Lisa Yan

Fully Automated Molecular Mechanics Based Induced Fit Protein−Ligand Docking Method

We describe a method for docking a ligand into a protein receptor while allowing flexibility of the protein binding site. The method employs a multistep procedure that begins with the generation of protein and ligand conformations. An initial placement of the ligand is then performed by computing binding site hotspots. This initial placement is followed by a protein side-chain refinement stage that models protein flexibility. The final step of the process is an energy minimization of the ligand pose in the presence of the rigid receptor. Thus the algorithm models flexibility of the protein at two stages, before and after ligand placement. We validated this method by performing docking and cross docking studies of eight protein systems for which crystal structures were available for at least two bound ligands. The resulting rmsd values of the 21 docked protein-ligand complexes showed values of 2 A or less for all but one of the systems examined. The method has two critical benefits for high throughput virtual screening studies. First, no user intervention is required in the docking once the initial binding site selection has been made in the protein. Second, the initial protein conformation generation needs to be performed only once for a given binding region. Also, the method may be customized in various ways depending on the particular scenario in which dockings are being performed. Each of the individual steps of the method is fully independent making it straightforward to explore different variants of the high level workflow to further improve accuracy and performance.

A fast and accurate computational approach to protein ionization

We report a very fast and accurate physics‐based method to calculate pH‐dependent electrostatic effects in protein molecules and to predict the pK values of individual sites of titration. In addition, a CHARMm‐based algorithm is included to construct and refine the spatial coordinates of all hydrogen atoms at a given pH. The present method combines electrostatic energy calculations based on the Generalized Born approximation with an iterative mobile clustering approach to calculate the equilibria of proton binding to multiple titration sites in protein molecules. The use of the GBIM (Generalized Born with Implicit Membrane) CHARMm module makes it possible to model not only water‐soluble proteins but membrane proteins as well. The method includes a novel algorithm for preliminary refinement of hydrogen coordinates. Another difference from existing approaches is that, instead of monopeptides, a set of relaxed pentapeptide structures are used as model compounds. Tests on a set of 24 proteins demonstrate the high accuracy of the method. On average, the RMSD between predicted and experimental pK values is close to 0.5 pK units on this data set, and the accuracy is achieved at very low computational cost. The pH‐dependent assignment of hydrogen atoms also shows very good agreement with protonation states and hydrogen‐bond network observed in neutron‐diffraction structures. The method is implemented as a computational protocol in Accelrys Discovery Studio and provides a fast and easy way to study the effect of pH on many important mechanisms such as enzyme catalysis, ligand binding, protein–protein interactions, and protein stability.

LOOPER: a molecular mechanics-based algorithm for protein loop prediction

We describe a new ab initio method and corresponding program, LOOPER, for the prediction of protein loop conformations. The method is based on a multi-step algorithm (developed as a set of CHARMm scripts) and uses standard CHARMm force field parameters for energy minimization and scoring. One of the main obstacles to ab initio computational loop modeling is the exponential growth of the backbone conformational states with the number of residues in the loop fragment. In contrast to many ab initio algorithms that use Monte-Carlo schemes or exhaustive sampling, LOOPER adopts a systematic search strategy with minimal sampling of the backbone torsion angles. During the initial conformational sampling, two representative states are sampled for each alanine-like residue based on pairs of initial varphi and psi dihedral angles, except glycine, which is sampled by four representative conformations. The initial (varphi, psi) values are determined from the analysis of a novel iso-energy contour map which is proposed as an alternative structure validation method to the widely used Ramachandra plot. The efficient sampling strategy is combined with energy minimization at each step. The initial energy minimization and scoring of the loop include the interactions of the protein core with loop backbone atoms only. Construction and optimization of the side-chain conformations is followed by a final ranking stage based on the CHARMm energy with a generalized Born solvation term as a scoring function. The systematic and efficient sampling strategy in LOOPER consistently finds near native loop conformations in our validation study. At the same time, the computational overhead of our method is significantly lower than many alternative approaches that use exhaustive search strategies.

The dominant role of side-chain backbone interactions in structural realization of amino acid code. ChiRotor: A side-chain prediction algorithm based on side-chain backbone interactions

The basic differences between the 20 natural amino acid residues are due to differences in their side‐chain structures. This characteristic design of protein building blocks implies that side‐chain–side‐chain interactions play an important, even dominant role in 3D‐structural realization of amino acid codes. Here we present the results of a comparative analysis of the contributions of side‐chain–side‐chain (s‐s) and side‐chain–backbone (s‐b) interactions to the stabilization of folded protein structures within the framework of the CHARMm molecular data model. Contrary to intuition, our results suggest that side‐chain–backbone interactions play the major role in side‐chain packing, in stabilizing the folded structures, and in differentiating the folded structures from the unfolded or misfolded structures, while the interactions between side chains have a secondary effect. An additional analysis of electrostatic energies suggests that combinatorial dominance of the interactions between opposite charges makes the electrostatic interactions act as an unspecific folding force that stabilizes not only native structure, but also compact random conformations. This observation is in agreement with experimental findings that, in the denatured state, the charge–charge interactions stabilize more compact conformations. Taking advantage of the dominant role of side‐chain–backbone interactions in side‐chain packing to reduce the combinatorial problem, we developed a new algorithm, ChiRotor, for rapid prediction of side‐chain conformations. We present the results of a validation study of the method based on a set of high resolution X‐ray structures.

pH‐selective mutagenesis of protein–protein interfaces: In silico design of therapeutic antibodies with prolonged half‐life

Understanding the effects of mutation on pH‐dependent protein binding affinity is important in protein design, especially in the area of protein therapeutics. We propose a novel method for fast in silico mutagenesis of protein–protein complexes to calculate the effect of mutation as a function of pH. The free energy differences between the wild type and mutants are evaluated from a molecular mechanics model, combined with calculations of the equilibria of proton binding. The predicted pH‐dependent energy profiles demonstrate excellent agreement with experimentally measured pH‐dependency of the effect of mutations on the dissociation constants for the complex of turkey ovomucoid third domain (OMTKY3) and proteinase B. The virtual scanning mutagenesis identifies all hotspots responsible for pH‐dependent binding of immunoglobulin G (IgG) to neonatal Fc receptor (FcRn) and the results support the current understanding of the salvage mechanism of the antibody by FcRn based on pH‐selective binding. The method can be used to select mutations that change the pH‐dependent binding profiles of proteins and guide the time consuming and expensive protein engineering experiments. As an application of this method, we propose a computational strategy to search for mutations that can alter the pH‐dependent binding behavior of IgG to FcRn with the aim of improving the half‐life of therapeutic antibodies in the target organism.

Automated antibody structure prediction using Accelrys tools: results and best practices.

We describe the methodology and results from our participation in the second Antibody Modeling Assessment experiment. During the experiment we predicted the structure of eleven unpublished antibody Fv fragments. Our prediction methods centered on template‐based modeling; potential templates were selected from an antibody database based on their sequence similarity to the target in the framework regions. Depending on the quality of the templates, we constructed models of the antibody framework regions either using a single, chimeric or multiple template approach. The hypervariable loop regions in the initial models were rebuilt by grafting the corresponding regions from suitable templates onto the model. For the H3 loop region, we further refined models using ab initio methods. The final models were subjected to constrained energy minimization to resolve severe local structural problems. The analysis of the models submitted show that Accelrys tools allow for the construction of quite accurate models for the framework and the canonical CDR regions, with RMSDs to the X‐ray structure on average below 1 Å for most of these regions. The results show that accurate prediction of the H3 hypervariable loops remains a challenge. Furthermore, model quality assessment of the submitted models show that the models are of quite high quality, with local geometry assessment scores similar to that of the target X‐ray structures. Proteins 2014; 82:1583–1598.

A hidden Markov model with molecular mechanics energy-scoring function for transmembrane helix prediction

A range of methods has been developed to predict transmembrane helices and their topologies. Although most of these algorithms give good predictions, no single method consistently outperforms the others. However, combining different algorithms is one approach that can potentially improve the accuracy of the prediction. We developed a new method that initially uses a hidden Markov model to predict alternative models for membrane spanning helices in proteins. The algorithm subsequently identifies the best among models by ranking them using a novel scoring function based on the folding energy of transmembrane helical fragments. This folding of helical fragments and the incorporation into membrane is modeled using CHARMm, extended with the Generalized Born surface area solvent model (GBSA/IM) with implicit membrane. The combined method reported here, TMHGB significantly increases the accuracy of the original hidden Markov model-based algorithm.

Assessment of putative protein targets derived from the SARS genome.

The ability to rapidly and reliably develop hypotheses on the function of newly discovered protein sequences requires systematic and comprehensive analysis. Such an analysis, embodied within the DS GeneAtlas™ pipeline, has been used to critically evaluate the severe acute respiratory syndrome (SARS) genome with the goal of identifying new potential targets for viral therapeutic intervention. This paper discusses several new functional hypotheses on the roles played by the constituent gene products of SARS, and will serve as an example of how such assignments can be developed or extended on other systems of interest.

From fold recognition to homology modeling: an analysis of protein modeling challenges at different levels of prediction complexity.

An analysis of different approaches to protein structure prediction is presented based solely on the range of models submitted to the third Critical Assessment of Protein Structure Prediction (CASP3) conference. CASP conferences evaluate the current state of the art of protein structure prediction by comparing blind prediction efforts of many groups for the same set of target sequences. Target sequences may be highly similar to those with known structure or can be totally (at least superficially) sequentially dissimilar. Techniques applied to those blind predictions (over 40 targets) ranges from a detailed homology prediction to the detection of remote homologues well below a twilight zone of protein sequence similarity. For the CASP3 conference, we have submitted predictions, totaling 35, with various levels of difficulty and complexity. For ten submitted homology targets, eight of them were determined by experiment so far. The RMSD of C-alpha atoms are 1.2-1.7, 2.3, and 4.6-17.9 A for the three easy targets, two hard targets, and three very hard homology targets, respectively. Out of 18-fold recognition predictions available for analysis, we got six correct predictions, five near misses, three tough near misses and four far misses. Here we analyze successes and failures of those predictions in an attempt to identify common problems and common achievements.

Target validation through high throughput proteomics analysis

Abstract High throughput functional annotation of the proteome has emerged as a standard tool for target identification. In contrast, target validation, which requires detailed analysis of biological function, has until recently remained an essentially experimental low throughput activity. Currently, there is considerable interest in accelerating and improving the validation process to counter the declining number of small-molecule-based therapeutics being released onto the market. Progress in high throughput proteomics is a key technology in this respect. Uniquely, it offers the ability to rapidly identify and characterize networks of interacting proteins, which in turn presents new opportunities to develop alternative lead development strategies.