Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Computational Approaches For Designing Protein/inhibitor Complexes And Membrane Protein Variants

Drug discovery of small-molecule protein inhibitors is a vast enterprise that involves several scientific disciplines (i.e. genomics, cell biology, x-ray crystallography, chemistry, computer science, statistics), with each discipline focusing on a particular aspect of the process. In this thesis, I use computational and experimental approaches to explore the most fundamental aspect of drug discovery: the molecular interactions of small-molecules inhibitors with proteins. In Part I (Chapters I and II), I describe how computational docking approaches can be used to identify structurally diverse molecules that can inhibit multiple protein targets in the brain. I illustrate this approach using the examples of microtubule-stabilizing agents and inhibitors of cyclooxygenase(COX)-I and 5-lipoxygenase (5-LOX). In Part II (Chapters III and IV), I focus on membrane proteins, which are notoriously difficult to work with due to their low natural abundances, low yields for heterologous over expression, and propensities toward aggregation. I describe a general approach for designing water-soluble variants of membrane proteins, for the purpose of developing cell-free, label-free, detergent-free, solution-phase studies of protein structure and small-molecule binding. I illustrate this approach through the design of a water-soluble variant of the membrane protein Smoothened, wsSMO. This wsSMO stands to serve as a first-step towards developing membrane protein analogs of this important signaling protein and drug target

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

Structure - Activity Relationships in the Antiinflammatory Steroids: A Pattern Recognition Approach

A pattern recognition technique has been used to determine structure-activity relationships for antiinflammatory steroids. Expe.:. rimental results using the human vasoconstrictor test of McKenzie and Stoughton and the rat granuloma cotton pellet method of Meier were correlated with the various substructural descriptors. Steroids were classified into two categories according to potency and a pattern recognition method was applied to determine their relative ranking. The resulting structure-activity relationships obtained and the relative contributions of the various structural variables for both bioassays are discussed. A synergistic effect was predicted to be in operation between certain pairs of substituents

Structure - Activity Relationships in the Antiinflammatory Steroids: A Pattern Recognition Approach

A pattern recognition technique has been used to determine structure-activity relationships for antiinflammatory steroids. Expe.:. rimental results using the human vasoconstrictor test of McKenzie and Stoughton and the rat granuloma cotton pellet method of Meier were correlated with the various substructural descriptors. Steroids were classified into two categories according to potency and a pattern recognition method was applied to determine their relative ranking. The resulting structure-activity relationships obtained and the relative contributions of the various structural variables for both bioassays are discussed. A synergistic effect was predicted to be in operation between certain pairs of substituents