Density functional theory for doped TiO2: current research strategies and advancements.

Since the inception of the density functional theory (DFT) by Hohenberg and Kohn in 1964, it rapidly became an indispensable theoretical tool across various disciplines, such as chemistry, biology, and materials science, among others. This theory has ushered in a new era of computational research, paving the way for substantial advancements in fundamental understanding. Today, DFT is routinely employed for a diverse range of applications, such as probing new material properties and providing a profound understanding of the mechanisms underlying physical, chemical, and biological processes. Even after decades of active utilization, the improvement of DFT principles has never been slowed down, meaning that more accurate theoretical results are continuously generated with time. This work highlights the latest achievements acquired by DFT in the specific research field, namely the theoretical investigations of doped TiO2systems, which have not been comprehensively reviewed and summarized yet. Successful progress in this niche is currently hard to imagine without the support by DFT. It can accurately reveal new TiO2properties after introducing the desired dopant and help to find the optimal system design for a specific application prior to proceeding to more time-consuming and expensive experimental research. Hence, by evaluating a selection of the most recent research studies, we aim to highlight the pertinent aspects of DFT as they relate to the study of doped TiO2systems. We also aim to shed light on the strengths and weaknesses of DFT and present the primary strategies employed thus far to predict the properties of various doped TiO2systems reliably.

Improving the Catalytic Performance of the Hydrogen Evolution Reaction of α-MoB2 via Rational Doping by Transition Metal Elements.

Abundant transition metal borides are emerging as promising electrochemical hydrogen evolution reaction (HER) catalysts which have a potential to substitute noble metals. Those containing graphene-like (flat) boron layers, such as α-MoB2, are particularly promising and their performance can be further enhanced via doping by the second metal. In order to understand intrinsic effect of doping and rationalize selection of dopants, we employ density functional theory (DFT) calculations to study substitutional doping of α-MoB2 by transition metals as a route towards systematic improvement of intrinsic catalytic activity towards HER. We calculated thermodynamic stability of various transition metal elements to select metals which form a stable ternary phase with α-MoB2 . We inspected surface stability of dopants and assessed catalytic activity of doped surface through hydrogen binding free energy at various hydrogen coverages. We calculated the reaction barriers and pathways for the Tafel step of HER for the most promising dopants. The results highlight iron as the best dopant, simultaneously lowering the reaction barrier of the Tafel step while having suitable thermodynamic stability within MoB2 lattice.

Ab Initio Modelling of the Structure of ToxA-like and MAX Fungal Effector Proteins.

Pathogenic fungal diseases in crops are mediated by the release of effector proteins that facilitate infection. Characterising the structure of these fungal effectors is vital to understanding their virulence mechanisms and interactions with their hosts, which is crucial in the breeding of plant cultivars for disease resistance. Several effectors have been identified and validated experimentally; however, their lack of sequence conservation often impedes the identification and prediction of their structure using sequence similarity approaches. Structural similarity has, nonetheless, been observed within fungal effector protein families, creating interest in validating the use of computational methods to predict their tertiary structure from their sequence. We used Rosetta ab initio modelling to predict the structures of members of the ToxA-like and MAX effector families for which experimental structures are known to validate this method. An optimised approach was then used to predict the structures of phenotypically validated effectors lacking known structures. Rosetta was found to successfully predict the structure of fungal effectors in the ToxA-like and MAX families, as well as phenotypically validated but structurally unconfirmed effector sequences. Interestingly, potential new effector structural families were identified on the basis of comparisons with structural homologues and the identification of associated protein domains.

Exploring the speed and performance of molecular replacement with AMPLE using QUARK ab initio protein models.

AMPLE clusters and truncates ab initio protein structure predictions, producing search models for molecular replacement. Here, an interesting degree of complementarity is shown between targets solved using the different ab initio modelling programs QUARK and ROSETTA. Search models derived from either program collectively solve almost all of the all-helical targets in the test set. Initial solutions produced by Phaser after only 5 min perform surprisingly well, improving the prospects for in situ structure solution by AMPLE during synchrotron visits. Taken together, the results show the potential for AMPLE to run more quickly and successfully solve more targets than previously suspected.

Predicting potential SARS-CoV-2 mutations of concern via full quantum mechanical modelling.

Ab initio quantum mechanical models can characterize and predict intermolecular binding, but only recently have models including more than a few hundred atoms gained traction. Here, we simulate the electronic structure for approximately 13 000 atoms to predict and characterize binding of SARS-CoV-2 spike variants to the human ACE2 (hACE2) receptor using the quantum mechanics complexity reduction (QM-CR) approach. We compare four spike variants in our analysis: Wuhan, Omicron, and two Omicron-based variants. To assess binding, we mechanistically characterize the energetic contribution of each amino acid involved, and predict the effect of select single amino acid mutations. We validate our computational predictions experimentally by comparing the efficacy of spike variants binding to cells expressing hACE2. At the time we performed our simulations (December 2021), the mutation A484K which our model predicted to be highly beneficial to ACE2 binding had not been identified in epidemiological surveys; only recently (August 2023) has it appeared in variant BA.2.86. We argue that our computational model, QM-CR, can identify mutations critical for intermolecular interactions and inform the engineering of high-specificity interactors.

Exploring the potential of Halalkalibacterium halodurans laccase for endosulfan and chlorophacinone degradation: insights from molecular docking and molecular dynamics simulations.

Pesticides are widely used in agriculture but at the same time, a majority of them are known to cause serious harm to health and the environment. In the recent past, laccases have been reported as key enzymes having the ability to degrade pollutants by converting them into less toxic forms. In this investigation, laccase from polyextremophilic bacterium Halalkalibacterium halodurans C-125 was analyzed for its structural, physicochemical, and functional characterization using in silico approaches. The 3D model of the said enzyme is unknown; therefore, the model was generated by template-independent modeling using ROBETTA, I-TASSER, and Alphafold server. The best-generated model from Alphafold with a confidence of 0.95 was validated from ERRAT and Verify 3D scores of 89.95 and 91.80%, respectively. The Ramachandran plot generated using the PROCHECK server further predicted the accuracy of the model with 93.7% and 5.9% of residues present in most favored and additional allowed regions of the plot respectively. The active sites, ion binding sites, and subcellular localization of laccase were also predicted. The generated model was docked with 121 pollutants (pesticides, insecticides, herbicides, fungicides, and rodenticides) for its degradation potential towards these pollutants. Two ligands chlorophacinone (based on the highest binding energy) and endosulfan (based on agricultural uses) were selected for molecular dynamic simulation studies. Endosulfan as a pesticide is banned but in some countries governments allow its use for special purposes which need serious consideration on developing bioremediation approaches for endosulfan degradation. MD simulation studies revealed that both chlorophacinone and endosulfan form hydrogen bonds and hydrophobic bonds with the active site of laccase and chlorophacinone-laccase complex were more stable in comparison to endosulfan. The present investigation provides insight into the structural features of laccase and its potential for the degradation of pesticides which can be further validated by experimental data.Communicated by Ramaswamy H. Sarma.

Ab initio modelling of an essential mammalian protein: Transcription Termination Factor 1 (TTF1).

Transcription Termination Factor 1 (TTF1) is an essential mammalian protein that regulates transcription, replication fork arrest, DNA damage repair, chromatin remodelling etc. TTF1 interacts with numerous cellular proteins to regulate various cellular phenomena which play a crucial role in maintaining normal cellular physiology, and dysregulation of this protein has been reported to induce oncogenic transformation of the cells. However, despite its key role in many cellular processes, the complete structure of human TTF1 has not been elucidated to date, neither experimentally nor computationally. Therefore, understanding the structure of human TTF1 is crucial for studying its functions and interactions with other cellular factors. The aim of this study was to construct the complete structure of human TTF1 protein, using molecular modelling approaches. Owing to the lack of suitable homologues in the Protein Data Bank (PDB), the complete structure of human TTF1 was constructed by ab initio modelling. The structural stability was determined with molecular dynamics (MD) simulations in explicit solvent, and trajectory analyses. The frequently occurring conformation of human TTF1 was selected by trajectory clustering, and the central residues of this conformation were determined by centrality analyses of the Residue Interaction Network (RIN) of TTF1. Two residue clusters, one in the oligomerization domain and other in the C-terminal domain, were found to be central to the structural stability of human TTF1. To the best of our knowledge, this study is the first to report the complete structure of this essential mammalian protein, and the results obtained herein will provide structural insights for future research including that in cancer biology and related studies.Communicated by Ramaswamy H. Sarma.

Computational insight into the three-dimensional structure of ADP ribosylation factor like protein 15, a novel susceptibility gene for rheumatoid arthritis.

The ARL15 gene (ADP ribosylation factor like protein 15) encodes for an uncharacterized small GTP-binding protein. Its exact role in human physiology remains unknown, but a number of genetic association studies have recognised different variants in this gene to be statistically associated with numerous traits and complex diseases. We have previously reported a novel association of ARL15 with rheumatoid arthritis (RA) based on a genome-wide association study in a north Indian cohort. Subsequent investigations have provided leads for its involvement in RA pathophysiology, especially its potential as a novel therapeutic target. However, the absence of an experimentally determined tertiary structure for ARL15 significantly hinders the understanding of its biochemical and physiological functions, as well as development of potential lead molecules. We, therefore, aimed to derive a high quality, refined model of the three dimensional structure of human ARL15 protein using two different computational protein structure prediction methods - template-based threading and ab initio modelling. The best model each from among the five each derived from both the approaches was selected based on stringent quality assessment and refinement. Molecular dynamics simulations over long timescales revealed the ab initio model to be relatively more stable, and it marginally outperformed the template-based model in the quality assessment as well. A putative GTP-binding site was also predicted using homology for the ARL15 protein, where potential competitive inhibitors can be targeted. This high quality predicted model may provide insights to the biological role(s) of ARL15 and inform and guide further experimental, structural and biochemical characterization efforts.Communicated by Ramaswamy H. Sarma.

First Principles Calculations of Atomic and Electronic Structure of TiAl3+- and TiAl2+-Doped YAlO3.

In this paper, the density functional theory accompanied with linear combination of atomic orbitals (LCAO) method is applied to study the atomic and electronic structure of the Ti3+ and Ti2+ ions substituted for the host Al atom in orthorhombic Pbnm bulk YAlO3 crystals. The disordered crystalline structure of YAlO3 was modelled in a large supercell containing 160 atoms, allowing simulation of a substitutional dopant with a concentration of about 3%. In the case of the Ti2+-doped YAlO3, compensated F-center (oxygen vacancy with two trapped electrons) is inserted close to the Ti to make the unit cell neutral. Changes of the interatomic distances and angles between the chemical bonds in the defect-containing lattices were analyzed and quantified. The positions of various defect levels in the host band gap were determined.

Ambiguities in and completeness of SAS data analysis of membrane proteins: the case of the sensory rhodopsin II-transducer complex.

Membrane proteins (MPs) play vital roles in the function of cells and are also major drug targets. Structural information on proteins is vital for understanding their mechanism of function and is critical for the development of drugs. However, obtaining high-resolution structures of membrane proteins, in particular, under native conditions is still a great challenge. In such cases, the low-resolution methods small-angle X-ray and neutron scattering (SAXS and SANS) might provide valuable structural information. However, in some cases small-angle scattering (SAS) provides ambiguous ab initio structural information if complementary measurements are not performed and/or a priori information on the protein is not taken into account. Understanding the nature of the limitations may help to overcome these problems. One of the main problems of SAS data analysis of solubilized membrane proteins is the contribution of the detergent belt surrounding the MP. Here, a comprehensive analysis of how the detergent belt contributes to the SAS data of a membrane-protein complex of sensory rhodopsin II with its cognate transducer from Natronomonas pharaonis (NpSRII-NpHtrII) was performed. The influence of the polydispersity of NpSRII-NpHtrII oligomerization is the second problem that is addressed here. It is shown that inhomogeneity in the scattering length density of the detergent belt surrounding a membrane part of the complex and oligomerization polydispersity significantly impacts on SAXS and SANS profiles, and therefore on 3D ab initio structures. It is described how both problems can be taken into account to improve the quality of SAS data treatment. Since SAS data for MPs are usually obtained from solubilized proteins, and their detergent belt and, to a certain extent, oligomerization polydispersity are sufficiently common phenomena, the approaches proposed in this work might be used in SAS studies of different MPs.

A glance into the evolution of template-free protein structure prediction methodologies.

Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in template-based modelling protocols, whereas strategies that involve template-free modelling still lag behind, specifically for larger proteins (>150 a.a.). Various improvements have been observed in ab initio protein structure prediction methodologies overtime, with recent ones attributed to the usage of deep learning approaches to construct protein backbone structure from its amino acid sequence. This review highlights the major strategies undertaken for template-free modelling of protein structures while discussing few tools developed under each strategy. It will also briefly comment on the progress observed in the field of ab initio modelling of proteins over the course of time as seen through the evolution of CASP platform.

In silico prediction of structure and function for a large family of transmembrane proteins that includes human Tmem41b.

Background: Recent strides in computational structural biology have opened up an opportunity to understand previously uncharacterised proteins.  The under-representation of transmembrane proteins in the Protein Data Bank highlights the need to apply new and advanced bioinformatics methods to shed light on their structure and function.  This study focuses on a family of transmembrane proteins containing the Pfam domain PF09335 ('SNARE_ASSOC'/ 'VTT '/'Tvp38'/'DedA'). One prominent member, Tmem41b, has been shown to be involved in early stages of autophagosome formation and is vital in mouse embryonic development as well as being identified as a viral host factor of SARS-CoV-2. Methods: We used evolutionary covariance-derived information to construct and validate ab initio models, make domain boundary predictions and infer local structural features.  Results: The results from the structural bioinformatics analysis of Tmem41b and its homologues showed that they contain a tandem repeat that is clearly visible in evolutionary covariance data but much less so by sequence analysis.  Furthermore, cross-referencing of other prediction data with covariance analysis showed that the internal repeat features two-fold rotational symmetry.  Ab initio modelling of Tmem41b and homologues reinforces these structural predictions.  Local structural features predicted to be present in Tmem41b were also present in Cl -/H + antiporters.  Conclusions: The results of this study strongly point to Tmem41b and its homologues being transporters for an as-yet uncharacterised substrate and possibly using H + antiporter activity as its mechanism for transport.

Structures and dynamics of the novel S1/S2 protease cleavage site loop of the SARS-CoV-2 spike glycoprotein.

At the end of 2019, a new highly virulent coronavirus known under the name SARS-CoV-2 emerged as a human pathogen. One key feature of SARS-CoV-2 is the presence of an enigmatic insertion in the spike glycoprotein gene representing a novel multibasic S1/S2 protease cleavage site. The proteolytic cleavage of the spike at this site is essential for viral entry into host cells. However, it has been systematically abrogated in structural studies in order to stabilize the spike in the prefusion state. In this study, multi-microsecond molecular dynamics simulations and ab initio modeling were leveraged to gain insights into the structures and dynamics of the loop containing the S1/S2 protease cleavage site. They unveiled distinct conformations, formations of short helices and interactions of the loop with neighboring glycans that could potentially regulate the accessibility of the cleavage site to proteases and its processing. In most conformations, this loop protrudes from the spike, thus representing an attractive SARS-CoV-2 specific therapeutic target.

An integrated in silico approach to understand protein-protein interactions: human meprin-ß with fetuin-A.

Human meprin-ß, a zinc metalloprotease belonging to the astacin family, have been found to be associated with many pathological conditions like inflammatory bowel disease, fibrosis and neurodegenerative disease. The inhibition of meprin-ß by various inhibitors, both macromolecular and small molecules, is crucial in the control of several diseases. Human fetuin-A, a negative acute phase protein involved in inflammatory disease, has recently been identified as an endogenous inhibitor for meprin-ß. In this computational study, an integrated in silico approach was performed using existing structural information of meprin-ß coupled with ab initio modelling of human fetuin-A to predict a rational model of the complex through protein-protein docking. Further, the models were optimized and validated to generate an ensemble of conformations through extensive molecular dynamics simulation. Virtual alanine scanning mutagenesis was explored to identify hotspot residues on both proteins significant for protein-protein interaction (PPI). The results of the study provide structural insight into PPI between meprin-ß and fetuin-A which can be useful in designing molecules to modulate meprin-ß activity. Communicated by Ramaswamy H. Sarma.

Molecular replacement using structure predictions from databases.

Molecular replacement (MR) is the predominant route to solution of the phase problem in macromolecular crystallography. Where the lack of a suitable homologue precludes conventional MR, one option is to predict the target structure using bioinformatics. Such modelling, in the absence of homologous templates, is called ab initio or de novo modelling. Recently, the accuracy of such models has improved significantly as a result of the availability, in many cases, of residue-contact predictions derived from evolutionary covariance analysis. Covariance-assisted ab initio models representing structurally uncharacterized Pfam families are now available on a large scale in databases, potentially representing a valuable and easily accessible supplement to the PDB as a source of search models. Here, the unconventional MR pipeline AMPLE is employed to explore the value of structure predictions in the GREMLIN and PconsFam databases. It was tested whether these deposited predictions, processed in various ways, could solve the structures of PDB entries that were subsequently deposited. The results were encouraging: nine of 27 GREMLIN cases were solved, covering target lengths of 109-355 residues and a resolution range of 1.4-2.9 Å, and with target-model shared sequence identity as low as 20%. The cluster-and-truncate approach in AMPLE proved to be essential for most successes. For the overall lower quality structure predictions in the PconsFam database, remodelling with Rosetta within the AMPLE pipeline proved to be the best approach, generating ensemble search models from single-structure deposits. Finally, it is shown that the AMPLE-obtained search models deriving from GREMLIN deposits are of sufficiently high quality to be selected by the sequence-independent MR pipeline SIMBAD. Overall, the results help to point the way towards the optimal use of the expanding databases of ab initio structure predictions.

Origin of low Young modulus of multicomponent, biomedical Ti alloys - Seeking optimal elastic properties through a first principles investigation.

Multicomponent, biomedical ß-Ti alloys offer ultra-low Young modulus values that are related to a unique and poorly understood reduction of C44 and C' elastic constants in comparison with binary systems. The elastic properties of such materials are difficult to control due to the large variations occurring even for a small change in chemical composition, which cannot be explained using existing theories. In this article, we investigate the above issues through systematic ab initio elastic constants calculations for a series of binary, ternary and quaternary Ti alloys. Special attention is paid to examining the reliability of the methodology adopted and to clarifying the atomic scale mechanisms that affect the mechanical properties of the systems analysed. It was found that the lower boundary of the polycrystalline Young modulus of Ti-Nb-base ß phase is close to 50 GPa, and strongly depends on two specific electronic hybridisations related to niobium and simple metals addition that control C44 and C'. Based on the relationship established between electronic structure and mechanical properties, we propose several quaternary alloys whose directional <100> Young modulus values are equal or similar to that of human bones. Some electronic-based guidelines for designing new multicomponent ß-Ti alloys are also formulated.

Approaches to ab initio molecular replacement of α-helical transmembrane proteins.

α-Helical transmembrane proteins are a ubiquitous and important class of proteins, but present difficulties for crystallographic structure solution. Here, the effectiveness of the AMPLE molecular replacement pipeline in solving α-helical transmembrane-protein structures is assessed using a small library of eight ideal helices, as well as search models derived from ab initio models generated both with and without evolutionary contact information. The ideal helices prove to be surprisingly effective at solving higher resolution structures, but ab initio-derived search models are able to solve structures that could not be solved with the ideal helices. The addition of evolutionary contact information results in a marked improvement in the modelling and makes additional solutions possible.

GP0.4 from bacteriophage T7: in silico characterisation of its structure and interaction with E. coli FtsZ.

BACKGROUND: Proteins produced by bacteriophages can have potent antimicrobial activity. The study of phage-host interactions can therefore inform small molecule drug discovery by revealing and characterising new drug targets. Here we characterise in silico the predicted interaction of gene protein 0.4 (GP0.4) from the Escherichia coli (E. coli) phage T7 with E. coli filamenting temperature-sensitive mutant Z division protein (FtsZ). FtsZ is a tubulin homolog which plays a key role in bacterial cell division and that has been proposed as a drug target. RESULTS: Using ab initio, fragment assembly structure modelling, we predicted the structure of GP0.4 with two programs. A structure similarity-based network was used to identify a U-shaped helix-turn-helix candidate fold as being favoured. ClusPro was used to dock this structure prediction to a homology model of E. coli FtsZ resulting in a favourable predicted interaction mode. Alternative docking methods supported the proposed mode which offered an immediate explanation for the anti-filamenting activity of GP0.4. Importantly, further strong support derived from a previously characterised insertion mutation, known to abolish GP0.4 activity, that is positioned in close proximity to the proposed GP0.4/FtsZ interface. CONCLUSIONS: The mode of interaction predicted by bioinformatics techniques strongly suggests a mechanism through which GP0.4 inhibits FtsZ and further establishes the latter's druggable intrafilament interface as a potential drug target.

Resolution of ab initio shapes determined from small-angle scattering.

Spatial resolution is an important characteristic of structural models, and the authors of structures determined by X-ray crystallography or electron cryo-microscopy always provide the resolution upon publication and deposition. Small-angle scattering of X-rays or neutrons (SAS) has recently become a mainstream structural method providing the overall three-dimensional structures of proteins, nucleic acids and complexes in solution. However, no quantitative resolution measure is available for SAS-derived models, which significantly hampers their validation and further use. Here, a method is derived for resolution assessment for ab initio shape reconstruction from scattering data. The inherent variability of the ab initio shapes is utilized and it is demonstrated how their average Fourier shell correlation function is related to the model resolution. The method is validated against simulated data for proteins with known high-resolution structures and its efficiency is demonstrated in applications to experimental data. It is proposed that henceforth the resolution be reported in publications and depositions of ab initio SAS models.

Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds.

For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurate ab initio (non-homology-based) structure prediction. Such models can be used in structure solution by molecular replacement (MR) where the target fold is novel or is only distantly related to known structures. Here, AMPLE, an MR pipeline that assembles search-model ensembles from ab initio structure predictions ('decoys'), is employed to assess the value of contact-assisted ab initio models to the crystallographer. It is demonstrated that evolutionary covariance-derived residue-residue contact predictions improve the quality of ab initio models and, consequently, the success rate of MR using search models derived from them. For targets containing ß-structure, decoy quality and MR performance were further improved by the use of a ß-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simple Rosetta decoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with ß-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing.