskeleton remodeling effector, VAV, and direct methylation of the cytoskeletal regulatory protein, Talin. However, it is unclear whether this extranuclear, epigenetic-independent function of EZH2 has a profound impact on the initiation of cellular transformation and metastasis. Here, we show that EZH2 increases Talin1 methylation and cleavage, thereby enhancing adhesion turnover and promoting accelerated tumorigenesis. This transforming capacity is abolished by targeted disruption of EZH2 interaction with VAV. Furthermore, our studies demonstrate that EZH2 in the cytoplasm is closely associated with cancer stem cell properties, and that overexpression of EZH2, a mutant EZH2 lacking its nuclear localization signal (EZH2ΔNLS), or a methyl-mimicking Talin1 mutant substantially promotes JAK2-dependent STAT3 activation and cellular transformation. Taken together, our results suggest a critical role for the VAV interaction-dependent, extranuclear action of EZH2 in neoplastic transformation.ASTAR (Agency for Sci., Tech. and Research, S’pore)MOE (Min. of Education, S’pore)NMRC (Natl Medical Research Council, S’pore)MOH (Min. of Health, S’pore)Accepted versio