Protein Models: The Grand Challenge of protein docking

Characterization of life processes at the molecular level requires structural details of protein–protein interactions (PPIs). The number of experimentally determined protein structures accounts only for a fraction of known proteins. This gap has to be bridged by modeling, typically using experimentally determined structures as templates to model related proteins. The fraction of experimentally determined PPI structures is even smaller than that for the individual proteins, due to a larger number of interactions than the number of individual proteins, and a greater difficulty of crystallizing protein–protein complexes. The approaches to structural modeling of PPI (docking) often have to rely on modeled structures of the interactors, especially in the case of large PPI networks. Structures of modeled proteins are typically less accurate than the ones determined by X-ray crystallography or nuclear magnetic resonance. Thus the utility of approaches to dock these structures should be assessed by thorough benchmarking, specifically designed for protein models. To be credible, such benchmarking has to be based on carefully curated sets of structures with levels of distortion typical for modeled proteins. This article presents such a suite of models built for the benchmark set of the X-ray structures from the Dockground resource (http://dockground.bioinformatics.ku.edu) by a combination of homology modeling and Nudged Elastic Band method. For each monomer, six models were generated with predefined Cα root mean square deviation from the native structure (1, 2, . . ., 6 Å). The sets and the accompanying data provide a comprehensive resource for the development of docking methodology for modeled proteins

Structural templates for comparative protein docking

Structural characterization of protein-protein interactions is important for understanding life processes. Because of the inherent limitations of experimental techniques, such characterization requires computational approaches. Along with the traditional protein-protein docking (free search for a match between two proteins), comparative (template-based) modeling of protein-protein complexes has been gaining popularity. Its development puts an emphasis on full and partial structural similarity between the target protein monomers and the protein-protein complexes previously determined by experimental techniques (templates). The template-based docking relies on the quality and diversity of the template set. We present a carefully curated, non-redundant library of templates containing 4,950 full structures of binary complexes and 5,936 protein-protein interfaces extracted from the full structures at 12Å distance cut-off. Redundancy in the libraries was removed by clustering the PDB structures based on structural similarity. The value of the clustering threshold was determined from the analysis of the clusters and the docking performance on a benchmark set. High structural quality of the interfaces in the template and validation sets was achieved by automated procedures and manual curation. The library is included in the Dockground resource for molecular recognition studies at http://dockground.bioinformatics.ku.edu

Protein Model Docking Benchmark 2

Structural characterization of protein-protein interactions is essential for our ability to understand life processes. However, only a fraction of known proteins have experimentally determined structures. Such structures provide templates for modeling of a large part of the proteome, where individual proteins can be docked by template-free or template-based techniques. Still, the sensitivity of the docking methods to the inherent inaccuracies of protein models, as opposed to the experimentally determined high-resolution structures, remains largely untested, primarily due to the absence of appropriate benchmark set(s). Structures in such a set should have pre-defined inaccuracy levels and, at the same time, resemble actual protein models in terms of structural motifs/packing. The set should also be large enough to ensure statistical reliability of the benchmarking results. We present a major update of the previously developed benchmark set of protein models. For each interactor, six models were generated with the model-to-native Cα RMSD in the 1 to 6 Å range. The models in the set were generated by a new approach, which corresponds to the actual modeling of new protein structures in the “real case scenario,” as opposed to the previous set, where a significant number of structures were model-like only. In addition, the larger number of complexes (165 vs. 63 in the previous set) increases the statistical reliability of the benchmarking. We estimated the highest accuracy of the predicted complexes (according to CAPRI criteria), which can be attained using the benchmark structures. The set is available at http://dockground.bioinformatics.ku.edu

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein–protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models

Content of Metals in Phragmites australis Trin. ex Steud and Potamogeton pectinatus L. from Water Bodies of Different Salinity

Металлы определяли в двух видах макрофитов – Phragmites australis Trin. ex Steud и Potamogeton pectinatus L., произрастающих в водоемах с разной соленостью, с помощью эмиссионного спектрометра с индуктивно-связанной плазмой. Анализ результатов методом главных компонент показал, что на валовое содержание металлов влияет вид макрофитов и условия окружающей среды, в частности химический состав воды. Оба вида макрофитов из пресноводного водохранилища Бугач отличались более высокими концентрациями железа, алюминия, никеля, ванадия и кобальта по сравнению с теми же видами макрофитов, собранными в солоноватоводных озерах. Однако для макрофитов из оз. Шира, отобранных в опресненной и солоноватоводной частях озера, расхождений в содержании данных металлов не выявлено. В ряде случаев пробы из одной точки, но собранные в разные годы имели существенные различия – это наблюдалось для растений тростника из солоноватоводной станции оз. Шира, и растений рдеста из оз. Шунет. Было установлено, что наиболее высокое валовое содержание большинства металлов характерно для P. pectinatusMetals were determined in two species of macrophytes Phragmites australis Trin. ex Steud and Potamogeton pectinatus L. grown in lakes of different salinity, using emission spectrometer with inductively coupled plasma. Principal component analysis revealed that the total metal content is influenced by species of macrophytes and environmental conditions (in particular water chemistry). Both species of macrophytes from freshwater reservoir Bugach were characterized by higher concentrations of Fe, Al, Ni, V and Co in comparison with the same species from brackish lakes. However, there were no significant differences in content of these metals between samples of macrophytes taken in desalinated and saltwater parts of Shira Lake. In some cases, metal content of samples collected in different years at the same place were significantly different. It was observed for plants of Ph. australis collected in brackish station of Shira Lake, and plants of P. pectinatus from Lake Shunet. It was found that the highest total content of most metals is typical for P. pectinatu

Assessment of Anthropogenic Impact on the Yenisei River Anabranch within the City of Krasnoyarsk Based on Elemental Analysis of Macrophytes and Water

Абаканская протока р. Енисей в черте г. Красноярска подвержена влиянию антропогенных факторов (дамба в верховьях, подогретые воды ТЭЦ, рыбоводное хозяйство). Цель работы – оценить антропогенное влияние на лентический участок реки Енисей в г. Красноярске на основе элементного анализа макрофитов и воды. С помощью атомно-эмиссионной спектрометрии с индуктивно-связанной плазмой (ИСП-АЭС) определено содержание макро- и микроэлементов в воде и макрофитах Абаканской протоки. В воде обнаружено превышение ПДК для рыбохозяйственных водоемов по Cu, Mn, Mo, Al и фоновых значений по минерализации, концентрации B, Ba, Ca, Mg, Li, Na, Sr, Mn, что могло быть связано с поступлением ливневых сточных и грунтовых вод; концентрации Cu, вероятно, поступающей с подогретыми водами ТЭЦ; концентрации K и NO2 - в воде, вероятно, под воздействием рыбоводного хозяйства. Выявлено увеличение содержания Ba, Сa, Cu, Sr, Zn в элодее, Ca, Cu, Pb, Sr, Li в урути на участках, подверженных антропогенному воздействию. Содержание металлов в погруженных макрофитах свидетельствовало о загрязнении экосистемы Cu, Sr, Fe, Ni и Zn. Выявлены три группы макрофитов, различающиеся по содержанию элементов: элодея (Elodea canadensis Michx.) и рдест стеблеобъемлющий (Potamogeton perfoliatus L.); уруть (Myriophyllum sp.), рдест гребенчатый (Stuckenia pectinata (L.) Börner), роголистник погруженный (Ceratophyllum demersum L.); спирогира (Spirogyra sp.). Отличия могут быть связаны с морфологическими и физиологическими особенностями аккумуляции эссенциальных (Mg, Zn, Fe и V) и неэссенциальных (As, Li, Sr) элементов погруженными макрофитамиThe ‘Abakanskaya’ anabranch of the Yenisei River located in Krasnoyarsk is influenced by several anthropogenic factors (a dam in the upper reaches; heated water discharge from a thermal power plant; fish farming). The aim of the present work was to assess the anthropogenic impact on the lentic part of the Yenisei River in Krasnoyarsk based on elemental analysis of macrophytes and water. Inductively coupled plasma atomic emission spectrometry (ICP-AES) was used to determine the contents of macro- and trace elements in water and macrophytes. Results showed that Cu, Mn, Mo, and Al concentrations in water were higher than their MACs for fishery reservoirs, and specific conductivity and concentrations of B, Ba, Ca, Mg, Li, Na, Sr, and Mn in water exceeded their background values, which could be associated with the input of sewage and ground waters. The elevated concentration of Cu could be attributed to the input of that element with the heated waters of the thermal power plant, and increased concentrations of K and NO2- in water were probably caused by fish farming. Increased contents of Ba, Ca, Cu, Sr, and Zn in Elodea canadensis Michx. and Ca, Cu, Pb, Sr, and Li in Myriophyllum sp. were revealed at sites subjected to anthropogenic impact. The contents of metals in submerged macrophytes were indicative of the contamination of the ecosystem with Cu, Sr, Fe, Ni, and Zn. Three groups of macrophytes have been identified, differing in the contents of elements: E. canadensis, Potamogeton perfoliatus L.; Myriophyllum sp., Stuckenia pectinata (L.) Börner, Ceratophyllum demersum L.; and Spirogyra sp. These dissimilarities may be related to the morphological and physiological differences in the accumulation of essential (Mg, Zn, Fe, and V) and non-essential (As, Li, and Sr) elements by submerged macrophyte

Molecular Modeling of Structural Complexes of Glycosphingolipids With the HIV-1 GP120 V3 Loop as a Framework for the Development of Anti-Aids Drugs of a New Generation

A sctructural complex of -galactosylceramide with the HIV-1 V3 loop peptides that imitate the most probable binding site for glycolipids was generated by molecular docking simulations. Based on the analysis of this complex, two water soluble analogs of -galactosylceramide, which should preserve a high affinity to V3 characteristic of the native molecule, were designed and their anti-HIV-1 activity was predicted by free energy calculations. In the light of the findings obtained, the simulated glycolipids may be considered as the promising basic structures for the development of novel potent antiviral agents that target (block) the envelope gp120 V3 loop

Protein oligomer modeling guided by predicted interchain contacts in CASP14

For CASP14, we developed deep learning-based methods for predicting homo-oligomeric and hetero-oligomeric contacts and used them for oligomer modeling. To build structure models, we developed an oligomer structure generation method that utilizes predicted interchain contacts to guide iterative restrained minimization from random backbone structures. We supplemented this gradient-based fold-and-dock method with template-based and ab initio docking approaches using deep learning-based subunit predictions on 29 assembly targets. These methods produced oligomer models with summed Z-scores 5.5 units higher than the next best group, with the fold-and-dock method having the best relative performance. Over the eight targets for which this method was used, the best of the five submitted models had average oligomer TM-score of 0.71 (average oligomer TM-score of the next best group: 0.64), and explicit modeling of inter-subunit interactions improved modeling of six out of 40 individual domains (Delta GDT-TS > 2.0).N