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

In silico identification and assessment of novel allosteric protein binding sites to expand the “druggable” human proteome

Ph. D. Thesis.Throughout the last years there has been a considerable number of drugs that were discovered thanks to computer aided drug design (CADD) techniques. Using the 3D information, such as protein structures obtained by X-ray crystallography or nuclear magnetic resonance (NMR), it is possible to identify the binding sites and to design molecules that may specifically target these sites. This approach saves a lot of time and money, as the lead search is more accurate: less compounds need to be synthesised and tested. Although a great number of proteins have been successfully targeted with this structure-based approach, there are a lot of disease-linked proteins that have been considered “undruggable” by conventional structure-based techniques. This is mainly due to failure in detection of potential binding sites, which precludes the structure-guided design of suitable ligands. There is the presumption that the “druggable” human proteome may be larger than previously expected. Protein structures may present multiple binding sites (allosteric and/or cryptic) that cannot be targeted by the means of conventional CADD techniques. In the past years, several novel methods have been developed to identify and/or unveil these binding hotspots. Amongst them cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterisation of allosteric and cryptic binding sites, which can be rendered “druggable” by small molecule ligands. Despite their conceptual simplicity and effectiveness, the analysis of cosolvent MD trajectories relies on pocket volume data, which requires a high level of manual investigation and may introduce a bias. The present study focused on the development of the novel cosolvent analysis toolkit (denoted as CAT), as an open-source, freely accessible analytical tool, suitable for automated analysis of cosolvent MD trajectories. CAT is compatible with popular molecular graphics software packages such as UCSF Chimera and VMD. Using a novel hybrid empirical force field scoring function, CAT accurately ranked the dynamic interactions between the macromolecular target and cosolvent molecular probes. Alongside the development of CAT, this work investigated the signal transducer activator of transcription 3 (STAT3) as the case study. STAT3 is among the most investigated oncogenic transcription factors, as it is highly associated with cancer initiation, progression, metastasis, chemoresistance, and immune evasion. Constitutive activation of STAT3 by mutations occurs frequently in tumour cells, and directly contributes to many malignant phenotypes. The evidence from both preclinical and clinical studies have demonstrated that STAT3 plays a critical role in several malignancies associated with poor prognosis such as glioblastoma and triple-negative breast cancer (TNBC), and STAT3 inhibitors have shown efficacy in inhibiting cancer growth and metastasis. Unfortunately, detailed structural biology studies on STAT3 as well as target-based drug discovery efforts have been hampered by difficulties in the expression and purification of the full length STAT3 and a lack of ligand-bound crystal structures. Considering these, computational methods offer an attractive strategy for the assessment of “druggability” of STAT3 dimers and allow investigations of reported activating and inhibiting STAT3 mutants at the atomistic level of detail. This work studied effects exerted by reported STAT3 mutations on the protein structure, dynamics, DNA binding and dimerisation, thus linking structure, dynamics, energetics, and the biological function. By employing a combination of equilibrium molecular dynamics (MD) and umbrella sampling (US) simulations to a series of human STAT3 dimers, which comprised wild-type protein and four mutations; the work presented herein explains the modulation of STAT3 activity by these mutations. The binding sites were mapped by the combination of MD simulations, molecular docking, and CAT analysis, and the binding mode of a clinical candidate napabucasin/BBI-608 at STAT3, which resembles the effect of D570K mutation, has been characterised. Collectively the results of this study demonstrate the robustness of the newly developed CAT methodology and its applicability in computational studies aiming at identification of protein “hotspots” in a wide range of protein targets, including the challenging ones. This work contributes to understanding the activation/inhibition mechanism of STAT3, and it explains the molecular mechanism of STAT3 inhibition by BBI-608. Alongside the characterisation of the BBI-608 binding mode, a novel binding site amenable to bind small molecule v ligands has been discovered in this work, which may pave the way to design novel STAT3 inhibitors and to suggest new strategies for pharmacological intervention to combat cancers associated with poor prognosis. It is expected that the results presented in this dissertation will contribute to an increase of the size of the potentially “druggable” human proteome

Thermodynamics and structure of methionine enkephalin using the statistical temperature molecular dynamics algorithm

Kim, Straub, and Keyes introduced the statistical temperature molecular dynamics (STMD) algorithm to overcome broken ergodicity by sampling a non­-Boltzmann flat energy histogram as noted in Kim, Straub, and Keyes, Phys. Rev. Lett. 97: 050601 (2007). Canonical averages are calculated via reweighting to the desired temperature. While STMD is promising, its application has been almost entirely to simple or model systems. In this dissertation the implementation of STMD into the biosimulation package CHARMM is used to simulate the methionine enkephalin pentamer peptide with a methione terminal cap in a droplet of CHARMM TIP3P water molecules. Chain thermodynamics is analyzed from the novel perspective of the statistical temperature as a function of potential energy, $TS(U), automatically generated by STMD. Both the minimum in the slope of$TS(U), and the peak in the heat capacity as a function of temperature, calculated via reweighting, indicate a collapse transition at Tθ ≈ 253K. Distributions of dihedral angles are obtained as a function of temperature. Rotamer regions found in the literature are reproduced, along with unique regions not found previously, including with advanced algorithms, indicating the power of STMD enhanced sampling

Computational Approaches To Anti-Toxin Therapies And Biomarker Identification

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data. Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification

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

Improved approaches to ligand growing through fragment docking and fragment-based library design

Die Fragment-basierte Wirkstoffforschung (“fragment-based drug discovery“ – FBDD) hat in den vergangenen zwei Jahrzehnten kontinuierlich an Beliebtheit gewonnen und sich zu einem dominanten Instrument der Erforschung neuer chemischer Moleküle als potentielle bioaktive Modulatoren entwickelt. FBDD ist eng mit Ansätzen zur Fragment-Erweiterung, wie etwa dem Fragment-„growing“, „merging“ oder dem „linking“, verknüpft. Diese Entwicklungsansätze können mit Hilfe von Computerprogrammen oder teilautomatischen Prozessen der „de novo“ Wirkstoffentwicklung beschleunigt werden. Obwohl Computer mühelos Millionen von Vorschlägen generieren können, geschieht dies allerdings oft auf Kosten unsicherer synthetischer Realisierbarkeit der Verbindungen mit einer potentiellen Sackgasse im Optimierungsprozess. Dieses Manuskript beschreibt die Entwicklung zweier computerbasierter Instrumente, PINGUI und SCUBIDOO, mit dem Ziel den FBDD Ausarbeitungs-Zyklus zu fördern. PINGUI ist ein halbautomatischer Arbeitsablauf zur Fragment-Erweiterung basierend auf der Proteinstruktur unter Berücksichtigung der synthetischen Umsetzbarkeit. SCUBIDOO ist eine freizugängliche Datenbank mit aktuell 21 Millionen verfügbaren virtuellen Produkten, entwickelt durch die Kombination kommerziell verfügbarer Bausteine („building blocks“) mit bewährten organischen Reaktionen. Zu jedem erzeugten virtuellen Produkt wird somit eine Synthesevorschrift geliefert. Die entscheidenden Funktionen von PINGUI, wie die Erzeugung abgeleiteter Bibliotheken oder das Anwenden organischer Reaktionen, wurden daraufhin in die SCUBIDOO Webseite integriert. PINGUI als auch SCUBIDOO wurden des Weiteren zur Erforschung Fragment-basierter Liganden („fragment-based ligand discovery“) mit dem β-2 adrenergen Rezeptor (β-2-AR) und der PIM1 Kinase als Zielproteine („targets“) eingesetzt. Im Rahmen einer ersten Studie zum β-2-AR wurden mit PINGUI acht unterschiedliche Erweiterungen für verschiedene Fragment-Treffer („hits“) vorhergesagt (ausgewählt?). Alle acht Verbindungen konnten dabei erfolgreich synthetisiert werden und vier der acht Produkte zeigten im Vergleich zu den Ausgangsfragmenten eine erhöhte Affinität zum target. Eine zweite Studie umfasste die Anwendung von SCUBIDOO zur schnellen Identifikation von Fragmenten und deren möglichen Erweiterungen mit potentieller Bindungsaktivität zur PIM-1 Kinase. Als Ergebnis ergab sich ein Fragment-Treffer mit der dazugehörigen Kristallstruktur. Weitere Folgeprodukte befinden sich derzeit in Synthese. Abschließend wurde SCUBIDOO an eine automatische Roboter- Synthese gekoppelt, wodurch hunderte von Verbindungen effizient parallel synthetisiert werden können. 127 der 240 vorhergesagten Produkte (53%) wurden mit dem Ziel an den β-2-AR zu binden bereits synthetisiert und werden in Kürze weitergehend getestet. Die beiden vorgestellten Computer-Tools könnten zur Verbesserung im Anfangsstadium befindlicher Projekte zur Fragment-basierten Wirkstoffentwicklung, vor allem hinsichtlich der Strategien im Bereich der Fragment Erweiterung, eingesetzt werden. PINGUI zum Beispiel generiert Vorschläge zur Fragment- Erweiterung, die sich mit hoher Wahrscheinlichkeit an die Zielstruktur anlagern, und stellt somit ein nützliches und kreatives Werkzeug zur Untersuchung von Struktur-Wirkungsbeziehungen („structure-activity relationship“ – SAR) dar. SCUBIDOO zeigte sich mit einem bisherigen 53-prozentigen Synthese-Erfolg als zugänglich für die Integration an die effiziente automatisierte Roboter-Synthese. Jede zukünftige Synthese liefert neue Kenntnisse innerhalb der Datenbank und wird somit nach und nach den Synthese-Erfolg erhöhen. Des Weiteren stellen alle synthetisierten Produkte neuartige Verbindungen dar, was umso mehr den möglichen Einfluss SCUBIDOOs bei der Entdeckung neuer chemischer Strukturen hervorhebt

Computational Investigations of Biomolecular Motions and Interactions in Genomic Maintenance and Regulation

The most critical biochemistry in an organism supports the central dogma of molecular biology: transcription of DNA to RNA and translation of RNA to peptide sequence. Proteins are then responsible for catalyzing, regulating and ensuring the fidelity of transcription and translation. At the heart of these processes lie selective biomolecular interactions and specific dynamics that are necessary for complex formation and catalytic activity. Through advanced biophysical and computational methods, it has become possible to probe these macromolecular dynamics and interactions at the molecular and atomic levels to tease out their underlying physical bases. To the end of a more thorough understanding of these physical bases, we have performed studies to probe the motions and interactions intrinsic to the function of biomolecular complexes: modeling the dual-base flipping strategy of alkylpurine glycosylase D, dynamically tracing evolution and epistasis in the 3-ketosteroid family of nuclear receptors, discovering the allosteric and conformational aspects of transcription regulation in liver receptor homologue 1, leveraging specific contacts in tyrosyl-DNA phosphodiesterase 2 for the development of novel inhibitor scaffolds, and detailing the experimentally observed connection between solvation and sequence-specific binding affinity in PU.1-DNA complexes at the atomic level. While each study seeks to solve system-specific problems, the collection outlines a general and broadly applicable description of the biophysical motivations of biochemical processes

Computational design and experimental characterization of metallopeptides as proteases for bioengineering applications

Enzymes are highly versatile catalysts present in biological systems and with high technological potential. Zinc metalloproteases are a major class of enzymes currently being employed in e.g. food, detergent, biopharmaceutical industries. In order to increase their robustness and range of biological and technological applications, metalloenzymes can be redesigned by exploring the chemical versatility of different metals along with protein sequence modifications. In this work, the computational design of new zinc metalloproteases was approached to test if proteolytic activity can be recapitulated in small scaffolds tailored for bioengineering applications. Structural and dynamical aspects of metalloproteases were first addressed to identify catalytically-relevant interactions. Models of the active site were developed to screen a set of 43 small scaffolds with the Rosetta software for their ability to recapitulate the native enzyme functionality. Two candidate scaffolds were selected for enzyme design and experimental characterization, namely the Sp1 zinc finger 2 and the villin headpiece subdomain. While metal coordination was achieved (binding constants KZnP,app in the 105 M-1 range), the scaffolds presented low stabilities (thermal unfolding bellow 50 °C) most likely due to perturbations introduced by the 4 to 10 sequence modifications. The metallopeptides presented catalytic activity towards ester substrates within the range of values found for other small scaffolds in the literature (second-order rate constants k2 in the 10-1 M-1s-1 range). The design approach developed in this work was successful in achieving catalytically-active metallopeptides, although target metalloprotease activity could not be achieved. Molecular dynamics simulations in microsecond regimes were subsequently used to detect design flaws related with high scaffold flexibility. This work contributes to the improvement of the computational enzyme design approaches by pointing out the need for a dynamical treatment of the designs in longer time-scales, and through the development of fast methods to rank and evaluate a large number of potential biocatalysts.Enzimas são versáteis catalisadoras presentes em sistemas biológicos e com elevado potencial tecnológico. Metaloproteases de zinco são uma classe de enzimas com aplicação corrente na indústria e.g. alimentar, biofarmacêutica e detergentes. A versatilidade química de diferentes metais combinada com modificações na sequência de metaloenzimas podem ser exploradas de forma a aumentar a sua robustez e leque de aplicações biológicas e tecnológicas, Neste trabalho, novos metaloproteases de zinco foram desenhados computacionalmente de forma a testar se atividade proteolítica pode ser reproduzida em proteínas pequenas adaptadas para bioengenharia. Análise de aspetos estruturais/dinâmicos de metaloproteases permitiram identificar interações cataliticamente relevantes. Modelos do centro ativo de zinco foram desenvolvidos para examinar com o software Rosetta um conjunto de 43 pequenas estruturas quanto ao seu potencial em recapitular a função nativa de enzimas. Duas estruturas foram selecionadas para design e caracterização experimental, nomeadamente o “zinc-finger” 2 da proteína Sp1 e o subdomínio cabeça da vilina. Embora coordenação com o metal tenha sido alcançada (constantes de afinidade KZnP,app na ordem 105 M-1), as estruturas apresentam baixa estabilidade (temperatura de desnaturação inferior a 50 °C), refletindo perturbações provavelmente causadas por 4-10 modificações de sequência. Os metalopéptidos apresentam actividade catalítica para ésteres semelhante aos valores de literatura obtidos para outras pequenas estruturas (constantes de segunda ordem k2 na ordem 10-1 M-1s-1). A metodologia desenvolvida neste trabalho foi bem sucedida em desenhar metalopéptidos catalíticos, embora a atividade alvo de metaloprotease não tenha sido alcançada. Simulações de dinâmica molecular na escala de microsegundo foram usadas posteriormente para detectar falhas nos designs relacionadas com elevada flexibilidade estrutural. Este trabalho contribui para o melhoramento de métodos de design computacional de enzimas, ao demonstrar a necessidade de considerar aspectos dinâmicos dos designs em escalas de tempo maiores, e no desenvolvimento de métodos rápidos para classificar e avaliar um vasto leque de potenciais biocatalisadores