MOTIVATION: Machine learning tools have become increasingly common in biological research, driven by the emergence of pre-trained large language models. However, training effective models remains a complex task, since many choices influence their performance. AutoML (automated machine learning) approaches help address these challenges by streamlining the entire model development pipeline. RESULTS: We developed aMLProt, an AutoML framework tailored specifically for protein applications, such as enzyme engineering and bioprospecting. It features a modular design, allowing each component to be used independently or in combination. Notably, aMLProt integrates 19 classifiers and 26 regressors, along with pre-trained protein language models. It also includes standalone applications proven useful for protein-related workflows. To enhance usability, aMLProt is integrated with Horus, a GUI-based application with a visual interface. AVAILABILITY AND IMPLEMENTATION: aMLProt is available on https://github.com/etiur/aMLProt.git and https://doi.org/10.5281/zenodo.14971157; The aMLProt plugin is available via the official Horus Plugin Repository https://horus.bsc.es/repo/plugins/amlprot, and Horus itself can be freely downloaded from https://horus.bsc.es. Moreover, a demo of aMLProt can be found, without previous registration or download, at the horus.bsc.es/amlprot and horus.bsc.es/amlprot-suggest. The results and data from the pH optima regression model are available at: https://zenodo.org/records/15394097.

MOTIVATION: Single protein residue mutations may reshape the binding affinity of protein-protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein-protein complexes trained on interactome data. RESULTS: Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein-protein mutations using UEP, a fast and reliable open-source code. AVAILABILITY AND IMPLEMENTATION: UEP algorithm can be found at: https://github.com/pepamengual/UEP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract The analysis of protein‐ligand interactions is complex because of the many factors at play. Most current methods for visual analysis provide this information in the form of simple 2D plots, which, besides being quite space hungry, often encode a low number of different properties. In this paper we present a system for compact 2D visualization of molecular simulations. It purposely omits most spatial information and presents physical information associated to single molecular components and their pairwise interactions through a set of 2D InfoVis tools with coordinated views, suitable interaction, and focus+context techniques to analyze large amounts of data. The system provides a wide range of motifs for elements such as protein secondary structures or hydrogen bond networks, and a set of tools for their interactive inspection, both for a single simulation and for comparing two different simulations. As a result, the analysis of protein‐ligand interactions of Molecular Simulation trajectories is greatly facilitated.

Vanillyl alcohol oxidase (VAO) is a homo-octameric flavoenzyme belonging to the VAO/PCMH family. Each VAO subunit consists of two domains, the FAD-binding and the cap domain. VAO catalyses, among other reactions, the two-step conversion of p-creosol (2-methoxy-4-methylphenol) to vanillin (4-hydroxy-3-methoxybenzaldehyde). To elucidate how different ligands enter and exit the secluded active site, Monte Carlo based simulations have been performed. One entry/exit path via the subunit interface and two additional exit paths have been identified for phenolic ligands, all leading to the si side of FAD. We argue that the entry/exit path is the most probable route for these ligands. A fourth path leading to the re side of FAD has been found for the co-ligands dioxygen and hydrogen peroxide. Based on binding energies and on the behaviour of ligands in these four paths, we propose a sequence of events for ligand and co-ligand migration during catalysis. We have also identified two residues, His466 and Tyr503, which could act as concierges of the active site for phenolic ligands, as well as two other residues, Tyr51 and Tyr408, which could act as a gateway to the re side of FAD for dioxygen. Most of the residues in the four paths are also present in VAO's closest relatives, eugenol oxidase and p-cresol methylhydroxylase. Key path residues show movements in our simulations that correspond well to conformations observed in crystal structures of these enzymes. Preservation of other path residues can be linked to the electron acceptor specificity and oligomerisation state of the three enzymes. This study is the first comprehensive overview of ligand and co-ligand migration in a member of the VAO/PCMH family, and provides a proof of concept for the use of an unbiased method to sample this process.

Molecular simulations are used in many areas of biotechnology, such as drug design and enzyme engineering. Despite the development of automatic computational protocols, analysis of molecular interactions is still a major aspect where human comprehension and intuition are key to accelerate, analyze, and propose modifications to the molecule of interest. Most visualization algorithms help the users by providing an accurate depiction of the spatial arrangement: the atoms involved in inter-molecular contacts. There are few tools that provide visual information on the forces governing molecular docking. However, these tools, commonly restricted to close interaction between atoms, do not consider whole simulation paths, long-range distances and, importantly, do not provide visual cues for a quick and intuitive comprehension of the energy functions (modeling intermolecular interactions) involved. In this paper, we propose visualizations designed to enable the characterization of interaction forces by taking into account several relevant variables such as molecule-ligand distance and the energy function, which is essential to understand binding affinities. We put emphasis on mapping molecular docking paths obtained from Molecular Dynamics or Monte Carlo simulations, and provide time-dependent visualizations for different energy components and particle resolutions: atoms, groups or residues. The presented visualizations have the potential to support domain experts in a more efficient drug or enzyme design process.

Today molecular simulations produce complex data sets capturing the interactions of molecules in detail. Due to the complexity of this time-varying data, advanced visualization techniques are required to support its visual analysis. Current molecular visualization techniques utilize ambient occlusion as a global illumination approximation to improve spatial comprehension. Besides these shadow-like effects, interreflections are also known to improve the spatial comprehension of complex geometric structures. Unfortunately, the inherent computational complexity of interreflections would forbid interactive exploration, which is mandatory in many scenarios dealing with static and time-varying data. In this paper, we introduce a novel analytic approach for capturing interreflections of molecular structures in real-time. By exploiting the knowledge of the underlying space filling representations, we are able to reduce the required parameters and can thus apply symbolic regression to obtain an analytic expression for interreflections. We show how to obtain the data required for the symbolic regression analysis, and how to exploit our analytic solution to enhance interactive molecular visualizations.

Correlated inter-domain motions in proteins can mediate fundamental biochemical processes such as signal transduction and allostery. Here we characterize at structural level the inter-domain coupling in a multidomain enzyme, Adenylate Kinase (AK), using computational methods that exploit the shape information encoded in residual dipolar couplings (RDCs) measured under steric alignment by nuclear magnetic resonance (NMR). We find experimental evidence for a multi-state equilibrium distribution along the opening/closing pathway of Adenylate Kinase, previously proposed from computational work, in which inter-domain interactions disfavour states where only the AMP binding domain is closed. In summary, we provide a robust experimental technique for study of allosteric regulation in AK and other enzymes.

SUMMARY: We introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matrices, implementing up to three well-known superposition algorithms. pyRMSD provides its own symmetric distance matrix class that, besides the fact that it can be used as a regular matrix, helps to save memory and increases memory access speed. This last feature can dramatically improve the overall performance of any Python algorithm using it. In addition, its extensibility, testing suites and documentation make it a good choice to those in need of a workbench for developing or testing new algorithms. AVAILABILITY: The source code (under MIT license), installer, test suites and benchmarks can be found at https://pele.bsc.es/ under the tools section. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The fast development of software and hardware is notably helping in closing the gap between macroscopic and microscopic data. Using a novel theoretical strategy combining molecular dynamics simulations, conformational clustering, ab-initio quantum mechanics and electronic coupling calculations, we show how computational methodologies are mature enough to provide accurate atomistic details into the mechanism of electron transfer (ET) processes in complex protein systems, known to be a significant challenge. We performed a quantitative study of the ET between Cytochrome c Peroxidase and its redox partner Cytochrome c. Our results confirm the ET mechanism as hole transfer (HT) through residues Ala194, Ala193, Gly192 and Trp191 of CcP. Furthermore, our findings indicate the fine evolution of the enzyme to approach an elevated turnover rate of 5.47 × 10(6) s(-1) for the ET between Cytc and CcP through establishment of a localized bridge state in Trp191.

The cytosol is the major environment in all bacterial cells. The true physical and dynamical nature of the cytosol solution is not fully understood and here a modeling approach is applied. Using recent and detailed data on metabolite concentrations, we have created a molecular mechanical model of the prokaryotic cytosol environment of Escherichia coli, containing proteins, metabolites and monatomic ions. We use 200 ns molecular dynamics simulations to compute diffusion rates, the extent of contact between molecules and dielectric constants. Large metabolites spend ∼80% of their time in contact with other molecules while small metabolites vary with some only spending 20% of time in contact. Large non-covalently interacting metabolite structures mediated by hydrogen-bonds, ionic and π stacking interactions are common and often associate with proteins. Mg(2+) ions were prominent in NIMS and almost absent free in solution. Κ(+) is generally not involved in NIMSs and populates the solvent fairly uniformly, hence its important role as an osmolyte. In simulations containing ubiquitin, to represent a protein component, metabolite diffusion was reduced owing to long lasting protein-metabolite interactions. Hence, it is likely that with larger proteins metabolites would diffuse even more slowly. The dielectric constant of these simulations was found to differ from that of pure water only through a large contribution from ubiquitin as metabolite and monatomic ion effects cancel. These findings suggest regions of influence specific to particular proteins affecting metabolite diffusion and electrostatics. Also some proteins may have a higher propensity for associations with metabolites owing to their larger electrostatic fields. We hope that future studies may be able to accurately predict how binding interactions differ in the cytosol relative to dilute aqueous solution.