DINC: a new AutoDock-based protocol for docking large ligands

Restoring adapter protein complex 4 function with small molecules: an in silico approach to spastic paraplegia 50

This study focuses on spastic paraplegia type 50 (SPG50), an adapter protein complex 4 deficiency syndrome caused by mutations in the adapter protein complex 4 subunit mu-1 (AP4M1) gene, and on the downstream alterations of the AP4M1 protein. We applied a battery of heterogeneous computational resources, encompassing two in-house tools described here for the first time, to (a) assess the druggability potential of AP4M1, (b) characterize SPG50-associated mutations and their 3D scenario, (c) identify mutation-tailored drug candidates for SPG50, and (d) elucidate their mechanisms of action by means of structural considerations on homology models of the adapter protein complex 4 core. Altogether, the collected results indicate R367Q as the mutation with the most promising potential of being corrected by small-molecule drugs, and the flavonoid rutin as best candidate for this purpose. Rutin shows promise in rescuing the interaction between the AP4M1 and adapter protein complex subunit beta-1 (AP4B1) subunits by means of a glue-like mode of action. Overall, this approach offers a framework that could be systematically applied to the investigation of mutation-wise molecular mechanisms in different hereditary spastic paraplegias, too.

Noncovalent binding of carbofuran to acetylcholinesterase from Homo sapiens, Danio rerio, Apis mellifera and Caenorhabditis elegans: Homology modelling, molecular docking and dynamics, and quantum biochemistry description

Although various regulatory agencies have banned or severely restricted the use of carbofuran (CAR), recent reports indicate the presence of CAR residues in both cultivated and wild areas. This pesticide is a potent inhibitor of acetylcholinesterase (AChE), which acts by preventing the hydrolysis of acetylcholine (ACh). Given the critical role of AChE::ACh in the proper functioning of the nervous system, we thought it appropriate to investigate the binding of CAR to AChEs from Homo sapiens, Danio rerio, Apis mellifera, and Caenorhabditis elegans using homology modelling, molecular docking, molecular dynamics, and quantum biochemistry. Molecular docking and dynamics results indicated peculiar structural behavior in each AChE::CAR system. Quantum biochemistry results showed similar affinities for all complexes, confirming the description of carbofuran as a broad-spectrum pesticide, and have a limited correlation with IC50 values. We found the following decreasing affinity order of AChE species: H. sapiens > A. mellifera > C. elegans > D. rerio. The computational results suggest that CAR occupies different pockets in the AChEs studied. In addition, our results showed that CAR binds to hsAChE and ceAChE in a very similar manner: it has high affinities for the same subsites in both species and forms hydrogen bonds with residues (hsTYR124 and ceTRP107) occupying homologous positions in the peripheral site. This suggests that this nematode is a potential model to evaluate the toxicity of carbamates, even though the sequence identity between them is only 41 %. Interestingly, we also observed that the catalytic histidines of drAChE and amAChE exhibited favorable contacts with carbofuran, suggesting that the non-covalent binding of carbofuran to these proteins may promote faster carbamylation rates than the binding modes to human and worm acetylcholinesterases. Our computational results provide a better understanding of the binding mechanisms in these complexes, as well as new insights into the mechanism of carbamylation.

Investigating In silico and In vitro Therapeutic Potential of Diosmetin as the Anti-Parkinson Agent

AIM

This study aimed to investigate how diosmetin interacts with seven target receptors associated with oxidative stress (OS) and validate its antioxidant properties for the potential management of Parkinson's disease (PD).

BACKGROUND

In PD, the degeneration of dopaminergic cells is strongly influenced by OS. This stressor is intricately connected to various mechanisms involved in neurodegeneration, such as mitochondrial dysfunction, neuroinflammation, and excitotoxicity induced by nitric oxide.

OBJECTIVE

The aim of this research was to establish a molecular connection between diosmetin and OS-associated target receptors was the goal, and it investigated how this interaction can lessen PD.

METHODS

Seven molecular targets - Adenosine A2A (AA2A), Peroxisome Proliferator-Activated Receptor Gamma (PPARγ), Protein Kinase AKT1, Nucleolar Receptor NURR1, Liver - X Receptor Beta (LXRβ), Monoamine Oxidase - B (MAO-B) and Tropomyosin receptor kinase B (TrkB) were obtained from RCSB. Molecular docking software was employed to determine molecular interactions, while antioxidant activity was assessed through in vitro assays against various free radicals.

RESULTS

Diosmetin exhibited interactions with all seven target receptors at their binding sites. Notably, it showed superior interaction with AA2A and NURR1 compared to native ligands, with binding energies of -7.55, and -6.34 kcal/mol, respectively. Additionally, significant interactions were observed with PPARγ, AKT1, LXRβ, MAO-B, and TrkB with binding energies of -8.34, -5.42, -7.66, -8.82, -8.45 kcal/mol, respectively. Diosmetin also demonstrated antioxidant activity against various free radicals, particularly against hypochlorous acid (HOCl) and nitric oxide (NO) free radicals.

CONCLUSION

Diosmetin possibly acts on several target receptors linked to the pathophysiology of PD, demonstrating promise as an OS inhibitor and scavenger.

Design of Tetra-Peptide Ligands of Antibody Fc Regions Using In Silico Combinatorial Library Screening

Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries' display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing.

Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management

Molecular docking is a computational technique that predicts the binding affinity of ligands to receptor proteins. Although it has potential uses in nutraceutical research, it has developed into a formidable tool for drug development. Bioactive substances called nutraceuticals are present in food sources and can be used in the management of diseases. Finding their molecular targets can help in the creation of disease-specific new therapies. The purpose of this review was to explore molecular docking's application to the study of dietary supplements and disease management. First, an overview of the fundamentals of molecular docking and the various software tools available for docking was presented. The limitations and difficulties of using molecular docking in nutraceutical research are also covered, including the reliability of scoring functions and the requirement for experimental validation. Additionally, there was a focus on the identification of molecular targets for nutraceuticals in numerous disease models, including those for sickle cell disease, cancer, cardiovascular, gut, reproductive, and neurodegenerative disorders. We further highlighted biochemistry pathways and models from recent studies that have revealed molecular mechanisms to pinpoint new nutraceuticals' effects on disease pathogenesis. It is convincingly true that molecular docking is a useful tool for identifying the molecular targets of nutraceuticals in the management of diseases. It may offer information about how nutraceuticals work and support the creation of new therapeutics. Therefore, molecular docking has a bright future in nutraceutical research and has a lot of potentials to lead to the creation of brand-new medicines for the treatment of disease.

Synthesis, In Silico Studies, and In Vitro Anti-Inflammatory Activity of Novel Imidazole Derivatives Targeting p38 MAP Kinase

A series of eight novel N-substituted [4-(trifluoro methyl)-1H-imidazole-1-yl] amide derivatives (AA1-AA8) were synthesized, characterized, and evaluated for their in vitro p38 MAP kinase anti-inflammatory inhibitory activity. The synthesized compounds were obtained by coupling [4-(trifluoromethyl)-1H-imidazole-1-yl] acetic acid with 2-amino-N-(Substituted)-3-phenylpropanamide derivatives utilizing 1-[bis(dimethylamino)methylene]-1H-1,2,3-triazolo[4,5-b] pyridinium 3-oxide hexafluorophosphate as a coupling agent. Various spectroscopic methods established and confirmed their structures, specifically, ¹H NMR, ¹³C NMR, Fourier transform infrared (FTIR), and mass spectrometry. In order to emphasize the binding site of the p38 MAP kinase protein and newly synthesized compounds, molecular docking studies were carried out. In the series, compound AA6 had the highest docking score of 7.83 kcal/mol. The ADME studies were performed using web software. Studies revealed that all the synthesized compounds were orally active and showed good gastrointestinal absorption within the acceptable range. Lipinski's "rule of five" was used to determine drug-likeness. The synthesized compounds were screened for their anti-inflammatory activity by performing an albumin denaturation assay in which five compounds (AA2, AA3, AA4, AA5, and AA6) were found to exhibit substantial activity. Hence, these were further selected and proceeded for the evaluation of p38 MAP kinase inhibitory activity. The compound AA6 possesses considerable p38 kinase inhibitory anti-inflammatory activity with an IC₅₀ value of 403.57 ± 6.35 nM compared to the prototype drug adezmapimod (SB203580) with an IC₅₀ value of 222.44 ± 5.98 nM. Some further structural modifications in compound AA6 could contribute to the development of new p38 MAP kinase inhibitors with an improved IC₅₀ value.

Drug and Anti-Viral Peptide Design to Inhibit the Monkeypox Virus by Restricting A36R Protein

Most recently, monkeypox virus (MPXV) has emanated as a global public health threat. Unavailability of effective medicament against MPXV escalates demand for new therapeutic agent. In this study, in silico strategies were conducted to identify novel drug against the A36R protein of MPXV. The A36R protein of MPXV is responsible for the viral migration, adhesion, and vesicle trafficking to the host cell. To block the A36R protein, 4893 potential antiviral peptides (AVPs) were retrieved from DRAMP and SATPdb databases. Finally, 57 sequences were screened based on peptide filtering criteria, which were then modeled. Likewise, 31 monkeypox virus A36R protein sequences were collected from NCBI protein database to find consensus sequence and to predict 3D protein model. The refined and validated models of the A36R protein and AVP peptides were used to predict receptor-ligand interactions using DINC 2 server. Three peptides that showed best interactions were SATPdb10193, SATPdb21850, and SATPdb26811 with binding energies -6.10, -6.10, and -6.30 kcal/mol, respectively. Small molecules from drug databases were also used to perform virtual screening against the A36R protein. Among databases, Enamine-HTSC showed strong affinity with docking scores ranging from -8.8 to 9.8 kcal/mol. Interaction of target protein A36R with the top 3 peptides and the most probable drug (Z55287118) examined by molecular dynamic (MD) simulation. Trajectory analyses (RMSD, RMSF, SASA, and Rg) confirmed the stable nature of protein-ligand and protein-peptide complexes. This work suggests that identified top AVPs and small molecules might interfere with the function of the A36R protein of MPXV.

Structural Prediction of Peptide-MHC Binding Modes

The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8⁺ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.

A novel framework integrating AI model and enzymological experiments promotes identification of SARS-CoV-2 3CL protease inhibitors and activity-based probe

The identification of protein-ligand interaction plays a key role in biochemical research and drug discovery. Although deep learning has recently shown great promise in discovering new drugs, there remains a gap between deep learning-based and experimental approaches. Here, we propose a novel framework, named AIMEE, integrating AI model and enzymological experiments, to identify inhibitors against 3CL protease of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2), which has taken a significant toll on people across the globe. From a bioactive chemical library, we have conducted two rounds of experiments and identified six novel inhibitors with a hit rate of 29.41%, and four of them showed an IC50 value <3 μM. Moreover, we explored the interpretability of the central model in AIMEE, mapping the deep learning extracted features to the domain knowledge of chemical properties. Based on this knowledge, a commercially available compound was selected and was proven to be an activity-based probe of 3CLpro. This work highlights the great potential of combining deep learning models and biochemical experiments for intelligent iteration and for expanding the boundaries of drug discovery. The code and data are available at https://github.com/SIAT-code/AIMEE.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Source and exploration of the peptides used to construct peptide-drug conjugates

Peptide-drug conjugates (PDCs) are a class of novel molecules widely designed and synthesized for delivering payload drugs. The peptide part plays a vital role in the whole molecule, because they determine the ability of the molecules to penetrate the membrane and target to the specific targets. Here, we introduce the source of different kinds of cell-penetrating peptides (CPPs) and cell-targeting peptides (CTPs) that have been used or could be used in constructing PDCs as well as their latest application in delivering drugs. What's more, the approaches of developing CPPs and CTPs and the techniques to discover novel peptides are focused on and summarized in the review. This review aims to help relevant researchers fast understand the research status of peptides in PDCs and carry forward the process of novel peptides discovery.

Discovery of new targeting agents against GAPDH receptor for antituberculosis drug delivery

OBJECTIVES

Tuberculosis (TB) remains a public health concern due to the emergence and evolution of multidrug-resistant strains. To overcome this issue, reinforcing the effectiveness of first line antituberculosis agents using targeted drug delivery approach is an option. Glyceraldehyde-3-Phosphate Dehydrogenase (GADPH), a common virulence factor found in the pathogenic microorganisms has recently been discovered on the cell-surface of Mycobacterium tuberculosis, allowing it to be used as a drug target for TB. This study aims to discover active small molecule(s) that target GAPDH and eventually enhance the delivery of antituberculosis drugs.

METHODS

Ten ligands with reported in vitro and/or in vivo activities against GAPDH were evaluated for their binding interactions through molecular docking studies using AutoDock 4.2 program. The ligand with the best binding energy was then modified to produce 10 derivatives, which were redocked against GAPDH using previous protocols. BIOVIA Discovery Studio Visualizer 2019 was used to explore the ligand-receptor interactions between the derivatives and GAPDH.

RESULTS

Among the 10 ligands, curcumin, koningic acid and folic acid showed the best binding energies. Further analysis on the docking of two folic acid derivatives, F7 (γ-{[tert-butyl-N-(6-aminohexyl)]carbamate}folic acid) and F8 (folic acid N-hydroxysuccinimide ester) showed that the addition of a bulky substituent at the carboxyl group of the glutamic acid subcomponent resulted in improved binding energy.

CONCLUSIONS

Folic acid and the two derivatives F7 and F8 have huge potentials to be developed as targeting agents against the GAPDH receptor. Further study is currently on-going to evaluate the effectiveness of these molecules in vitro.

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search

AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening.

HLA-Arena: A Customizable Environment for the Structural Modeling and Analysis of Peptide-HLA Complexes for Cancer Immunotherapy

PURPOSE

HLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunotherapy projects. Unfortunately, this kind of analysis is limited by the small number of experimentally determined structures of peptide-HLA complexes. Overcoming this limitation requires developing novel computational methods to model and analyze peptide-HLA structures.

METHODS

Here we describe a new platform for the structural modeling and analysis of peptide-HLA complexes, called HLA-Arena, which we have implemented using Jupyter Notebook and Docker. It is a customizable environment that facilitates the use of computational tools, such as APE-Gen and DINC, which we have previously applied to peptide-HLA complexes. By integrating other commonly used tools, such as MODELLER and MHCflurry, this environment includes support for diverse tasks in structural modeling, analysis, and visualization.

RESULTS

To illustrate the capabilities of HLA-Arena, we describe 3 example workflows applied to peptide-HLA complexes. Leveraging the strengths of our tools, DINC and APE-Gen, the first 2 workflows show how to perform geometry prediction for peptide-HLA complexes and structure-based binding prediction, respectively. The third workflow presents an example of large-scale virtual screening of peptides for multiple HLA alleles.

CONCLUSION

These workflows illustrate the potential benefits of HLA-Arena for the structural modeling and analysis of peptide-HLA complexes. Because HLA-Arena can easily be integrated within larger computational pipelines, we expect its potential impact to vastly increase. For instance, it could be used to conduct structural analyses for personalized cancer immunotherapy, neoantigen discovery, or vaccine development.

In silico vaccine design against Chlamydia trachomatis infection

Chlamydia trachomatis, a gram-negative bacterium known to infect the genital sites mainly columnar epithelial cells of the cervix, urethra and rectum in women and causes acute epididymitis, urinary tract inflammation and DNA damage to the sperms in men, hence considered to be one of the major sexually transmitted infections. The infection is asymptomatic in many people and remains untreated leading to serious health complications, including pelvic inflammatory disease, ectopic pregnancy and infertility. The current treatment options include antibiotics, but the pathogen has gained resistance against many antibiotics. The present work involves an in silico reverse vaccinology approach for identifying the immunogens as vaccine candidates that can be effective against reinfections and should be capable of inducing long-term protective immunity against Chlamydial infections. This study identifies the putative vaccine candidates that are membrane bound with high antigenicity properties; antigenicity induces the immunogenicity which involves identification of T-cell and B-cell epitopes that induce both humoral and cell-mediated immunity. The epitopes 'LSWEMELAY', 'LSNTEGYRY', 'TSDLGQMEY', 'FIDLLQAIY' and 'FSNNFSDIY' were predicted as core sequences for class I MHC molecules. The identified epitopes showed promising ability to interact with the human leukocyte antigens (HLA). These epitopes showed maximum population coverage with epitope conservancy above 80%. Molecular docking was performed to test the binding affinities of the identified epitopes with the HLA molecule to study the binding cleft interactions. The vaccine candidate thus identified from this study showed to possess the potential to activate the B- and T-cell immune responses which are more specific and make the body stronger against infections and effective for reinfections.

Antimicrobial peptide Temporin-L complexed with anionic cyclodextrins results in a potent and safe agent against sessile bacteria

Concern over antibiotic resistance is growing, and new classes of antibiotics, particularly against Gram-negative bacteria, are needed. Antimicrobial peptides (AMPs) have been proposed as a new class of clinically useful antimicrobials. Special attention has been devoted to frog-skin temporins. In particular, temporin L (TL) is strongly active against Gram-positive, Gram-negative bacteria and yeast strains. With the aim of overcoming some of the main drawbacks preventing the widespread clinical use of this peptide, i.e. toxicity and unfavorable pharmacokinetics profile, we designed new formulations combining TL with different types of cyclodextrins (CDs). TL was associated to a panel of neutral or negatively charged, monomeric and polymeric CDs. The impact of CDs association on TL solubility, as well as the transport through bacterial alginates was assessed. The biocompatibility on human cells together with the antimicrobial and antibiofilm properties of TL/CD systems was explored.

Novel Benzo[B]Furans with Anti-Microtubule Activity Upregulate Expression of Apoptotic Genes and Arrest Leukemia Cells in G2/M Phase

BACKGROUND

Novel derivatives of benzo[b]furan were found to be highly toxic towards human chronic myelogenous (K562), acute myelogenous (HL-60) and acute lymphoblastic (MOLT-4) leukemia cells.

OBJECTIVE

The objective was the characterization of the biological activity of novel benzofurans (influence on apoptosis, mitogen-activated protein kinases and on the cell cycle). Cellular protein(s) targeted by test benzofurans and mechanism of action were identified.

METHODS

The methods utilized in the study were chemical synthesis, fluorescence assays, flow cytometry, gene expression by DNA microarray and real-time RT-PCR, western blotting, cytotoxicity assays, pull-down assay, mass spectroscopy, in vitro polymerization of tubulin, molecular docking.

RESULTS

1,1'-[3-(bromomethyl)-5,6- dimethoxy-1-benzofuran-2,7-diyldiethanone (1) and methyl 4-bromo-6- (dibromoacetyl)-5-hydroxy-2-methyl-1-benzofuran-3-carboxylate (2) induced apoptosis in K562 and MOLT-4 cells. The profiling of gene expression revealed that 1 and 2 increased the expression of proapoptotic genes involved in both receptor (TNFRSF 10A, TNFRSF 10B, CASP8) and mitochondrial (BAX, BID, NOXA, APAF1) pathways of apoptosis. Test benzo[b]furans activated c-Jun N-terminal kinase (JNK) and p38 kinase in K562 cells. Tubulin was identified as a protein target for benzo[b]furans in pull-down experiments with biotinylated 2. Test benzo[b]furans inhibited polymerization of tubulin monomers in vitro, decreased the level of cellular microtubules and arrested cells in a G2/M phase. Molecular docking suggests that benzo[b]furans 1 and 2 bind to tubulin via colchicine binding pocket and the complex is stabilized mainly by hydrophobic interactions.

CONCLUSION

Novel benzo[b]furans with anti-microtubule activity were identified. They induce apoptosis in cancer cells and cause G2/M cell cycle arrest. Biological activity of 1 and 2 makes them potential lead compounds for development as anticancer drugs.

Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins

BACKGROUND

Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use five datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies.

RESULTS

Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneficial. Instead, it is more efficient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and efficiency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional benefits of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered.

CONCLUSIONS

Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking.

General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept

The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC complexes. Several attempts have been made to use molecular docking for such analyses, but pMHC structure remains too challenging for even state-of-the-art docking tools. To overcome these limitations, we describe the use of an incremental meta-docking approach for structural prediction of pMHC complexes. Previous methods applied in this context used specific constraints to reduce the complexity of this prediction problem, at the expense of generality. Our strategy makes no assumption and can potentially be used to predict binding modes for any pMHC complex. Our method has been tested in a re-docking experiment, reproducing the binding modes of 25 pMHC complexes whose crystal structures are available. This study is a proof of concept that incremental docking strategies can lead to general geometry prediction of pMHC complexes, with potential applications for immunotherapy against cancer or infectious diseases.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

DINC 2.0: A New Protein-Peptide Docking Webserver Using an Incremental Approach

Molecular docking is a standard computational approach to predict binding modes of protein-ligand complexes by exploring alternative orientations and conformations of the ligand (i.e., by exploring ligand flexibility). Docking tools are largely used for virtual screening of small drug-like molecules, but their accuracy and efficiency greatly decays for ligands with more than 10 flexible bonds. This prevents a broader use of these tools to dock larger ligands, such as peptides, which are molecules of growing interest in cancer research. To overcome this limitation, our group has previously proposed a meta-docking strategy, called DINC, to predict binding modes of large ligands. By incrementally docking overlapping fragments of a ligand, DINC allowed predicting binding modes of peptide-based inhibitors of transcription factors involved in cancer. Here, we describe DINC 2.0, a revamped version of the DINC webserver with enhanced capabilities and a more user-friendly interface. DINC 2.0 allows docking ligands that were previously too challenging for DINC, such as peptides with more than 25 flexible bonds. The webserver is freely accessible at http://dinc.kavrakilab.org, together with additional documentation and video tutorials. Our team will provide continuous support for this tool and is working on extending its applicability to other challenging fields, such as personalized immunotherapy against cancer. Cancer Res; 77(21); e55-57. ©2017 AACR.

Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration

"Virtual Screening" is a common step of in silico drug design, where researchers screen a large library of small molecules (ligands) for interesting hits, in a process known as "Docking". However, docking is a computationally intensive and time-consuming process, usually restricted to small size binding sites (pockets) and small number of interacting residues. When the target site is not known (blind docking), researchers split the docking box into multiple boxes, or repeat the search several times using different seeds, and then merge the results manually. Otherwise, the search time becomes impractically long. In this research, we studied the relation between the search progression and Average Sum of Proximity relative Frequencies (ASoF) of searching threads, which is closely related to the search speed and accuracy. A new inter-process spatio-temporal integration method is employed in Quick Vina 2, resulting in a new docking tool, QuickVina-W, a suitable tool for "blind docking", (not limited in search space size or number of residues). QuickVina-W is faster than Quick Vina 2, yet better than AutoDock Vina. It should allow researchers to screen huge ligand libraries virtually, in practically short time and with high accuracy without the need to define a target pocket beforehand.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

Structural insight into the glucokinase-ligands interactions. Molecular docking study

Glucokinase (GK) plays a key role in the regulation of hepatic glucose metabolism. Inactivation of GK is associated with diabetes, and an increase of its activity is linked to hypoglycemia. Possibility to regulate the GK activity using small chemical compounds as allosteric activators induces the scientific interest to the study of the activation mechanism and to the development of new allosteric glucokinase activators. Interaction of glucokinase with ligands is the first step of the complicated mechanism of regulation of the GK functioning. In this paper, we study the interaction of GK with native (glucose) and synthetic (allosteric activators) ligands using molecular docking method. Calculations demonstrate the ability of molecular docking programs to accurately reproduce crystallized ligand poses and conformations and to calculate a free energy of binding with satisfactory accuracy. Correlation between the free energy of binding and the bioactivity of activators is discussed. These results provide a new insight into protein-ligand interactions and can be used for the engineering of new activators.

Molecular Docking of Biologically Active Substances to Double Helical Nucleic Acids

Molecular docking of ligands to DNA-targets is of great importance for the design of new anticancer drugs. Unfortunately, most docking programs were developed for protein-ligand docking which raises a question about their applicability for the DNA-ligand docking. In this study, the popular docking programs AutoDock Vina, AutoDock4 and AutoDock3 were compared for a test set of 50 DNA-ligand complexes taken from the Nucleic Acid Database. It was shown that the version 3.05 of the AutoDock program was the most successful in reproducing the structures of intercalation and minor-groove complexes. The program AutoDock4 was able to re-dock to within 2 Å RMSD most of the intercalation complexes of the test set, but showed poor performance for minor groove binders. While Vina, on the contrary, failed to construct six intercalation complexes of the test set, but showed satisfactory results for DNA-ligand minor-groove complexes when small search space was used.

Trehalose-8-hydroxyquinoline conjugates as antioxidant modulators of Aβ aggregation

The conjugation of trehalose with 8-hydroxyquinoline induces synergistic effects that lead to good antiaggregant ability. The difunctionalization of trehalose produces a better-performing antiaggregant compound.

Targeting the Src Homology 2 (SH2) Domain of Signal Transducer and Activator of Transcription 6 (STAT6) with Cell-Permeable, Phosphatase-Stable Phosphopeptide Mimics Potently Inhibits Tyr641 Phosphorylation and Transcriptional Activity

Signal transducer and activator of transcription 6 (STAT6) transmits signals from cytokines IL-4 and IL-13 and is activated in allergic airway disease. We are developing phosphopeptide mimetics targeting the SH2 domain of STAT6 to block recruitment to phosphotyrosine residues on IL-4 or IL-13 receptors and subsequent Tyr641 phosphorylation to inhibit the expression of genes contributing to asthma. Structure-affinity relationship studies showed that phosphopeptides based on Tyr631 from IL-4Rα bind with weak affinity to STAT6, whereas replacing the pY+3 residue with simple aryl and alkyl amides resulted in affinities in the mid to low nM range. A set of phosphatase-stable, cell-permeable prodrug analogues inhibited cytokine-stimulated STAT6 phosphorylation in both Beas-2B human airway cells and primary mouse T-lymphocytes at concentrations as low as 100 nM. IL-13-stimulated expression of CCL26 (eotaxin-3) was inhibited in a dose-dependent manner, demonstrating that targeting the SH2 domain blocks both phosphorylation and transcriptional activity of STAT6.

Unusual Cyclodextrin Derivatives as a New Avenue to Modulate Self- and Metal-Induced Aβ Aggregation

Mounting evidence suggests an important role of cyclodextrins in providing protection in neurodegenerative disorders. Metal dyshomeostasis is reported to be a pathogenic factor in neurodegeneration because it could be responsible for damage involving oxidative stress and protein aggregation. As such, metal ions represent an effective target. To improve the metal-binding ability of cyclodextrin, we synthesized three new 8-hydroxyquinoline-cyclodextrin conjugates with difunctionalized cyclodextrins. In particular, the 3-difunctionalized regioisomer represents the first example of cyclodextrin with two pendants at the secondary rim, resulting in a promising compound. The derivatives have significant antioxidant capacity and the powerful activity in inhibiting self-induced amyloid-β aggregation seems to be led by synergistic effects of both cyclodextrin and hydroxyquinoline. Moreover, the derivatives are also able to complex metal ions and to inhibit metal-induced protein aggregation. Therefore, these compounds could have potential as therapeutic agents in diseases related to protein aggregation and metal dyshomeostasis.

Synthesis of a DNA-targeting nickel (II) complex with testosterone thiosemicarbazone which exhibits selective cytotoxicity towards human prostate cancer cells (LNCaP)

Testosterone thiosemicarbazone, L and its nickel (II) complex 1 were synthesized and characterized by using FTIR, CHN, (1)H NMR, and X-ray crystallography. X-ray diffraction study confirmed the formation of L from condensation of testosterone and thiosemicarbazide. Mononuclear complex 1 is coordinated to two Schiff base ligands via two imine nitrogens and two tautomeric thiol sulfurs. The cytotoxicity of both compounds was investigated via MTT assay with cisplatin as positive reference standard. L is more potent towards androgen-dependent LNCaP (prostate) and HCT 116 (colon). On the other hand, complex 1, which is in a distorted square planar environment with L acting as a bidentate NS-donor ligand, is capable of inhibiting the growth of all the cancer cell lines tested, including PC-3 (prostate). It is noteworthy that both compounds are less toxic towards human colon cell CCD-18Co. The intrinsic DNA binding constant (Kb) of both compounds were evaluated via UV-Vis spectrophotometry. Both compounds showed Kb values which are comparable to the reported Kb value of typical classical intercalator such as ethidium bromide. The binding constant of the complex is almost double compared with ligand L. Both compounds were unable to inhibit the action topoisomerase I, which is the common target in cancer treatment (especially colon cancer). This suggest a topoisomerase I independent-cell death mechanism.

Docking small peptides remains a great challenge: an assessment using AutoDock Vina

There is a growing interest in the mechanisms and the prediction of how flexible peptides bind proteins, often in a highly selective and conserved manner. While both existing small-molecule docking methods and custom protocols can be used, even short peptides make difficult targets owing to their high torsional flexibility. Any benchmarking should therefore start with those. We compiled a meta-data set of 47 complexes with peptides up to five residues, based on 11 related studies from the past decade. Although their highly varying strategies and constraints preclude direct, quantitative comparisons, we still provide a comprehensive overview of the reported results, using a simple yet stringent measure: the quality of the top-scoring peptide pose. Using the entire data set, this is augmented by our own benchmark of AutoDock Vina, a freely available, fast and widely used docking tool. It particularly addresses non-expert users and was therefore implemented in a highly integrated manner. Guidelines addressing important issues such as the amount of sampling required for result reproducibility are so far lacking. Using peptide docking as an example, this is the first study to address these issues in detail. Finally, to encourage further, standardized benchmarking efforts, the compiled data set is made available in an accessible, transparent and extendable manner.

Selection pressure in CD8⁺ T-cell epitopes in the pol gene of HIV-1 infected individuals in Colombia. A bioinformatic approach

One of the main characteristics of the human immunodeficiency virus is its genetic variability and rapid adaptation to changing environmental conditions. This variability, resulting from the lack of proofreading activity of the viral reverse transcriptase, generates mutations that could be fixed either by random genetic drift or by positive selection. Among the forces driving positive selection are antiretroviral therapy and CD8+ T-cells, the most important immune mechanism involved in viral control. Here, we describe mutations induced by these selective forces acting on the pol gene of HIV in a group of infected individuals. We used Maximum Likelihood analyses of the ratio of non-synonymous to synonymous mutations per site (dN/dS) to study the extent of positive selection in the protease and the reverse transcriptase, using 614 viral sequences from Colombian patients. We also performed computational approaches, docking and algorithmic analyses, to assess whether the positively selected mutations affected binding to the HLA molecules. We found 19 positively-selected codons in drug resistance-associated sites and 22 located within CD8+ T-cell epitopes. A high percentage of mutations in these epitopes has not been previously reported. According to the docking analyses only one of those mutations affected HLA binding. However, algorithmic methods predicted a decrease in the affinity for the HLA molecule in seven mutated peptides. The bioinformatics strategies described here are useful to identify putative positively selected mutations associated with immune escape but should be complemented with an experimental approach to define the impact of these mutations on the functional profile of the CD8+ T-cells.