Loopholes and missing links in protein modeling

Exploring Niclosamide as a Multi-target Drug Against SARS-CoV-2: Molecular Dynamics Simulation Studies on Host and Viral Proteins

Niclosamide has emerged as a promising repurposed drug against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In vitro studies suggested that niclosamide inhibits the host transmembrane protein 16F (hTMEM16F), crucial for lipid scramblase activity, which consequently reduces syncytia formation that aids viral spread. Based on other in vitro reports, niclosamide may also target viral proteases such as papain-like protease (PLpro) and main protease (Mpro), essential for viral replication and maturation. However, the precise interactions by which niclosamide interacts with these multiple targets remain largely unclear. Docking and molecular dynamics (MD) simulation studies were undertaken based on a homology model of the hTMEM16F and available crystal structures of SARS-CoV-2 PLpro and Mpro. Niclosamide was observed to bind stably throughout a 400 ns MD simulation at the extracellular exit gate of the hTMEM16F tunnel, forming crucial interactions with residues spanning the TM1-TM2 loop (Gln350), TM3 (Phe481), and TM5-TM6 loop (Lys573, Glu594, and Asp596). Among the SARS-CoV-2 proteases, niclosamide was found to interact effectively with conserved active site residues of PLpro (Tyr268), exhibiting better stability in comparison to the control inhibitor, GRL0617. In conclusion, our in silico analyses support niclosamide as a multi-targeted drug inhibiting viral and host proteins involved in SARS-CoV-2 infections.

The validation of new CHD1L inhibitors as a therapeutic strategy for cancer

Chromodomain helicase DNA-binding protein 1 like (CHD1L) is an oncogene that promotes tumor progression, metastasis, and multidrug resistance. CHD1L expression is indicative of poor outcomes and low survival in cancer patients with various cancer types. Herein, we report a set of CHD1L inhibitors (CHD1Li) discovered from high-throughput screening and evaluated using enzyme inhibition, 3D tumor organoid cytotoxicity and mechanistic assays. The structurally distinct compounds 8-11 emerged as hits with promising bioactivity by targeting CHD1L. CHD1Li were further examined for their stability in human and mouse liver microsomes, which showed compounds 9 and 11 to be the most metabolically stable. Additionally, molecular modeling studies of CHD1Li with the target protein shed light on key pharmacophore features driving CHD1L binding. Taken together, these results expand the chemical space of CHD1Li as a potential targeted therapy for colorectal cancer and other cancers.

Molecular insights into Mmpl3 leads to the development of novel indole-2-carboxamides as antitubercular agents

Tuberculosis (TB) is an air-borne infectious disease and is the leading cause of death among all infectious diseases globally. The current treatment regimen for TB is overtly long and patient non-compliance often leads to drug resistant TB resulting in a need to develop new drugs that will act via novel mechanisms. In this research work, we selected Mycobacterium membrane protein large (MmpL3) as the drug target and indole-2-carboximide as our molecule of interest for further designing new molecules. A homology model was prepared for the Mycobacterium tuberculosis MmpL3 from the crystal structure of Mycobacterium smegmatis MmpL3. A series of indoles which are known to be MmpL3 inhibitors were docked in the prepared protein and the binding site properties were identified. Based on that, 10 molecules were designed and synthesized and their antitubercular activities evaluated. We identified four hits among which the highest potency candidate possessed a minimum inhibitory concentration (MIC) of 1.56 μM at 2-weeks. Finally, molecular dynamics simulation studies were done with 3b and a previously reported MmpL3 inhibitor to understand the intricacies of their binding in real time and to correlate the experimental findings with the simulation data.

Protein Predictive Modeling and Simulation of Mutations of Presenilin-1 Familial Alzheimer's Disease on the Orthosteric Site

Alzheimer's disease pathology is characterized by β-amyloid plaques and neurofibrillary tangles. Amyloid precursor protein is processed by β and γ secretase, resulting in the production of β-amyloid peptides with a length ranging from 38 to 43 amino acids. Presenilin 1 (PS1) is the catalytic unit of γ-secretase, and more than 200 PS1 pathogenic mutations have been identified as causative for Alzheimer's disease. A complete monocrystal structure of PS1 has not been determined so far due to the presence of two flexible domains. We have developed a complete structural model of PS1 using a computational approach with structure prediction software. Missing fragments Met1-Glut72 and Ser290-Glu375 were modeled and validated by their energetic and stereochemical characteristics. Then, with the complete structure of PS1, we defined that these fragments do not have a direct effect in the structure of the pore. Next, we used our hypothetical model for the analysis of the functional effects of PS1 mutations Ala246GLu, Leu248Pro, Leu248Arg, Leu250Val, Tyr256Ser, Ala260Val, and Val261Phe, localized in the catalytic pore. For this, we used a quantum mechanics/molecular mechanics (QM/MM) hybrid method, evaluating modifications in the topology, potential surface density, and electrostatic potential map of mutated PS1 proteins. We found that each mutation exerts changes resulting in structural modifications of the active site and in the shape of the pore. We suggest this as a valid approach for functional studies of PS1 in view of the possible impact in substrate processing and for the design of targeted therapeutic strategies.

Comparative Modeling and Evaluation of Leukotriene B4 Receptors for Selective Drug Discovery Towards the Treatment of Inflammatory Diseases

Leukotriene B4 (LTB4) exerts its biological effects through stimulation of specific G protein-coupled receptors (GPCRs)-namely BLT1 and BLT2. Due to the absence of human BLT1 and BLT2 crystal structures, the current study was set to predict the 3D structures of these two receptors for structure-based anti-inflammatory drug discovery. Homology modeling of the BLT1 receptor was first constructed, based on various X-ray and NMR GPCR templates, followed by molecular dynamics (MD) refinement. Using a single-template approach, nine well-established alignment methods and ten secondary structure prediction methods during the backbone generation were implemented and assessed. The binding sites of the BLT1 receptor were then mapped using fifteen chemical probes with the help of FTMAP and AutoDock Vina 4.2 software. Model validation was performed through the docking of eight specific antagonists that have experimental inhibition constants (k_i) towards BLT1. The antagonists-BLT1 docked structures were then subjected to AMBER-based molecular mechanical minimization and the corresponding binding energies were calculated using molecular mechanics-generalized Born surface area (MM/GBSA) approach. According to the results, the most energetically stable models were constructed using SAlign method for the alignment process and PSIPRED for secondary structure prediction. In comparison, the refined BLT1 model built on 2KS9 as an NMR template has the lowest DOPE energy compared to those built on 4EA3 and 4XT1 as X-ray templates. According to the mapping results, two main binding sites were identified: one was among TMs II, III and VII and the other was among TMs III, IV and V. For the antagonists, correlation between binding energies and experimental data was in a good agreement, with a correlation coefficient (R² value) of 0.91. Due to the great amino acid sequence similarity between BLT1 and BLT2 receptors (calculated as 45.2%), BLT2 model was constructed based on the predicted BLT1 model.

The C-Box Region of MAF1 Regulates Transcriptional Activity and Protein Stability

MAF1 is a conserved negative regulator of RNA polymerase (pol) III and intracellular lipid homeostasis across species. Here, we show that the MAF1 C-box region negatively regulates its activity. Mutations in Caenorhabditis elegans mafr-1 that truncate the C-box retain the ability to inhibit the transcription of RNA pol III targets, reduce lipid biogenesis, and lower reproductive output. In human cells, C-box deletion of MAF1 leads to increased MAF1 nuclear localization and enhanced repression of ACC1 and FASN, but with impaired repression of RNA pol III targets. Surprisingly, C-box mutations render MAF1 insensitive to rapamycin, further defining a regulatory role for this region. Two MAF1 species, MAF1_L and MAF1_S, are regulated by the C-box YSY motif, which, when mutated, alters species stoichiometry and proteasome-dependent turnover of nuclear MAF1. Our results reveal a role for the C-box region as a critical determinant of MAF1 stability, activity, and response to cellular stress.

Identification of novel natural compound inhibitors for human complement component 5a receptor by homology modeling and virtual screening

Abstract

Neuropathic pain and inflammatory pain are two common types of pathological pain in human health problems. To date, normal painkillers are only partially effective in treating such pain, leading to a tremendous demand to develop new chemical entities to combat pain and inflammation. A promising pharmacological treatment is to control signal transduction via the inflammatory mediator-coupled receptor protein C5aR by finding antagonists to inhibit C5aR activation. Here, we report the first computational study on the identification of non-peptide natural compound inhibitors for C5aR by homology modeling and virtual screening. Our study revealed a novel natural compound inhibitor Acteoside with better docking scores than all four existing non-peptidic natural compounds. The MM-GBSA binding free energy calculations confirmed that Acteoside has a decrease of ~39 kcal/mol in the free energy of binding compared to the strongest binding reference compound. Main contributions to the higher affinity of Acteoside to C5aR are the exceptionally strong lipophilic interaction, enhanced electrostatics and hydrogen bond interactions. Detailed analysis on the physiochemical properties of Acteoside suggests further directions in lead optimization. Taken together, our study proposes that Acteoside is a potential lead molecule targeting the C5aR allosteric site and provides helpful information for further experimental studies.

Graphical Abstract

SL2: an interactive webtool for modeling of missing segments in proteins

SuperLooper2 (SL2) (http://proteinformatics.charite.de/sl2) is the updated version of our previous web-server SuperLooper, a fragment based tool for the prediction and interactive placement of loop structures into globular and helical membrane proteins. In comparison to our previous version, SL2 benefits from both a considerably enlarged database of fragments derived from high-resolution 3D protein structures of globular and helical membrane proteins, and the integration of a new protein viewer. The database, now with double the content, significantly improved the coverage of fragment conformations and prediction quality. The employment of the NGL viewer for visualization of the protein under investigation and interactive selection of appropriate loops makes SL2 independent of third-party plug-ins and additional installations.

Conformational thermodynamics guided structural reconstruction of biomolecular fragments

Conformational thermodynamics compares the modeling protocols to identify the conformation of the missing region leading to a suitable model for metal ion free (apo) skeletal muscle Troponin C.

Monte Carlo loop refinement and virtual screening of the thyroid-stimulating hormone receptor transmembrane domain

Metropolis Monte Carlo (MMC) loop refinement has been performed on the three extracellular loops (ECLs) of rhodopsin and opsin-based homology models of the thyroid-stimulating hormone receptor transmembrane domain, a class A type G protein-coupled receptor. The Monte Carlo sampling technique, employing torsion angles of amino acid side chains and local moves for the six consecutive backbone torsion angles, has previously reproduced the conformation of several loops with known crystal structures with accuracy consistently less than 2 Å. A grid-based potential map, which includes van der Waals, electrostatics, hydrophobic as well as hydrogen-bond potentials for bulk protein environment and the solvation effect, has been used to significantly reduce the computational cost of energy evaluation. A modified sigmoidal distance-dependent dielectric function has been implemented in conjunction with the desolvation and hydrogen-bonding terms. A long high-temperature simulation with 2 kcal/mol repulsion potential resulted in extensive sampling of the conformational space. The slow annealing leading to the low-energy structures predicted secondary structure by the MMC technique. Molecular docking with the reported agonist reproduced the binding site within 1.5 Å. Virtual screening performed on the three lowest structures showed that the ligand-binding mode in the inter-helical region is dependent on the ECL conformations.

Random Coordinate Descent with Spinor-matrices and Geometric Filters for Efficient Loop Closure

Protein loop closure constitutes a critical step in loop and protein modeling whereby geometrically feasible loops must be found between two given anchor residues. Here, a new analytic/iterative algorithm denoted random coordinate descent (RCD) to perform protein loop closure is described. The algorithm solves loop closure through minimization as in cyclic coordinate descent but selects bonds for optimization randomly, updates loop conformations by spinor-matrices, performs loop closure in both chain directions, and uses a set of geometric filters to yield efficient conformational sampling. Geometric filters allow one to detect clashes and constrain dihedral angles on the fly. The RCD algorithm is at least comparable to state of the art loop closure algorithms due to an excellent balance between efficiency and intrinsic sampling capability. Furthermore, its efficiency allows one to improve conformational sampling by increasing the sampling number without much penalty. Overall, RCD turns out to be accurate, fast, robust, and applicable over a wide range of loop lengths. Because of the versatility of RCD, it is a solid alternative for integration with current loop modeling strategies.

How long is a piece of loop?

Loops are irregular structures which connect two secondary structure elements in proteins. They often play important roles in function, including enzyme reactions and ligand binding. Despite their importance, their structure remains difficult to predict. Most protein loop structure prediction methods sample local loop segments and score them. In particular protein loop classifications and database search methods depend heavily on local properties of loops. Here we examine the distance between a loop's end points (span). We find that the distribution of loop span appears to be independent of the number of residues in the loop, in other words the separation between the anchors of a loop does not increase with an increase in the number of loop residues. Loop span is also unaffected by the secondary structures at the end points, unless the two anchors are part of an anti-parallel beta sheet. As loop span appears to be independent of global properties of the protein we suggest that its distribution can be described by a random fluctuation model based on the Maxwell-Boltzmann distribution. It is believed that the primary difficulty in protein loop structure prediction comes from the number of residues in the loop. Following the idea that loop span is an independent local property, we investigate its effect on protein loop structure prediction and show how normalised span (loop stretch) is related to the structural complexity of loops. Highly contracted loops are more difficult to predict than stretched loops.

Computer-aided antibody design

Recent clinical trials using antibodies with low toxicity and high efficiency have raised expectations for the development of next-generation protein therapeutics. However, the process of obtaining therapeutic antibodies remains time consuming and empirical. This review summarizes recent progresses in the field of computer-aided antibody development mainly focusing on antibody modeling, which is divided essentially into two parts: (i) modeling the antigen-binding site, also called the complementarity determining regions (CDRs), and (ii) predicting the relative orientations of the variable heavy (V(H)) and light (V(L)) chains. Among the six CDR loops, the greatest challenge is predicting the conformation of CDR-H3, which is the most important in antigen recognition. Further computational methods could be used in drug development based on crystal structures or homology models, including antibody-antigen dockings and energy calculations with approximate potential functions. These methods should guide experimental studies to improve the affinities and physicochemical properties of antibodies. Finally, several successful examples of in silico structure-based antibody designs are reviewed. We also briefly review structure-based antigen or immunogen design, with application to rational vaccine development.

Fluorescence polarization assay for inhibitors of the kinase domain of receptor interacting protein 1

Necrotic cell death is prevalent in many different pathological disease states and in traumatic injury. Necroptosis is a form of necrosis that stems from specific signaling pathways, with the key regulator being receptor interacting protein 1 (RIP1), a serine/threonine kinase. Specific inhibitors of RIP1, termed necrostatins, are potent inhibitors of necroptosis. Necrostatins are structurally distinct from one another yet still possess the ability to inhibit RIP1 kinase activity. To further understand the differences in the binding of the various necrostatins to RIP1 and to develop a robust high-throughput screening (HTS) assay, which can be used to identify new classes of RIP1 inhibitors, we synthesized fluorescein derivatives of Necrostatin-1 (Nec-1) and Nec-3. These compounds were used to establish a fluorescence polarization (FP) assay to directly measure the binding of necrostatins to RIP1 kinase. The fluorescein-labeled compounds are well suited for HTS because the assays have a dimethyl sulfoxide (DMSO) tolerance up to 5% and Z' scores of 0.62 (fluorescein-Nec-1) and 0.57 (fluorescein-Nec-3). In addition, results obtained from the FP assays and ligand docking studies provide insights into the putative binding sites of Nec-1, Nec-3, and Nec-4.

Modeling large regions in proteins: applications to loops, termini, and folding

Template-based methods for predicting protein structure provide models for a significant portion of the protein but often contain insertions or chain ends (InsEnds) of indeterminate conformation. The local structure prediction "problem" entails modeling the InsEnds onto the rest of the protein. A well-known limit involves predicting loops of ≤12 residues in crystal structures. However, InsEnds may contain as many as ~50 amino acids, and the template-based model of the protein itself may be imperfect. To address these challenges, we present a free modeling method for predicting the local structure of loops and large InsEnds in both crystal structures and template-based models. The approach uses single amino acid torsional angle "pivot" moves of the protein backbone with a C(β) level representation. Nevertheless, our accuracy for loops is comparable to existing methods. We also apply a more stringent test, the blind structure prediction and refinement categories of the CASP9 tournament, where we improve the quality of several homology based models by modeling InsEnds as long as 45 amino acids, sizes generally inaccessible to existing loop prediction methods. Our approach ranks as one of the best in the CASP9 refinement category that involves improving template-based models so that they can function as molecular replacement models to solve the phase problem for crystallographic structure determination.

The FALC-Loop web server for protein loop modeling

The FALC-Loop web server provides an online interface for protein loop modeling by employing an ab initio loop modeling method called FALC (fragment assembly and analytical loop closure). The server may be used to construct loop regions in homology modeling, to refine unreliable loop regions in experimental structures or to model segments of designed sequences. The FALC method is computationally less expensive than typical ab initio methods because the conformational search space is effectively reduced by the use of fragments derived from a structure database. The analytical loop closure algorithm allows efficient search for loop conformations that fit into the protein framework starting from the fragment-assembled structures. The FALC method shows prediction accuracy comparable to other state-of-the-art loop modeling methods. Top-ranked model structures can be visualized on the web server, and an ensemble of loop structures can be downloaded for further analysis. The web server can be freely accessed at http://falc-loop.seoklab.org/.

Modeling the possible conformations of the extracellular loops in G-protein-coupled receptors

This study presents the results of a de novo approach modeling possible conformational dynamics of the extracellular (EC) loops in G-protein-coupled receptors (GPCRs), specifically in bovine rhodopsin (bRh), squid rhodopsin (sRh), human beta-2 adrenergic receptor (beta2AR), turkey beta-1 adrenergic receptor (beta1AR), and human A2 adenosine receptor (A2AR). The approach deliberately sacrificed a detailed description of any particular 3D structure of the loops in GPCRs in favor of a less precise description of many possible structures. Despite this, the approach found ensembles of the low-energy conformers of the EC loops that contained structures close to the available X-ray snapshots. For the smaller EC1 and EC3 loops (6-11 residues), our results were comparable with the best recent results obtained by other authors using much more sophisticated techniques. For the larger EC2 loops (25-34 residues), our results consistently yielded structures significantly closer to the X-ray snapshots than the results of the other authors for loops of similar size. The results suggested possible large-scale movements of the EC loops in GPCRs that might determine their conformational dynamics. The approach was also validated by accurately reproducing the docking poses for low-molecular-weight ligands in beta2AR, beta1AR, and A2AR, demonstrating the possible influence of the conformations of the EC loops on the binding sites of ligands. The approach correctly predicted the system of disulfide bridges between the EC loops in A2AR and elucidated the probable pathways for forming this system.

Modeling of loops in proteins: a multi-method approach

Background

Template-target sequence alignment and loop modeling are key components of protein comparative modeling. Short loops can be predicted with high accuracy using structural fragments from other, not necessairly homologous proteins, or by various minimization methods. For longer loops multiscale approaches employing coarse-grained de novo modeling techniques should be more effective.

Results

For a representative set of protein structures of various structural classes test predictions of loop regions have been performed using MODELLER, ROSETTA, and a CABS coarse-grained de novo modeling tool. Loops of various length, from 4 to 25 residues, were modeled assuming an ideal target-template alignment of the remaining portions of the protein. It has been shown that classical modeling with MODELLER is usually better for short loops, while coarse-grained de novo modeling is more effective for longer loops. Even very long missing fragments in protein structures could be effectively modeled. Resolution of such models is usually on the level 2-6 Å, which could be sufficient for guiding protein engineering. Further improvement of modeling accuracy could be achieved by the combination of different methods. In particular, we used 10 top ranked models from sets of 500 models generated by MODELLER as multiple templates for CABS modeling. On average, the resulting molecular models were better than the models from individual methods.

Conclusions

Accuracy of protein modeling, as demonstrated for the problem of loop modeling, could be improved by the combinations of different modeling techniques.

Closing the side-chain gap in protein loop modeling

The success of structure-based drug design relies on accurate protein modeling where one of the key issues is the modeling and refinement of loops. This study takes a critical look at modeled loops, determining the effect of re-sampling side-chains after the loop conformation has been generated. The results are evaluated in terms of backbone and side-chain conformations with respect to the native loop. While models can contain loops with high quality backbone conformations, the side-chain orientations could be poor, and therefore unsuitable for ligand docking and structure-based design. In this study, we report on the ability to model loop side-chains accurately using a variety of commercially available algorithms that include rotamer libraries, systematic torsion scans and knowledge-based methods.

SuperLooper--a prediction server for the modeling of loops in globular and membrane proteins

SuperLooper provides the first online interface for the automatic, quick and interactive search and placement of loops in proteins (LIP). A database containing half a billion segments of water-soluble proteins with lengths up to 35 residues can be screened for candidate loops. A specified database containing 180 000 membrane loops in proteins (LIMP) can be searched, alternatively. Loop candidates are scored based on sequence criteria and the root mean square deviation (RMSD) of the stem atoms. Searching LIP, the average global RMSD of the respective top-ranked loops to the original loops is benchmarked to be <2 Å, for loops up to six residues or <3 Å for loops shorter than 10 residues. Other suitable conformations may be selected and directly visualized on the web server from a top-50 list. For user guidance, the sequence homology between the template and the original sequence, proline or glycine exchanges or close contacts between a loop candidate and the remainder of the protein are denoted. For membrane proteins, the expansions of the lipid bilayer are automatically modeled using the TMDET algorithm. This allows the user to select the optimal membrane protein loop concerning its relative orientation to the lipid bilayer. The server is online since October 2007 and can be freely accessed at URL: http://bioinformatics.charite.de/superlooper/

Comparison of Composer and ORCHESTRAR

Although the number of known protein structures is increasing, the number of protein sequences without determined structures is still much larger. Three-dimensional (3D) protein structure information helps in the understanding of functional mechanisms, but solving structures by X-ray crystallography or NMR is often a lengthy and difficult process. A relatively fast way of determining a protein's 3D structure is to construct a computer model using homologous sequence and structure information. Much work has gone into algorithms that comprise the ORCHESTRAR homology modeling program in the SYBYL software package. This novel homology modeling tool combines algorithms for modeling conserved cores, variable regions, and side chains. The paradigm of using existing knowledge from multiple templates and the underlying protein environment knowledgebase is used in all of these algorithms, and will become even more powerful as the number of experimentally derived protein structures increases. To determine how ORCHESTRAR compares to Composer (a broadly used, but an older tool), homology models of 18 proteins were constructed using each program so that a detailed comparison of each step in the modeling process could be carried out. Proteins modeled include kinases, dihydrofolate reductase, HIV protease, and factor Xa. In almost all cases ORCHESTRAR produces models with lower root-mean-squared deviation (RMSD) values when compared with structures determined by X-ray crystallography or NMR. Moreover, ORCHESTRAR produced a homology model for three target sequences where Composer failed to produce any. Data for RMSD comparisons between structurally conserved cores, structurally variable regions, side-chain conformations are presented, as well as analyses of active site and protein-protein interface configurations.