Efficient algorithms in protein modelling

Proteins are key players in the complex world of living cells. No matter whether they are involved in enzymatic reactions, inter-cell communication or numerous other processes, knowledge of their structure is vital for a detailed understanding of their function. However, structure determination by experiment is often a laborious process that cannot keep up with the ever increasing pace of sequencing methodologies. As a consequence, the gap between proteins where we only know the sequence and the proteins where we additionally have detailed structural information is growing rapidly. Computational modelling methods that extrapolate structural information from homologous structures have established themselves as a valuable complement to experiment and help bridging this gap. This thesis addresses two key aspects in protein modelling. (1) It investigates and improves methodologies that assign reliability estimates to protein models, so called quality estimation (QE) methods. Even a human expert cannot immediately detect errors introduced in the modelling process, thus the importance of automated methods performing this task. (2) It assesses the available methods that perform the modelling itself, discusses solutions for current shortcomings and provides efficient implementations thereof. When detecting errors in protein models, many knowledge based methods are biased towards the physio-chemical properties observed in soluble protein structures. This limits their applicability for the important class of membrane protein models. In an effort to improve the situation, QMEANBrane has been developed. QMEANBrane is specifically designed to detect local errors in membrane protein models by membrane specific statistical potentials of mean force that nowadays approach statistical saturation given the increase of available experimental data. Considering the improvement of quality estimation for soluble proteins, instead of solely applying the widely used statistical potentials of mean force, QMEANDisCo incorporates the observed structural variety of experimentally determined protein structures homologous to the model being assessed. Valuable ensemble information can be gathered without the need of actually depending on a large ensemble of protein models, thus circumventing a main limitation of consensus QE methods. Apart from improving QE methods, in an effort of implementing and extending state-of-the-art modelling algorithms, the lack of a free and efficient modelling engine became obvious. No available modelling engine provided an open-source codebase as a basis for novel, innovative algorithms and, at the same time, had no restrictions for usage. This contradicts our efforts to make protein modelling available to all biochemists and molecular biologists worldwide. As a consequence we implemented a new free and open modelling engine from scratch - ProMod3. ProMod3 allows to combine extremely efficient, state-of-the-art modelling algorithms in a flexible manner to solve various modelling problems. To weaken the dogma of one template one model, basic algorithms have been explored to incorporate structural information from multiple templates into one protein model. The algorithms are built using ProMod3 and have extensively been tested in the context of the CAMEO continuous evaluation platform. The result is a highly competitive modelling pipeline that excels with extremely low runtimes and excellent performance

In-silico study of the Effectiveness of Allium sativum L. extract as an Angiotensin-Converting Enzyme (ACE) Inhibitor in Hypertension

Over the last decade, the global prevalence of hypertension rate has increased by 5.2% and, in Indonesia, the prevalence rate has increased significantly from 25.8% in 2013 to 34.1% in 2018. Hypertension treatments using blood pressure-lowering drugs, such as angiotensin-converting enzyme (ACE) inhibitors, often cause unpleasant side effects. These side effects increase the interest in using potentially effective natural remedies, such as garlic. This study aimed to determine which organosulfur compounds in garlic can act as an ACE inhibitor to reduce blood pressure in hypertension using a cheminformatics approach. Eighteen organosulfur compounds of Allium sativum L. were screened based on Lipinski’s rules and ADMET evaluation. Seven compounds passed the screening and were subjected to QSAR analysis, molecular docking analysis, and molecular dynamics simulations to assess the stability of the protein. The seven compounds then underwent molecular docking and QSAR analysis. Ajoene (4,5,9-trithiadodeca-1,6,11-triene-9-oxide) and S-allylmercaptocysteine (SAMC) were two compounds with better docking values compared to the positive control compound. The QSAR analysis also showed that SAMC had an activity as an ACE inhibitor. The ADMET evaluation showed that Ajoene and SAMC had good absorption and could not penetrate the blood-brain barrier. Molecular dynamics simulation of ACE complexes Ajoene, SAMC, and Captopril ranged from 0.05 to 5.61 Å but exhibited a pattern of synonymous fluctuations for most residues. Based on the simulation data, the organosulfur compounds from garlic, Ajoene, and SAMC are proven to have a mechanism of action as ACE inhibitors to reduce blood pressure in hypertension

In silico characterization of Cnidarian’s antimicrobial peptides

The characterization of active compounds in Cnidaria sheds light on a large bank of substances against multiresistant bacteria related to diseases in humans, which makes it a cutting edge with a repertoire of antimicrobial molecules worthy of bioprospecting analysis. Thus, the main nabof this research was to characterize antimicrobial peptides (AMP) belonging to the defensin family in different species of Cnidarians through bioinformatic approaches. To this, an exhaustive search was carried out for sequences homologous to antimicrobial peptides belonging to the defensin family in genomes availables for Cnidarians. Also, 3D models of AMP were obtained by modeling based on homology, functional characterization of peptides found was performed with machine learning approaches. Characterization of twelve peptides derived from 11 Cnidarian species was possible due to 3D modeling, which showed structural similarity with defensins reported in several species such as Nasonia vitripennis, Pisum sativum, Solanum lycopersicum, and Aurelia aurita. Also, different physicochemical properties such as hydrophobic moment, hydrophobicity, net charge, amphiphilic index, and isoelectric point were evaluated. These peptides showed values ​​that are ideal for AMP. Further, functional characterization showed a bactericidal potential of 20 peptides against multiresistant bacteria Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae. These peptides with action potential were found in 17 species from Cnidarians and obtained by homology through the defensin Aurelin, described in the Cnidarian Aurelia aurita, and Mus musculus’ Beta-defensin 7. Finally, a phylogenetic tree was performed, it showed that defensins are distributed in all Cnidarians regardless of the taxonomic group. Thus, the origin of defensins in the Phylum Cnidaria is not monophyletic. Our results show that Cnidaria has AMP with structural and physicochemical characteristics similar to those described in defensins of insects, mammals, and plants. The structural characteristics of these peptides, their physicochemical properties, and their functional potential outline them as promising templates for the discovery of new antibiotics

3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

While scientists can often infer the biological function of proteins from their 3-dimensional quaternary structures, the gap between the number of known protein sequences and their experimentally determined structures keeps increasing. A potential solution to this problem is presented by ever more sophisticated computational protein modeling approaches. While often powerful on their own, most methods have strengths and weaknesses. Therefore, it benefits researchers to examine models from various model providers and perform comparative analysis to identify what models can best address their specific use cases. To make data from a large array of model providers more easily accessible to the broader scientific community, we established 3D-Beacons, a collaborative initiative to create a federated network with unified data access mechanisms. The 3D-Beacons Network allows researchers to collate coordinate files and metadata for experimentally determined and theoretical protein models from state-of-the-art and specialist model providers and also from the Protein Data Bank

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn Asp, Phe Tyr, Lys Arg, Gln Glu, Ile Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\overline{R} R = 0.85) between thirty amino acid mutability scales and the mutational inertia (I X ), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions. © 2017 The Author(s)

In Silico Phylogeny, Sequence and Structure Analyses of Fungal Thermoacidophilic GH11 Xylanases

Thermoacidophilic xylanase enzymes are mostly preferred for use as animal feed additives. In this study, we performed in silico phylogeny, sequence, structure, and enzyme-docked complex analyses of six thermoacidophilic GH11 xylanases belonging to various fungal species (Gymnopus androsaceus xylanase = GaXyl, Penicilliopsis zonata xylanase = PzXyl, Aspergillus neoniger xylanase = AnXyl, Calocera viscosa xylanase = CvXyl, Acidomyces richmondensis xylanase = ArXyl, Oidiodendron maius xylanase = OmXyl). To do this, amino acid sequences of six fungal thermoacidophilic GH11 xylanases, belonging to unreviewed protein entries in the UniProt/TrEMBL database, were investigated at molecular phylogeny and amino acid sequence levels. In addition, three-dimensional predicted enzyme models were built and then validated by using various bioinformatics programs computationally. The interactions between enzyme and the substrate were analyzed via docking program in the presence of two substrates (xylotetraose = X-4 and xylopentaose = X-5). According to molecular phylogeny analysis, three clusters of these enzymes occurred: the first group had PzXyl, AnXyl, and CvXyl, and the second group possessed GaXyl and OmXyl, and the third group included ArXyl. Multiple sequence alignment analysis demonstrated that the five xylanases (ArXyl, OmXyl, CvXyl, PzXyl, AnXyl) had longer N-terminal regions, indicating greater thermal stability, relative to the GaXyl. Homology modeling showed that all the predicted model structures were, to a great extent, conserved. Docking analysis results indicated that CvXyl, OmXyl, and AnXyl had higher binding efficiency to two substrates, compared to the GaXyl, PzXyl, and ArXyl xylanases, and CvXyl-X-4 docked complex had the highest substrate affinity with a binding energy of -9.8 kCal/mol. CvXyl, OmXyl, and AnXyl enzymes commonly had arginine in B8 beta- strand interacted with two substrates, different from the other enzymes having lower binding efficiency. As a result, it was concluded that the three thermoacidophilic xylanase enzymes might be better candidates as the animal feed additive

Feature Importance in the Quality of Protein Templates

Proteins are in the focus of research due to their importance as biological catalysts in various cellular processes and diseases. Since the experimental study of proteins is time-consuming and expensive, in silico prediction and analysis of proteins is common. Template-based prediction is the most reliable, which is why the aim of this study is to analyze how important are the primary features of proteins for their quality score. Statistical analysis shows that protein models with a resolution lower than 3 Å or R value lower than 0.25 have higher quality scores when compared individually to their counterparts. Machine learning algorithm random forest analysis also shows resolution to have the highest importance, while other features have lower but moderate importance scores. The exception is the presence of ligand in protein models, which does not have an effect on the global protein quality scores, both through statistical and machine learning analyses

Structural analysis of a simplified model reproducing SARS-CoV-2 S RBD/ACE2 binding site

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an RNA virus identified as the cause of the coronavirus outbreak in December 2019 (COVID-19). Like all the RNA viruses, SARS-CoV-2 constantly evolves through mutations in its genome, accumulating 1–2 nucleotide changes every month, giving the virus a selective advantage through enhanced transmissibility, greater pathogenicity, and the possibility of circumventing immunity previously acquired by an individual either by natural infection or by vaccination. Several SARS-CoV-2 variants of concern (VoC) have been identified, among which we find Alpha (Lineage B.1.1.7), Beta (Lineage B.1.351), and Gamma (Lineage P.1) variants. Most of the mutations occur in the spike (S) protein, a surface glycoprotein that plays a crucial role in viral infection; the S protein binds the host cell receptor, the angiotensin-converting enzyme of type 2 (ACE2) via the receptor binding domain (RBD) and catalyzes the fusion of the viral membrane with the host cell. In this work, we present the development of a simplified system that would afford to study the change in the SARS-CoV-2 S RBD/ACE2 binding related to the frequent mutations. In particular, we synthesized and studied the structure of short amino acid sequences, mimicking the two proteins’ critical portions. Variations in the residues were easily managed through the one-point alteration of the sequences. Nuclear magnetic resonance (NMR) and circular dichroism (CD) spectroscopies provide insights into ACE2 and SARS-CoV-2 S RBD structure with its related three variants (Alpha, Beta, and Gamma). Spectroscopy data supported by molecular dynamics lead to the description of an ACE2/RBD binding model in which the effect of a single amino acid mutation in changing the binding of S protein to the ACE2 receptor is predictable

An Evolutionary Comparative Study of Congenital Stationary Night Blindness-Associated TRPM1 Genetic Variants of Uncertain Significance in Horses and Humans Utilizing Caenorhabditis elegans

Congenital stationary night blindness (CSNB) is a heterogeneous collection of genetic diseases affecting the eyes and vision in horses and humans. Current research has implicated several genetic mutations impacting different genes involved in phototransduction and signal transmission, including TRPM1. In horses, genetic mutations in TRPM1 also result in a leopard spotting pattern or leopard complex. The goal of this study is to examine the potential impact of CSNB associated TRPM1 missense variants of uncertain significance (VUS). Previous research in Caenorhabditis elegans have revealed an orthologous TRPM1 gene known as gon-2 that allows for comparative studies. The evolutionary relationship of TRPM1 and other orthologous genes were examined along with the evolutionary conservation of TRPM1 missense VUS. Three TRPM1 VUS were identified in conserved loci across human, horse, C. elegans, and other species. A gene mutational analysis, predictive missense variant analysis, and protein modeling predict TRPM1 the c.2572A\u3eG (p.Ile875Val) to be likely pathogenic or damaging. These findings support further in vivo assessment of the VUS. To assess this, a CRISPR-Cas9-engineered C. elegans model containing the TRPM1 missense VUS in the nematode loci of gon-2 was proposed. Two sets of DNA primers were designed and tested to amplify the VUS region in gon-2 using polymerase chain reaction (PCR) and gel electrophoresis. CRISPR RNA guides were also designed to target gon-2 and will be used in future microinjection experiments. A PCR assay was optimized to be utilized for downstream screening and genotyping to identify the gon-2 VUS C. elegans strain. This mutant strain will allow for further in vivo investigation of the missense VUS in the C. elegans model

Using Steady-State Kinetics to Quantitate Substrate Selectivity and Specificity: A Case Study with Two Human Transaminases

We examined the ability of two human cytosolic transaminases, aspartate aminotransferase (GOT1) and alanine aminotransferase (GPT), to transform their preferred substrates whilst discriminating against similar metabolites. This offers an opportunity to survey our current understanding of enzyme selectivity and specificity in a biological context. Substrate selectivity can be quantitated based on the ratio of the kcat /KM values for two alternative substrates (the ‘discrimination index’). After assessing the advantages, implications and limits of this index, we analyzed the reactions of GOT1 and GPT with alternative substrates that are metabolically available and show limited structural differences with respect to the preferred substrates. The transaminases’ observed selectivities were remarkably high. In particular, GOT1 reacted ~106-fold less efficiently when the side-chain carboxylate of the ’physiological’ substrates (aspartate and glutamate) was replaced by an amido group (asparagine and glutamine). This represents a current empirical limit of discrimination associated with this chemical difference. The structural basis of GOT1 selectivity was addressed through substrate docking simulations, which highlighted the importance of electrostatic interactions and proper substrate positioning in the active site. We briefly discuss the biological implications of these results and the possibility of using kcat /KM values to derive a global measure of enzyme specificity