Computational studies for prediction of protein folding and ligand binding

This dissertation comprises four projects. I) Glycosylation is a post-translational modification that affects many physiological processes, including protein folding, cell interaction and host immune response. PglC, a phosphoglycosyl transferase (PGT) involved in the biosynthesis of N-linked glycoproteins in Campylobacter jejuni, is representative of one of the structurally simplest members of the small bacterial PGT family. The research utilizes sequence similarity network and evolutionary covariance studies to identify the catalytic core of PglC, followed by modeling its three-dimensional structure using the covariance as constraints. II) Rapid growth of fragment-based drug discovery necessitates accurate fragment library screening for targets of interest, finding strong binders with specific binding. While many high-resolution biophysical methods for fragment screening work well, docking-based virtual screening is highly desired due to the speed and cost efficiency. Beyond the key performance-determining factors like score function and search method, the goal is to learn from the experimental fragment bound structures in the PDBbinder database and to evaluate the profile of side-chain flexibility in the interface and its contribution to docking performance. III) Protein docking procedures carry out the task of predicting the structure of a protein–protein complex starting from the known structures of the individual protein components. However, the structure of one or both components frequently must be obtained by homology modeling based on known structures. This work presents a benchmark dataset of experimentally determined target complexes with a large set of sufficiently diverse template complexes identified for each target. The dataset allows developers to test their algorithms combining homology modeling and docking, in order to determine the factors that critically influence the prediction performance. IV) Human Eukaryotic Initiation Factor 4AI (heIF4AI) is the enzymatic component of a highly efficient complex, heIF4F. Its helicase activity binds and unwinds the secondary structure of mRNA at the 5' end and thus plays a crucial role in translation initiation. This research focuses on the C-terminal domain of heIF4AI and investigates its potential as an anti-cancer target by integrating the approaches of solvent mapping, docking, crystallization and NMR

Bioactive conformational ensemble server and database. A public framework to speed up in silico drug discovery.

Modern high-throughput structure-based drug discovery algorithms consider ligand flexibility, but typically with low accuracy, which results in a loss of performance in the derived models. Here we present the Bioactive Conformational Ensemble (BCE) server and its associated database. The server creates conformational ensembles of drug-like ligands and stores them in the BCE database, where a variety of analyses are offered to the user. The workflow implemented in the BCE server combines enhanced sampling molecular dynamics with self-consistent reaction field quantum mechanics (SCRF/QM) calculations. The server automatizes all the steps to transform 1D or 2D representation of drugs into three dimensional molecules, which are then titrated, parametrized, hydrated and optimized before being subjected to Hamiltonian replica-exchange (HREX) molecular dynamics simulations. Ensembles are collected and subjected to a clustering procedure to derive representative conformers, which are then analyzed at the SCRF/QM level of theory. All structural data is organized in a noSQL database accessible through a graphical interface and in a programmatic manner through a REST API. The server allows the user to define a private workspace and offers a deposition protocol as well as input files for "in house" calculations in those cases where confidentiality is a must. The database and the associated server are available at https://mmb.irbbarcelona.org/BC

In Silico Design and Selection of CD44 Antagonists:implementation of computational methodologies in drug discovery and design

Drug discovery (DD) is a process that aims to identify drug candidates through a thorough evaluation of the biological activity of small molecules or biomolecules. Computational strategies (CS) are now necessary tools for speeding up DD. Chapter 1 describes the use of CS throughout the DD process, from the early stages of drug design to the use of artificial intelligence for the de novo design of therapeutic molecules. Chapter 2 describes an in-silico workflow for identifying potential high-affinity CD44 antagonists, ranging from structural analysis of the target to the analysis of ligand-protein interactions and molecular dynamics (MD). In Chapter 3, we tested the shape-guided algorithm on a dataset of macrocycles, identifying the characteristics that need to be improved for the development of new tools for macrocycle sampling and design. In Chapter 4, we describe a detailed reverse docking protocol for identifying potential 4-hydroxycoumarin (4-HC) targets. The strategy described in this chapter is easily transferable to other compounds and protein datasets for overcoming bottlenecks in molecular docking protocols, particularly reverse docking approaches. Finally, Chapter 5 shows how computational methods and experimental results can be used to repurpose compounds as potential COVID-19 treatments. According to our findings, the HCV drug boceprevir could be clinically tested or used as a lead molecule to develop compounds that target COVID-19 or other coronaviral infections. These chapters, in summary, demonstrate the importance, application, limitations, and future of computational methods in the state-of-the-art drug design process

An enhanced-sampling MD-based protocol for molecular docking

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g. from molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the unbound (apo) protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables able to sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. In this work, we assessed the method on re-docking and cross-docking calculations. In first case, we selected three different protein targets undergoing different extent of conformational changes upon binding and, for each of them, we docked the experimental ligand conformation into an ensemble of receptor structures generated by EDES. In the second case, in the contest of a blind docking challenge, we generated the 3D structures of a set of different ligands of the same receptor and docked them into a set of EDES-generated conformations of that receptor. In all cases, for both re-docking and cross-docking experiments, our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry of the receptor. Moreover, ensemble docking calculations using those conformations yielded in almost all cases to native-like poses among the top-ranked ones. Finally, we also tested an improved EDES recipe on a further target, known to be extremely challenging due to its extended binding region and the large extent of conformational changes accompanying the binding of its ligands

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used

Dynamics of protein-drug interactions inferred from structural ensembles and physics-based models

The conformational flexibility of target proteins is a major challenge in understanding and modeling protein-drug interactions. A fundamental issue, yet to be clarified, is whether the observed conformational changes are controlled by the protein, or induced by the inhibitor. While the concept of induced fit has been widely adopted for describing the structural changes that accompany ligand binding, there is growing evidence in support of the dominance of proteins' intrinsic dynamics, which has been evolutionarily optimized to accommodate its functional interactions. The wealth of structural data for target proteins in the presence of different ligands now permits us to make a critical assessment of the balance between these two effects in selecting the bound forms. We focused on three widely studied drug targets, HIV-1 reverse transcriptase, p38 MAP kinase, and cyclin-dependent kinase 2. A total of 292 structures determined for these enzymes in the presence of different inhibitors as well as unbound form permitted us to perform an extensive comparative analysis of the conformational space accessed upon ligand binding, and its relation to the intrinsic dynamics prior to ligand binding as predicted by elastic network model analysis. Further, we analyzed NMR ensembles of ubiquitin and calmodulin representing their microseconds range solution dynamics. Our results show that the ligand selects the conformer that best matches its structural and dynamic properties amongst the conformers intrinsically accessible to the protein in the unliganded form. The results suggest that simple but robust rules encoded in the protein structure play a dominant role in pre-defining the mechanisms of ligand binding, which may be advantageously exploited in designing inhibitors. We apply these lessons to the study of MAP kinase phosphatases (MKPs), which are therapeutically relevant but challenging signaling enzymes. Our study provides insights into the interactions and selectivity of MKP inhibitors and shows how an allosteric inhibition mechanism holds for a recently discovered inhibitor of MKP-3. We also provide evidence for the functional significance of the structure-encoded dynamics of rhodopsin and nicotinic acetylcholine receptor, members of two membrane proteins classes serving as targets for more than 40% of all current FDA approved drugs

Computational Modeling of Designed Ankyrin Repeat Protein Complexes with their Targets

Recombinant therapeutic proteins are playing an ever-increasing role in the clinic. High-affinity binding candidates can be produced in a high-throughput manner through the process of selection and evolution from large libraries, but the structures of the complexes with target protein can only be determined for a small number of them in a costly, low-throughput manner, typically by x-ray crystallography. Reliable modeling of complexes would greatly help to understand their mode of action and improve them by further engineering, for example, by designing bi-paratopic binders. Designed ankyrin repeat proteins (DARPins) are one such class of antibody mimetics that have proven useful in the clinic, in diagnostics and research. Here we have developed a standardized procedure to model DARPin–target complexes that can be used to predict the structures of unknown complexes. It requires only the sequence of a DARPin and a structure of the unbound target. The procedure includes homology modeling of the DARPin, modeling of the flexible parts of a target, rigid body docking to ensembles of the target and docking with a partially flexible backbone. For a set of diverse DARPin–target complexes tested it generated a single model of the complex that well approximates the native state of the complex. We provide a protocol that can be used in a semi-automated way and with tools that are freely available. The presented concepts should help to accelerate the development of novel bio-therapeutics for scaffolds with similar properties