Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis

Aim: Molecular dynamics simulations and normal mode analysis are well-established approaches to generate receptor conformational ensembles (RCEs) for ligand docking and virtual screening. Here, we report new fast molecular dynamics-based and normal mode analysis-based protocols combined with conformational pocket classifications to efficiently generate RCEs. Materials \& methods: We assessed our protocols on two well-characterized protein targets showing local active site flexibility, dihydrofolate reductase and large collective movements, CDK2. The performance of the RCEs was validated by distinguishing known ligands of dihydrofolate reductase and CDK2 among a dataset of diverse chemical decoys. Results \& discussion: Our results show that different simulation protocols can be efficient for generation of RCEs depending on different kind of protein flexibility

Utilizing Experimental Data for Reducing Ensemble Size in Flexible-Protein Docking

Efficient and sufficient incorporation of protein flexibility into docking is still a challenging task. Docking to an ensemble of protein structures has proven its utility for docking, but using a large ensemble of structures can reduce the efficiency of docking and can increase the number of false positives in virtual screening. In this paper, we describe the application of our new methodology, Limoc, to generate an ensemble of holo-like protein structures in combination with the relaxed complex scheme (RCS), to virtual screening. We describe different schemes to reduce the ensemble of protein structures to increase efficiency and enrichment quality. Utilizing experimental knowledge about actives for a target protein allows the reduction of ensemble members to a minimum of three protein structures, increasing enrichment quality and efficiency simultaneously

Development and optimisation of computational tools for drug discovery

The aim of my PhD project was the development, optimisation, and implementation of new in silico virtual screening protocols. Specifically, this thesis manuscript is divided into three main parts, presenting some of the papers published during my doctoral work. The first one, here named CHEMOMETRIC PROTOCOLS IN DRUG DISCOVERY, is about the optimisation and application of an in house developed chemometric protocol. This part has been entirely developed at the University of Palermo - STEBICEF Department - under the guide of my supervisors. During the development of this part I have personally worked on the tuning and optimisation of the algorithm and on the docking campaigns to obtain molecule conformaitons. The second part, THE APPLICATION OF MOLECULAR DYNAMICS TO VIRTUAL SCREENING, presents a new approach to virtual screening, in particular the attention is focused on different approaches to the application of protein flexibility and dynamics to virtual screening. This part, has been carried out in cooperation with the University of Vienna - Department of Pharmaceutical Chemistry. For these works I have worked in the development of the general workflow, to a lesser extent to the programming (coding) part of the applications used and I mainly focused on the realisation of the screening campaigns and results interpretation. The third and last part, COMPUTATIONAL CHEMISTRY IN POLY-PHARMACOLOGY AND DRUG REPURPOSING, concerns the study of the in silico methods applied to two main topics of the drug discovery process, such as the drug repurposing and the polypharmacology. In this part I will briefly describe what published in two reviews dealing to the above mentioned topics. In conclusion during this doctoral project, I have demonstrated how the use of in silico tools can be useful in the drug discovery process. The Chemometric protocols developed and optimised represent in fact a helpful strategy to use for target fishing. Whereas, the application of molecular dynamics to virtual screening, especially for pharmacophore modelling, is a new way to deepen crucial features to be adopted in the search of new putative active compounds.The aim of my PhD project was the development, optimisation, and implementation of new in silico virtual screening protocols. Specifically, this thesis manuscript is divided into three main parts, presenting some of the papers published during my doctoral work. The first one, here named CHEMOMETRIC PROTOCOLS IN DRUG DISCOVERY, is about the optimisation and application of an in house developed chemometric protocol. This part has been entirely developed at the University of Palermo - STEBICEF Department - under the guide of my supervisors. During the development of this part I have personally worked on the tuning and optimisation of the algorithm and on the docking campaigns to obtain molecule conformaitons. The second part, THE APPLICATION OF MOLECULAR DYNAMICS TO VIRTUAL SCREENING, presents a new approach to virtual screening, in particular the attention is focused on different approaches to the application of protein flexibility and dynamics to virtual screening. This part, has been carried out in cooperation with the University of Vienna - Department of Pharmaceutical Chemistry. For these works I have worked in the development of the general workflow, to a lesser extent to the programming (coding) part of the applications used and I mainly focused on the realisation of the screening campaigns and results interpretation. The third and last part, COMPUTATIONAL CHEMISTRY IN POLY-PHARMACOLOGY AND DRUG REPURPOSING, concerns the study of the in silico methods applied to two main topics of the drug discovery process, such as the drug repurposing and the polypharmacology. In this part I will briefly describe what published in two reviews dealing to the above mentioned topics. In conclusion during this doctoral project, I have demonstrated how the use of in silico tools can be useful in the drug discovery process. The Chemometric protocols developed and optimised represent in fact a helpful strategy to use for target fishing. Whereas, the application of molecular dynamics to virtual screening, especially for pharmacophore modelling, is a new way to deepen crucial features to be adopted in the search of new putative active compounds

Modeling Interactions of Flexible Proteins

Proteins are dynamic molecules that mediate most biological processes through interactions with other proteins and biomolecules. A fundamental understanding of the mechanisms governing protein interactions requires intricate knowledge of the three-dimensional structures of biomolecular complexes. Despite advances in experimental structure determination, we have structural insights into only a small fraction of known complexes. Computational modeling provides an invaluable complementary tool to explore protein interactions in a rapid and high-throughput manner. A principal challenge limiting the accuracy of current computational methods is the ability to predict binding-induced conformational changes during protein–protein association. In this dissertation, I address this challenge by creating new tools to predict atomistic models of flexible protein complexes. First, I develop a heterodimer docking protocol that incorporates flexibility by efficiently simulating conformational selection from hundreds of pre-generated backbone conformations and identifies the near-native models with a novel, coarse-grained score function called Motif Dock Score (MDS). On a benchmark of 88 complexes with different degrees of flexibility, this protocol, RosettaDock 4.0, is the first method to successfully dock approximately 50% of complexes with conformational change of up to 2.2 Å. Next, I present the results of our participation in the community-wide blind experiment, Critical Assessment of PRedicted Interactions (CAPRI) rounds 37–45, where I use various docking methods to predict the structures of protein homomer, heteromer and oligosaccharide complexes. In the process, I identify inadequacies in these methods and propose enhancements. Based on the shortcomings identified in CAPRI, I develop a protocol to predict the structure of symmetric homomers from monomeric inputs with a focus on tightly-packed complexes. This method, Rosetta SymDock2, leverages MDS in the coarse-grained phase and simulates subunit flexibility through induced fit by all-atom flexible-backbone refinement. It outperforms competing algorithms by docking 61% of cyclic complexes and 42% of dihedral complexes in a diverse benchmark of 43 homomers. In the course of developing these algorithms, I also discover that the binding energy wells of homomers are narrower, steeper and deeper than those of heterodimers, thus explaining their increased stability. Finally, I present preliminary results to propose data-driven strategies that can overcome current barriers to accurate modeling