| 1. |
- Hassan, Sameer, et al.
(författare)
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Elucidation of ligand binding and dimerization of NADPH:protochlorophyllide (Pchlide) oxidoreductase from pea (Pisum sativum L.) by structural analysis and simulations
- 2021
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Ingår i: Proteins-Structure Function and Bioinformatics. - : Wiley. - 0887-3585 .- 1097-0134. ; 89:10, s. 1300-1314
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Tidskriftsartikel (refereegranskat)abstract
- NADPH:protochlorophyllide (Pchlide) oxidoreductase (POR) is a key enzyme of chlorophyll biosynthesis in angiosperms. It is one of few known photoenzymes, which catalyzes the light-activated trans-reduction of the C17-C18 double bond of Pchlide's porphyrin ring. Due to the light requirement, dark-grown angiosperms cannot synthesize chlorophyll. No crystal structure of POR is available, so to improve understanding of the protein's three-dimensional structure, its dimerization, and binding of ligands (both the cofactor NADPH and substrate Pchlide), we computationally investigated the sequence and structural relationships among homologous proteins identified through database searches. The results indicate that alpha 4 and alpha 7 helices of monomers form the interface of POR dimers. On the basis of conserved residues, we predicted 11 functionally important amino acids that play important roles in POR binding to NADPH. Structural comparison of available crystal structures revealed that they participate in formation of binding pockets that accommodate the Pchlide ligand, and that five atoms of the closed tetrapyrrole are involved in non-bonding interactions. However, we detected no clear pattern in the physico-chemical characteristics of the amino acids they interact with. Thus, we hypothesize that interactions of these atoms in the Pchlide porphyrin ring are important to hold the ligand within the POR binding site. Analysis of Pchlide binding in POR by molecular docking and PELE simulations revealed that the orientation of the nicotinamide group is important for Pchlide binding. These findings highlight the complexity of interactions of porphyrin-containing ligands with proteins, and we suggest that fit-inducing processes play important roles in POR-Pchlide interactions.
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| 2. |
- Hassan, Sameer, et al.
(författare)
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Ligand Binding Site Comparison - LiBiSCo - a web-based tool for analyzing interactions between proteins and ligands to explore amino acid specificity within active sites
- 2021
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Ingår i: Proteins-Structure Function and Bioinformatics. - : Wiley. - 0887-3585 .- 1097-0134. ; 89:11, s. 1530-1540
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Tidskriftsartikel (refereegranskat)abstract
- Interaction between protein and ligands are ubiquitous in a biological cell, and understanding these interactions at the atom level in protein-ligand complexes is crucial for structural bioinformatics and drug discovery. Here, we present a web-based protein-ligand interaction application named Ligand Binding Site Comparison (LiBiSCo) for comparing the amino acid residues interacting with atoms of a ligand molecule between different protein-ligand complexes available in the Protein Data Bank (PDB) database. The comparison is performed at the ligand atom level irrespectively of having binding site similarity or not between the protein structures of interest. The input used in LiBiSCo is one or several PDB IDs of protein-ligand complex(es) and the tool returns a list of identified interactions at ligand atom level including both bonded and non-bonded interactions. A sequence profile for the interaction for each ligand atoms is provided as a WebLogo. The LiBiSco is useful in understanding ligand binding specificity and structural promiscuity among families that are structurally unrelated. The LiBiSCo tool can be accessed through .
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| 3. |
- Laine, Elodie, et al.
(författare)
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Protein sequence-to-structure learning : Is this the end(-to-end revolution)?
- 2021
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Ingår i: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 89:12, s. 1770-1786
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Forskningsöversikt (refereegranskat)abstract
- The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; and (vi) finally truly end-to-end architectures, that is, differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last 2 years and widely used in CASP14.
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| 4. |
- Lensink, Marc F., et al.
(författare)
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Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment
- 2023
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Ingår i: Proteins. - : WILEY. - 0887-3585 .- 1097-0134.
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Tidskriftsartikel (refereegranskat)abstract
- We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average similar to 70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.
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| 5. |
- Pozzati, Gabriele, et al.
(författare)
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Scoring of protein-protein docking models utilizing predicted interface residues
- 2022
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Ingår i: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 90:7, s. 1493-1505
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Tidskriftsartikel (refereegranskat)abstract
- Scoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today's best scoring functions can significantly increase the number of top-ranked models but still fail for most targets. Here, we examine the possibility of utilizing predicted interface residues to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the regions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. This study systematically tests different interface prediction methods for scoring >300.000 low-resolution rigid-body template free docking decoys. Overall we find that contact-based interface prediction by BIPSPI is the best method to score docking solutions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high-importance metric when estimating interface prediction quality, focusing on docking constraints production. Finally, we discussed several limitations for adopting interface predictions as constraints in a docking protocol.
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| 6. |
- Sjöström, Dick J., et al.
(författare)
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Computational backbone design enables soluble engineering of transferrin receptor apical domain
- 2020
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Ingår i: Proteins. - : John Wiley & Sons. - 0887-3585 .- 1097-0134. ; 88:12, s. 1569-1577
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Tidskriftsartikel (refereegranskat)abstract
- Supply of iron into human cells is achieved by iron carrier protein transferrin and its receptor that upon complex formation get internalized by endocytosis. Similarly, the iron needs to be delivered into the brain, and necessitates the transport across the blood-brain barrier. While there are still unanswered questions about these mechanisms, extensive efforts have been made to use the system for delivery of therapeutics into biological compartments. The dimeric form of the receptor, where each subunit consists of three domains, further complicates the detailed investigation of molecular determinants responsible for guiding the receptor interactions with other proteins. Especially the apical domain's biological function has been elusive. To further the study of transferrin receptor, we have computationally decoupled the apical domain for soluble expression, and validated the design strategy by structure determination. Besides presenting a methodology for solubilizing domains, the results will allow for study of apical domain's function.
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| 7. |
- Sjöström, Dick J., et al.
(författare)
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Tuning the binding interface between Machupo virus glycoprotein and human transferrin receptor
- 2021
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Ingår i: Proteins. - : John Wiley & Sons. - 0887-3585 .- 1097-0134. ; 89:3, s. 311-321
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Tidskriftsartikel (refereegranskat)abstract
- Machupo virus, known to cause hemorrhagic fevers, enters human cells via binding with its envelope glycoprotein to transferrin receptor 1 (TfR). Similarly, the receptor interactions have been explored in biotechnological applications as a molecular system to ferry therapeutics across the cellular membranes and through the impenetrable blood-brain barrier that effectively blocks any such delivery into the brain. Study of the experimental structure of Machupo virus glycoprotein 1 (MGP1) in complex with TfR and glycoprotein sequence homology has identified some residues at the interface that influence binding. There are, however, no studies that have attempted to optimize the binding potential between MGP1 and TfR. In pursuits for finding therapeutic solutions for the New World arenaviruses, and to gain a greater understanding of MGP1 interactions with TfR, it is crucial to understand the structure-sequence relationship driving the interface formation. By displaying MGP1 on yeast surface we have examined the contributions of individual residues to the binding of solubilized ectodomain of TfR. We identified MGP1 binding hot spot residues, assessed the importance of posttranslational N-glycan modifications, and used a selection with random mutagenesis for affinity maturation. We show that the optimized MGP1 variants can bind more strongly to TfR than the native MGP1, and there is an MGP1 sequence that retains binding in the absence of glycosylation, but with the addition of further amino acid substitutions. The engineered variants can be used to probe cellular internalization or the blood-brain barrier crossing to achieve greater understanding of TfR mediated internalization.
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| 8. |
- Swain, Shasank S., et al.
(författare)
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Integrated bioinformatics-cheminformatics approach toward locating pseudo-potential antiviral marine alkaloids against SARS-CoV-2-Mpro
- 2022
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Ingår i: Proteins. - : John Wiley & Sons. - 0887-3585 .- 1097-0134. ; 90:9, s. 1617-1633
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Tidskriftsartikel (refereegranskat)abstract
- The emergence of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) with the most contagious variants, alpha (B.1.1.7), beta (B.1.351), delta (B.1.617.2), and Omicron (B.1.1.529) has continuously added a higher number of morbidity and mortality, globally. The present integrated bioinformatics-cheminformatics approach was employed to locate potent antiviral marine alkaloids that could be used against SARS-CoV-2. Initially, 57 antiviral marine alkaloids and two repurposing drugs were selected from an extensive literature review. Then, the putative target enzyme SARS-CoV-2 main protease (SARS-CoV-2-Mpro) was retrieved from the protein data bank and carried out a virtual screening-cum-molecular docking study with all candidates using PyRx 0.8 and AutoDock 4.2 software. Further, the molecular dynamics (MD) simulation of the two most potential alkaloids and a drug docking complex at 100 ns (with two ligand topology files from PRODRG and ATB server, separately), the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) free energy, and contributions of entropy were investigated. Then, the physicochemical-toxicity-pharmacokinetics-drug-likeness profiles, the frontier molecular orbitals energies (highest occupied molecular orbital, lowest unoccupied molecular orbital, and Delta E), and structural-activity relationship were assessed and analyzed. Based on binding energy, 8-hydroxymanzamine (-10.5 kcal/mol) and manzamine A (-10.1 kcal/mol) from all alkaloids with darunavir (-7.9 kcal/mol) and lopinavir (-7.4 kcal/mol) against SARS-CoV-2-Mpro were recorded. The MD simulation (RMSD, RMSF, Rg, H-bond, MM/PBSA binding energy) illustrated that the 8-hydroxymanzamine exhibits a static thermodynamic feature than the other two complexes. The predicted physicochemical, toxicity, pharmacokinetics, and drug-likeness profiles also revealed that the 8-hydroxymanzamine could be used as a potential lead candidate individually and/or synergistically with darunavir or lopinavir to combat SARS-CoV-2 infection after some pharmacological validation.
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| 9. |
- Wallner, Björn
(författare)
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Improved multimer prediction using massive sampling with AlphaFold in CASP15
- 2023
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Ingår i: Proteins. - : WILEY. - 0887-3585 .- 1097-0134. ; 91:12, s. 1734-1746
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Tidskriftsartikel (refereegranskat)abstract
- AlphaFold2 has revolutionized structure prediction by achieving high accuracy comparable to experimentally determined structures. However, there is still room for improvement, especially for challenging cases like multimers. A key to the success of AlphaFold is its ability to assess and rank its own predictions. Our basic idea for the Wallner group in CASP15 was to exploit this excellent scoring function in AlphaFold by massive sampling. To achieve this goal, we conducted AlphaFold runs using six different settings, using templates, without templates, and with an increased number of recycles for both multimer v1 and v2 weights. In all instances, we enabled dropout layers during inference, allowing for sampling of uncertainty and enhancing the diversity of the generated models. In total, 274 289 models were generated for the 38 targets in CASP15, with a median of 4810 models per target. Of these 38 targets, 10 were high quality, 11 were medium quality, 11 were acceptable, and only 6 were incorrect. The improvement over the baseline method, NBIS-AF2-multimer, is substantial, with the mean DockQ increasing from 0.43 to 0.56, with several targets showing a DockQ score increase of +0.6 units. Remarkable, considering Wallner and NBIS-AF2-multimer were using identical input data. The success can be attributed to the diversified sampling using dropout with different settings and, in particular, the use of multimer v1, which is much more susceptible to sampling compared with v2. The method is available here: .
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| 10. |
- Zhao, Lina, et al.
(författare)
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Histidine protonation states are key in the LigI catalytic reaction mechanism
- 2022
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Ingår i: Proteins. - : Wiley. - 0887-3585. ; 90:1, s. 123-130
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Tidskriftsartikel (refereegranskat)abstract
- Lignin is one of the world's most abundant organic polymers, and 2-pyrone-4,6-dicarboxylate lactonase (LigI) catalyzes the hydrolysis of 2-pyrone-4,6-dicarboxylate (PDC) in the degradation of lignin. The pH has profound effects on enzyme catalysis and therefore we studied this in the context of LigI. We found that changes of the pH mostly affects surface residues, while the residues at the active site are more subject to changes of the surrounding microenvironment. In accordance with this, a high pH facilitates the deprotonation of the substrate. Detailed free energy calculations by the empirical valence bond (EVB) approach revealed that the overall hydrolysis reaction is more likely when the three active site histidines (His31, His33 and His180) are protonated at the &ip.eop; site, however, protonation at the δ site may be favored during specific steps of the reaction. Our studies have uncovered the determinant role of the protonation state of the active site residues His31, His33 and His180 in the hydrolysis of PDC.
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