Sense and simplicity in HADDOCK scoring: Lessons from CASP-CAPRI round 1

In silico analysis of aptamer-RNA conjugate interactions with human transferrin receptor

The human transmembrane protein Transferrin Receptor-1 is regarded as a promising target for the systemic delivery of therapeutic agents, particularly of nucleic acid therapeutics, such as short double stranded RNAs. This ubiquitous receptor is involved in cellular iron uptake, keeping intracellular homeostasis. It is overexpressed in multiple cancer cell types and is internalized via clathrin-mediated endocytosis. In previous studies, a human transferrin receptor-1 RNA aptamer, identified as TR14 ST1-3, was shown to be capable of effectively internalizing into cells in culture and to deliver small, double stranded RNAs in vitro and in vivo, via systemic administration. To understand, at the molecular level, the aptamer binding to the receptor and the impact of conjugation with the therapeutic RNA, a multi-level in silico protocol was employed, including protein-aptamer docking, molecular dynamics simulations and free energy calculations. The competition for the binding pocket, between the aptamer and the natural ligand human Transferrin, was also evaluated. The results show that the aptamer binds to the same region as Transferrin, with residues from the helical domain showing a critical role. Moreover, the conjugation to the therapeutic RNA, was shown not to affect aptamer binding. Overall, this study provides an atomic-level understanding of aptamer association to human Transferrin Receptor-1 and of its conjugation with a short model-therapeutic RNA, providing also important clues for futures studies aiming to deliver other oligonucleotide-based therapeutics via Transferrin Receptor.

A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models

The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them appropriately in biomedical research, such as protein-protein interaction studies, protein design, and drug discovery. With the advent of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing attention. Many deep learning methods have been developed to tackle this problem; however, there is a noticeable absence of a comprehensive overview of these methods to facilitate future development. Addressing this gap, we present a review of deep learning EMA methods for protein complex structures developed in the past several years, analyzing their methodologies, data and feature construction. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.

Unveiling the structure and interactions of SOG1, a NAC domain transcription factor: An in-silico perspective

SOG1 is a crucial plant-specific NAC domain family transcription factor and functions as the central regulator of DNA damage response, acting downstream of ATM and ATR kinases. In this study, various in-silico approaches have been employed for the characterization of SOG1 transcription factor in a comparative manner with its orthologues from various plant species. Amino acid sequences of more than a hundred SOG1 or SOG1-like proteins were retrieved and their relationship was determined through phylogenetic and motif analyses. Various physiochemical properties and secondary structural components of SOG1 orthologues were determined in selective plant species including Arabidopsis thaliana, Oryza sativa, Amborella trichopoda, and Physcomitrella patens. Furthermore, fold recognition or threading and homology-based three-dimensional models of SOG1 were constructed followed by subsequent evaluation of quality and accuracy of the generated protein models. Finally, extensive DNA-Protein and Protein-Protein interaction studies were performed using the HADDOCK server to give an insight into the mechanism of how SOG1 binds with the promoter region of its target genes or interacts with other proteins to regulate the DNA damage responses in plants. Our docking analysis data have shown the molecular mechanism of SOG1's binding with 5'-CTT(N)7AAG-3' and 5'-(N)4GTCAA(N)4-3' consensus sequences present in the promoter region of its target genes. Moreover, SOG1 physically interacts and forms a thermodynamically stable complex with NAC103 and BRCA1 proteins, which possibly serve as coactivators or mediators in the transcription regulatory network of SOG1. Overall, our in-silico study will provide meaningful information regarding the structural and functional characterization of the SOG1 transcription factor.

Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning

Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties.

ARCTIC-3D: automatic retrieval and clustering of interfaces in complexes from 3D structural information

The formation of a stable complex between proteins lies at the core of a wide variety of biological processes and has been the focus of countless experiments. The huge amount of information contained in the protein structural interactome in the Protein Data Bank can now be used to characterise and classify the existing biological interfaces. We here introduce ARCTIC-3D, a fast and user-friendly data mining and clustering software to retrieve data and rationalise the interface information associated with the protein input data. We demonstrate its use by various examples ranging from showing the increased interaction complexity of eukaryotic proteins, 20% of which on average have more than 3 different interfaces compared to only 10% for prokaryotes, to associating different functions to different interfaces. In the context of modelling biomolecular assemblies, we introduce the concept of "recognition entropy", related to the number of possible interfaces of the components of a protein-protein complex, which we demonstrate to correlate with the modelling difficulty in classical docking approaches. The identified interface clusters can also be used to generate various combinations of interface-specific restraints for integrative modelling. The ARCTIC-3D software is freely available at github.com/haddocking/arctic3d and can be accessed as a web-service at wenmr.science.uu.nl/arctic3d.

An in silico study of protein-protein interactions and design of novel peptides for TrkA in ameloblastoma

Ameloblastoma is a benign odontogenic jawbone tumor. The binding of Nerve growth factor (NGF) to receptor tyrosine kinase A (TrkA) promotes cell survival, proliferation, and differentiation via PI3K/AKT and Ras/MAPK signaling. Although the exact cause of ameloblastoma remains unknown, elevated levels of NGF and TrkA expression in ameloblastoma are associated with aggressive tumor behavior and poor patient outcomes. It is previously demonstrated that His 4, Arg 9, and Glu 11 residues of NGF made crucial interactions with the TrkA subunit. The main aim of our present study to develop potential therapeutic strategies by identifying promising peptide candidates. The objectives include starting with a detailed in silico analysis to identify a crucial peptide sequence of NGF that is bound by TrkA, creating a library of novel peptides from the identified peptide sequence through a single-point mutation on interacting residues (His 4, Arg 9, and Glu 11), and selecting the top peptides based on docking score, interactions analysis, and desirable pose analysis. The study ultimately designed a hybrid peptide candidate through the simultaneous and continuous mutation of the top residues, resulting in a peptide that exhibited a more specific interaction with TrkA, blocking the binding site and preventing the interaction between NGF and TrkA.Communicated by Ramaswamy H. Sarma.

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

PURPOSE OF REVIEW

Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH.

RECENT FINDINGS

In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

Breaking the 'don't eat me' signal: in silico design of CD47-directed peptides for cancer immunotherapy

The leading cause of death worldwide is cancer. Although there are various therapies available to treat cancer, finding a successful one can be like searching for a needle in a haystack. Immunotherapy appears to be one of those needles in the haystack of cancer treatment. Immunotherapeutic agents enhance the immune response of the patient's body to tumor cells. One of the immunotherapeutic targets, Cluster of Differentiation 47 (CD47), releases the "don't eat me" signal when it binds to its receptor, Signal Regulatory Protein (SIRPα). Tumor cells use this signal to circumvent the immune system, rendering it ineffective. To stop tumor cells from releasing the "don't eat me" signal, the CD47-SIRPα interaction is specifically targeted in this study. To do so, in silico peptides were designed based on the structural analysis of the interaction between two proteins using point mutations on the interacting residues with the other amino acids. The peptide library was designed and docked on SIRPα using computational tools. Later on, after analyzing the docked complex, the best of them was selected for MD simulation studies of 100 ns. Further analysis after MD studies was carried out to determine the possible potential anti-SIRPα peptides.

A Structural Model for the Core Nup358-BicD2 Interface

Dynein motors facilitate the majority of minus-end-directed transport events on microtubules. The dynein adaptor Bicaudal D2 (BicD2) recruits the dynein machinery to several cellular cargo for transport, including Nup358, which facilitates a nuclear positioning pathway that is essential for the differentiation of distinct brain progenitor cells. Previously, we showed that Nup358 forms a "cargo recognition α-helix" upon binding to BicD2; however, the specifics of the BicD2-Nup358 interface are still not well understood. Here, we used AlphaFold2, complemented by two additional docking programs (HADDOCK and ClusPro) as well as mutagenesis, to show that the Nup358 cargo-recognition α-helix binds to BicD2 between residues 747 and 774 in an anti-parallel manner, forming a helical bundle. We identified two intermolecular salt bridges that are important to stabilize the interface. In addition, we uncovered a secondary interface mediated by an intrinsically disordered region of Nup358 that is directly N-terminal to the cargo-recognition α-helix and binds to BicD2 between residues 774 and 800. This is the same BicD2 domain that binds to the competing cargo adapter Rab6, which is important for the transport of Golgi-derived and secretory vesicles. Our results establish a structural basis for cargo recognition and selection by the dynein adapter BicD2, which facilitates transport pathways that are important for brain development.

Unveiling the functional epitopes of cobra venom cytotoxin by immunoinformatics and epitope-omic analyses

Approximate 70% of cobra venom is composed of cytotoxin (CTX), which is responsible for the dermonecrotic symptoms of cobra envenomation. However, CTX is generally low in immunogenicity, and the antivenom is ineffective in attenuating its in vivo toxicity. Furthermore, little is known about its epitope properties for empirical antivenom therapy. This study aimed to determine the epitope sequences of CTX using the immunoinformatic analyses and epitope-omics profiling. A conserved CTX was used in this study to determine its T-cell and B-cell epitope sequences using immunoinformatic tools and molecular docking simulation with different Human Leukocyte Antigens (HLAs). The potential T-cell and B-cell epitopes were 'KLVPLFY,' 'CPAGKNLCY,' 'MFMVSTPTK,' and 'DVCPKNSLL.' Molecular docking simulations disclosed that the HLA-B62 supertype exhibited the greatest binding affinity towards cobra venom cytotoxin. The namely L7, G18, K19, N20, M25, K33, V43, C44, K46, N47, and S48 of CTX exhibited prominent intermolecular interactions with HLA-B62. The multi-enzymatic-limited-digestion/liquid chromatography-mass spectrometry (MELD/LC-MS) also revealed three potential epitope sequences as 'LVPLFYK,' 'MFMVS,' and 'TVPVKR'. From different epitope mapping approaches, we concluded four potential epitope sites of CTX as 'KLVPLFYK', 'AGKNL', 'MFMVSTPKVPV' and 'DVCPKNSLL'. Site-directed mutagenesis of these epitopes confirmed their locations at the functional loops of CTX. These epitope sequences are crucial to CTX's structural folding and cytotoxicity. The results concluded the epitopes that resided within the functional loops constituted potential targets to fabricate synthetic epitopes for CTX-targeted antivenom production.

Ensemble-based molecular docking and spectrofluorometric analysis of interaction between cytotoxin and tumor necrosis factor receptor 1

Cytotoxin (CTX) is a three-finger toxin presents predominantly in cobra venom. The functional site of the toxin is located at its three hydrophobic loop tips. Its actual mechanism of cytotoxicity remains inconclusive as few conflicting hypotheses have been proposed in addition to direct cytolytic effects. The present work investigated the interaction between CTX and death receptor families via ensemble-based molecular docking and fluorescence titration analysis. Multiple sequence alignments of different CTX isoforms obtained a conserved CTX sequence. The three-dimensional structure of the conserved CTX was later determined using homology modelling, and its quality was validated. Ensemble-based molecular docking of CTX was performed with different death receptors, such as Fas-ligand and tumor necrosis factor receptor families. Our results showed that tumor necrosis factor receptor 1 (TNFR1) was the best receptor interacting with CTX attributed to the interaction of all three functional loops and evinced with low HADDOCK, Z-score and RMSD value. The interaction between CTX and TNFR1 was also supported by a concentration-dependent reduction of fluorescence intensity with increasing binding affinity. The possible intermolecular interactions between CTX and TNFR1 were Van der Waals forces and hydrogen bonding. Our findings suggest a possibility that CTX triggers apoptosis cell death through non-covalent interactions with TNFR1.Communicated by Ramaswamy H. Sarma.

Target Recognition in Tandem WW Domains: Complex Structures for Parallel and Antiparallel Ligand Orientation in h-FBP21 Tandem WW

Protein-protein interactions often rely on specialized recognition domains, such as WW domains, which bind to specific proline-rich sequences. The specificity of these protein-protein interactions can be increased by tandem repeats, i.e., two WW domains connected by a linker. With a flexible linker, the WW domains can move freely with respect to each other. Additionally, the tandem WW domains can bind in two different orientations to their target sequences. This makes the elucidation of complex structures of tandem WW domains extremely challenging. Here, we identify and characterize two complex structures of the tandem WW domain of human formin-binding protein 21 and a peptide sequence from its natural binding partner, the core-splicing protein SmB/B'. The two structures differ in the ligand orientation and, consequently, also in the relative orientation of the two WW domains. We analyze and probe the interactions in the complexes by molecular simulations and NMR experiments. The workflow to identify the complex structures uses molecular simulations, density-based clustering, and peptide docking. It is designed to systematically generate possible complex structures for repeats of recognition domains. These structures will help us to understand the synergistic and multivalency effects that generate the astonishing versatility and specificity of protein-protein interactions.

Identification of Potential ACE2-Derived Peptide Mimetics in SARS-CoV-2 Omicron Variant Therapeutics using Computational Approaches

The COVID-19 pandemic has become a global health challenge because of the emergence of distinct variants. Omicron, a new variant, is recognized as a variant of concern (VOC) by the World Health Organization (WHO) because of its higher mutations and accelerated human infection. The infection rate is strongly dependent on the binding rate of the receptor binding domain (RBD) against human angiotensin converting enzyme-2 (ACE2human) receptor. Inhibition of protein-protein (RBDs(SARS-CoV-2/omicron)-ACE2human) interaction has been already proven to inhibit viral infection. We have systematically designed ACE2human-derived peptides and peptide mimetics that have high binding affinity toward RBDomicron. Our peptide mutational analysis indicated the influence of canonical amino acids on the peptide binding process. Herein, efforts have been made to explore the atomistic details and events of RBDs(SARS-CoV-2/omicron)-ACE2human interactions by using molecular dynamics simulation. Our studies pave a path for developing therapeutic peptidomimetics against omicron.

Induced fit with replica exchange improves protein complex structure prediction

Despite the progress in prediction of protein complexes over the last decade, recent blind protein complex structure prediction challenges revealed limited success rates (less than 20% models with DockQ score > 0.4) on targets that exhibit significant conformational change upon binding. To overcome limitations in capturing backbone motions, we developed a new, aggressive sampling method that incorporates temperature replica exchange Monte Carlo (T-REMC) and conformational sampling techniques within docking protocols in Rosetta. Our method, ReplicaDock 2.0, mimics induced-fit mechanism of protein binding to sample backbone motions across putative interface residues on-the-fly, thereby recapitulating binding-partner induced conformational changes. Furthermore, ReplicaDock 2.0 clocks in at 150-500 CPU hours per target (protein-size dependent); a runtime that is significantly faster than Molecular Dynamics based approaches. For a benchmark set of 88 proteins with moderate to high flexibility (unbound-to-bound iRMSD over 1.2 Å), ReplicaDock 2.0 successfully docks 61% of moderately flexible complexes and 35% of highly flexible complexes. Additionally, we demonstrate that by biasing backbone sampling particularly towards residues comprising flexible loops or hinge domains, highly flexible targets can be predicted to under 2 Å accuracy. This indicates that additional gains are possible when mobile protein segments are known.

Domain architecture and catalysis of the Staphylococcus aureus fatty acid kinase

Fatty acid kinase (Fak) is a two-component enzyme that generates acyl-phosphate for phospholipid synthesis. Fak consists of a kinase domain protein (FakA) that phosphorylates a fatty acid enveloped by a fatty acid binding protein (FakB). The structural basis for FakB function has been established, but little is known about FakA. Here, we used limited proteolysis to define three separate FakA domains: the amino terminal FakA_N, the central FakA_L, and the carboxy terminal FakA_C. The isolated domains lack kinase activity, but activity is restored when FakA_N and FakA_L are present individually or connected as FakA_NL. The X-ray structure of the monomeric FakA_N captures the product complex with ADP and two Mg2+ ions bound at the nucleotide site. The FakA_L domain encodes the dimerization interface along with conserved catalytic residues Cys240, His282, and His284. AlphaFold analysis of FakA_L predicts the catalytic residues are spatially clustered and pointing away from the dimerization surface. Furthermore, the X-ray structure of FakA_C shows that it consists of two subdomains that are structurally related to FakB. Analytical ultracentrifugation demonstrates that FakA_C binds FakB, and site-directed mutagenesis confirms that a positively charged wedge on FakB meshes with a negatively charged groove on FakA_C. Finally, small angle X-ray scattering analysis is consistent with freely rotating FakA_N and FakA_C domains tethered by flexible linkers to FakA_L. These data reveal specific roles for the three independently folded FakA protein domains in substrate binding and catalysis.

Designing Short Peptides to Block the Interaction of SARS-CoV-2 and Human ACE2 for COVID-19 Therapeutics

To date, the current COVID-19 pandemic caused by SARS-CoV-2 has infected 99.2 million while killed 2.2 million people throughout the world and is still spreading widely. The unavailability of potential therapeutics against this virus urges to search and develop new drugs. SARS-CoV-2 enters human cells by interacting with human angiotensin-converting enzyme 2 (ACE2) receptor expressed on human cell surface through utilizing receptor-binding domain (RBD) of its spike glycoprotein. The RBD is highly conserved and is also a potential target for blocking its interaction with human cell surface receptor. We designed short peptides on the basis of our previously reported truncated ACE2 (tACE2) for increasing the binding affinity as well as the binding interaction network with RBD. These peptides can selectively bind to RBD with much higher affinities than the cell surface receptor. Thus, these can block all the binding residues required for binding to cell surface receptor. We used selected amino acid regions (21–40 and 65–75) of ACE2 as scaffold for the de novo peptide design. Our designed peptide Pep1 showed interactions with RBD covering almost all of its binding residues with significantly higher binding affinity (−13.2 kcal mol⁻¹) than the cell surface receptor. The molecular dynamics (MD) simulation results showed that designed peptides form a stabilized complex with RBD. We suggest that blocking the RBD through de novo designed peptides can serve as a potential candidate for COVID-19 treatment after further clinical investigations.

Computational Design and Analysis of a Multi-epitope Against Influenza A virus

Influenza A viruses are among the most studied viruses, however no effective prevention against influenza infection has been developed. So, designing an effective vaccine against Influenza A virus is a critical issue in the field of medical biotechnology. For this reason, to combat this disease, we have designed a novel multi-epitope vaccine candidate based on the several conserved and potential linear B-cell and T-cell binding epitopes by using the in silico approach. This vaccine consists of an ER signal conserved sequence, the PADRE conserved epitope and two conserved epitopes of Influenza matrix protein 2. T-cell binding epitopes from Matrix protein 2 were predicted by in silico tools of epitope prediction. The selected epitopes were joined by flexible linkers and physicochemical properties, toxicity, and allergenecity were investigated. The designed vaccine was antigenic, immunogenic, and non-allergenic with suitable physicochemical properties and has higher solubility. The final multi-epitope construct was modeled, confirmed by different programs and the molecular interactions with immune receptors were considered. The molecular docking assay indicated the interactions with immune-stimulatory toll-like receptor 3 (TLR3) and major histocompatibility complex class I (MHCI). The HADDOCK and H DOCK servers were used to make docking analysis, respectively. The docking analysis indicated a strong and stable binding interaction between the vaccine construct with major histocompatibility complex (MHC) class I and toll-like receptor 3. Overall, the findings suggest that the current vaccine may be a promising vaccine to prevent Influenza infection.

Using yeast two-hybrid system and molecular dynamics simulation to detect venom protein-protein interactions

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The venom protein-protein interactions in snake venom remain largely unknown.

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Y2H coupled with MD simulations was used to detect venom protein interactions.

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Venom PLA₂s interact with themselves and Lys49 PLA₂ interacts with venom CRISP.

Proteins and peptides are major components of snake venom. Venom protein transcriptomes and proteomes of many snake species have been reported; however, snake venom complexity (i.e., the venom protein-protein interactions, PPIs) remains largely unknown. To detect the venom protein interactions, we used the most common snake venom component, phospholipase A₂s (PLA₂s) as a “bait” to identify the interactions between PLA₂s and 14 of the most common proteins in Western diamondback rattlesnake (Crotalus atrox) venom by using yeast two-hybrid (Y2H) analysis, a technique used to detect PPIs. As a result, we identified PLA₂s interacting with themselves, and lysing-49 PLA₂ (Lys49 PLA₂) interacting with venom cysteine-rich secretory protein (CRISP). To reveal the complex structure of Lys49 PLA₂-CRISP interaction at the structural level, we first built the three-dimensional (3D) structures of Lys49 PLA₂ and CRISP by a widely used computational program-MODELLER. The binding modes of Lys49 PLA₂-CRISP interaction were then predicted through three different docking programs including ClusPro, ZDOCK and HADDOCK. Furthermore, the most likely complex structure of Lys49 PLA₂-CRISP was inferred by molecular dynamic (MD) simulations with GROMACS software. The techniques used and results obtained from this study strengthen the understanding of snake venom protein interactions and pave the way for the study of animal venom complexity.

The milk-derived lactoferrin inhibits V-ATPase activity by targeting its V1 domain

Lactoferrin (Lf), a bioactive milk protein, exhibits strong anticancer and antifungal activities. The search for Lf targets and mechanisms of action is of utmost importance to enhance its effective applications. A common feature among Lf-treated cancer and fungal cells is the inhibition of a proton pump called V-ATPase. Lf-driven V-ATPase inhibition leads to cytosolic acidification, ultimately causing cell death of cancer and fungal cells. Given that a detailed elucidation of how Lf and V-ATPase interact is still missing, herein we aimed to fill this gap by employing a five-stage computational approach. Molecular dynamics simulations of both proteins were performed to obtain a robust sampling of their conformational landscape, followed by clustering, which allowed retrieving representative structures, to then perform protein-protein docking. Subsequently, molecular dynamics simulations of the docked complexes and free binding energy calculations were carried out to evaluate the dynamic binding process and build a final ranking based on the binding affinities. Detailed atomist analysis of the top ranked complexes clearly indicates that Lf binds to the V₁ cytosolic domain of V-ATPase. Particularly, our data suggest that Lf binds to the interfaces between A/B subunits, where the ATP hydrolysis occurs, thus inhibiting this process. The free energy decomposition analysis further identified key binding residues that will certainly aid in the rational design of follow-up experimental studies, hence bridging computational and experimental biochemistry.

Truncated human angiotensin converting enzyme 2; a potential inhibitor of SARS-CoV-2 spike glycoprotein and potent COVID-19 therapeutic agent

The current pandemic of Covid-19 caused by SARS-CoV-2 is continued to spread globally and no potential drug or vaccine against it is available. Spike (S) glycoprotein is the structural protein of SARS-CoV-2 located on the envelope surface, involve in interaction with angiotensin converting enzyme 2 (ACE2), a cell surface receptor, followed by entry into the host cell. Thereby, blocking the S glycoprotein through potential inhibitor may interfere its interaction with ACE2 and impede its entry into the host cell. Here, we present a truncated version of human ACE2 (tACE2), comprising the N terminus region of the intact ACE2 from amino acid position 21-119, involved in binding with receptor binding domain (RBD) of SARS-CoV-2. We analyzed the in-silico potential of tACE2 to compete with intact ACE2 for binding with RBD. The protein-protein docking and molecular dynamic simulation showed that tACE2 has higher binding affinity for RBD and form more stabilized complex with RBD than the intact ACE2. Furthermore, prediction of tACE2 soluble expression in E. coli makes it a suitable candidate to be targeted for Covid-19 therapeutics. This is the first MD simulation based findings to provide a high affinity protein inhibitor for SARS-CoV-2 S glycoprotein, an important target for drug designing against this unprecedented challenge.Communicated by Ramaswamy H. Sarma.

How Far Are We from the Rapid Prediction of Drug Resistance Arising Due to Kinase Mutations?

In all living organisms, protein kinases regulate various cell signaling events through phosphorylation. The phosphorylation occurs upon transferring an ATP's terminal phosphate to a target residue. Because of the central role of protein kinases in several proliferative pathways, point mutations occurring within the kinase's ATP-binding site can lead to a constitutively active enzyme, and ultimately, to cancer. A select set of these point mutations can also make the enzyme drug resistant toward the available kinase inhibitors. Because of technical and economical limitations, rapid experimental exploration of the impact of these mutations remains to be a challenge. This underscores the importance of kinase-ligand binding affinity prediction tools that are poised to measure the efficacy of inhibitors in the presence of kinase mutations. To this end, here, we compare the performances of six web-based scoring tools (DSX-ONLINE, KDEEP, HADDOCK2.2, PDBePISA, Pose&Rank, and PRODIGY-LIG) in assessing the impact of kinase mutations on their interactions with their inhibitors. This assessment is carried out on a new structure-based BINDKIN benchmark we compiled. BINDKIN contains wild-type and mutant structure pairs of kinase-inhibitor complexes, together with their corresponding experimental binding affinities (in the form of IC₅₀, K _d, and K _i). The performance of various web servers over BINDKIN shows that they cannot predict the binding affinities (ΔGs) of wild-type and mutant cases directly. Still, they could catch whether a mutation improves or worsens the ligand binding (ΔΔGs) where the highest Pearson's R correlation coefficient is reached by DSX-ONLINE over the K _i dataset. When homology models are used instead of K _i-associated crystal structures, DSX-ONLINE loses its predictive capacity. These results highlight that there is room to improve the available scoring functions to estimate the impact of protein kinase point mutations on inhibitor binding. The BINDKIN benchmark with all related results is freely accessible online (https://github.com/CSB-KaracaLab/BINDKIN).

Computational Methods for the Elucidation of Protein Structure and Interactions

Biologists are increasingly aware of the importance of protein structure in revealing function. The computational tools now exist which allow researchers to model unknown proteins simply on the basis of their primary sequence. However, for the non-specialist bioinformatician, there is a dazzling array of terminology, acronyms, and competing computer software available for this process. This review is intended to highlight the key stages of computational protein structure prediction, as well as explain the reasons behind some of the procedures and list some established workarounds for common pitfalls. Thereafter follows a review of five one-stop servers for start-to-finish structure prediction.

Structural models of human ACE2 variants with SARS-CoV-2 Spike protein for structure-based drug design

Emergence of coronaviruses poses a threat to global health and economy. The current outbreak of SARS-CoV-2 has infected more than 28,000,000 people and killed more than 915,000. To date, there is no treatment for coronavirus infections, making the development of therapies to prevent future epidemics of paramount importance. To this end, we collected information regarding naturally-occurring variants of the Angiotensin-converting enzyme 2 (ACE2), an epithelial receptor that both SARS-CoV and SARS-CoV-2 use to enter the host cells. We built 242 structural models of variants of human ACE2 bound to the receptor binding domain (RBD) of the SARS-CoV-2 surface spike glycoprotein (S protein) and refined their interfaces with HADDOCK. Our dataset includes 140 variants of human ACE2 representing missense mutations found in genome-wide studies, 39 mutants with reported effects on the recognition of the RBD, and 63 predictions after computational alanine scanning mutagenesis of ACE2-RBD interface residues. This dataset will help accelerate the design of therapeutics against SARS-CoV-2, as well as contribute to prevention of possible future coronaviruses outbreaks.

Measurement(s)	Molecular Genetic Variation
Technology Type(s)	digital curation
Factor Type(s)	ACE2 variants
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12902498

Performance and Its Limits in Rigid Body Protein-Protein Docking

The development of fast Fourier transform (FFT) algorithms enabled the sampling of billions of complex conformations and thus revolutionized protein-protein docking. FFT-based methods are now widely available and have been used in hundreds of thousands of docking calculations. Although the methods perform "soft" docking, which allows for some overlap of component proteins, the rigid body assumption clearly introduces limitations on accuracy and reliability. In addition, the method can work only with energy expressions represented by sums of correlation functions. In this paper we use a well-established protein-protein docking benchmark set to evaluate the results of these limitations by focusing on the performance of the docking server ClusPro, which implements one of the best rigid body methods. Furthermore, we explore the theoretical limits of accuracy when using established energy terms for scoring, provide comparison with flexible docking algorithms, and review the historical performance of servers in the CAPRI docking experiment.

Truncated human angiotensin converting enzyme 2; a potential inhibitor of SARS-CoV-2 spike glycoprotein and potent COVID-19 therapeutic agent

The current pandemic of Covid-19 caused by SARS-CoV-2 is continued to spread globally and no potential drug or vaccine against it is available. Spike (S) glycoprotein is the structural protein of SARS-CoV-2 located on the envelope surface, involve in interaction with angiotensin converting enzyme 2 (ACE2), a cell surface receptor, followed by entry into the host cell. Thereby, blocking the S glycoprotein through potential inhibitor may interfere its interaction with ACE2 and impede its entry into the host cell. Here, we present a truncated version of human ACE2 (tACE2), comprising the N terminus region of the intact ACE2 from amino acid position 21-119, involved in binding with receptor binding domain (RBD) of SARS-CoV-2. We analyzed the in-silico potential of tACE2 to compete with intact ACE2 for binding with RBD. The protein-protein docking and molecular dynamic simulation showed that tACE2 has higher binding affinity for RBD and form more stabilized complex with RBD than the intact ACE2. Furthermore, prediction of tACE2 soluble expression in E. coli makes it a suitable candidate to be targeted for Covid-19 therapeutics. This is the first MD simulation based findings to provide a high affinity protein inhibitor for SARS-CoV-2 S glycoprotein, an important target for drug designing against this unprecedented challenge.Communicated by Ramaswamy H. Sarma.

Coarse-grained (hybrid) integrative modeling of biomolecular interactions

The computational modeling field has vastly evolved over the past decades. The early developments of simplified protein systems represented a stepping stone towards establishing more efficient approaches to sample intricated conformational landscapes. Downscaling the level of resolution of biomolecules to coarser representations allows for studying protein structure, dynamics and interactions that are not accessible by classical atomistic approaches. The combination of different resolutions, namely hybrid modeling, has also been proved as an alternative when mixed levels of details are required. In this review, we provide an overview of coarse-grained/hybrid models focusing on their applicability in the modeling of biomolecular interactions. We give a detailed list of ready-to-use modeling software for studying biomolecular interactions allowing various levels of coarse-graining and provide examples of complexes determined by integrative coarse-grained/hybrid approaches in combination with experimental information.

Integrative biology of native cell extracts: a new era for structural characterization of life processes

Advances in electron microscopy have provided unprecedented access to the structural characterization of large, flexible and heterogeneous complexes. Until recently, cryo-electron microscopy (cryo-EM) has been applied to understand molecular organization in either highly purified, isolated biomolecules or in situ. An emerging field is developing, bridging the gap between the two approaches, and focuses on studying molecular organization in native cell extracts. This field has demonstrated its potential by resolving the structure of fungal fatty acid synthase (FAS) at 4.7 Å [Fourier shell correlation (FSC) = 0.143]; FAS was not only less than 50% enriched, but also retained higher-order binders, previously unknown. Although controversial in the sense that the lysis step might introduce artifacts, cell extracts preserve aspects of cellular function. In addition, cell extracts are accessible, besides cryo-EM, to modern proteomic methods, chemical cross-linking, network biology and biophysical modeling. We expect that automation in imaging cell extracts, along with the integration of molecular/cell biology approaches, will provide remarkable achievements in the study of closer-to-life biomolecular states of pronounced biotechnological and medical importance. Such steps will, eventually, bring us a step closer to the biophysical description of cellular processes in an integrative, holistic approach.

iScore: a novel graph kernel-based function for scoring protein-protein docking models

MOTIVATION

Protein complexes play critical roles in many aspects of biological functions. Three-dimensional (3D) structures of protein complexes are critical for gaining insights into structural bases of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determinations of 3D protein complex structures, computational docking has evolved as a valuable tool to predict 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge.

RESULTS

Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein-protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to, that of state-of-the-art scoring functions on two independent datasets: (i) Docking software-specific models and (ii) the CAPRI score set generated by a wide variety of docking approaches (i.e. docking software-non-specific). iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary, topological and energetic information for scoring docked conformations. This work represents the first successful demonstration of graph kernels to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes.

AVAILABILITY AND IMPLEMENTATION

The iScore code is freely available from Github: https://github.com/DeepRank/iScore (DOI: 10.5281/zenodo.2630567). And the docking models used are available from SBGrid: https://data.sbgrid.org/dataset/684).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38-45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement/CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher-quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.

Efficient flexible backbone protein-protein docking for challenging targets

Motivation

Binding-induced conformational changes challenge current computational docking algorithms by exponentially increasing the conformational space to be explored. To restrict this search to relevant space, some computational docking algorithms exploit the inherent flexibility of the protein monomers to simulate conformational selection from pre-generated ensembles. As the ensemble size expands with increased flexibility, these methods struggle with efficiency and high false positive rates.

Results

Here, we develop and benchmark RosettaDock 4.0, which efficiently samples large conformational ensembles of flexible proteins and docks them using a novel, six-dimensional, coarse-grained score function. A strong discriminative ability allows an eight-fold higher enrichment of near-native candidate structures in the coarse-grained phase compared to RosettaDock 3.2. It adaptively samples 100 conformations each of the ligand and the receptor backbone while increasing computational time by only 20-80%. In local docking of a benchmark set of 88 proteins of varying degrees of flexibility, the expected success rate (defined as cases with ≥50% chance of achieving 3 near-native structures in the 5 top-ranked ones) for blind predictions after resampling is 77% for rigid complexes, 49% for moderately flexible complexes and 31% for highly flexible complexes. These success rates on flexible complexes are a substantial step forward from all existing methods. Additionally, for highly flexible proteins, we demonstrate that when a suitable conformer generation method exists, the method successfully docks the complex.

Availability and implementation

As a part of the Rosetta software suite, RosettaDock 4.0 is available at https://www.rosettacommons.org to all non-commercial users for free and to commercial users for a fee.

Supplementary information

Supplementary data are available at Bioinformatics online.

Pre- and post-docking sampling of conformational changes using ClustENM and HADDOCK for protein-protein and protein-DNA systems

Incorporating the dynamic nature of biomolecules in the modeling of their complexes is a challenge, especially when the extent and direction of the conformational changes taking place upon binding is unknown. Estimating whether the binding of a biomolecule to its partner(s) occurs in a conformational state accessible to its unbound form (“conformational selection”) and/or the binding process induces conformational changes (“induced‐fit”) is another challenge. We propose here a method combining conformational sampling using ClustENM—an elastic network‐based modeling procedure—with docking using HADDOCK, in a framework that incorporates conformational selection and induced‐fit effects upon binding. The extent of the applied deformation is estimated from its energetical costs, inspired from mechanical tensile testing on materials. We applied our pre‐ and post‐docking sampling of conformational changes to the flexible multidomain protein‐protein docking benchmark and a subset of the protein‐DNA docking benchmark. Our ClustENM‐HADDOCK approach produced acceptable to medium quality models in 7/11 and 5/6 cases for the protein‐protein and protein‐DNA complexes, respectively. The conformational selection (sampling prior to docking) has the highest impact on the quality of the docked models for the protein‐protein complexes. The induced‐fit stage of the pipeline (post‐sampling), however, improved the quality of the final models for the protein‐DNA complexes. Compared to previously described strategies to handle conformational changes, ClustENM‐HADDOCK performs better than two‐body docking in protein‐protein cases but worse than a flexible multidomain docking approach. However, it does show a better or similar performance compared to previous protein‐DNA docking approaches, which makes it a suitable alternative.

What method to use for protein-protein docking?

A number of well-established servers perform 'free' docking of proteins of known structures. In contrast, template-based docking can start from sequences if structures are available for complexes that are homologous to the target. On the basis of the results of the CAPRI-CASP structure prediction experiments, template-based methods yield more accurate predictions if good templates can be found, but generally fail without such templates. However, free global docking, or focused docking around even poor quality template-based models, can still generate acceptable docked structures in these cases. In accordance with the analysis of a benchmark set, free docking of heterodimers yields acceptable or better predictions in the top 10 models for around 40% of structures. However, it is likely that a combination of template-based and free docking methods can perform better for targets that have template structures available. Another way of improving the reliability of predictions is adding experimental information as restraints, an option built into several docking servers.

West-Life: A Virtual Research Environment for structural biology

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Data processing and data management services for structural biology.

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Enhancements to existing web services for structure solution and analysis.

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New pipelines to link these services into more complex higher-level workflows.

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New data management facilities.

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Making the benefits of European e-Infrastructures more accessible to structural biologists.

The West-Life project (https://about.west-life.eu/) is a Horizon 2020 project funded by the European Commission to provide data processing and data management services for the international community of structural biologists, and in particular to support integrative experimental approaches within the field of structural biology. It has developed enhancements to existing web services for structure solution and analysis, created new pipelines to link these services into more complex higher-level workflows, and added new data management facilities. Through this work it has striven to make the benefits of European e-Infrastructures more accessible to life-science researchers in general and structural biologists in particular.

iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations

Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning‐based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy‐based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein‐protein complexes. It competes with existing state‐of‐the‐art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC < 0.42), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2–p53 complex.

Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2

We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall's Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening.

Determining protein similarity by comparing hydrophobic core structure

Formal assessment of structural similarity is - next to protein structure prediction - arguably the most important unsolved problem in proteomics. In this paper we propose a similarity criterion based on commonalities between the proteins' hydrophobic cores. The hydrophobic core emerges as a result of conformational changes through which each residue reaches its intended position in the protein body. A quantitative criterion based on this phenomenon has been proposed in the framework of the CASP challenge. The structure of the hydrophobic core - including the placement and scope of any deviations from the idealized model - may indirectly point to areas of importance from the point of view of the protein's biological function. Our analysis focuses on an arbitrarily selected target from the CASP11 challenge. The proposed measure, while compliant with CASP criteria (70-80% correlation), involves certain adjustments which acknowledge the presence of factors other than simple spatial arrangement of solids.