Here we introduce Geomfinder, an intuitive, flexible, alignment-free and ligand-independent web server for detailed estimation of similarities between all pairs of 3D patterns detected in any two given protein structures. We used around 1100 protein structures to form pairs of proteins which were assessed with Geomfinder. In these analyses each protein was considered in only one pair (e.g. in a subset of 100 different proteins, 50 pairs of proteins can be defined). Thus: (a) Geomfinder detected identical pairs of 3D patterns in a series of monoamine oxidase-B structures, which corresponded to the effectively similar ligand binding sites at these proteins; (b) we identified structural similarities among pairs of protein structures which are targets of compounds such as acarbose, benzamidine, adenosine triphosphate and pyridoxal phosphate; these similar 3D patterns are not detected using sequence-based methods; (c) the detailed evaluation of three specific cases showed the versatility of Geomfinder, which was able to discriminate between similar and different 3D patterns related to binding sites of common substrates in a range of diverse proteins.

Proteins are macromolecules involved in the biological functions. Protein-protein interactions that occur between two or more proteins are called protein complexes. These complexes are of vital importance as they play a key role in most biological processes, such as biosynthesis, cellular regulation and signal translation. For this reason, the researchers try to understand these protein complexes in their three-dimensional structure, through data that are generated from methods such as X-ray crystallography or nuclear magnetic resonance. Because the tertiary structure is unique to each protein it is also directly responsible for its biological properties. These data are used for the prediction of protein-protein interactions. The prediction seeks by means of computational tools, to identify and to categorize the type of interaction that occurs in a given protein complex. This through the use of different features of the complex. In this study we try to determine if using only the distance between amino acids belonging to the interaction zone in protein complexes influences whether the interaction is classified as transient or permanent (duration time of interaction). For this we use a set of protein complexes already classified as transient or permanent, in addition to its three-dimensional structure. The distance measures used as a feature, are selected considering the energy produced in the interaction zone and the structure of the amino acids involved and their position. A 75% accuracy was obtained, using distance as a discriminant factor in the classification of protein complexes. This suggests that, there is a relation between the distance as a discriminating factor to classify these complexes as transient or permanent.

Discovering conserved three-dimensional (3D) patterns among protein structures may provide valuable insights into protein classification, functional annotations or the rational design of multi-target drugs. Thus, several computational tools have been developed to discover and compare protein 3D-patterns. However, most of them only consider previously known 3D-patterns such as orthosteric binding sites or structural motifs. This fact makes necessary the development of new methods for the identification of all possible 3D-patterns that exist in protein structures (allosteric sites, enzyme-cofactor interaction motifs, among others). In this work, we present 3D-PP, a new free access web server for the discovery and recognition all similar 3D amino acid patterns among a set of proteins structures (independent of their sequence similarity). This new tool does not require any previous structural knowledge about ligands, and all data are organized in a high-performance graph database. The input can be a text file with the PDB access codes or a zip file of PDB coordinates regardless of the origin of the structural data: X-ray crystallographic experiments or in silico homology modeling. The results are presented as lists of sequence patterns that can be further analyzed within the web page. We tested the accuracy and suitability of 3D-PP using two sets of proteins coming from the Protein Data Bank: (a) Zinc finger containing and (b) Serotonin target proteins. We also evaluated its usefulness for the discovering of new 3D-patterns, using a set of protein structures coming from in silico homology modeling methodologies, all of which are overexpressed in different types of cancer. Results indicate that 3D-PP is a reliable, flexible and friendly-user tool to identify conserved structural motifs, which could be relevant to improve the knowledge about protein function or classification.

Root mean square deviation (RMSD) is one of the most useful and straightforward features for structural comparison between different conformations of the same molecule. Commonly, protein-ligand docking programs have included some utilities that allow the calculation of this value; however, they only work efficiently when exists a complete atom label equivalence between the evaluated conformations. We present LigRMSD, a free web-server for the automatic matching and RMSD calculations among identical or similar chemical compounds. This server allows the user to submit only a pair of identical or similar molecules or dataset of similar compounds to compare their three-dimensional conformations.

Plants synthesize a large number of natural products, many of which are bioactive and have practical values as well as commercial potential. To explore this vast structural diversity, we present PSC-db, a unique plant metabolite database aimed to categorize the diverse phytochemical space by providing 3D-structural information along with physicochemical and pharmaceutical properties of the most relevant natural products. PSC-db may be utilized, for example, in qualitative estimation of biological activities (Quantitative Structure-Activity Relationship, QSAR) or massive docking campaigns to identify new bioactive compounds, as well as potential binding sites in target proteins. PSC-db has been implemented using the open-source PostgreSQL database platform where all compounds with their complementary and calculated information (classification, redundant names, unique IDs, physicochemical properties, etc.) were hierarchically organized. The source organism for each compound, as well as its biological activities against protein targets, cell lines and different organism were also included.

The identification of similar three-dimensional (3D) amino acid patterns among different proteins might be helpful to explain the polypharmacological profile of many currently used drugs. Also, it would be a reasonable first step for the design of novel multitarget compounds. Most of the current computational tools employed for this aim are limited to the comparisons among known binding sites, and do not consider several additional important 3D patterns such as allosteric sites or other conserved motifs. In the present work, we introduce Geomfinder2.0, which is a new and improved version of our previously described algorithm for the deep exploration and discovery of similar and druggable 3D patterns. As compared with the original version, substantial improvements that have been incorporated to our software allow: (i) to compare quaternary structures, (ii) to deal with a list of pairs of structures, (iii) to know how druggable is the zone where similar 3D patterns are detected and (iv) to significantly reduce the execution time. Thus, the new algorithm achieves up to 353x speedup as compared to the previous sequential version, allowing the exploration of a significant number of quaternary structures in a reasonable time. In order to illustrate the potential of the updated Geomfinder version, we show a case of use in which similar 3D patterns were detected in the cardiac ions channels NaV1.5 and TASK-1. These channels are quite different in terms of structure, sequence and function and both have been regarded as important targets for drugs aimed at treating atrial fibrillation. Finally, we describe the in vitro effects of tafluprost (a drug currently used to treat glaucoma, which was identified as a novel putative ligand of NaV1.5 and TASK-1) upon both ion channels’ activity and discuss its possible repositioning as a novel antiarrhythmic drug.

Abstract: The discovery of conserved three-dimensional (3D) amino-acid patterns among a set of protein structures can be useful, for instance, to predict the functions of unknown proteins or for the rational design of multi-target drugs. There are several applications that perform a three-dimensional search of patterns in the structures of proteins. However, discovering conserved 3D patterns in a set of proteins with no other baseline patterns is a challenge. In this paper, we analyze and improve a state-of-the-art algorithm, 3D-PP, that implements this discovery. In this algorithm, the 3D patterns are detected and clustered using the root mean square deviation value, measured among each pair of 3D patterns (topological variability indicator). Even when 3D-PP deals with this task, the simultaneous processing of high amounts of proteins becomes a computational challenge with the size and the number of proteins to be evaluated. In this work, we present and analyze different shared memory parallel strategies of 3D-PP, using OpenMP. Those strategies improve the overall performance of the original implementation by reducing parallel load unbalance among threads and overall increasing parallelism. The results show significant performance improvements compared to the original version, achieving up to 13x speedup for a small number of proteins and 17.7× for a larger set.

Alzheimer’s disease (AD), a prevalent neurodegenerative disorder, presents significant challenges in drug development due to its multifactorial nature and complex pathophysiology. The AlzyFinder Platform, introduced in this study, addresses these challenges by providing a comprehensive, free web-based tool for parallel ligand-based virtual screening and network pharmacology, specifically targeting over 85 key proteins implicated in AD. This innovative approach is designed to enhance the identification and analysis of potential multitarget ligands, thereby accelerating the development of effective therapeutic strategies against AD. AlzyFinder Platform incorporates machine learning models to facilitate the ligand-based virtual screening process. As the first open-access platform of its kind, AlzyFinder stands as a valuable resource for the AD research community, available at http://www.alzyfinder-platform.udec.cl with supporting data and scripts accessible via GitHub https://github.com/ramirezlab/AlzyFinder.

Identifying conserved (similar) three-dimensional patterns among a set of proteins can be helpful for the rational design of polypharmacological drugs. Some available tools allow this identification from a limited perspective, only considering the available information, such as known binding sites or previously annotated structural motifs. Thus, these approaches do not look for similarities among all putative orthosteric and or allosteric bindings sites between protein structures. To overcome this tech-weakness Geomfinder was developed, an algorithm for the estimation of similarities between all pairs of three-dimensional amino acids patterns detected in any two given protein structures, which works without information about their known patterns. Even though Geomfinder is a functional alternative to compare small structural proteins, it is computationally unfeasible for the case of large protein processing and the algorithm needs to improve its performance. This work presents several parallel versions of the Geomfinder to exploit SMPs, distributed memory systems, hybrid version of SMP and distributed memory systems, and GPU based systems. Results show significant improvements in performance as compared to the original version and achieve up to 24.5x speedup when analyzing proteins of average size and up to 95.4x in larger proteins.

The discovery and comparison of three-dimensional (3D) patterns in protein structures are critical tasks in structural bioinformatics, particularly for identifying functional sites and potential drug targets. In this work, we present significant enhancements of Geomfinder, a ligand-independent algorithm for identifying and comparing 3D protein patterns. Our contributions include the following key improvements: i) a mechanism that reduces the number of unnecessary site comparisons; ii) an optimization that lowers the computational complexity of partial score calculations from O(N×M) to O(N+M); and iii) an updated OpenMP-based parallel version to accelerate the site discovery and comparison processes further. Our results show significant performance improvements for a base dataset of proteins. Results show that the optimized sequential Geomfinder achieves up to 26.3x speedup, compared to the original sequential Geomfinder, for relatively large proteins (reducing from approximately 1 hour to approximately 2 and a half minutes). And our OpenMP Geomfinder proposal, combined with the sequential optimization, reaches a speedup of more than 1000x compared to the sequential original Geomfinder. This novel optimized and parallel Geomfinder version allows for processing large protein structures that were not reasonable to compare before.

The discovery and clustering of three-dimensional protein patterns in a set of protein structures, without relying on predefined search patterns, can be highly beneficial for predicting the functions of unknown proteins and facilitating rational multi-target drug design. This work introduces a novel OpenMP parallelization of the 3D-PP algorithm using explicit and nested tasks, which balances data sharing synchronization and load unbalance, improving previous implementations based on implicit tasks. Given the vast number of protein structures available in the Protein Data Bank (over 231,000 from PDB and more than 1,068,000 from AlphaFold), processing large datasets locally can be constrained by available resources such as main memory and secondary storage. To address this challenge, we propose parallel approaches to the 3D-PP algorithm for distributed memory systems (using MPI) and hybrid systems (combining MPI and OpenMP). The evaluated strategies distribute the workload based on the entire protein structure. Experimental results using a dataset of 8,344 protein structures show that the new OpenMP taskified version is 1.6x faster than the previous best OpenMP implementation. Moreover, the hybrid MPI+OpenMP version achieves a speedup of up to 162.5x, demonstrating its scalability and efficiency for large-scale protein structure analysis.

Sequence logos are a common way to visually represent amino acid frequencies and conserved sequence patterns of multiple sequence alignments (MSAs). MSA Class Logos is a free web server that enables users to browse and explore sequence logos of several sequence groups or classes simultaneously online, and to download amino acid frequencies for the entire MSA or the sequence classes. Amino acid frequencies can be downloaded in tabular or graphical form for further offline analysis. The core feature of MSA Class Logos is the user’s ability to group amino acid sequences into classes or subgroups, generate sequence logos for each class, and compare the clearly arranged logos online. Sequence logos for the entire MSA and user-defined classes are generated in separate tabs and can be explored conveniently in parallel. Class-specific sequence logos facilitate the identification of class-specific conserved residues unique in evolutionary distant or functionally differing protein groups. Results can be explored visually directly on the web page or downloaded as tables in CSV format. Additionally, segments of each sequence logo can be downloaded in SVG format. MSA Class Logos is primarily intended for investigators without programming and scripting skills to ease detailed sequence analysis.