Exploring Protein Cavities through Rigidity Analysis

Analysis of changes of cavity volumes in predefined directions of protein motions and cavity flexibility

Dynamics of protein cavities associated with protein fluctuations and conformational plasticity is essential for their biological function. NMR ensembles, molecular dynamics (MD) simulations, and normal mode analysis (NMA) provide appropriate frameworks to explore functionally relevant protein dynamics and cavity changes relationships. Within this context, we have recently developed analysis of null areas (ANA), an efficient method to calculate cavity volumes. ANA is based on a combination of algorithms that guarantees its robustness against numerical differentiations. This is a unique feature with respect to other methods. Herein, we present an updated and improved version that expands it use to quantify changes in cavity features, like volume and flexibility, due to protein structural distortions performed on predefined biologically relevant directions, for example, directions of largest contribution to protein fluctuations (principal component analysis [PCA modes]) obtained by MD simulations or ensembles of NMR structures, collective NMA modes or any other direction of motion associated with specific conformational changes. A web page has been developed where its facilities are explained in detail. First, we show that ANA can be useful to explore gradual changes of cavity volume and flexibility associated with protein ligand binding. Secondly, we perform a comparison study of the extent of variability between protein backbone structural distortions, and changes in cavity volumes and flexibilities evaluated for an ensemble of NMR active and inactive conformers of the epidermal growth factor receptor structures. Finally, we compare changes in size and flexibility between sets of NMR structures for different homologous chains of dynein.

Quantitative Characterization of Binding Pockets and Binding Complementarity by Means of Zernike Descriptors

In this work, we describe the application of the Zernike formalism to quantitatively characterize the binding pockets of two sets of biologically relevant systems. Such an approach, when applied to molecular dynamics trajectories, is able to pinpoint the subtle differences between very similar molecular regions and their impact on the local propensity to ligand binding, allowing us to quantify such differences. The statistical robustness of our procedure suggests that it is very suitable to describe protein binding sites and protein-ligand interactions within a rigorous and well-defined framework.

Trehalose synthesis inhibitor: A molecular in silico drug design

Infectious diseases are serious public health problems, affecting a large portion of the world's population. A molecule that plays a key role in pathogenic organisms is trehalose and recently has been an interest in the metabolism of this molecule for drug development. The trehalose-6-phosphate synthase (TPS1) is an enzyme responsible for the biosynthesis of trehalose-6-phosphate (T6P) in the TPS1/TPS2 pathway, which results in the formation of trehalose. Studies carried out by our group demonstrated the inhibitory capacity of T6P in the TPS1 enzyme from Saccharomyces cerevisiae, preventing the synthesis of trehalose. By in silico techniques, we compiled sequences and experimentally determined structures of TPS1. Sequence alignments and molecular modeling were performed. The generated structures were submitted in validation of algorithms, aligned structurally and analyzed evolutionarily. Molecular docking methodology was applied to analyze the interaction between T6P and TPS1 and ADMET properties of T6P were analyzed. The results demonstrated the models created presented sequence and structural similarities with experimentally determined structures. With the molecular docking, a cavity in the protein surface was identified and the molecule T6P was interacting with the residues TYR-40, ALA-41, MET-42, and PHE-372, indicating the possible uncompetitive inhibition mechanism provided by this ligand, which can be useful in directing the molecular design of inhibitors. In ADMET analyses, T6P had acceptable risk values compared with other compounds from World Drug Index. Therefore, these results may present a promising strategy to explore to develop a broad-spectrum antibiotic of this specific target with selectivity, potency, and reduced side effects, leading to a new way to treat infectious diseases like tuberculosis and candidiasis.

Computational Structural Biology: Successes, Future Directions, and Challenges

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.