i3Drefine software for protein 3D structure refinement and its assessment in CASP10

ABC transporter inhibition by beauvericin partially overcomes drug resistance in Leishmania tropica

Leishmaniasis is a neglected tropical disease infecting the world's poorest populations. Miltefosine (ML) remains the primary oral drug against the cutaneous form of leishmaniasis. The ATP-binding cassette (ABC) transporters are key players in the xenobiotic efflux, and their inhibition could enhance the therapeutic index. In this study, the ability of beauvericin (BEA) to overcome ABC transporter-mediated resistance of Leishmania tropica to ML was assessed. In addition, the transcription profile of genes involved in resistance acquisition to ML was inspected. Finally, we explored the efflux mechanism of the drug and inhibitor. The efficacy of ML against all developmental stages of L. tropica in the presence or absence of BEA was evaluated using an absolute quantification assay. The expression of resistance genes was evaluated, comparing susceptible and resistant strains. Finally, the mechanisms governing the interaction between the ABC transporter and its ligands were elucidated using molecular docking and dynamic simulation. Relative quantification showed that the expression of the ABCG sub-family is mostly modulated by ML. In this study, we used BEA to impede resistance of Leishmania tropica. The IC50 values, following BEA treatment, were significantly reduced from 30.83, 48.17, and 16.83 µM using ML to 8.14, 11.1, and 7.18 µM when using a combinatorial treatment (ML + BEA) against promastigotes, axenic amastigotes, and intracellular amastigotes, respectively. We also demonstrated a favorable BEA-binding enthalpy to L. tropica ABC transporter compared to ML. Our study revealed that BEA partially reverses the resistance development of L. tropica to ML by blocking the alternate ATP hydrolysis cycle.

Validating Fractalkine receptor as a target and identifying candidates for drug discovery against type 2 diabetes

Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases employing abnormal levels of insulin. Enhancing the insulin production is greatly aided by the regulatory mechanisms of the Fractalkine receptor (CX3CR1) system in islet β-cell function. However, elements including a high-fat diet, obesity, and ageing negatively impact the expression of CX3CR1 in islets. CX3CL1/CX3CR1 receptor-ligand complex is now recognized as a novel therapeutic target. It suggests that T2DM-related β-cell dysfunction may result from lower amount of these proteins. We analyzed the differential expression of CX3CR1 gene samples taken from persons with T2DM using data obtained from the Gene Expression Omnibus database. Homology modeling enabled us to generate the three-dimensional structure of CX3CR1 and a possible binding pocket. The optimized CX3CR1 structure was subjected to rigorous screening against a massive library of 693 million drug-like molecules from the ZINC15 database. This screening process led to the identification of three compounds with strong binding affinity at the identified binding pocket of CX3CR1. To further evaluate the potential of these compounds, molecular dynamics simulations were conducted over a 50 ns time scale to assess the stability of the protein-ligand complexes. These simulations revealed that ZINC000032506419 emerged as the most promising drug-like compound among the three potent molecules. The discovery of ZINC000032506419 holds exciting promise as a potential therapeutic agent for T2D and other related metabolic disorders. These findings pave the way for the development of effective medications to address the complexities of T2DM and its associated metabolic diseases.

Structure-based virtual screening against multiple Plasmodium falciparum kinases reveals antimalarial compounds

The continuous emergence of resistance against most frontline antimalarial drugs has led to countless deaths in malaria-endemic countries, counting 619,000 deaths in 2021, with mutation in drug targets being the sole cause. As mutation is correlated frequently with fitness cost, the likelihood of mutation emergence in multiple targets at a time is extremely low. Hence, multitargeting compounds may seem promising to address drug resistance issues with additional benefits like increased efficacy, improved safety profile, and the requirement of fewer pills compared to traditional single and combinational drugs. In this study, we attempted to use the High Throughput Virtual Screening approach to predict multitarget inhibitors against six chemically validated Plasmodium falciparum (Pf) kinases (PfPKG, PfMAP2, PfCDPK4, PfTMK, PfPK5, PfPI4K), resulting in 21 multitargeting hits. The molecular dynamic simulation of the top six complexes (Myricetin-MAP2, Quercetin-CDPK4, Myricetin-TMK, Quercetin-PKG, Salidroside-PK5, and Salidroside-PI4K) showed stable interactions. Moreover, hierarchical clustering reveals the structural divergence of the compounds from the existing antimalarials, indicating less chance of cross-resistance. Additionally, the top three hits were validated through parasite growth inhibition assays, with quercetin and myricetin exhibiting an IC50 value of 1.84 and 3.93 µM, respectively.

Characterization of novel CD19-specific VHHs isolated from a camelid immune library by phage display

BACKGROUND

Monoclonal antibody (mAb)-based immunotherapies have achieved promising outcomes in the treatment of immunological and oncological indications. CD19 is considered one of the most qualified antigens in the treatment of B-cell neoplasms. VHHs (nanobodies) are known for their physicochemical advantages over conventional mAbs rendering them suitable therapeutics and diagnostic tools. Herein, we aimed to isolate CD19-specific VHHs from a novel immune library using phage display.

METHODS

An immune VHH gene library was constructed. Using phage display and after five biopanning rounds, two monoclonal CD19-specific VHHs were isolated. The selected VHHs were expressed, purified, and characterized in terms of their affinity, specificity, sensitivity, and ability to target CD19-positive cell lines. Moreover, in silico analyses were employed for further characterization.

RESULTS

A VHH library was developed, and because the outputs of the 4th biopanning round exhibited the most favorable characteristics, a panel of random VHHs was selected from them. Ultimately, two of the most favorable VHHs were selected and DNA sequenced (designated as GR37 and GR41). Precise experiments indicated that GR37 and GR41 exhibited considerable specificity, sensitivity, and affinity (1.15 × 107 M-1 and 2.08 × 107 M-1, respectively) to CD19. Flow cytometric analyses revealed that GR37 and GR41 could bind CD19 on the surface of cell lines expressing the antigen. Moreover, in silico experiments predicted that both VHHs target epitopes that are distinct from that targeted by the CD19-specific single-chain variable fragment (scFv) FMC63.

CONCLUSION

The selected VHHs can be used as potential targeting tools for the development of CD19-based immunotherapeutics.

In-silico drug design for the novel Karachi-NF001 strain of brain-eating amoeba: Naegleria fowleri

Naegleria fowleri (N. fowleri) is a free-living thermophilic amoeba of fresh water and soil. The amoeba primarily feeds on bacteria but can be transmitted to humans upon contact with freshwater sources. Furthermore, this brain-eating amoeba enters the human body through the nose and travels to the brain to cause primary amebic meningoencephalitis (PAM). N. fowleri has been reported globally since its discovery in 1961. Recently a new strain of N. fowleri named Karachi-NF001 was found in a patient who had traveled from Riyadh, Saudi Arabia to Karachi in 2019. There were 15 unique genes identified in the genome of the Karachi-NF001 strain compared to all the previously reported strains of N. fowleri worldwide. Six of these genes encode well-known proteins. In this study, we performed in-silico analysis on 5 of these 6 proteins, namely, Rab family small GTPase, NADH dehydrogenase subunit 11, two Glutamine-rich protein 2 proteins (locus tags: 12086 and 12110), and Tigger transposable element-derived protein 1. We conducted homology modeling of these 5 proteins followed by their active site identification. These proteins were subjected to molecular docking against 105 anti-bacterial ligand compounds as potential drugs. Subsequently, the 10 best-docked compounds were determined for each protein and ranked according to the number of interactions and their binding energies. The highest binding energy was recorded for the two Glutamine-rich protein 2 proteins with different locus tags, and results have shown that the protein-inhibitor complex was stable throughout the simulation run. Moreover, future in-vitro studies could validate the findings of our in-silico analysis and identify potential therapeutic drugs against N. fowleri infections.

Contact-Assisted Threading in Low-Homology Protein Modeling

The ability to successfully predict the three-dimensional structure of a protein from its amino acid sequence has made considerable progress in the recent past. The progress is propelled by the improved accuracy of deep learning-based inter-residue contact map predictors coupled with the rising growth of protein sequence databases. Contact map encodes interatomic interaction information that can be exploited for highly accurate prediction of protein structures via contact map threading even for the query proteins that are not amenable to direct homology modeling. As such, contact-assisted threading has garnered considerable research effort. In this chapter, we provide an overview of existing contact-assisted threading methods while highlighting the recent advances and discussing some of the current limitations and future prospects in the application of contact-assisted threading for improving the accuracy of low-homology protein modeling.

Using Local Protein Model Quality Estimates to Guide a Molecular Dynamics-Based Refinement Strategy

The refinement of predicted 3D models aims to bring them closer to the native structure by fixing errors including unusual bonds and torsion angles and irregular hydrogen bonding patterns. Refinement approaches based on molecular dynamics (MD) simulations using different types of restraints have performed well since CASP10. ReFOLD, developed by the McGuffin group, was one of the many MD-based refinement approaches, which were tested in CASP 12. When the performance of the ReFOLD method in CASP12 was evaluated, it was observed that ReFOLD suffered from the absence of a reliable guidance mechanism to reach consistent improvement for the quality of predicted 3D models, particularly in the case of template-based modelling (TBM) targets. Therefore, here we propose to utilize the local quality assessment score produced by ModFOLD6 to guide the MD-based refinement approach to further increase the accuracy of the predicted 3D models. The relative performance of the new local quality assessment guided MD-based refinement protocol and the original MD-based protocol ReFOLD are compared utilizing many different official scoring methods. By using the per-residue accuracy (or local quality) score to guide the refinement process, we are able to prevent the refined models from undesired structural deviations, thereby leading to more consistent improvements. This chapter will include a detailed analysis of the performance of the local quality assessment guided MD-based protocol versus that deployed in the original ReFOLD method.

Molecular modelling on multiepitope-based vaccine against SARS-CoV-2 using immunoinformatics, molecular docking, and molecular dynamics simulation

The pandemic of COVID-19 caused by SARS-CoV-2 has made a worldwide health emergency. Despite the fact that current vaccines are readily available, several SARSCoV-2 variants affecting the existing vaccine are to be less effective due to the mutations in the structural proteins. Furthermore, the appearance of the new variants cannot be easily predicted in the future. Therefore, the attempts to construct new vaccines or to modify the current vaccines are still pivotal works for preventing the spread of the virus. In the present investigation, the computational analysis through immunoinformatics, molecular docking, and molecular dynamics (MD) simulation is employed to construct an effective vaccine against SARS-CoV2. The structural proteins of SARS-CoV2 are utilized to create a multiepitope-based vaccine (MEV). According to our findings presented by systematic procedures in the current investigation, the MEV construct may be able to trigger a strong immunological response against the virus. Therefore, the designed MEV could be a potential vaccine candidate against SARS-CoV-2, and also it is expected to be effective for other variants.

A short framework-III (mini-M-2) conotoxin from the venom of a vermivorous species, Conus archon, inhibits human neuronal nicotinic acetylcholine receptors

The venoms of Conus snails contain neuroactive peptides named conotoxins (CTXs). Some CTXs are nicotinic acetylcholine receptor (nAChRs) antagonists. nAChRs modulate the release of neurotransmitters and are implicated in several pathophysiologies. One venom peptide from Conus archon, a vermivorous species from the Mexican Pacific, was purified by RP-HPLC and its activity on human α7, α3β2, and α7β2 nAChRs was assessed by the two-electrode voltage clamp technique. At 36.3 µM the purified peptide (F27-1, renamed tentatively ArchIIIA) slowly reversibly inhibited the ACh-induced response of the hα7 subtype by 44.52 ± 5.83%, while it had low or no significant effect on the response of the hα3β2 and hα7β2 subtypes; the EC₅₀ of the inhibiting effect was 45.7 µM on the hα7 subtype. This peptide has 15 amino acid residues and a monoisotopic mass of 1654.6 Da (CCSALCSRYHCLPCC), with three disulfide bridges and a free C-terminus. This sequence with a CC-C-C-CC arrangement (framework III) belongs to the M superfamily of conotoxins, corresponding to the mini-M´s (M-1-M-3) conotoxins; due to its size and inter-Cys spacings it is an M-2 conotoxin. This toxin is a novel mini-M conotoxin affecting ligand-gated ion channels, like the maxi-M CTX ψ-conotoxins and α-MIIIJ conotoxin (nAChRs blockers). This peptide seems to be homologous to the reg3b conotoxin (from Conus regius) with an identity of 93.3%, differing only in the third residue in the sequence, serine for threonine, both uncharged polar residues. We obtained, in silico, a probable 3D structure, which is consistent with its effect on neuronal subtypes.

Experimental Study of Potential CD8+ Trivalent Synthetic Peptides for Liver Cancer Vaccine Development Using Sprague Dawley Rat Models

Background. Liver cancer (LC) is the most devastating disease affecting a large set of populations in the world. The mortality due to LC is escalating, indicating the lack of effective therapeutic options. Immunotherapeutic agents may play an important role against cancer cells. As immune cells, especially T lymphocytes, which are part of cancer immunology, the design of vaccine candidates for cytotoxic T lymphocytes may be an effective strategy for curing liver cancer. Results. In our study, based on an immunoinformatics approach, we predicted potential T cell epitopes of MHC class I molecules using integrated steps of data retrieval, screening of antigenic proteins, functional analysis, peptide synthesis, and experimental in vivo investigations. We predicted the binding affinity of epitopes LLECADDRADLAKY, VSEHRIQDKDGLFY, and EYILSLEELVNGMY of LC membrane-bounded extracellular proteins including butyrophilin-like protein-2 (BTNL2), glypican-3 (GPC3), and serum albumin (ALB), respectively, with MHC class I molecules (allele: HLA-A $\ast$ 01:01). These T cell epitopes rely on the level of their binding energy and antigenic properties. We designed and constructed a trivalent immunogenic model by conjugating these epitopes with linkers to activate cytotoxic T cells. For validation, the nonspecific hematological assays showed a significant rise in the count of white blood cells ( $5\times {10}^9/\text{l}$ ), lymphocytes ( $13\times {10}^9/\text{l}$ ), and granulocytes ( $5\times {10}^9/\text{l}$ ) compared to the control after administration of trivalent peptides. Specific immunoassays including granzyme B and IgG ELISA exhibited the significant concentration of these effector molecules in blood serum, indicating the activity of cytotoxic T cells. Granzyme concentration increased to 1050 pg/ml at the second booster dose compared to the control (95 pg/ml), while the concentration of IgG raised to 6 g/l compared to the control (2 g/l). Conclusion. We concluded that a potential therapeutic trivalent vaccine can activate and modulate the immune system to cure liver cancer on the basis of significant outcomes of specific and nonspecific assays.

Molecular insights into the differential efflux mechanism of Rv1634 protein, a multidrug transporter of major facilitator superfamily in Mycobacterium tuberculosis

Currently, multidrug-resistant tuberculosis (MDR-TB) is a public health crisis and a major health security threat globally. In Mycobacterium tuberculosis (Mtb), major facilitator superfamily (MFS) is the largest group of secondary active transporters. Along with the transport of their natural substrates, MFS proteins were involved in a drug efflux mechanism that ultimately lead to resistance against available anti-TB drugs in Mtb. In the present study, the three-dimensional structure model of an MFS protein, Rv1634, a probable multidrug transporter from Mtb, was generated using homology modeling. The protein structure model was found in inward-open conformation having 14 transmembrane helices. In addition, a central transport channel was deduced across the protein, and a single binding pocket was identified halfway through the central cavity by structural alignment with the homologous protein structures. Further, Rv1634 protein was studied based on the differential structural behavior of apo and ligand-bound forms. All the protein systems were inserted into a phospholipid bilayer to characterize the conformational dynamics of the protein using molecular dynamics (MD) simulations. Detailed analysis of the MD trajectories showed the diverse substrate specificity of the binding pocket for the antibiotics that caused differential movement in the ciprofloxacin and norfloxacin, to which Mtb strains have now become resistant. The expulsion of the drugs outside the bacterial cell occurs through the alternating-access mechanism of N and C-terminal domains, which is intriguing and essential to the understanding the drug resistance mechanism in pathogenic bacteria.

ReFOLD3: refinement of 3D protein models with gradual restraints based on predicted local quality and residue contacts

ReFOLD3 is unique in its application of gradual restraints, calculated from local model quality estimates and contact predictions, which are used to guide the refinement of theoretical 3D protein models towards the native structures. ReFOLD3 achieves improved performance by using an iterative refinement protocol to fix incorrect residue contacts and local errors, including unusual bonds and angles, which are identified in the submitted models by our leading ModFOLD8 model quality assessment method. Following refinement, the likely resulting improvements to the submitted models are recognized by ModFOLD8, which produces both global and local quality estimates. During the CASP14 prediction season (May-Aug 2020), we used the ReFOLD3 protocol to refine hundreds of 3D models, for both the refinement and the main tertiary structure prediction categories. Our group improved the global and local quality scores for numerous starting models in the refinement category, where we ranked in the top 10 according to the official assessment. The ReFOLD3 protocol was also used for the refinement of the SARS-CoV-2 targets as a part of the CASP Commons COVID-19 initiative, and we provided a significant number of the top 10 models. The ReFOLD3 web server is freely available at https://www.reading.ac.uk/bioinf/ReFOLD/.

Design of a multi-epitope vaccine against cervical cancer using immunoinformatics approaches

Cervical cancer, caused by human papillomavirus (HPV), is the fourth most common type of cancer among women worldwide. While HPV prophylactic vaccines are available, they have no therapeutic effects and do not clear up existing infections. This study aims to design a therapeutic vaccine against cervical cancer using reverse vaccinology. In this study, the E6 and E7 oncoproteins from HPV16 were chosen as the target antigens for epitope prediction. Cytotoxic T lymphocytes (CTL) and helper T lymphocytes (HTL) epitopes were predicted, and the best epitopes were selected based on antigenicity, allergenicity, and toxicity. The final vaccine construct was composed of the selected epitopes, along with the appropriate adjuvant and linkers. The multi-epitope vaccine was evaluated in terms of physicochemical properties, antigenicity, and allergenicity. The tertiary structure of the vaccine construct was predicted. Furthermore, several analyses were also carried out, including molecular docking, molecular dynamics (MD) simulation, and in silico cloning of the vaccine construct. The results showed that the final proposed vaccine could be considered an effective therapeutic vaccine for HPV; however, in vitro and in vivo experiments are required to validate the efficacy of this vaccine candidate.

Exploring SARS-COV-2 structural proteins to design a multi-epitope vaccine using immunoinformatics approach: An in silico study

In December 2019, a new virus called SARS-CoV-2 was reported in China and quickly spread to other parts of the world. The development of SARS-COV-2 vaccines has recently received much attention from numerous researchers. The present study aims to design an effective multi-epitope vaccine against SARS-COV-2 using the reverse vaccinology method. In this regard, structural proteins from SARS-COV-2, including the spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins, were selected as target antigens for epitope prediction. A total of five helper T lymphocytes (HTL) and five cytotoxic T lymphocytes (CTL) epitopes were selected after screening the predicted epitopes for antigenicity, allergenicity, and toxicity. Subsequently, the selected HTL and CTL epitopes were fused via flexible linkers. Next, the cholera toxin B-subunit (CTxB) as an adjuvant was linked to the N-terminal of the chimeric structure. The proposed vaccine was analyzed for the properties of physicochemical, antigenicity, and allergenicity. The 3D model of the vaccine construct was predicted and docked with the Toll-like receptor 4 (TLR4). The molecular dynamics (MD) simulation was performed to evaluate the stable interactions between the vaccine construct and TLR4. The immune simulation was also conducted to explore the immune responses induced by the vaccine. Finally, in silico cloning of the vaccine construct into the pET-28 (+) vector was conducted. The results obtained from all bioinformatics analysis stages were satisfactory; however, in vitro and in vivo tests are essential to validate these results.

Designing of Potential Polyvalent Vaccine Model for Respiratory Syncytial Virus by System Level Immunoinformatics Approaches

BACKGROUND

Respiratory syncytial virus (RSV) infection is a public health epidemic, leading to around 3 million hospitalization and about 66,000 deaths each year. It is a life-threatening condition exclusive to children with no effective treatment.

METHODS

In this study, we used system-level and vaccinomics approaches to design a polyvalent vaccine for RSV, which could stimulate the immune components of the host to manage this infection. Our framework involves data accession, antigenicity and subcellular localization analysis, T cell epitope prediction, proteasomal and conservancy evaluation, host-pathogen-protein interactions, pathway studies, and in silico binding affinity analysis.

RESULTS

We found glycoprotein (G), fusion protein (F), and small hydrophobic protein (SH) of RSV as potential vaccine candidates. Of these proteins (G, F, and SH), we found 9 epitopes for multiple alleles of MHC classes I and II bear significant binding affinity. These potential epitopes were linked to form a polyvalent construct using AAY, GPGPG linkers, and cholera toxin B adjuvant at N-terminal with a 23.9 kDa molecular weight of 224 amino acid residues. The final construct was a stable, immunogenic, and nonallergenic protein containing cleavage sites, TAP transport efficiency, posttranslation shifts, and CTL epitopes. The molecular docking indicated the optimum binding affinity of RSV polyvalent construct with MHC molecules (-12.49 and -10.48 kcal/mol for MHC classes I and II, respectively). This interaction showed that a polyvalent construct could manage and control this disease.

CONCLUSION

Our vaccinomics and system-level investigation could be appropriate to trigger the host immune system to prevent RSV infection.

Beauvericin potentiates the activity of pesticides by neutralizing the ATP-binding cassette transporters in arthropods

Multi-drug resistance is posing major challenges in suppressing the population of pests. Many herbivores develop resistance, causing a prolonged survival after exposure to a previously effective pesticide. Consequently, resistant pests reduce the yield of agricultural production, causing significant economic losses and reducing food security. Therefore, overpowering resistance acquisition of crop pests is a must. The ATP binding cassette transporters (ABC transporters) are considered as the main participants to the pesticide efflux and their neutralization will greatly contribute to potentiate failed treatments. Real-Time PCR analysis of 19 ABC transporter genes belonging to the ABCB, ABCC, ABCG, and ABCH revealed that a broad range of efflux pumps is activated in response to the exposure to pesticides. In this study, we used beauvericin (BEA), a known ABC transporters modulator, to resensitize different strains of Tetranychus urticae after artificial selection for resistance to cyflumetofen, bifenazate, and abamectin. Our results showed that the combinatorial treatment of pesticide (manufacturer's recommended doses) + BEA (sublethal doses: 0.15 mg/L) significantly suppressed the resistant populations of T. urticae when compared to single-drug treatments. Moreover, after selective pressure for 40 generations, the LC₅₀ values were significantly reduced from 36.5, 44.7, and 94.5 (pesticide) to 8.3, 12.5, and 23.4 (pesticide + BEA) for cyflumetofen, bifenazate, and abamectin, respectively. While the downstream targets for BEA are still elusive, we demonstrated hereby that it synergizes with sub-lethal doses of different pesticides and increases their effect by inhibiting ABC transporters. This is the first report to document such combinatorial activity of BEA against higher invertebrates paving the way for its usage in treating refractory cases of resistance to pesticides. Moreover, we demonstrated, for the first time, using in silico techniques, the higher affinity of BEA to ABC transformers subfamilies when compared to xenobiotics; thus, elucidating the pathway of the mycotoxin.

Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading

Sequence-based protein homology detection has emerged as one of the most sensitive and accurate approaches to protein structure prediction. Despite the success, homology detection remains very challenging for weakly homologous proteins with divergent evolutionary profile. Very recently, deep neural network architectures have shown promising progress in mining the coevolutionary signal encoded in multiple sequence alignments, leading to reasonably accurate estimation of inter-residue interaction maps, which serve as a rich source of additional information for improved homology detection. Here, we summarize the latest developments in protein homology detection driven by inter-residue interaction map threading. We highlight the emerging trends in distant-homology protein threading through the alignment of predicted interaction maps at various granularities ranging from binary contact maps to finer-grained distance and orientation maps as well as their combination. We also discuss some of the current limitations and possible future avenues to further enhance the sensitivity of protein homology detection.

Polyvalent therapeutic vaccine for type 2 diabetes mellitus: Immunoinformatics approach to study co-stimulation of cytokines and GLUT1 receptors

Background

Type 2 diabetes mellitus (T2DM) is a worldwide disease that have an impact on individuals of all ages causing micro and macro vascular impairments due to hyperglycemic internal environment. For ultimate treatment to cure T2DM, association of diabetes with immune components provides a strong basis for immunotherapies and vaccines developments that could stimulate the immune cells to minimize the insulin resistance and initiate gluconeogenesis through an insulin independent route.

Methodology

Immunoinformatics based approach was used to design a polyvalent vaccine for T2DM that involved data accession, antigenicity analysis, T-cell epitopes prediction, conservation and proteasomal evaluation, functional annotation, interactomic and in silico binding affinity analysis.

Results

We found the binding affinity of antigenic peptides with major histocompatibility complex (MHC) Class-I molecules for immune activation to control T2DM. We found 13-epitopes of 9 amino acid residues for multiple alleles of MHC class-I bears significant binding affinity. The downstream signaling resulted by T-cell activation is directly regulated by the molecular weight, amino acid properties and affinity of these epitopes. Each epitope has important percentile rank with significant ANN IC₅₀ values. These high score potential epitopes were linked using AAY, EAAAK linkers and HBHA adjuvant to generate T-cell polyvalent vaccine with a molecular weight of 35.6 kDa containing 322 amino acids residues. In silico analysis of polyvalent construct showed the significant binding affinity (− 15.34 Kcal/mol) with MHC Class-I. This interaction would help to understand our hypothesis, potential activation of T-cells and stimulatory factor of cytokines and GLUT1 receptors.

Conclusion

Our system-level immunoinformatics approach is suitable for designing potential polyvalent therapeutic vaccine candidates for T2DM by reducing hyperglycemia and enhancing metabolic activities through the immune system.

Structure based drug designing and discovery of promising lead molecules against UDP-N-acetylenolpyruvoylglucosamine reductase (MurB): A potential drug target in multi-drug resistant Acinetobacter baumannii

According to the world health organization (WHO) reports, Acinetobacter baumannii was considered as one of the significant and first-line priority pathogens, which causes hospital-acquired nosocomial infections in human. The enzymes involved in the peptidoglycan biosynthetic pathway are critical for the survival of this bacterium. Therefore, these enzymes are ideal drug target since they are conserved among most of the species and non-homologous to human. Here, we utilized the structure-based virtual screening (SBVS) technique to identify the promising lead molecules against MurB (UDP-N-acetylenolpyruvoylglucosamine reductase) protein using computational approaches. Initially, the three-dimensional structure of MurB was predicted based on MurB from P. aeruginosa (PDB ID: 4JAY), which is used as a structural template for homology modeling. During the High-throughput Virtual screening (HTVS) analysis, we started with 30,792 molecules against MurB model, among these; only 5238 molecules could be considered suitable for further step. Finally, only twenty molecules were able to pass Lipinski's and ADMET properties. After a thorough examination of interaction analysis, higher ΔG and Ki values, we had chosen five promising molecules (ZINC IDs: ZINC12530134, ZINC15675540, ZINC15675762, ZINC15675624 and ZINC15707270) and three control molecules (PubChem IDs: 54682555, 729933 and 39964628) for Molecular dynamics (MD) simulation to understand the effect of ligands towards the structural stability, structural integrity and structural compactness of MurB protein. Further, the MM/PBSA binding free energy analysis was performed for eight ligands bound MurB structures. Together the results obtained from global dynamics, essential dynamics and MM-PBSA binding free energy analysis, we concluded that apart from the control molecules, ZINC12530134 should be considered as one of the most promising ones and it could be the potent inhibitor against A baumannii and provide valuable insight for further experimental studies.

refineD: improved protein structure refinement using machine learning based restrained relaxation

MOTIVATION

Protein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraints remains a major issue in structure refinement.

RESULTS

Here, we develop a machine learning based restrained relaxation protocol that uses deep discriminative learning based binary classifiers to predict multi-resolution probabilistic restraints from the starting structure and subsequently converts these restraints to be integrated into Rosetta all-atom energy function as additional scoring terms during structure refinement. We use four restraint resolutions as adopted in GDT-HA (0.5, 1, 2 and 4 Å), centered on the Cα atom of each residue that are predicted by ensemble of four deep discriminative classifiers trained using combinations of sequence and structure-derived features as well as several energy terms from Rosetta centroid scoring function. The proposed method, refineD, has been found to produce consistent and substantial structural refinement through the use of cumulative and non-cumulative restraints on 150 benchmarking targets. refineD outperforms unrestrained relaxation strategy or relaxation that is restrained to starting structures using the FastRelax application of Rosetta or atomic-level energy minimization based ModRefiner method as well as molecular dynamics (MD) simulation based FG-MD protocol. Furthermore, by adjusting restraint resolutions, the method addresses the tradeoff that exists between degree and consistency of refinement. These results demonstrate a promising new avenue for improving accuracy of template-based protein models by effectively guiding conformational sampling during structure refinement through the use of machine learning based restraints.

AVAILABILITY AND IMPLEMENTATION

http://watson.cse.eng.auburn.edu/refineD/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High throughput virtual screening reveals SARS-CoV-2 multi-target binding natural compounds to lead instant therapy for COVID-19 treatment

The present-day world is severely suffering from the recently emerged SARS-CoV-2. The lack of prescribed drugs for the deadly virus has stressed the likely need to identify novel inhibitors to alleviate and stop the pandemic. In the present high throughput virtual screening study, we used in silico techniques like receptor-ligand docking, Molecular dynamic (MD), and ADME properties to screen natural compounds. It has been documented that many natural compounds display antiviral activities, including anti–SARS-CoV effect. The present study deals with compounds of Natural Product Activity and Species Source (NPASS) database with known biological activity that probably impedes the activity of six essential enzymes of the virus. Promising drug-like compounds were identified, demonstrating better docking score and binding energy for each druggable targets. After an extensive screening analysis, three novel multi-target natural compounds were predicted to subdue the activity of three/more major drug targets simultaneously. Concerning the utility of natural compounds in the formulation of many therapies, we propose these compounds as excellent lead candidates for the development of therapeutic drugs against SARS-CoV-2.

Optimisation of estrogen receptor subtype-selectivity of a 4-Aryl-4H-chromene scaffold previously identified by virtual screening

4-Aryl-4H-Chromene derivatives have been previously shown to exhibit anti-proliferative, apoptotic and anti-angiogenic activity in a variety of tumor models in vitro and in vivo generally via activation of caspases through inhibition of tubulin polymerisation. We have previously identified by Virtual Screening (VS) a 4-aryl-4H-chromene scaffold, of which two examples were shown to bind Estrogen Receptor α and β with low nanomolar affinity and <20-fold selectivity for α over β and low micromolar anti-proliferative activity in the MCF-7 cell line. Thus, using the 4-aryl-4H-chromene scaffold as a starting point, a series of compounds with a range of basic arylethers at C-4 and modifications at the C3-ester substituent of the benzopyran ring were synthesised, producing some potent ER antagonists in the MCF-7 cell line which were highly selective for ERα (compound 35; 350-fold selectivity) or ERβ (compound 42; 170-fold selectivity).

The effect of high pressure on the NMDA receptor: molecular dynamics simulations

Professional divers exposed to ambient pressures above 11 bar develop the high pressure neurological syndrome (HPNS), manifesting as central nervous system (CNS) hyperexcitability, motor disturbances, sensory impairment, and cognitive deficits. The glutamate-type N-methyl-D-aspartate receptor (NMDAR) has been implicated in the CNS hyperexcitability of HPNS. NMDARs containing different subunits exhibited varying degrees of increased/decreased current at high pressure. The mechanisms underlying this phenomenon remain unclear. We performed 100 ns molecular dynamics (MD) simulations of the NMDAR structure embedded in a dioleoylphosphatidylcholine (DOPC) lipid bilayer solvated in water at 1 bar, hydrostatic 25 bar, and in helium at 25 bar. MD simulations showed that in contrast to hydrostatic pressure, high pressure helium causes substantial distortion of the DOPC membrane due to its accumulation between the two monolayers: reduction of the Sn-1 and Sn-2 DOPC chains and helium-dependent dehydration of the NMDAR pore. Further analysis of important regions of the NMDAR protein such as pore surface (M2 α-helix), Mg²⁺ binding site, and TMD-M4 α-helix revealed significant effects of helium. In contrast with previous models, these and our earlier results suggest that high pressure helium, not hydrostatic pressure per se, alters the receptor tertiary structure via protein-lipid interactions. Helium in divers’ breathing mixtures may partially contribute to HPNS symptoms.

An extracellular cation coordination site influences ion conduction of OsHKT2;2

BACKGROUND

HKT channels mediate sodium uniport or sodium and potassium symport in plants. Monocotyledons express a higher number of HKT proteins than dicotyledons, and it is only within this clade of HKT channels that cation symport mechanisms are found. The prevailing ion composition in the extracellular medium affects the transport abilities of various HKT channels by changing their selectivity or ion transport rates. How this mutual effect is achieved at the molecular level is still unknown. Here, we built a homology model of the monocotyledonous OsHKT2;2, which shows sodium and potassium symport activity. We performed molecular dynamics simulations in the presence of sodium and potassium ions to investigate the mutual effect of cation species.

RESULTS

By analyzing ion-protein interactions, we identified a cation coordination site on the extracellular protein surface, which is formed by residues P71, D75, D501 and K504. Proline and the two aspartate residues coordinate cations, while K504 forms salt bridges with D75 and D501 and may be involved in the forwarding of cations towards the pore entrance. Functional validation via electrophysiological experiments confirmed the biological relevance of the predicted ion coordination site and identified K504 as a central key residue. Mutation of the cation coordinating residues affected the functionality of HKT only slightly. Additional in silico mutants and simulations of K504 supported experimental results.

CONCLUSION

We identified an extracellular cation coordination site, which is involved in ion coordination and influences the conduction of OsHKT2;2. This finding proposes a new viewpoint in the discussion of how the mutual effect of variable ion species may be achieved in HKT channels.

Interactome Analysis and Docking Sites of MutS Homologs Reveal New Physiological Roles in Arabidopsis thaliana

Due to their sedentary lifestyle, plants are constantly exposed to different stress stimuli. Stress comes in variety of forms where factors like radiation, free radicals, “replication errors, polymerase slippage”, and chemical mutagens result in genotoxic or cytotoxic damage. In order to face “the base oxidation or DNA replication stress”, plants have developed many sophisticated mechanisms. One of them is the DNA mismatch repair (MMR) pathway. The main part of the MMR is the MutS homologue (MSH) protein family. The genome of Arabidopsis thaliana encodes at least seven homologues of the MSH family: AtMSH1, AtMSH2, AtMSH3, AtMSH4, AtMSH5, AtMSH6, and AtMSH7. Despite their importance, the functions of AtMSH homologs have not been investigated. In this work, bioinformatics tools were used to obtain a better understanding of MSH-mediated DNA repair mechanisms in Arabidopsis thaliana and to understand the additional biological roles of AtMSH family members. In silico analysis, including phylogeny tracking, prediction of 3D structure, interactome analysis, and docking site prediction, suggested interactions with proteins were important for physiological development of A. thaliana. The MSH homologs extensively interacted with both TIL1 and TIL2 (DNA polymerase epsilon catalytic subunit), proteins involved in cell fate determination during plant embryogenesis and involved in flowering time repression. Additionally, interactions with the RECQ protein family (helicase enzymes) and proteins of nucleotide excision repair pathway were detected. Taken together, the results presented here confirm the important role of AtMSH proteins in mismatch repair and suggest important new physiological roles.

Methods for the Refinement of Protein Structure 3D Models

The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.

Immunomics Datasets and Tools: To Identify Potential Epitope Segments for Designing Chimeric Vaccine Candidate to Cervix Papilloma

Immunomics tools and databases play an important role in the designing of prophylactic or therapeutic vaccines against pathogenic bacteria and viruses. Therefore, we aimed to illustrate the different immunological databases and web servers used to design a chimeric vaccine candidate against human cervix papilloma. Initially, cellular immunity inducing major histocompatibility complex class I and II epitopes from L2 protein of papilloma 58 strain were predicted using the IEDB, NetMHC, and Tepi tools. Then, the overlapped segments from the above analysis were used to calculate efficiency on interferon-gamma and humoral immunity production. In addition, the allergenicity, antigenicity, cross-reactivity with human proteomes, and epitope conservancy of elite segments were determined. The chimeric vaccine candidate (SGD58) was constructed with two different overlapped peptide segments (23–36) and (29–42), adjuvants (flagellin and RS09), two Th epitopes, and amino acid linkers. The results of homology modeling demonstrated that SGD58 have 88.6% of favored regions based on Ramachandran plot. Protein–protein docking with Swarm Dock reveals SGD58 with receptor complex have −54.74 kcal/mol of binding energy with more than 20 interacting residues. Docked complex are stable in 100ns of molecular dynamic simulation. Further, coding sequences of SGD58 also show elevated gene expression in E. coli. In conclusion, SGD58 may prompt vaccine against cervix papilloma. This study provides insight of vaccine design against different pathogenic microbes as well.

Exploring the Papillomaviral Proteome to Identify Potential Candidates for a Chimeric Vaccine against Cervix Papilloma Using Immunomics and Computational Structural Vaccinology

The human papillomavirus (HPV) 58 is considered to be the second most predominant genotype in cervical cancer incidents in China. HPV type-restriction, non-targeted delivery, and the highcost of existing vaccines necessitate continuing research on the HPV vaccine. We aimed to explore the papillomaviral proteome in order to identify potential candidates for a chimeric vaccine against cervix papilloma using computational immunology and structural vaccinology approaches. Two overlapped epitope segments (23⁻36) and (29⁻42) from the N-terminal region of the HPV58 minor capsid protein L2 are selected as capable of inducing both cellular and humoral immunity. In total, 318 amino acid lengths of the vaccine construct SGD58 contain adjuvants (Flagellin and RS09), two Th epitopes, and linkers. SGD58 is a stable protein that is soluble, antigenic, and non-allergenic. Homology modeling and the structural refinement of the best models of SGD58 and TLR5 found 96.8% and 93.9% favored regions in Rampage, respectively. The docking results demonstrated a HADDOCK score of -62.5 ± 7.6, the binding energy (-30 kcal/mol) and 44 interacting amino acid residues between SGD58-TLR5 complex. The docked complex are stable in 100 ns of simulation. The coding sequences of SGD58 also show elevated gene expression in Escherichia coli with 1.0 codon adaptation index and 59.92% glycine-cysteine content. We conclude that SGD58 may prompt the creation a vaccine against cervix papilloma.

StackSSSPred: A Stacking-Based Prediction of Supersecondary Structure from Sequence

Supersecondary structure (SSS) refers to specific geometric arrangements of several secondary structure (SS) elements that are connected by loops. The SSS can provide useful information about the spatial structure and function of a protein. As such, the SSS is a bridge between the secondary structure and tertiary structure. In this chapter, we propose a stacking-based machine learning method for the prediction of two types of SSSs, namely, β-hairpins and β-α-β, from the protein sequence based on comprehensive feature encoding. To encode protein residues, we utilize key features such as solvent accessibility, conservation profile, half surface exposure, torsion angle fluctuation, disorder probabilities, and more. The usefulness of the proposed approach is assessed using a widely used threefold cross-validation technique. The obtained empirical result shows that the proposed approach is useful and prediction can be improved further.

Structure refinement of membrane proteins via molecular dynamics simulations

A refinement protocol based on physics-based techniques established for water soluble proteins is tested for membrane protein structures. Initial structures were generated by homology modeling and sampled via molecular dynamics simulations in explicit lipid bilayer and aqueous solvent systems. Snapshots from the simulations were selected based on scoring with either knowledge-based or implicit membrane-based scoring functions and averaged to obtain refined models. The protocol resulted in consistent and significant refinement of the membrane protein structures similar to the performance of refinement methods for soluble proteins. Refinement success was similar between sampling in the presence of lipid bilayers and aqueous solvent but the presence of lipid bilayers may benefit the improvement of lipid-facing residues. Scoring with knowledge-based functions (DFIRE and RWplus) was found to be as good as scoring using implicit membrane-based scoring functions suggesting that differences in internal packing is more important than orientations relative to the membrane during the refinement of membrane protein homology models.

Searching whole genome sequences for biochemical identification features of emerging and reemerging pathogenic Corynebacterium species

Biochemical tests are traditionally used for bacterial identification at the species level in clinical microbiology laboratories. While biochemical profiles are generally efficient for the identification of the most important corynebacterial pathogen Corynebacterium diphtheriae, their ability to differentiate between biovars of this bacterium is still controversial. Besides, the unambiguous identification of emerging human pathogenic species of the genus Corynebacterium may be hampered by highly variable biochemical profiles commonly reported for these species, including Corynebacterium striatum, Corynebacterium amycolatum, Corynebacterium minutissimum, and Corynebacterium xerosis. In order to identify the genomic basis contributing for the biochemical variabilities observed in phenotypic identification methods of these bacteria, we combined a comprehensive literature review with a bioinformatics approach based on reconstruction of six specific biochemical reactions/pathways in 33 recently released whole genome sequences. We used data retrieved from curated databases (MetaCyc, PathoSystems Resource Integration Center (PATRIC), The SEED, TransportDB, UniProtKB) associated with homology searches by BLAST and profile Hidden Markov Models (HMMs) to detect enzymes participating in the various pathways and performed ab initio protein structure modeling and molecular docking to confirm specific results. We found a differential distribution among the various strains of genes that code for some important enzymes, such as beta-phosphoglucomutase and fructokinase, and also for individual components of carbohydrate transport systems, including the fructose-specific phosphoenolpyruvate-dependent sugar phosphotransferase (PTS) and the ribose-specific ATP-binging cassette (ABC) transporter. Horizontal gene transfer plays a role in the biochemical variability of the isolates, as some genes needed for sucrose fermentation were seen to be present in genomic islands. Noteworthy, using profile HMMs, we identified an enzyme with putative alpha-1,6-glycosidase activity only in some specific strains of C. diphtheriae and this may aid to understanding of the differential abilities to utilize glycogen and starch between the biovars.

Predictive and Experimental Approaches for Elucidating Protein-Protein Interactions and Quaternary Structures

The elucidation of protein-protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development.

Computational protein structure refinement: Almost there, yet still so far to go

Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.

3Drefine: an interactive web server for efficient protein structure refinement

3Drefine is an interactive web server for consistent and computationally efficient protein structure refinement with the capability to perform web-based statistical and visual analysis. The 3Drefine refinement protocol utilizes iterative optimization of hydrogen bonding network combined with atomic-level energy minimization on the optimized model using a composite physics and knowledge-based force fields for efficient protein structure refinement. The method has been extensively evaluated on blind CASP experiments as well as on large-scale and diverse benchmark datasets and exhibits consistent improvement over the initial structure in both global and local structural quality measures. The 3Drefine web server allows for convenient protein structure refinement through a text or file input submission, email notification, provided example submission and is freely available without any registration requirement. The server also provides comprehensive analysis of submissions through various energy and statistical feedback and interactive visualization of multiple refined models through the JSmol applet that is equipped with numerous protein model analysis tools. The web server has been extensively tested and used by many users. As a result, the 3Drefine web server conveniently provides a useful tool easily accessible to the community. The 3Drefine web server has been made publicly available at the URL: http://sysbio.rnet.missouri.edu/3Drefine/.

Homology modeling and molecular dynamics study on Schwanniomyces occidentalis alpha-amylase

With consumers growing increasingly aware of environmental issues, industries find enzymes as a reasonable alternative over physical conditions and chemical catalysts. Amylases are important hydrolase enzymes, which have been widely used in variety of industrial process such as pharmaceutical, food, and fermentation industries. Among amylases α-Amylase is in maximum demand due to its wide range of applications. The homology modeling study on Schwanniomyces occidentalis amylase (AMY1, UniProt identifier number: P19269) was performed by Modeller using Aspergillus oryzae (6TAA) as the template. The resulting structure was analyzed for validity and subjected to 14 ns of molecular dynamics (MD) simulation trough GROMACS. The validity of obtained model may represent that utilized OPLS force field is suitable for calcium-containing enzymes. DSSP secondary structure and contact map analysis represent the conservation of domain A TIM barrel feature together with calcium ion coordination sphere. Investigating the covariance matrix followed by principle component analyses for the first five eigenvectors of both trajectories indicate a little more flexibility for AMY1 structure. The electrostatic calculation for the final structures shows similar isoelectric point and superimposed buffering zone in the 5-8 pH range.

Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking.

Multi-Dimensional Scaling and MODELLER-Based Evolutionary Algorithms for Protein Model Refinement

Protein structure prediction, i.e., computationally predicting the three-dimensional structure of a protein from its primary sequence, is one of the most important and challenging problems in bioinformatics. Model refinement is a key step in the prediction process, where improved structures are constructed based on a pool of initially generated models. Since the refinement category was added to the biennial Critical Assessment of Structure Prediction (CASP) in 2008, CASP results show that it is a challenge for existing model refinement methods to improve model quality consistently. This paper presents three evolutionary algorithms for protein model refinement, in which multidimensional scaling(MDS), the MODELLER software, and a hybrid of both are used as crossover operators, respectively. The MDS-based method takes a purely geometrical approach and generates a child model by combining the contact maps of multiple parents. The MODELLER-based method takes a statistical and energy minimization approach, and uses the remodeling module in MODELLER program to generate new models from multiple parents. The hybrid method first generates models using the MDS-based method and then run them through the MODELLER-based method, aiming at combining the strength of both. Promising results have been obtained in experiments using CASP datasets. The MDS-based method improved the best of a pool of predicted models in terms of the global distance test score (GDT-TS) in 9 out of 16test targets.