1,721,006 research outputs found
Un modello di soluto polarizzabile per migliorare la procedura di stima delle cariche parziali
No full textApplication of conformational clustering in protein-ligand docking
No full textProtein-Ligand docking is a powerful technique routinely employed in structure-based drug design. Despite many reported success stories, docking is not always able to provide an accurate and easily interpretable prediction of the structure of the bound complex formed by a small organic molecule and a pharmacologically relevant target. Cluster analysis can represent a versatile and readily available postprocessing tool to be employed in combination with protein-ligand docking to simplify the evaluation of the results and help to overcome present limitations of docking protocols
Enhanced Molecular Dynamics Method to Efficiently Increase the Discrimination Capability of Computational Protein-Protein Docking
Full text linkProtein-protein docking typically consists of the generation of putative binding conformations, which are subsequently ranked by fast heuristic scoring functions. The simplicity of these functions allows for computational efficiency but has severe repercussions on their discrimination capabilities. In this work, we show the effectiveness of suitable descriptors calculated along short scaled molecular dynamics runs in recognizing the nearest-native bound conformation among a set of putative structures generated by the HADDOCK tool for eight protein-protein systems
AClAP, Autonomous hierarchical agglomerative Cluster Analysis based protocol to partition conformational datasets
No full textMOTIVATION: Sampling the conformational space is a fundamental step for both ligand- and structure-based drug design. However, the rational organization of different molecular conformations still remains a challenge. In fact, for drug design applications, the sampling process provides a redundant conformation set whose thorough analysis can be intensive, or even prohibitive. We propose a statistical approach based on cluster analysis aimed at rationalizing the output of methods such as Monte Carlo, genetic, and reconstruction algorithms. Although some software already implements clustering procedures, at present, a universally accepted protocol is still missing. RESULTS: We integrated hierarchical agglomerative cluster analysis with a clusterability assessment method and a user independent cutting rule, to form a global protocol that we implemented in a MATLAB metalanguage program (AClAP). We tested it on the conformational space of a quite diverse set of drugs generated via Metropolis Monte Carlo simulation, and on the poses we obtained by reiterated docking runs performed by four widespread programs. In our tests, AClAP proved to remarkably reduce the dimensionality of the original datasets at a negligible computational cost. Moreover, when applied to the outcomes of many docking programs together, it was able to point to the crystallographic pose
The role of histone tails in nucleosome stability: An electrostatic perspective
No full textWe propose a methodology for the study of protein-DNA electrostatic interactions and apply it to clarify the effect of histone tails in nucleosomes. This method can be used to correlate electrostatic interactions to structural and functional features of protein-DNA systems, and can be combined with coarse-grained representations. In particular, we focus on the electrostatic field and resulting forces acting on the DNA. We investigate the electrostatic origins of effects such as different stages in DNA unwrapping, nucleosome destabilization upon histone tail truncation, and the role of specific arginines and lysines undergoing Post-Translational Modifications. We find that the positioning of the histone tails can oppose the attractive pull of the histone core, locally deform the DNA, and tune DNA unwrapping. Small conformational variations in the often overlooked H2A C-terminal tails had significant electrostatic repercussions near the DNA entry and exit sites. The H2A N-terminal tail exerts attractive electrostatic forces towards the histone core in positions where Polymerase II halts its progress. We validate our results with comparisons to previous experimental and computational observations
Kinetics of Drug Binding and Residence Time
No full textThe kinetics of drug binding and unbinding is assuming an increasingly crucial role in the long, costly process of bringing a new medicine to patients. For example, the time a drug spends in contact with its biological target is known as residence time (the inverse of the kinetic constant of the drug-target unbinding, 1/kbinfoffeinf). Recent reports suggest that residence time could predict drug efficacy in vivo, perhaps even more effectively than conventional thermodynamic parameters (free energy, enthalpy, entropy). There are many experimental and computational methods for predicting drug-target residence time at an early stage of drug discovery programs. Here, we review and discuss the methodological approaches to estimating drug binding kinetics and residence time. We first introduce the theoretical background of drug binding kinetics from a physicochemical standpoint. We then analyze the recent literature in the field, starting from the experimental methodologies and applications thereof and moving to theoretical and computational approaches to the kinetics of drug binding and unbinding. We acknowledge the central role of molecular dynamics and related methods, which comprise a great number of the computational methods and applications reviewed here. However, we also consider kinetic Monte Carlo. We conclude with the outlook that drug (un)binding kinetics may soon become a go/no go step in the discovery and development of new medicines
AClAP, Autonomous hierarchical agglomerative Cluster Analysis based protocol to partition conformational datasets
No full textMOTIVATION: Sampling the conformational space is a fundamental step for both ligand- and structure-based drug design. However, the rational organization of different molecular conformations still remains a challenge. In fact, for drug design applications, the sampling process provides a redundant conformation set whose thorough analysis can be intensive, or even prohibitive. We propose a statistical approach based on cluster analysis aimed at rationalizing the output of methods such as Monte Carlo, genetic, and reconstruction algorithms. Although some software already implements clustering procedures, at present, a universally accepted protocol is still missing. RESULTS: We integrated hierarchical agglomerative cluster analysis with a clusterability assessment method and a user independent cutting rule, to form a global protocol that we implemented in a MATLAB metalanguage program (AClAP). We tested it on the conformational space of a quite diverse set of drugs generated via Metropolis Monte Carlo simulation, and on the poses we obtained by reiterated docking runs performed by four widespread programs. In our tests, AClAP proved to remarkably reduce the dimensionality of the original datasets at a negligible computational cost. Moreover, when applied to the outcomes of many docking programs together, it was able to point to the crystallographic pose
The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning
No full textThe study of biomolecular interactions between a drug and its biological target is of paramount importance for the design of novel bioactive compounds. In this paper, we report on the use of molecular dynamics (MD) simulations and machine learning to study the binding mechanism of a transition state analogue (DADMe-immucillin-H) to the purine nucleoside phosphorylase (PNP) enzyme. Microsecond-long MD simulations allow us to observe several binding events, following different dynamical routes and reaching diverse binding configurations. These simulations are used to estimate kinetic and thermodynamic quantities, such as k(on) and binding free energy, obtaining a good agreement with available experimental data. In addition, we advance a hypothesis for the slow-onset inhibition mechanism of DADMe-immucillin-H against PNP. Combining extensive MD simulations with machine learning algorithms could therefore be a fruitful approach for capturing key aspects of drug-target recognition and binding
Replica-exchange optimization of antibody fragments
No full textIn the framework of the rational design of macromolecules capable of binding to a specific target for biosensing applications, we here further develop an evolutionary protocol designed to optimize the binding affinity of protein binders. In particular we focus on the optimization of the binding portion of small antibody fragments known as nanobodies (or VHH) and choose the hen egg white lysozyme (HEWL) as our target. By implementing a replica exchange scheme for this optimization, we show that an initial hit is not needed and similar solutions can be found by either optimizing an already known anti-HEWL VHH or a randomly selected binder (here a VHH selective towards another macromolecule). While we believe that exhaustive searches of the mutation space are most appropriate when only few key residues have to be optimized, in case a lead binder is not available the proposed evolutionary algorithm should be instead the method of choice
Enhanced sampling methods in drug design
No full textThe contribution reports on the use of enhanced sampling methods in computational drug discovery
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