Sequence Coverage Visualizer: A web application for protein sequence coverage 3D visualization

Protein structure is connected with its function and interaction and plays an extremely important role in protein characterization. As one of the most important analytical methods for protein characterization, Proteomics is widely used to determine protein composition, quantitation, interaction, and even structures. However, due to the gap between identified proteins by proteomics and available 3D structures, it was very challenging, if not impossible, to visualize proteomics results in 3D and further explore the structural aspects of proteomics experiments. Recently, two groups of researchers from DeepMind and Baker lab have independently published protein structure prediction tools that can help us obtain predicted protein structures for the whole human proteome. Although there is still debate on the validity of some of the predicted structures, it is no doubt that these represent the most accurate predictions to date. More importantly, this enabled us to visualize the majority of human proteins for the first time. To help other researchers best utilize these protein structure predictions, we present the Sequence Coverage Visualizer (SCV), http://scv.lab.gy, a web application for protein sequence coverage 3D visualization. Here we showed a few possible usages of the SCV, including the labeling of post-translational modifications and isotope labeling experiments. These results highlight the usefulness of such 3D visualization for proteomics experiments and how SCV can turn a regular result list into structural insights. Furthermore, when used together with limited proteolysis, we demonstrated that SCV can help validate and compare different protein structures, including predicted ones and existing PDB entries. By performing limited proteolysis on native proteins at various time points, SCV can visualize the progress of the digestion. This time-series data further allowed us to compare the predicted structure and existing PDB entries. Although not deterministic, these comparisons could be used to refine current predictions further and represent an important step towards a complete and correct protein structure database. Overall, SCV is a convenient and powerful tool for visualizing proteomics results.

De novo protein structure prediction by incremental inter-residue geometries prediction and model quality assessment using deep learning

Motivation: The successful application of deep learning has promoted progress in protein model quality assessment. How to use model quality assessment to further improve the accuracy of protein structure prediction, especially not reliant on the existing templates, is helpful for unraveling the folding mechanism. Here, we investigate whether model quality assessment can be introduced into structure prediction to form a closed-loop feedback, and iteratively improve the accuracy of de novo protein structure prediction. Results: In this study, we propose a de novo protein structure prediction method called RocketX. In RocketX, a feedback mechanism is constructed through the geometric constraint prediction network GeomNet, the structural simulation module, and the model quality evaluation network EmaNet. In GeomNet, the co-evolutionary features extracted from MSA that search from the sequence databases are sent to an improved residual neural network to predict the inter-residue geometric constraints. The structure model is folded based on the predicted geometric constraints. In EmaNet, the 1D and 2D features are extracted from the folded model and sent to the deep residual neural network to estimate the inter-residue distance deviation and per-residue lDDT of the model, which will be fed back to GeomNet as dynamic features to correct the geometries prediction and progressively improve model accuracy. RocketX is tested on 483 benchmark proteins and 20 FM targets of CASP14. Experimental results show that the closed-loop feedback mechanism significantly contributes to the performance of RocketX, and the prediction accuracy of RocketX outperforms that of the state-of-the-art methods trRosetta (without templates) and RaptorX. In addition, the blind test results on CAMEO show that although no template is used, the prediction accuracy of RocketX on medium and hard targets is comparable to the advanced methods that integrate templates.

Exploring the folding energy landscapes of heme proteins using a hybrid AWSEM-heme model

Heme is an active center in many proteins. Here we explore computationally the role of heme in protein folding and protein structure. We model heme proteins using a hybrid model employing the AWSEM Hamiltonian, a coarse-grained forcefield for the protein chain along with AMBER, an all-atom forcefield for the heme. We carefully designed transferable force fields that model the interactions between the protein and the heme. The types of protein–ligand interactions in the hybrid model include thioester covalent bonds, coordinated covalent bonds, hydrogen bonds, and electrostatics. We explore the influence of different types of hemes (heme b and heme c) on folding and structure prediction. Including both types of heme improves the quality of protein structure predictions. The free energy landscape shows that both types of heme can act as nucleation sites for protein folding and stabilize the protein folded state. In binding the heme, coordinated covalent bonds and thioester covalent bonds for heme c drive the heme toward the native pocket. The electrostatics also facilitates the search for the binding site.

Predicting pathogenicity for novel hearing loss mutations based on genetic and protein structure approaches

Hearing loss is a heterogeneous disorder. Identification of causative mutations is demanding due to genetic heterogeneity. In this study, we investigated the genetic cause of sensorineural hearing loss in patients with severe/profound deafness. After the exclusion of GJB2-GJB6 mutations, we performed whole exome sequencing in 32 unrelated Argentinean families. Mutations were detected in 16 known deafness genes in 20 patients: ACTG1, ADGRV1 (GPR98), CDH23, COL4A3, COL4A5, DFNA5 (GSDDE), EYA4, LARS2, LOXHD1, MITF, MYO6, MYO7A, TECTA, TMPRSS3, USH2A and WSF1. Notably, 11 variants affecting 9 different non-GJB2 genes resulted novel: c.12829C > T, p.(Arg4277*) in ADGRV1; c.337del, p.(Asp109*) and c.3352del, p.(Gly1118Alafs*7) in CDH23; c.3500G > A, p.(Gly1167Glu) in COL4A3; c.1183C > T, p.(Pro395Ser) and c.1759C > T, p.(Pro587Ser) in COL4A5; c.580 + 2 T > C in EYA4; c.1481dup, p.(Leu495Profs*31) in LARS2; c.1939 T > C, p.(Phe647Leu), in MYO6; c.733C > T, p.(Gln245*) in MYO7A and c.242C > G, p.(Ser81*) in TMPRSS3 genes. To predict the effect of these variants, novel protein modeling and protein stability analysis were employed. These results highlight the value of whole exome sequencing to identify candidate variants, as well as bioinformatic strategies to infer their pathogenicity.

Effects of the Pathological E200K Mutation on Human Prion Protein: A Computational screening and Molecular Dynamic approach

The Human Prion protein gene (PRNP) is mapped to short arm of chromosome 20 (20pter-12). Prion disease is associated with mutations in the Prion Protein encoding gene sequence. The mutations that occur in the prion protein could be divided into two types based on their influence on pathogenic potential: 1. Mutations that cause disease. 2. Disease-resistance mutations. Earlier studies found that the mutation G127V in the PRNP increases protein stability, whereas the mutation E200K, which has the highest mutation rate in the Prion protein, causes Creutzfeldt–Jakob disease (CJD) in humans and induces protein aggregation. We used a variety of bioinformatic algorithms, including SIFT, PolyPhen, I-Mutant, PhD-SNP, and SNP&GO, to predict the association of the E200K mutation with Prion disease. MD simulation is performed and graphs for RMSD, RMSF, Rg, DSSP, PCA, porcupine and FEL are generated to confirm and prove the stability of the wild type and mutant protein structures. The protein is analyzed for aggregation, and the results indicates more fluctuations in the protein structure during the simulation by the E200K mutation, however the G127V mutation makes protein structure stable against aggregation during the simulation.

The SARS-CoV-2 infection in Thailand: analysis of spike variants complemented by protein structure insights

Thailand was the first country outside China to officially report COVID-19 cases. Despite the strict regulations for international arrivals, up until February 2021, Thailand had been hit by two major outbreaks. With a large number of SARS-CoV-2 sequences collected from patients, the effects of many genetic variations, especially those unique to Thai strains, are yet to be elucidated. In this study, we analysed 439,197 sequences of the SARS-CoV-2 spike protein collected from NCBI and GISAID databases. 595 sequences were from Thailand and contained 52 variants, of which 6 had not been observed outside Thailand (p.T51N, p.P57T, p.I68R, p.S205T, p.K278T, p.G832C). These variants were not predicted to be of concern. We demonstrate that the p.D614G, although already present during the first Thai outbreak, became the prevalent strain during the second outbreak, similarly to what was described in other countries. Moreover, we show that the most common variants detected in Thailand (p.A829T, p.S459F and p.S939F) do not appear to cause any major structural change to the spike trimer or the spike-ACE2 interaction. Among the variants identified in Thailand was p.N501T. This variant, which involves an asparagine critical for spike-ACE2 binding, was not predicted to increase SARS-CoV-2 binding, thus in contrast to the variant of global concern p.N501Y. In conclusion, novel variants identified in Thailand are unlikely to increase the fitness of SARS-CoV-2. The insights obtained from this study could aid SARS-CoV-2 variants prioritisations and help molecular biologists and virologists working on strain surveillance.