The Protein Data Bank and structural genomics

Prediction rigidities for data-driven chemistry

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency whilst guaranteeing interpretability and transferability. In this work, we demonstrate the wide utility of prediction rigidities, a family of metrics derived from the loss function, in understanding the robustness of ML model predictions. We show that the prediction rigidities allow the assessment of the model not only at the global level, but also on the local or the component-wise level at which the intermediate (e.g. atomic, body-ordered, or range-separated) predictions are made. We leverage these metrics to understand the learning behavior of different ML models, and to guide efficient dataset construction for model training. We finally implement the formalism for a ML model targeting a coarse-grained system to demonstrate the applicability of the prediction rigidities to an even broader class of atomistic modeling problems.

Potential Serotonin 5-HT2A Receptor Agonist of Psychoactive Components of Silene undulata Aiton: LC-MS/MS, ADMET, and Molecular Docking Studies

BACKGROUND

Silene undulata is historically used for inducing vivid and prophetic lucid dreams, but limited information exists on its phytochemical composition and potential pharmacological properties.

OBJECTIVE

This study aimed to investigate the phytochemical composition of S. undulata through LC-MS/MS analysis and explore its potential serotonergic activity, which could support and confirm the traditional use of S. undulata as a dream-inducing plant.

METHODS

LC-MS/MS analysis was conducted on S. undulata extract, identifying 51 phytochemicals, including norharman, harmalol, harmaline, harmine, and ibogaine alkaloids. ADMET and Molecular docking investigations were employed to assess the serotonergic potential of these compounds.

RESULTS

The analysis revealed the presence of β-carboline alkaloids, such as norharman, harmalol, harmaline, harmine, and ibogaine, within S. undulata extract. ADMET analysis showed that these compounds have a favourable pharmacokinetic properties. In addition, molecular docking investigations showed that harmaline (-8.90 Kcal/mol), harmalol (-8.56 Kcal/mol), and ibogaine (-8.75 Kcal/mol) exhibited binding affinities comparable to the control molecule, LSD (-9.14 Kcal/mol), indicating potential agonistic activity at serotonin 5-HT2A receptor.

CONCLUSION

These findings provide insights into the potential therapeutic benefits of S. undulata, supporting its traditional use as a psychoactive plant. This study investigated the chemical constituents and potential serotonergic agonist activity of S. undulata for the first time. While promising, further research is necessary to uncover additional medicinal properties associated with the identified phytochemical components.

Protein Structure Inspired Discovery of a Novel Inducer of Anoikis in Human Melanoma

Drug discovery historically starts with an established function, either that of compounds or proteins. This can hamper discovery of novel therapeutics. As structure determines function, we hypothesized that unique 3D protein structures constitute primary data that can inform novel discovery. Using a computationally intensive physics-based analytical platform operating at supercomputing speeds, we probed a high-resolution protein X-ray crystallographic library developed by us. For each of the eight identified novel 3D structures, we analyzed binding of sixty million compounds. Top-ranking compounds were acquired and screened for efficacy against breast, prostate, colon, or lung cancer, and for toxicity on normal human bone marrow stem cells, both using eight-day colony formation assays. Effective and non-toxic compounds segregated to two pockets. One compound, Dxr2-017, exhibited selective anti-melanoma activity in the NCI-60 cell line screen. In eight-day assays, Dxr2-017 had an IC50 of 12 nM against melanoma cells, while concentrations over 2100-fold higher had minimal stem cell toxicity. Dxr2-017 induced anoikis, a unique form of programmed cell death in need of targeted therapeutics. Our findings demonstrate proof-of-concept that protein structures represent high-value primary data to support the discovery of novel acting therapeutics. This approach is widely applicable.

Exploring Radioiodinated Anastrozole and Epirubicin as AKT1-Targeted Radiopharmaceuticals in Breast Cancer: In Silico Analysis and Potential Therapeutic Effect with Functional Nuclear Imagining Implications

This study evaluates radio-iodinated anastrozole ([125I]anastrozole) and epirubicin ([125I]epirubicin) for AKT1-targeted breast cancer therapy, utilizing radiopharmaceutical therapy (RPT) for personalized treatment. Through molecular docking and dynamics simulations (200 ns), it investigates these compounds' binding affinities and mechanisms to the AKT1 enzyme, compared to the co-crystallized ligand, a known AKT1 inhibitor. Molecular docking results show that [125I]epirubicin has the highest ΔGbind (-11.84 kcal/mol), indicating a superior binding affinity compared to [125I] anastrozole (-10.68 kcal/mol) and the co-crystallized ligand (-9.53 kcal/mol). Molecular dynamics (MD) simulations confirmed a stable interaction with the AKT1 enzyme, with [125I]anastrozole and [125I]epirubicin reaching stability after approximately 68 ns with an average RMSD of around 2.2 Å, while the co-crystallized ligand stabilized at approximately 2.69 Å after 87 ns. RMSF analysis showed no significant shifts in residues or segments, with consistent patterns and differences of less than 2 Å, maintaining enzyme stability. The [125I]epirubicin complex maintained an average of four H-bonds, indicating strong and stable interactions, while [125I]anastrozole consistently formed three H-bonds. The average Rg values for both complexes were ~16.8 ± 0.1 Å, indicating no significant changes in the enzyme's compactness, thus preserving structural integrity. These analyses reveal stable binding and minimal structural perturbations, suggesting the high potential for AKT1 inhibition. MM-PBSA calculations confirm the potential of these radio-iodinated compounds as AKT1 inhibitors, with [125I]epirubicin exhibiting the most favorable binding energy (-23.57 ± 0.14 kcal/mol) compared to [125I]anastrozole (-20.03 ± 0.15 kcal/mol) and the co-crystallized ligand (-16.38 ± 0.14 kcal/mol), highlighting the significant role of electrostatic interactions in stabilizing the complex. The computational analysis shows [125I]anastrozole and [125I]epirubicin may play promising roles as AKT1 inhibitors, especially [125I]epirubicin for its high binding affinity and dynamic receptor interactions. These findings, supported by molecular docking scores and MM-PBSA binding energies, advocate for their potential superior inhibitory capability against the AKT1 enzyme. Nevertheless, it is crucial to validate these computational predictions through in vitro and in vivo studies to thoroughly evaluate the therapeutic potential and viability of these compounds for AKT1-targeted breast cancer treatment.

Protein Structure Inspired Drug Discovery

Drug discovery starts with known function, either of a compound or a protein, in-turn prompting investigations to probe 3D structure of the compound-protein interface. As protein structure determines function, we hypothesized that unique 3D structural motifs represent primary information denoting unique function that can drive discovery of novel agents. Using a physics-based protein structure analysis platform developed by us, designed to conduct computationally intensive analysis at supercomputing speeds, we probed a high-resolution protein x-ray crystallographic library developed by us. We selected 3D structural motifs whose function was not otherwise established, that offered environments supporting binding of drug-like chemicals and were present on proteins that were not established therapeutic targets. For each of eight potential binding pockets on six different proteins we accessed a 60 million compound library and used our analysis platform to evaluate binding. Using eight-day colony formation assays acquired compounds were screened for efficacy against human breast, prostate, colon and lung cancer cells and toxicity against human bone marrow stem cells. Compounds selectively inhibiting cancer growth segregated to two pockets on separate proteins. The compound, Dxr2-017, exhibited selective activity against human melanoma cells in the NCI-60 cell line screen, had an IC50 of 19 nM against human melanoma M14 cells in our eight-day assay, while over 2100-fold higher concentrations inhibited stem cells by less than 30%. We show that Dxr2-017 induces anoikis, a unique form of programmed cell death in need of targeted therapeutics. The predicted target protein for Dxr2-017 is expressed in bacteria, not in humans. This supports our strategy of focusing on unique 3D structural motifs. It is known that functionally important 3D structures are evolutionarily conserved. Here we demonstrate proof-of-concept that protein structure represents high value primary data to support discovery of novel therapeutics. This approach is widely applicable.

Radiological and Molecular Analysis of Radioiodinated Anastrozole and Epirubicin as Innovative Radiopharmaceuticals Targeting Methylenetetrahydrofolate Dehydrogenase 2 in Solid Tumors

In the dynamic field of radiopharmaceuticals, innovating targeted agents for cancer diagnosis and therapy is crucial. Our study enriches this evolving landscape by evaluating the potential of radioiodinated anastrozole ([125I]anastrozole) and radioiodinated epirubicin ([125I]epirubicin) as targeting agents against MTHFD2-driven tumors. MTHFD2, which is pivotal in one-carbon metabolism, is notably upregulated in various cancers, presenting a novel target for radiopharmaceutical application. Through molecular docking and 200 ns molecular dynamics (MD) simulations, we assess the binding efficiency and stability of [125I]anastrozole and [125I]epirubicin with MTHFD2. Molecular docking illustrates that [125I]epirubicin has a superior binding free energy (∆Gbind) of -41.25 kJ/mol compared to -39.07 kJ/mol for [125I]anastrozole and -38.53 kJ/mol for the control ligand, suggesting that it has a higher affinity for MTHFD2. MD simulations reinforce this, showing stable binding, as evidenced by root mean square deviation (RMSD) values within a narrow range, underscoring the structural integrity of the enzyme-ligand complexes. The root mean square fluctuation (RMSF) analysis indicates consistent dynamic behavior of the MTHFD2 complex upon binding with [125I]anastrozole and [125I]epirubicin akin to the control. The radius of gyration (RG) measurements of 16.90 Å for MTHFD2-[125I]anastrozole and 16.84 Å for MTHFD2-[125I]epirubicin confirm minimal structural disruption upon binding. The hydrogen bond analysis reveals averages of two and three stable hydrogen bonds for [125I]anastrozole and [125I]epirubicin complexes, respectively, highlighting crucial stabilizing interactions. The MM-PBSA calculations further endorse the thermodynamic favorability of these interactions, with binding free energies of -48.49 ± 0.11 kJ/mol for [125I]anastrozole and -43.8 kJ/mol for MTHFD2-. The significant contribution of Van der Waals and electrostatic interactions to the binding affinities of [125I]anastrozole and [125I]epirubicin, respectively, underscores their potential efficacy for targeted tumor imaging and therapy. These computational findings lay the groundwork for the future experimental validation of [125I]anastrozole and [125I]epirubicin as MTHFD2 inhibitors, heralding a notable advancement in precision oncology tools. The data necessitate subsequent in vitro and in vivo assays to corroborate these results.

Antioxidant and antibacterial activities of 5-mercapto(substitutedthio)-4-substituted-1,2,4-triazol based on nalidixic acid: A comprehensive study on its synthesis, characterization, and In silico evaluation

This study introduces a series of novel Alkyl thio-1,2,4-triazole (4a-p) and mercapto-1,2,4-triazole (3a-d) compounds derived from nalidixic acid. The synthesis was streamlined, involving interactions between nalidixic acid hydrazide and various isothiocyanates to yield cyclic and alkyl(aryl) sulfide compounds, characterized using 1H NMR, 13C NMR, IR, and elemental analysis. Antioxidant capabilities were quantified through DPPH and ABTS assays, highlighting significant potential, especially for compound 3d, which demonstrated an ABTS IC50 value of 0.397 μM, on par with ascorbic acid (IC50 = 0.87 μM). Antibacterial efficacy was established through MIC assessments against a broad spectrum of Gram-positive and Gram-negative bacteria, including Candida albicans. Compounds 3b, 4e, 4h, 4j, 4i, 4m, and 4o showed broad-spectrum activity, with 4k and 4m exhibiting pronounced potency against E. coli. Molecular docking studies validated the antibacterial potential, with compounds 4f and 4h showing high binding affinities (docking scores of -9.8 and -9.6 kcal/mol, respectively), indicating robust interactions with the bacterial enzyme targets. These scores underscore the compounds' mechanistic basis for their antibacterial action and support their therapeutic promise. Furthermore, compounds 3b, 4i, and 4m, identified through drug-likeness and toxicity predictions, were highlighted for their favorable profiles, suggesting their suitability for oral antibiotic therapies. This comprehensive study, blending synthetic, in vitro, and in silico approaches, emphasizes the triazole derivatives' potential as future candidates for antibiotic and antioxidant applications, particularly spotlighting compounds 3b, 4i, and 4m due to their promising efficacy and safety profiles.

Selectivity Mechanism of Pyrrolopyridone Analogues Targeting Bromodomain 2 of Bromodomain-Containing Protein 4 from Molecular Dynamics Simulations

Bromodomain and extra-terminal domain (BET) proteins play an important role in epigenetic regulation and are linked to several diseases; therefore, they are interesting targets. BET has two bromodomains: bromodomain 1 (BD1) and BD2. Selective targeting of BD1 or BD2 may produce different activities and greater effects than pan-BD inhibitors. However, the selective mechanism of the specific core must be studied at the atomic level. This study determined the effectiveness of pyrrolopyridone analogues to selectively inhibit BD2 using a pan-BD inhibitor (ABBV-075) and a selective-BD2 inhibitor (ABBV-744). Molecular dynamics simulations and calculations of binding free energies were used to systematically study the selectivity of BD2 inhibition by the pyrrolopyridone analogues. Overall, the pyrrolopyridone analogue inhibitors targeting BD2 interacted mainly with the following amino acid pairs between bromodomain-containing protein 4 (BRD4)-BD1 and BRD4-BD2 complexes: I146/V439, N140/N433, D144/H437, P82/P375, V87/V380, D88/D381, and Y139/Y432. The pyrrolopyridone analogues targeting BRD4-BD2 were divided into five regions based on selectivity mechanism. These results suggest that the R3 and R5 regions of pyrrolopyridone analogues can be modified to improve the selectivity between BRD4-BD1 and BRD4-BD2. The selectivity of BD2 inhibition by pyrrolopyridone analogues can be used to design novel BD2 inhibitors based on a pyrrolopyridone core.

Diosgenin and Monohydroxy Spirostanol from Prunus amygdalus var amara Seeds as Potential Suppressors of EGFR and HER2 Tyrosine Kinases: A Computational Approach

Cancer continues to be leading cause of death globally, with nearly 7 million deaths per year. Despite significant progress in cancer research and treatment, there remain several challenges to overcome, including drug resistance, the presence of cancer stem cells, and high interstitial fluid pressure in tumors. To tackle these challenges, targeted therapy, specifically targeting HER2 (Human Epidermal Growth Factor Receptor 2) as well as EGFR (Epidermal Growth Factor Receptor), is considered a promising approach in cancer treatment. In recent years, phytocompounds have gained recognition as a potential source of chemopreventive and chemotherapeutic agents in tumor cancer treatment. Phytocompounds are compounds derived from medicinal plants that have the potential to treat and prevent cancer. This study aimed to investigate phytocompounds from Prunus amygdalus var amara seeds as inhibitors against EGFR and HER2 enzymes using in silico methods. In this study, fourteen phytocompounds were isolated from Prunus amygdalus var amara seeds and subjected to molecular docking studies to determine their ability to bind to EGFR and HER2 enzymes. The results showed that diosgenin and monohydroxy spirostanol exhibited binding energies comparable to those of the reference drugs, tak-285, and lapatinib. Furthermore, the drug-likeness and ADMET predictions, performed using the admetSAR 2.0 web-server tool, suggested that diosgenin and monohydroxy spirostanol have similar safety and ADMET properties as the reference drugs. To get deeper insight into the structural steadiness and flexibility of the complexes formed between these compounds and theEGFR and HER2 proteins, molecular dynamics simulations were performed for 100 ns. The results showed that the hit phytocompounds did not significantly affect the stability of the EGFR and HER2 proteins and were able to form stable interactions with the catalytic binding sites of the proteins. Additionally, the MM-PBSA analysis revealed that the binding free energy estimates for diosgenin and monohydroxy spirostanol is comparable to the reference drug, lapatinib. This study provides evidence that diosgenin and monohydroxy spirostanol may have the potential to act as dual suppressors of EGFR and HER2. Additional in vivo and in vitro research are needed to certify these results and assess their efficacy and safety as cancer therapy agents. The experimental data reported and these results are in agreement.

Mycobacterium tuberculosis Rv0229c Shows Ribonuclease Activity and Reveals Its Corresponding Role as Toxin VapC51

The VapBC system, which belongs to the type II toxin-antitoxin (TA) system, is the most abundant and widely studied system in Mycobacterium tuberculosis. The VapB antitoxin suppresses the activity of the VapC toxin through a stable protein-protein complex. However, under environmental stress, the balance between toxin and antitoxin is disrupted, leading to the release of free toxin and bacteriostatic state. This study introduces the Rv0229c, a putative VapC51 toxin, and aims to provide a better understanding of its discovered function. The structure of the Rv0229c shows a typical PIN-domain protein, exhibiting an β1-α1-α2-β2-α3-α4-β3-α5-α6-β4-α7-β5 topology. The structure-based sequence alignment showed four electronegative residues in the active site of Rv0229c, which is composed of Asp8, Glu42, Asp95, and Asp113. By comparing the active site with existing VapC proteins, we have demonstrated the justification for naming it VapC51 at the molecular level. In an in vitro ribonuclease activity assay, Rv0229c showed ribonuclease activity dependent on the concentration of metal ions such as Mg²⁺ and Mn²⁺. In addition, magnesium was found to have a greater effect on VapC51 activity than manganese. Through these structural and experimental studies, we provide evidence for the functional role of Rv0229c as a VapC51 toxin. Overall, this study aims to enhance our understanding of the VapBC system in M. tuberculosis.

Secondary and Topological Structural Merge Prediction of Alpha-Helical Transmembrane Proteins Using a Hybrid Model Based on Hidden Markov and Long Short-Term Memory Neural Networks

Alpha-helical transmembrane proteins (αTMPs) play essential roles in drug targeting and disease treatments. Due to the challenges of using experimental methods to determine their structure, αTMPs have far fewer known structures than soluble proteins. The topology of transmembrane proteins (TMPs) can determine the spatial conformation relative to the membrane, while the secondary structure helps to identify their functional domain. They are highly correlated on αTMPs sequences, and achieving a merge prediction is instructive for further understanding the structure and function of αTMPs. In this study, we implemented a hybrid model combining Deep Learning Neural Networks (DNNs) with a Class Hidden Markov Model (CHMM), namely HDNNtopss. DNNs extract rich contextual features through stacked attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNNs), and CHMM captures state-associative temporal features. The hybrid model not only reasonably considers the probability of the state path but also has a fitting and feature-extraction capability for deep learning, which enables flexible prediction and makes the resulting sequence more biologically meaningful. It outperforms current advanced merge-prediction methods with a Q4 of 0.779 and an MCC of 0.673 on the independent test dataset, which have practical, solid significance. In comparison to advanced prediction methods for topological and secondary structures, it achieves the highest topology prediction with a Q2 of 0.884, which has a strong comprehensive performance. At the same time, we implemented a joint training method, Co-HDNNtopss, and achieved a good performance to provide an important reference for similar hybrid-model training.

Hit-to-Lead Short Peptides against Dengue Type 2 Envelope Protein: Computational and Experimental Investigations

Data from the World Health Organisation show that the global incidence of dengue infection has risen drastically, with an estimated 400 million cases of dengue infection occurring annually. Despite this worrying trend, there is still no therapeutic treatment available. Herein, we investigated short peptide fragments with a varying total number of amino acid residues (peptide fragments) from previously reported dengue virus type 2 (DENV2) peptide-based inhibitors, DN58wt (GDSYIIIGVEPGQLKENWFKKGSSIGQMF), DN58opt (TWWCFYFCRRHHPFWFFYRHN), DS36wt (LITVNPIVTEKDSPVNIEAE), and DS36opt (RHWEQFYFRRRERKFWLFFW), aided by in silico approaches: peptide–protein molecular docking and 100 ns of molecular dynamics (MD) simulation via molecular mechanics using Poisson–Boltzmann surface area (MMPBSA) and molecular mechanics generalised Born surface area (MMGBSA) methods. A library of 11,699 peptide fragments was generated, subjected to in silico calculation, and the candidates with the excellent binding affinity and shown to be stable in the DI-DIII binding pocket of DENV2 envelope (E) protein were determined. Selected peptides were synthesised using conventional Fmoc solid-phase peptide chemistry, purified by RP-HPLC, and characterised using LCMS. In vitro studies followed, to test for the peptides’ toxicity and efficacy in inhibiting the DENV2 growth cycle. Our studies identified the electrostatic interaction (from free energy calculation) to be the driving stabilising force for the E protein–peptide interactions. Five key E protein residues were also identified that had the most interactions with the peptides: (polar) LYS36, ASN37, and ARG350, and (nonpolar) LEU351 and VAL354; these residues might play crucial roles in the effective binding interactions. One of the peptide fragments, DN58opt_8-13 (PFWFFYRH), showed the best inhibitory activity, at about 63% DENV2 plague reduction, compared with no treatment. This correlates well with the in silico studies in which the peptide possessed the lowest binding energy (−9.0 kcal/mol) and was maintained steadily within the binding pocket of DENV2 E protein during the MD simulations. This study demonstrates the use of computational studies to expand research on lead optimisation of antiviral peptides, thus explaining the inhibitory potential of the designed peptides.

Accurate calculation of absolute free energy of binding for SHP2 allosteric inhibitors using free energy perturbation

Accurate prediction of binding affinity is a primary objective in structure-based drug discovery. A free energy perturbation (FEP) method based on molecular dynamics simulation shows great promise for protein-ligand binding affinity predictions. However, accurate calculation of binding affinity for allosteric inhibitors remains unknown and elusive, which hampers the discovery of allosteric inhibitors. Allosteric inhibitors exhibit several significant advantages over orthosteric inhibitors including higher specificity and lower side effects. Allosteric inhibitors against SHP2 are thought to be beneficial not only for diseases related to metabolism, but also for cancer, which make SHP2 a potential drug target. However, high structural sensitivity makes structural optimization of SHP2 allosteric inhibitors face challenges. Herein, we calculated the absolute binding free energy of SHP2 allosteric inhibitors using the FEP method by employing different λ-windows/simulation time sampling strategies. A simulation run with 32 λ-windows/64 ps sampling strategy delivered an excellent correlation (r = 0.96) and an unprecedented low mean absolute error of 0.5 kcal mol^-1 between predicted binding free energies and experimental ones, outperforming the MM/PBSA method. Our study demonstrates the possibility to accurately calculate the absolute binding free energy of allosteric inhibitors using FEP, which offers exciting prospects for the discovery of more effective allosteric inhibitors.

Prediction of Secondary and Tertiary Structure and Docking of Rb1WT And Rb1R661W Proteins

Background:

Retinoblastoma, a malignancy occurring in the juvenile cells of the retina, is responsible for light detection. It is one of the most emerging ra re childhood and infant cancer. It is initiated by the mutation in Rb1, a first tumor suppressor gene located on chromosome 13q14. Rb1 protein is responsible for cell cycle regulation.

Methods:

In our study, secondary and 3D-Structural predictions of Rb1WT and Rb1R661W were made by comparative or homology modeling to find any structural change leading to the disruption in its further interactions. Quality assurance of the structures was done by Ramachandran Plot for a stable structure. Both the proteins were then applied by docking process with proteins of interest.

Results:

Secondary structure showed a number of mutations in helixes, β-Hairpins of Rb1R661W. The major change was the loss of β-Hairpin loop, extension and shortening of helixes. 3D comparison structure showed a change in the groove of Rb1R661W. Docking results, unlike RB1 WT, had different and no interactions with some of the proteins of interest. This mutation in Rb1 protein had a deleterious effect on the protein functionality.

Conclusion:

This study will help to design the appropriate therapy and also understand the mechanism of disease of retinoblastoma, for researchers and pharmaceuticals.

Functional Characterization of Structural Genomics Proteins in the Crotonase Superfamily

Members of the Crotonase superfamily, a mechanistically diverse family of proteins that share a conserved quaternary structure, can often catalyze more than one reaction. However, the spectrum of activity for its members has not been well studied. We report on measured crotonase and hydrolase activity for eight structural genomics (SG) proteins from the Crotonase superfamily plus two previously characterized proteins, intended as controls: human enoyl CoA hydratase (ECH) and Anabaena β-diketone hydrolase. Like most of the 15,000+ SG protein structures deposited in the Protein Data Bank (PDB), the eight SG proteins are of unknown or uncertain biochemical function. The functional characterization of the eight SG proteins is guided by the Structurally Aligned Local Sites of Activity (SALSA), a local-structure-based computational approach to functional annotation. For human ECH, the turnover number for hydrolase activity is threefold higher than that for ECH activity, although the catalytic efficiency is 160-fold higher for ECH. Three SG proteins originally annotated as ECHs were predicted by SALSA to be hydrolases and are observed to have higher catalytic efficiencies for hydrolase activity than for ECH activity, on par with the previously characterized hydrolase. Among the five SG proteins predicted by SALSA to be ECHs, all but one also show some hydrolase activity; all five exhibit lower ECH activity than the human ECH with respect to the crotonyl-CoA substrate. Here, we show examples demonstrating that SALSA can correct functional misannotations even within enzyme families that display promiscuous activity.

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.

RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal.

Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS

BACKGROUND

Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs.

OBJECTIVE

Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes.

METHODS

SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions.

RESULTS

Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions.

CONCLUSION

Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.

Structural genomics and the Protein Data Bank

The field of Structural Genomics arose over the last 3 decades to address a large and rapidly growing divergence between microbial genomic, functional, and structural data. Several international programs took advantage of the vast genomic sequence information and evaluated the feasibility of structure determination for expanded and newly discovered protein families. As a consequence, structural genomics has developed structure-determination pipelines and applied them to a wide range of novel, uncharacterized proteins, often from “microbial dark matter,” and later to proteins from human pathogens. Advances were especially needed in protein production and rapid de novo structure solution. The experimental three-dimensional models were promptly made public, facilitating structure determination of other members of the family and helping to understand their molecular and biochemical functions. Improvements in experimental methods and databases resulted in fast progress in molecular and structural biology. The Protein Data Bank structure repository played a central role in the coordination of structural genomics efforts and the structural biology community as a whole. It facilitated development of standards and validation tools essential for maintaining high quality of deposited structural data.

An Unsual Cys-Glu-Lys Catalytic Triad is Responsible for the Catalytic Mechanism of the Nitrilase Superfamily: A QM/MM Study on Nit2

Nitrilase 2 (Nit2) is a representative member of the nitrilase superfamily that catalyzes the hydrolysis of α-ketosuccinamate into oxaloacetate. It has been associated with the metabolism of rapidly dividing cells like cancer cells. The catalytic mechanism of Nit2 employs a catalytic triad formed by Cys191, Glu81 and Lys150. The Cys191 and Glu81 play an active role during the catalytic process while the Lys150 is shown to play only a secondary role. The results demonstrate that the catalytic mechanism of Nit2 involves four steps. The nucleophilic attack of Cys191 to the α-ketosuccinamate, the formation of two tetrahedral enzyme adducts and the hydrolysis of a thioacyl-enzyme intermediate, from which results the formation of oxaloacetate and enzymatic turnover. The rate limiting step of the catalytic process is the formation of the first tetrahedral intermediate with a calculated activation free energy of 18.4 kcal/mol, which agrees very well with the experimental k_cat (17.67 kcal/mol).

The Nucleome Data Bank: web-based resources to simulate and analyze the three-dimensional genome

We introduce the Nucleome Data Bank (NDB), a web-based platform to simulate and analyze the three-dimensional (3D) organization of genomes. The NDB enables physics-based simulation of chromosomal structural dynamics through the MEGABASE + MiChroM computational pipeline. The input of the pipeline consists of epigenetic information sourced from the Encode database; the output consists of the trajectories of chromosomal motions that accurately predict Hi-C and fluorescence insitu hybridization data, as well as multiple observations of chromosomal dynamics in vivo. As an intermediate step, users can also generate chromosomal sub-compartment annotations directly from the same epigenetic input, without the use of any DNA-DNA proximity ligation data. Additionally, the NDB freely hosts both experimental and computational structural genomics data. Besides being able to perform their own genome simulations and download the hosted data, users can also analyze and visualize the same data through custom-designed web-based tools. In particular, the one-dimensional genetic and epigenetic data can be overlaid onto accurate 3D structures of chromosomes, to study the spatial distribution of genetic and epigenetic features. The NDB aims to be a shared resource to biologists, biophysicists and all genome scientists. The NDB is available at https://ndb.rice.edu.

RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including >660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with >40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.

Targeting Beta-Blocker Drug-Drug Interactions with Fibrinogen Blood Plasma Protein: A Computational and Experimental Study

In this work, one of the most prevalent polypharmacology drug–drug interaction events that occurs between two widely used beta-blocker drugs—i.e., acebutolol and propranolol—with the most abundant blood plasma fibrinogen protein was evaluated. Towards that end, molecular docking and Density Functional Theory (DFT) calculations were used as complementary tools. A fibrinogen crystallographic validation for the three best ranked binding-sites shows 100% of conformationally favored residues with total absence of restricted flexibility. From those three sites, results on both the binding-site druggability and ligand transport analysis-based free energy trajectories pointed out the most preferred biophysical environment site for drug–drug interactions. Furthermore, the total affinity for the stabilization of the drug–drug complexes was mostly influenced by steric energy contributions, based mainly on multiple hydrophobic contacts with critical residues (THR22: P and SER50: Q) in such best-ranked site. Additionally, the DFT calculations revealed that the beta-blocker drug–drug complexes have a spontaneous thermodynamic stabilization following the same affinity order obtained in the docking simulations, without covalent-bond formation between both interacting beta-blockers in the best-ranked site. Lastly, experimental ultrasound density and velocity measurements were performed and allowed us to validate and corroborate the computational obtained results.

An assessment of water placement algorithms in quantum mechanics/molecular mechanics modeling: the case of rhodopsins' first spectral absorption band maxima

Quantum mechanics/molecular mechanics (QM/MM) models are a widely used tool to obtain detailed insight into the properties and functioning of proteins. The outcome of QM/MM studies heavily depends on the quality of the applied QM/MM model. Prediction and right placement of internal water molecules in protein cavities is one of the critical parts of any QM/MM model construction. Herein, we performed a systematic study of four protein hydration algorithms. We tested these algorithms for their ability to predict X-ray-resolved water molecules for a set of membrane photosensitive rhodopsin proteins, as well as the influence of the applied water placement algorithms on the QM/MM calculated absorption maxima (λ_max) of these proteins. We used 49 rhodopsins and their intermediates with available X-ray structures as the test set. We found that a proper choice of hydration algorithms and setups is needed to predict functionally important water molecules in the chromophore-binding cavity of rhodopsins, such as the water cluster in the N-H region of bacteriorhodopsin or two water molecules in the binding pocket of bovine visual rhodopsin. The QM/MM calculated λ_max of rhodopsins is also quite sensitive to the applied protein hydration protocols. The best methodology allows obtaining an 18.0 nm average value for the absolute deviation of the calculated λ_max from the experimental λ_max. Although the major effect of water molecules on λ_max originates from the water molecules located in the binding pocket, the water molecules outside the binding pocket also affect the calculated λ_max mainly by causing a reorganization of the protein structure. The results reported in this study can be used for the evaluation and further development of hydration methodologies, in general, and rhodopsin QM/MM models, in particular.

Modeling and simulation study to identify threonine synthase as possible drug target in Leishmania major

Leishmaniasis is one of the most neglected tropical diseases that demand immediate attention to the identification of new drug targets and effective drug candidates. The present study demonstrates the possibility of using threonine synthase (TS) as a putative drug target in leishmaniasis disease management. We report the construction of an effective homology model of the enzyme that appears to be structurally as well as functionally well conserved. The 200 nanosecond molecular dynamics data on TS with and without pyridoxal phosphate (PLP) shed light on mechanistic details of PLP-induced conformational changes. Moreover, we address some important structural and dynamic interactions in the PLP binding region of TS that are in good agreement with previously speculated crystallographic estimations. Additionally, after screening more than 44,000 compounds, we propose 10 putative inhibitor candidates for TS based on virtual screening data and refined Molecular Mechanics Generalized Born Surface Area calculations. We expect that structural and functional dynamics data disclosed in this study will help initiate experimental endeavors toward establishing TS as an effective antileishmanial drug target.

Molecular Dynamics Simulations with NAMD2

X-ray diffraction crystallography is the primary technique to determine the three-dimensional structures of biomolecules. Although a robust method, X-ray crystallography is not able to access the dynamical behavior of macromolecules. To do so, we have to carry out molecular dynamics simulations taking as an initial system the three-dimensional structure obtained from experimental techniques or generated using homology modeling. In this chapter, we describe in detail a tutorial to carry out molecular dynamics simulations using the program NAMD2. We chose as a molecular system to simulate the structure of human cyclin-dependent kinase 2.

Docking with AutoDock4

AutoDock is one of the most popular receptor-ligand docking simulation programs. It was first released in the early 1990s and is in continuous development and adapted to specific protein targets. AutoDock has been applied to a wide range of biological systems. It has been used not only for protein-ligand docking simulation but also for the prediction of binding affinity with good correlation with experimental binding affinity for several protein systems. The latest version makes use of a semi-empirical force field to evaluate protein-ligand binding affinity and for selecting the lowest energy pose in docking simulation. AutoDock4.2.6 has an arsenal of four search algorithms to carry out docking simulation including simulated annealing, genetic algorithm, and Lamarckian algorithm. In this chapter, we describe a tutorial about how to perform docking with AutoDock4. We focus our simulations on the protein target cyclin-dependent kinase 2.

X-ray Structure-Based Chemoinformatic Analysis Identifies Promiscuous Ligands Binding to Proteins from Different Classes with Varying Shapes

(1) Background: Compounds with multitarget activity are of interest in basic research to explore molecular foundations of promiscuous binding and in drug discovery as agents eliciting polypharmacological effects. Our study has aimed to systematically identify compounds that form complexes with proteins from distinct classes and compare their bioactive conformations and molecular properties. (2) Methods: A large-scale computational investigation was carried out that combined the analysis of complex X-ray structures, ligand binding modes, compound activity data, and various molecular properties. (3) Results: A total of 515 ligands with multitarget activity were identified that included 70 organic compounds binding to proteins from different classes. These multiclass ligands (MCLs) were often flexible and surprisingly hydrophilic. Moreover, they displayed a wide spectrum of binding modes. In different target structure environments, binding shapes of MCLs were often similar, but also distinct. (4) Conclusions: Combined structural and activity data analysis identified compounds with activity against proteins with distinct structures and functions. MCLs were found to have greatly varying shape similarity when binding to different protein classes. Hence, there were no apparent canonical binding shapes indicating multitarget activity. Rather, conformational versatility characterized MCL binding.

RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, rcsb.org), the US data center for the global PDB archive, serves thousands of Data Depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without usage restrictions to more than 1 million rcsb.org Users worldwide and 600 000 pdb101.rcsb.org education-focused Users around the globe. PDB Data Depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy and 3D electron microscopy. PDB Data Consumers include researchers, educators and students studying Fundamental Biology, Biomedicine, Biotechnology and Energy. Recent reorganization of RCSB PDB activities into four integrated, interdependent services is described in detail, together with tools and resources added over the past 2 years to RCSB PDB web portals in support of a ‘Structural View of Biology.’

Discovery of potential targets of Triptolide through inverse docking in ovarian cancer cells

Triptolide (TPL) is proposed as an effective anticancer agent known for its anti-proliferation of a variety of cancer cells including ovarian cancer cells. Although some studies have been conducted, the mechanism by which TPL acts on ovarian cancer remains to be clearly described. Herein, systematic work based on bioinformatics was carried out to discover the potential targets of TPL in SKOV-3 cells. TPL induces the early apoptosis of SKOV-3 cells in a dose- and time-dependent manner with an IC₅₀ = 40 ± 0.89 nM when cells are incubated for 48 h. Moreover, 20 nM TPL significantly promotes early apoptosis at a rate of 40.73%. Using a self-designed inverse molecular docking protocol, we fish the top 19 probable targets of TPL from the target library, which was built on 2,250 proteins extracted from the Protein Data Bank. The 2D-DIGE assay reveals that the expression of eight genes is affected by TPL. The results of western blotting and qRT-PCR assay suggest that 40 nM of TPL up-regulates the level of Annexin A5 (6.34 ± 0.07 fold) and ATP syn thase (4.08 ± 0.08 fold) and down-regulates the level of β-Tubulin (0.11 ± 0.12 fold) and HSP90 (0.21 ± 0.09 fold). More details of TPL affecting on Annexin A5 signaling pathway will be discovered in the future. Our results define some potential targets of TPL, with the hope that this agent could be used as therapy for the preclinical treatment of ovarian cancer.

Structural Basis for Inhibition of Enoyl-[Acyl Carrier Protein] Reductase (InhA) from Mycobacterium tuberculosis

Background::

The enzyme trans-enoyl-[acyl carrier protein] reductase (InhA) is a central protein for the development of antitubercular drugs. This enzyme is the target for the pro-drug isoniazid, which is catalyzed by the enzyme catalase-peroxidase (KatG) to become active.

Objective::

Our goal here is to review the studies on InhA, starting with general aspects and focusing on the recent structural studies, with emphasis on the crystallographic structures of complexes involving InhA and inhibitors.

Method::

We start with a literature review, and then we describe recent studies on InhA crystallographic structures. We use this structural information to depict protein-ligand interactions. We also analyze the structural basis for inhibition of InhA. Furthermore, we describe the application of computational methods to predict binding affinity based on the crystallographic position of the ligands.

Results::

Analysis of the structures in complex with inhibitors revealed the critical residues responsible for the specificity against InhA. Most of the intermolecular interactions involve the hydrophobic residues with two exceptions, the residues Ser 94 and Tyr 158. Examination of the interactions has shown that many of the key residues for inhibitor binding were found in mutations of the InhA gene in the isoniazid-resistant Mycobacterium tuberculosis. Computational prediction of the binding affinity for InhA has indicated a moderate uphill relationship with experimental values.

Conclusion::

Analysis of the structures involving InhA inhibitors shows that small modifications on these molecules could modulate their inhibition, which may be used to design novel antitubercular drugs specific for multidrug-resistant strains.

Biological Activity Profiles of Multitarget Ligands from X-ray Structures

In pharmaceutical research, compounds with multitarget activity receive increasing attention. Such promiscuous chemical entities are prime candidates for polypharmacology, but also prone to causing undesired side effects. In addition, understanding the molecular basis and magnitude of multitarget activity is a stimulating topic for exploratory research. Computationally, compound promiscuity can be estimated through large-scale analysis of activity data. To these ends, it is critically important to take data confidence criteria and data consistency across different sources into consideration. Especially the consistency aspect has thus far only been little investigated. Therefore, we have systematically determined activity annotations and profiles of known multitarget ligands (MTLs) on the basis of activity data from different sources. All MTLs used were confirmed by X-ray crystallography of complexes with multiple targets. One of the key questions underlying our analysis has been how MTLs act in biological screens. The results of our analysis revealed significant variations of MTL activity profiles originating from different data sources. Such variations must be carefully considered in promiscuity analysis. Our study raises awareness of these issues and provides guidance for large-scale activity data analysis.

Off-Pocket Activity Cliffs: A Puzzling Facet of Molecular Recognition

While accurate quantitative prediction of ligand-protein binding affinity remains an elusive goal, high-affinity ligands to therapeutic targets are being designed through heuristic optimization of ligand-protein contacts. However, herein, through large-scale data mining and analyses, we demonstrate that a ligand's binding can also be strongly affected through modifying its solvent-exposed portion that does not make contacts with the protein, thus resulting in "off-pocket activity cliffs" (OAC). We then exposed the roots of the OAC phenomenon by means of molecular dynamics (MD) simulations and MD data analyses. We expect OAC to extend our knowledge of molecular recognition and enhance the drug designer's toolkit.

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued. These computational approaches aim to narrow down the search space for novel DTIs and shed light on drug functioning context. Most methods developed to date use binary classification to predict if the interaction between a drug and its target exists or not. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If that strength is not sufficiently strong, such DTI may not be useful. Therefore, the methods developed to predict drug-target binding affinities (DTBA) are of great value. In this study, we provide a comprehensive overview of the existing methods that predict DTBA. We focus on the methods developed using artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches, as well as related benchmark datasets and databases. Furthermore, guidance and recommendations are provided that cover the gaps and directions of the upcoming work in this research area. To the best of our knowledge, this is the first comprehensive comparison analysis of tools focused on DTBA with reference to AI/ML/DL.

Identifying Promiscuous Compounds with Activity against Different Target Classes

Compounds with multitarget activity are of high interest for polypharmacological drug discovery. Such promiscuous compounds might be active against closely related target proteins from the same family or against distantly related or unrelated targets. Compounds with activity against distinct targets are not only of interest for polypharmacology but also to better understand how small molecules might form specific interactions in different binding site environments. We have aimed to identify compounds with activity against drug targets from different classes. To these ends, a systematic analysis of public biological screening data was carried out. Care was taken to exclude compounds from further consideration that were prone to experimental artifacts and false positive activity readouts. Extensively assayed compounds were identified and found to contain molecules that were consistently inactive in all assays, active against a single target, or promiscuous. The latter included more than 1000 compounds that were active against 10 or more targets from different classes. These multiclass ligands were further analyzed and exemplary compounds were found in X-ray structures of complexes with distinct targets. Our collection of multiclass ligands should be of interest for pharmaceutical applications and further exploration of binding characteristics at the molecular level. Therefore, these highly promiscuous compounds are made publicly available.

High-accuracy protein structures by combining machine-learning with physics-based refinement

Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from coevolutionary analysis via machine learning to have significantly expanded the ability to predict structures for sequences without templates. One such method, AlphaFold, also performs well on sequences where templates are available but without using such information directly. Here we show that combining machine-learning based models from AlphaFold with state-of-the-art physics-based refinement via molecular dynamics simulations further improves predictions to outperform any other prediction method tested during the latest round of CASP. The resulting models have highly accurate global and local structures, including high accuracy at functionally important interface residues, and they are highly suitable as initial models for crystal structure determination via molecular replacement.

Discovery of potent apoptosis signal-regulating kinase 1 inhibitors via integrated computational strategy and biological evaluation

Apoptosis signal-regulating Kinase 1 (ASK1) has been confirmed as a potential therapeutic target for the treatment of non-alcoholic steatohepatitis (NASH) disorder and the discovery of ASK1 inhibitors has attracted increasing attention. In this work, a series of in silico methods including pharmacophore screening, docking binding site analysis, protein-ligand interaction fingerprint (PLIF) similarity investigation and molecular docking were applied to find the potential hits from commercial compound databases. Five compounds with potential inhibitory activity were purchased and submitted to biological activity validation. Thus, one hit compound was discovered with micromolar IC₅₀ value (10.59 μM) against ASK1. Results demonstrated that the integration of computation methods and biological test was quite reliable for the discovery of potent ASK1 inhibitors and the strategy could be extended to other similar targets of interest.

Whole-Cell Models and Simulations in Molecular Detail

Comprehensive data about the composition and structure of cellular components have enabled the construction of quantitative whole-cell models. While kinetic network-type models have been established, it is also becoming possible to build physical, molecular-level models of cellular environments. This review outlines challenges in constructing and simulating such models and discusses near- and long-term opportunities for developing physical whole-cell models that can connect molecular structure with biological function.

How Docking Programs Work

Protein-ligand docking simulations are of central interest for computer-aided drug design. Docking is also of pivotal importance to understand the structural basis for protein-ligand binding affinity. In the last decades, we have seen an explosion in the number of three-dimensional structures of protein-ligand complexes available at the Protein Data Bank. These structures gave further support for the development and validation of in silico approaches to address the binding of small molecules to proteins. As a result, we have now dozens of open source programs and web servers to carry out molecular docking simulations. The development of the docking programs and the success of such simulations called the attention of a broad spectrum of researchers not necessarily familiar with computer simulations. In this scenario, it is essential for those involved in experimental studies of protein-ligand interactions and biophysical techniques to have a glimpse of the basics of the protein-ligand docking simulations. Applications of protein-ligand docking simulations to drug development and discovery were able to identify hits, inhibitors, and even drugs. In the present chapter, we cover the fundamental ideas behind protein-ligand docking programs for non-specialists, which may benefit from such knowledge when studying molecular recognition mechanism.

Assembling multidomain protein structures through analogous global structural alignments

Most proteins exist with multiple domains in cells for cooperative functionality. However, structural biology and protein folding methods are often optimized for single-domain structures, resulting in a rapidly growing gap between the improved capability for tertiary structure determination and high demand for multidomain structure models. We have developed a pipeline, termed DEMO, for constructing multidomain protein structures by docking-based domain assembly simulations, with interdomain orientations determined by the distance profiles from analogous templates as detected through domain-level structure alignments. The pipeline was tested on a comprehensive benchmark set of 356 proteins consisting of 2-7 continuous and discontinuous domains, for which DEMO generated models with correct global fold (TM-score > 0.5) for 86% of cases with continuous domains and for 100% of cases with discontinuous domain structures, starting from randomly oriented target-domain structures. DEMO was also applied to reassemble multidomain targets in the CASP12 and CASP13 experiments using domain structures excised from the top server predictions, where the full-length DEMO models showed a significantly improved quality over the original server models. Finally, sparse restraints of mass spectrometry-generated cross-linking data and cryo-EM density maps are incorporated into DEMO, resulting in improvements in the average TM-score by 6.3% and 12.5%, respectively. The results demonstrate an efficient approach to assembling multidomain structures, which can be easily used for automated, genome-scale multidomain protein structure assembly.

Trypanosoma brucei centrin5 is enriched in the flagellum and interacts with other centrins in a calcium-dependent manner

Centrin is an evolutionarily conserved EF‐hand‐containing protein, which is present in eukaryotic organisms as diverse as algae, yeast, and humans. Centrins are associated with the microtubule‐organizing center and with centrosome‐related structures, such as basal bodies in flagellar and ciliated cells, and the spindle pole body in yeast. Five centrin genes have been identified in Trypanosoma brucei (T. brucei), a protozoan parasite that causes sleeping sickness in humans and nagana in cattle in sub‐Saharan Africa. In the present study, we identified that centrin5 of T. brucei (TbCentrin5) is localized throughout the cytosol and nucleus and enriched in the flagellum. We further identified that TbCentrin5 binds Ca²⁺ ions with a high affinity and constructed a model of TbCentrin5 bound by Ca²⁺ ions. Meanwhile, we observed that TbCentrin5 interacts with TbCentrin1, TbCentrin3, and TbCentrin4 and that the interactions are Ca²⁺‐dependent, suggesting that TbCentrin5 is able to form different complexes with other TbCentrins to participate in relevant cellular processes. Our study provides a foundation for better understanding of the biological roles of TbCentrin5.

Integration of Bioinformatics and in vitro Analysis Reveal Anti-leishmanial Effects of Azithromycin and Nystatin

Background:

Leishmaniasis is the major cause of mortality in under-developed countries. One of the main problems in leishmaniasis is the limited number of drug options, resistance and side effects. Such a situation requires to study the new chemical series with anti-leishmanial activity.

Objective:

To assess the anti-leishmanial activity of antibacterial and antifungal drugs.

Methods:

We have applied an integrative approach based on computational and in vitro methods to elucidate the efficacy of different antibacterial and antifungal drugs against Leishmania tropica (KWH23). Firstly these compounds were analyzed using in silico molecular docking. This analysis showed that the nystatin and azithromycin interacted with the active site amino acids of the target protein leishmanolysin. The nystatin, followed by azithromycin, produced the lowest binding energies indicating their inhibitive activity against the target. The efficacy of the docked drugs was further validated in vitro which showed that our bioinformatics based predictions completely agreed with experimental results. Stock solutions of drugs, media preparation and parasites cultures were performed according to the standard in-vitro protocol.

Results:

We found that the half maximal inhibitory concentration (IC50) value of dosage form of nystatin (10,000,00 U) and pure nystatin was 0.05701 µM and 0.00324 µM respectively. The IC50 value of combined azithromycin and nystatin (dosage and pure form) was 0.156 µg/ml and 0.0023 µg /ml (0.00248 µM) respectively. It was observed that IC50 value of nystatin is better than azithromycin and pure form of drugs had significant activity than the dosage form of drugs.

Conclusion:

From these results, it was also proven that pure drugs combination result is much better than all tested drugs results. The results of both in vitro and in silico studies clearly indicated that comparatively, nystatin is the potential candidate drug in combat against Leishmania tropica.

Advances in the Understanding of the Cannabinoid Receptor 1 – Focusing on the Inverse Agonists Interactions

Background:

Cannabinoid Receptor 1 (CB1) is a membrane protein prevalent in the central nervous system, whose crystallographic structure has recently been solved. Studies will be needed to investigate CB1 complexes with its ligands and its role in the development of new drugs.

Objective:

Our goal here is to review the studies on CB1, starting with general aspects and focusing on the recent structural studies, with emphasis on the inverse agonists bound structures.

Methods:

We start with a literature review, and then we describe recent studies on CB 1 crystallographic structure and docking simulations. We use this structural information to depict protein-ligand interactions. We also describe the molecular docking method to obtain complex structures of CB 1 with inverse agonists.

Results:

Analysis of the crystallographic structure and docking results revealed the residues responsible for the specificity of the inverse agonists for CB 1. Most of the intermolecular interactions involve hydrophobic residues, with the participation of the residues Phe 170 and Leu 359 in all complex structures investigated in the present study. For the complexes with otenabant and taranabant, we observed intermolecular hydrogen bonds involving residues His 178 (otenabant) and Thr 197 and Ser 383 (taranabant).

Conclusion:

Analysis of the structures involving inverse agonists and CB 1 revealed the pivotal role played by residues Phe 170 and Leu 359 in their interactions and the strong intermolecular hydrogen bonds highlighting the importance of the exploration of intermolecular interactions in the development of novel inverse agonists.

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.

Going beyond base-pairs: topology-based characterization of base-multiplets in RNA

Identification and characterization of base-multiplets, which are essentially mediated by base-pairing interactions, can provide insights into the diversity in the structure and dynamics of complex functional RNAs, and thus facilitate hypothesis driven biological research. The necessary nomenclature scheme, an extension of the geometric classification scheme for base-pairs by Leontis and Westhof, is however available only for base-triplets. In the absence of information on topology, this scheme is not applicable to quartets and higher order multiplets. Here we propose a topology-based classification scheme which, in conjunction with a graph-based algorithm, can be used for the automated identification and characterization of higher order base-multiplets in RNA structures. Here, the RNA structure is represented as a graph, where nodes represent nucleotides and edges represent base-pairing connectivity. Sets of connected components (of n nodes) within these graphs constitute subgraphs representing multiplets of "n" nucleotides. The different topological variants of the RNA multiplets thus correspond to different nonisomorphic forms of these subgraphs. To annotate RNA base-multiplets unambiguously, we propose a set of topology-based nomenclature rules for quartets, which are extendable to higher multiplets. We also demonstrate the utility of our approach toward the identification and annotation of higher order RNA multiplets, by investigating the occurrence contexts of selected examples in order to gain insights regarding their probable functional roles.