In Silico Optimisation Of Domain Antibodies Against HSP16.3 From Mycobacterium Tuberculosis

Heat shock protein 16.3 (HSP16.3) from Mycobacterium tuberculosis (Mtb) is critical for its survival during latent infection in human, thus making it an attractive target for developing diagnostic and therapeutic strategies. The predicted structure of HSP16.3 was docked against a known HSP hydrophobic probe, namely 4,4′-dianilino-1,1′-binaphthyl-5,5′-disulfonic acid (bisANS) and to the comparative models of HSP16.3 specific single domain antibodies (sdAbs), clone E3 and F1. The binding interactions were further elucidated by free energy calculations. The non-polar interactions were identified as the main force for antigen-antibody association

Structure and mechanism of monoclonal antibody binding to the junctional epitope of Plasmodium falciparum circumsporozoite protein.

Lasting protection has long been a goal for malaria vaccines. The major surface antigen on Plasmodium falciparum sporozoites, the circumsporozoite protein (PfCSP), has been an attractive target for vaccine development and most protective antibodies studied to date interact with the central NANP repeat region of PfCSP. However, it remains unclear what structural and functional characteristics correlate with better protection by one antibody over another. Binding to the junctional region between the N-terminal domain and central NANP repeats has been proposed to result in superior protection: this region initiates with the only NPDP sequence followed immediately by NANP. Here, we isolated antibodies in Kymab mice immunized with full-length recombinant PfCSP and two protective antibodies were selected for further study with reactivity against the junctional region. X-ray and EM structures of two monoclonal antibodies, mAb667 and mAb668, shed light on their differential affinity and specificity for the junctional region. Importantly, these antibodies also bind to the NANP repeat region with equal or better affinity. A comparison with an NANP-only binding antibody (mAb317) revealed roughly similar but statistically distinct levels of protection against sporozoite challenge in mouse liver burden models, suggesting that junctional antibody protection might relate to the ability to also cross-react with the NANP repeat region. Our findings indicate that additional efforts are necessary to isolate a true junctional antibody with no or much reduced affinity to the NANP region to elucidate the role of the junctional epitope in protection

Using advanced computational methods to model the binding of antibody complexes: a case study from the coagulation cascade

Haemophilia A is a congenital bleeding disorder affecting one in 5,000 to 10,000 males. To prevent symptomatic disease, injections of recombinant factor VIII (FVIII) are administered to compensate for insufficient levels of this essential clotting factor. Patients suffering from a severe form of haemophilia A are at increased risk of forming neutralising antibodies — known as inhibitors — against therapeutic FVIII. A better understanding of the binding characteristics of inhibitors may aid the selection of optimal haemophilia A therapies, lead to the development of new therapeutics that are less antigenic, and support future initiatives in personalised and precision medicine. With this goal in mind, Classical Molecular Dynamics (CMD) in conjunction with Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) free energy calculations, together with enhanced sampling techniques, have been used to investigate interactions and the dynamics of binding site residues of the human inhibitory antibody BO2C11 bound to the C2-domain of factor VIII. In parallel, recombinant bacterial expressions of the C2-domain were initiated with the aim to explore structural changes induced by mutations that abrogate binding as described previously in surface plasmon resonance experiments. Computational binding affinity predictions were generally shown to be in good agreement with experimental findings. Additionally, binding site dynamics were investigated in detail using customized visualization techniques and an interpretable machine learning approach. Nevertheless, CMD simulations were insufficient for gaining insights into structural changes induced by mutations that were determined experimentally to be non-binding, and for exploring the underlying differences between the bound and unbound structures of the FVIII-C2 domain. To this end, Accelerated Molecular Dynamics (AMD) and Umbrella Sampling (US) simulations proved to be appropriate additions to investigate the conformational changes and energetic differences associated with the binding of BO2C11

Predicting multibody assembly of proteins

textThis thesis addresses the multi-body assembly (MBA) problem in the context of protein assemblies. [...] In this thesis, we chose the protein assembly domain because accurate and reliable computational modeling, simulation and prediction of such assemblies would clearly accelerate discoveries in understanding of the complexities of metabolic pathways, identifying the molecular basis for normal health and diseases, and in the designing of new drugs and other therapeutics. [...] [We developed] F²Dock (Fast Fourier Docking) which includes a multi-term function which includes both a statistical thermodynamic approximation of molecular free energy as well as several of knowledge-based terms. Parameters of the scoring model were learned based on a large set of positive/negative examples, and when tested on 176 protein complexes of various types, showed excellent accuracy in ranking correct configurations higher (F² Dock ranks the correcti solution as the top ranked one in 22/176 cases, which is better than other unsupervised prediction software on the same benchmark). Most of the protein-protein interaction scoring terms can be expressed as integrals over the occupied volume, boundary, or a set of discrete points (atom locations), of distance dependent decaying kernels. We developed a dynamic adaptive grid (DAG) data structure which computes smooth surface and volumetric representations of a protein complex in O(m log m) time, where m is the number of atoms assuming that the smallest feature size h is [theta](r[subscript max]) where r[subscript max] is the radius of the largest atom; updates in O(log m) time; and uses O(m)memory. We also developed the dynamic packing grids (DPG) data structure which supports quasi-constant time updates (O(log w)) and spherical neighborhood queries (O(log log w)), where w is the word-size in the RAM. DPG and DAG together results in O(k) time approximation of scoring terms where k << m is the size of the contact region between proteins. [...] [W]e consider the symmetric spherical shell assembly case, where multiple copies of identical proteins tile the surface of a sphere. Though this is a restricted subclass of MBA, it is an important one since it would accelerate development of drugs and antibodies to prevent viruses from forming capsids, which have such spherical symmetry in nature. We proved that it is possible to characterize the space of possible symmetric spherical layouts using a small number of representative local arrangements (called tiles), and their global configurations (tiling). We further show that the tilings, and the mapping of proteins to tilings on arbitrary sized shells is parameterized by 3 discrete parameters and 6 continuous degrees of freedom; and the 3 discrete DOF can be restricted to a constant number of cases if the size of the shell is known (in terms of the number of protein n). We also consider the case where a coarse model of the whole complex of proteins are available. We show that even when such coarse models do not show atomic positions, they can be sufficient to identify a general location for each protein and its neighbors, and thereby restricts the configurational space. We developed an iterative refinement search protocol that leverages such multi-resolution structural data to predict accurate high resolution model of protein complexes, and successfully applied the protocol to model gp120, a protein on the spike of HIV and currently the most feasible target for anti-HIV drug design.Computer Science

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Funding Information: This research was funded by Fundação para a Ciência e Tecnologia (FCT) Portugal, grant number UIDB/50006/2020 (LAQV-REQUIMTE), UIDP/04378/2020 and UIDB/04378/2020 (UCIBIO) and LA/P/0140/2020 (i4HB), the European Commission GLYCOTwinning (GA 101079417), the EJPRD ProDGNE (EJPRD/0001/2020 EU 825575) and SI I&DT, DCMatters (AVISO Nº 17/SI/2019) REF 47212. F.P. gratefully acknowledges FCT for an Assistant Research Position (CEECIND/01649/2021). Publisher Copyright: © 2023 by the authors.Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein–protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.publishersversionpublishe

Computational methodologies applied to Protein-Protein Interactions for molecular insights in Medicinal Chemistry

In living systems, proteins usually team up into \u201cmolecular machinery\u201d implementing several protein-to-protein physical contacts \u2013 or protein-protein interactions (PPIs) \u2013 to exert biological effects at both cellular and systems levels. Deregulations of protein-protein contacts have been associated with a huge number of diseases in a wide range of medical areas, such as oncology, cancer immunotherapy, infectious diseases, neurological disorders, heart failure, inflammation and oxidative stress. PPIs are very complex and usually characterised by specific shape, size and complementarity. The protein interfaces are generally large, broad and shallow, and frequently protein-protein contacts are established between non-continuous epitopes, that conversely are dislocated across the protein interfaces. For this reason, in the past two decades, PPIs were thought to be \u201cundruggable\u201d targets by the scientific research community with scarce or no chance of success. However, in recent years the Medicinal Chemistry frontiers have been changing and PPIs have gained popularity amongst the research groups due to their key roles in such a huge number of diseases. Until recently, PPIs were determined by experimental evidence through techniques specifically developed to target a small group of interactions. However, these methods present several limitations in terms of high costs and labour- and time-wasting. Nowadays, a large number of computational methods have been successfully applied to evaluate, validate, and deeply analyse the experimentally determined protein interactomes. In this context, a high number of computational tools and techniques have been developed, such as methods designed to construct interaction databases, quantum mechanics and molecular mechanics (QM/MM) to study the electronic properties, simulate chemical reactions, and calculate spectra, and all-atom molecular dynamics simulations to simulate temporal and spatial scales of inter- and intramolecular interactions. These techniques have allowed to explore PPI networks and predict the related functional features. In this PhD work, an extensive use of computational techniques has been reported as valuable tool to explore protein-protein interfaces, identify their hot spot residues, select small molecules and design peptides with the aim of inhibiting six different studied PPIs. Indeed, in this thesis, a success story of in silico approaches to PPI study has been described, where MD simulations, docking and pharmacophore screenings led to the identification of a set of PPI modulators. Among these, two molecules, RIM430 and RIM442, registered good inhibitory activity with IC50 values even within the nanomolar range against the interaction between MUC1 and CIN85 proteins in cancer disease. Furthermore, computational alanine scanning, all-atom molecular dynamics simulations, docking and pharmacophore screening were exploited to (1) rationally predict three potential interaction models of NLRP3PYD-ASCPYD complex involved in inflammatory and autoimmune diseases; (2) identify a potentially druggable region on the surface of SARS-CoV-2 Spike protein interface and select putative inhibitors of the interaction between Spike protein and the host ACE2 receptor against COVID-19 (CoronaVIrus Disease 2019); (3) investigate intramolecular modifications as a consequence of a point mutation on C3b protein (R102G) associated with the age-related macular degeneration (AMD) disease; (4) design non-standard peptides to inhibit transcriptional events associated with HOX-PBX complex involved in cancer diseases; and (5) to optimise a patented peptide sequence by designing helix-shaped peptides embedded with the hydrogen bond surrogate approach to tackle cocaine abuse relapses associated with Ras-RasGRF1 interaction. Although all the herein exploited techniques are based on predictive calculations and need experimental evidence to confirm the findings, the results and molecular insights retrieved and collected show the potential of the computer-aided drug design applied to the Medicinal Chemistry, guaranteeing labour- and time-saving to the research groups. On the other hand, computing ability, improved algorithms and fast-growing data sets are rapidly fostering advances in multiscale molecular modelling, providing a powerful emerging paradigm for drug discovery. It means that more and more research efforts will be done to invest in novel and more precise computational techniques and fine-tune the currently employed methodologies

Antigenic Peptide Prediction From E6 and E7 Oncoproteins of HPV Types 16 and 18 for Therapeutic Vaccine Design Using Immunoinformatics and MD Simulation Analysis

Human papillomavirus (HPV) induced cervical cancer is the second most common cause of death, after breast cancer, in females. Three prophylactic vaccines by Merck Sharp &amp; Dohme (MSD) and GlaxoSmithKline (GSK) have been confirmed to prevent high-risk HPV strains but these vaccines have been shown to be effective only in girls who have not been exposed to HPV previously. The constitutively expressed HPV oncoproteins E6 and E7 are usually used as target antigens for HPV therapeutic vaccines. These early (E) proteins are involved, for example, in maintaining the malignant phenotype of the cells. In this study, we predicted antigenic peptides of HPV types 16 and 18, encoded by E6 and E7 genes, using an immunoinformatics approach. To further evaluate the immunogenic potential of the predicted peptides, we studied their ability to bind to class I major histocompatibility complex (MHC-I) molecules in a computational docking study that was supported by molecular dynamics (MD) simulations and estimation of the free energies of binding of the peptides at the MHC-I binding cleft. Some of the predicted peptides exhibited comparable binding free energies and/or pattern of binding to experimentally verified MHC-I-binding epitopes that we used as references in MD simulations. Such peptides with good predicted affinity may serve as candidate epitopes for the development of therapeutic HPV peptide vaccines

Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs

Computational design of the affinity and specificity of a therapeutic T cell receptor

T cell receptors (TCRs) are key to antigen-specific immunity and are increasingly being explored as therapeutics, most visibly in cancer immunotherapy. As TCRs typically possess only low-to-moderate affinity for their peptide/MHC (pMHC) ligands, there is a recognized need to develop affinity-enhanced TCR variants. Previous in vitro engineering efforts have yielded remarkable improvements in TCR affinity, yet concerns exist about the maintenance of peptide specificity and the biological impacts of ultra-high affinity. As opposed to in vitro engineering, computational design can directly address these issues, in theory permitting the rational control of peptide specificity together with relatively controlled increments in affinity. Here we explored the efficacy of computational design with the clinically relevant TCR DMF5, which recognizes nonameric and decameric epitopes from the melanoma-associated Melan-A/MART-1 protein presented by the class I MHC HLA-A2. We tested multiple mutations selected by flexible and rigid modeling protocols, assessed impacts on affinity and specificity, and utilized the data to examine and improve algorithmic performance. We identified multiple mutations that improved binding affinity, and characterized the structure, affinity, and binding kinetics of a previously reported double mutant that exhibits an impressive 400-fold affinity improvement for the decameric pMHC ligand without detectable binding to non-cognate ligands. The structure of this high affinity mutant indicated very little conformational consequences and emphasized the high fidelity of our modeling procedure. Overall, our work showcases the capability of computational design to generate TCRs with improved pMHC affinities while explicitly accounting for peptide specificity, as well as its potential for generating TCRs with customized antigen targeting capabilities

Molecular dynamics simulations of binding, unfolding, and global conformational changes of signaling and adhesion molecules

Molecular dynamics (MD) simulations were used to investigate the structural basis for the functions of three proteins: Fc(gamma) receptor III (CD16), von Willebrand factor (VWF), and integrin. CD16, a heavily glycosylated protein expressed on human immune cells, plays a crucial role in immune defense by linking antibody-antigen complexes with cellular effector functions. Glycosylation of CD16 decreases its affinity for IgG. MD simulations were run for CD16-IgG Fc complexes with or without an N-glycan on CD16. The two simulated complexes show different conformations. Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) approach was used to calculate the binding free energy of the CD16-IgG Fc complexes. The calculated binding free energy helped to identify critical residues. VWF, a multimeric multidomain glycoprotein, initiates platelet adhesion at the sites of vascular injury. A specific VWF metalloprotease, A Disintegrin And Metalloprotease with ThromboSpondin motifs member 13 (ADAMTS-13), cleaves the Tyr1605-Met1606 bond in the VWF A2 domain to generate the full spectrum of plasma VWF species. Shear stress or denaturants assist VWF cleavage by ADAMTS-13 due to the unfolding of A2. MD was used to simulate the unfolding processes of A2 under force or high temperature. The beta-strands of A2 were pulled out sequentially by force, during which the cleavage site changed in steps from the fully buried state to the fully exposed state. Thermal unfolding follows a very different pathway. Integrins are adhesion molecules mediating cell-cell, cell-extracellular matrix, and cell-pathogen interactions. Experiments suggest that integrins can undergo a large-scale change from a bent to an extended conformation, associating with a transition from low to high affinity states, i.e., integrin activation. Steered MD was utilized to simulate the bent-to-extended conformational transition in time of aVb3 integrin. The integrin was observed to change smoothly from the bent to the extended conformation. One major energy barrier was overcome, corresponding to the disruption of the interactions at Hybrid/EGF4/bTD interfaces. A partially extended conformation tends to bend back while a fully extended conformation is stabilized by the coordination of Asp457 with Ca2+ at alpha-genu. Unbending with separated legs overcomes more energy barriers.Ph.D.Committee Chair: Zhu, Cheng; Committee Member: Harvey, Stephen; Committee Member: Hud, Nicholas; Committee Member: Zamir, Evan; Committee Member: Zhu, Tin