CompASM: an Amber-VMD alanine scanning mutagenesis plug-in

Designing the Sniper: Improving Targeted Human Cytolytic Fusion Proteins for Anti-Cancer Therapy via Molecular Simulation

Targeted human cytolytic fusion proteins (hCFPs) are humanized immunotoxins for selective treatment of different diseases including cancer. They are composed of a ligand specifically binding to target cells genetically linked to a human apoptosis-inducing enzyme. hCFPs target cancer cells via an antibody or derivative (scFv) specifically binding to e.g., tumor associated antigens (TAAs). After internalization and translocation of the enzyme from endocytosed endosomes, the human enzymes introduced into the cytosol are efficiently inducing apoptosis. Under in vivo conditions such enzymes are subject to tight regulation by native inhibitors in order to prevent inappropriate induction of cell death in healthy cells. Tumor cells are known to upregulate these inhibitors as a survival mechanism resulting in escape of malignant cells from elimination by immune effector cells. Cytosolic inhibitors of Granzyme B and Angiogenin (Serpin P9 and RNH1, respectively), reduce the efficacy of hCFPs with these enzymes as effector domains, requiring detrimentally high doses in order to saturate inhibitor binding and rescue cytolytic activity. Variants of Granzyme B and Angiogenin might feature reduced affinity for their respective inhibitors, while retaining or even enhancing their catalytic activity. A powerful tool to design hCFPs mutants with improved potency is given by in silico methods. These include molecular dynamics (MD) simulations and enhanced sampling methods (ESM). MD and ESM allow predicting the enzyme-protein inhibitor binding stability and the associated conformational changes, provided that structural information is available. Such "high-resolution" detailed description enables the elucidation of interaction domains and the identification of sites where particular point mutations may modify those interactions. This review discusses recent advances in the use of MD and ESM for hCFP development from the viewpoints of scientists involved in both fields.

Glutamine Synthetase Drugability beyond Its Active Site: Exploring Oligomerization Interfaces and Pockets

BACKGROUND

Glutamine synthetase (GS) is a crucial enzyme to the nitrogen cycle with great commercial and pharmaceutical value. Current inhibitors target the active site, affecting GS activity indiscriminately in all organisms. As the active site is located at the interface between two monomers, the protein-protein interface (PPI) of GSs gains a new role, by providing new targets for enzyme inhibition. Exploring GSs PPI could allow for the development of inhibitors selective for specific organisms. Here we map the PPI of three GSs-human (hsGS), maize (zmGS) and Mycobacterium tuberculosis (mtGS)-and unravel new drugable pockets.

METHODS

The PPI binding free energy coming from key residues on three GSs from different organisms were mapped by computational alanine scan mutagenesis, applying a multiple dielectric constant MM-PBSA methodology. The most relevant residues for binding are referred as hot-spots. Drugable pockets on GS were detected with the Fpocket software.

RESULTS AND CONCLUSIONS

A total of 23, 19 and 30 hot-spots were identified on hsGS, zmGS and mtGS PPI. Even possessing differences in the hot-spots, hsGS and zmGS PPI are overall very similar. On the other hand, mtGS PPI differs greatly from hsGS and zmGS PPI. A novel drugable pocket was detected on the mtGS PPI. It seems particularly promising for the development of selective anti-tuberculosis drugs given its location on a PPI region that is highly populated with hot-spots and is completely different from the hsGS and zmGS PPIs. Drugs targeting this pockets should be inactive on eukaryotic GS II enzymes.

Overcoming drug resistance through in silico prediction

Prediction tools are commonly used in pre-clinical research to assist target selection, to optimize drug potency or to predict the pharmacological profile of drug candidates. In silico prediction and overcoming drug resistance is a new opportunity that creates a high interest in pharmaceutical research. This review presents two main in silico strategies to meet this challenge: a structure-based approach to study the influence of mutations on the drug-target interaction and a system-biology approach to identify resistance pathways for a given drug. In silico screening of synergies between therapeutic and resistant pathways through biological network analysis is an example of technique to escape drug resistance. Structure-based drug design and in silico system biology are complementary approaches to reach few objectives at once: increase efficiency, reduce toxicity and overcoming drug resistance.

A new scoring function for protein-protein docking that identifies native structures with unprecedented accuracy

Protein-protein (P-P) 3D structures are fundamental to structural biology and drug discovery. However, most of them have never been determined. Many docking algorithms were developed for that purpose, but they have a very limited accuracy in generating native-like structures and identifying the most correct one, in particular when a single answer is asked for. With such a low success rate it is difficult to point out one docked structure as being native-like. Here we present a new, high accuracy, scoring method to identify the 3D structure of P-P complexes among a set of trial poses. It incorporates alanine scanning mutagenesis experimental data that need to be obtained a priori. The scoring scheme works by matching the computational and the experimental alanine scanning mutagenesis results. The size of the trial P-P interface area is also taken into account. We show that the method ranks the trial structures and identifies the native-like structures with unprecedented accuracy (∼94%), providing the correct P-P 3D structures that biochemists and molecular biologists need to pursue their studies. With such a success rate, the bottleneck of protein-protein docking moves from the scoring to searching algorithms.

Insights into the structural determinants of substrate specificity and activity in mouse aldehyde oxidases

In this work, a combination of homology modeling and molecular dynamics (MD) simulations was used to investigate the factors that modulate substrate specificity and activity of the mouse AOX isoforms: mAOX1, mAOX2 (previously mAOX3l1), mAOX3 and mAOX4. The results indicate that the AOX isoform structures are highly preserved and even more conserved than the corresponding amino acid sequences. The only differences are at the protein surface and substrate-binding site region. The substrate-binding site of all isoforms consists of two regions: the active site, which is highly conserved among all isoforms, and a isoform-specific region located above. We predict that mAOX1 accepts a broader range of substrates of different shape, size and nature relative to the other isoforms. In contrast, mAOX4 appears to accept a more restricted range of substrates. Its narrow and hydrophobic binding site indicates that it only accepts small hydrophobic substrates. Although mAOX2 and mAOX3 are very similar to each other, we propose the following pairs of overlapping substrate specificities: mAOX2/mAOX4 and mAOX3/mAXO1. Based on these considerations, we propose that the catalytic activity between all isoforms should be similar but the differences observed in the binding site might influence the substrate specificity of each enzyme. These results also suggest that the presence of several AOX isoforms in mouse allows them to oxidize more efficiently a wider range of substrates. This contrasts with the same or other organisms that only express one isoform and are less efficient or incapable of oxidizing the same type of substrates.

Discovery of new sites for drug binding to the hypertension-related renin-angiotensinogen complex

Renin (REN) is a key drug target to stop the hypertension cascade, but thus far only one direct inhibitor has been made commercially available. In this study, we assess an innovative REN inhibition strategy, by targeting the interface of the renin:angiotensinogen (REN:ANG) complex. We characterized the energetic role of interfacial residues of REN:ANG and identified the ones responsible for protein:protein binding, which can serve as drug targets for disruption of the REN:ANG association. For this purpose, we applied a computational alanine scanning mutagenesis protocol, which measures the contribution of each side chain for the protein:protein binding free energy with an accuracy of ≈ 1 kcal/mol. As a result, in REN and ANG, six and eight residues were found to be critical for binding, respectively. The leading force behind REN:ANG complexation was found to be the hydrophobic effect. The binding free energy per residue was found to be proportional to the buried area. Residues responsible for binding were occluded from water at the complex, which promotes an efficient pairing between the two proteins. Two druggable pockets involving critical residues for binding were found on the surface of REN, where small druglike molecules can bind and disrupt the ANG:REN association that may provide an efficient way to achieve REN inhibition and control hypertension.

Computational Alanine Scanning Mutagenesis-An Improved Methodological Approach for Protein-DNA Complexes

Proteins and protein-based complexes are the basis of many key systems in nature and have been the subject of intense research in the last decades, in an attempt to acquire comprehensive knowledge of reactions that take place in nature. Computational Alanine Scanning Mutagenesis approaches have been extensively used in the study of protein interfaces and in the determination of the most important residues for complex formation, the Hot-spots. However, as it is usually applied to the study of protein-protein interfaces, we tried to modify and apply it to the study of protein-DNA interfaces, which are also crucial in nature but have not been the subject of as much research. In this work, we carry out MD simulations of seven protein-DNA complexes and tested the influence of the variation of different parameters on the determination of the binding free energy terms (ΔΔGbinding) of 78 mutations: solvent representation, internal dielectric constant, Linear and Nonlinear Poisson-Boltzmann equation, Generalized Born model, simulation time, number of structures analyzed, number of MD trajectories, force field used, and energetic terms involved. Overall, this new approach gave an average error of 1.55 kcal/mol, and P, R, F1, accuracy, and specificity values of 0.78, 0.50, 0.61, 0.77, and 0.92, respectively. This improved computational alanine scanning mutagenesis approach may serve as a tool to explore the behavior of this important class of complexes.

Computational Alanine Scanning Mutagenesis: MM-PBSA vs TI

Understanding protein-protein association and being able to determine the crucial residues responsible for their association (hot-spots) is a key issue with huge practical applications such as rational drug design and protein engineering. A variety of computational methods exist to detect hot-spots residues, but the development of a fast and accurate quantitative alanine scanning mutagenesis (ASM) continues to be crucial. Using four protein-protein complexes, we have compared a variation of the standard computational ASM protocol developed at our group, based on the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM-PBSA) approach, against Thermodynamic Integration (TI), a well-known and accurate but computationally expensive method. To compare the efficiency and the accuracy of the two methods, we have calculated the protein-protein binding free energy differences upon alanine mutation of interfacial residues (ΔΔGbind). In relation to the experimental ΔΔGbind values, the average error obtained with TI was 1.53 kcal/mol, while the ASM protocol resulted in an average error of 1.18 kcal/mol. The results demonstrate that the much faster ASM protocol gives results at the same level of accuracy as the TI method but at a fraction of the computational time required to run TI. This ASM protocol is therefore a strong and efficient alternative to the systematic evaluation of protein-protein interfaces, involving hundreds of amino acid residues in search of hot-spots.