Similarities and Differences in Ligand Binding to Protein and RNA Targets: The Case of Riboflavin.

It is nowadays clear that RNA molecules can play active roles in several biological processes. As a result, an increasing number of RNAs are gradually being identified as potentially druggable targets. In particular, noncoding RNAs can adopt highly organized conformations that are suitable for drug binding. However, RNAs are still considered challenging targets due to their complex structural dynamics and high charge density. Thus, elucidating relevant features of drug-RNA binding is fundamental for advancing drug discovery. Here, by using Molecular Dynamics simulations, we compare key features of ligand binding to proteins with those observed in RNA. Specifically, we explore similarities and differences in terms of (i) conformational flexibility of the target, (ii) electrostatic contribution to binding free energy, and (iii) water and ligand dynamics. As a test case, we examine binding of the same ligand, namely riboflavin, to protein and RNA targets, specifically the riboflavin (RF) kinase and flavin mononucleotide (FMN) riboswitch. The FMN riboswitch exhibited enhanced fluctuations and explored a wider conformational space, compared to the protein target, underscoring the importance of RNA flexibility in ligand binding. Conversely, a similar electrostatic contribution to the binding free energy of riboflavin was found. Finally, greater stability of water molecules was observed in the FMN riboswitch compared to the RF kinase, possibly due to the different shape and polarity of the pockets.

GSK-3ß Allosteric Inhibition: A Dead End or a New Pharmacological Frontier?

Most kinase inhibitors are designed to bind to highly homologous ATP-binding sites, which leads to promiscuity and possible off-target effects. Allostery is an alternative approach to pursuing selectivity. However, allostery is difficult to exploit due to the wide variety of underlying mechanisms and the potential involvement of long-range conformational effects that are difficult to pinpoint. GSK-3ß is involved in several pathologies. This critical target has an ATP-binding site that is highly homologous with the orthosteric sites of other kinases. Unsurprisingly, there is also great similarity between the ATP-binding sites of GSK-3ß and its isomer, which is not redundant and thus would benefit from selective inhibition. Allostery would also allow for a moderate and tunable inhibition, which is ideal for GSK-3ß, because this target is involved in multiple pathways, some of which must be preserved. However, despite considerable research efforts, only one allosteric GSK-3ß inhibitor has reached the clinic. Moreover, unlike other kinases, there are no X-ray structures of GSK-3ß in complex with allosteric inhibitors in the PDB data bank. This review aims to summarize the state of the art in allosteric GSK-3ß inhibitor investigations, highlighting the aspects that make this target challenging for an allosteric approach.

The Mechanistic Understanding of RAD51 Defibrillation: A Critical Step in BRCA2-Mediated DNA Repair by Homologous Recombination.

The cytotoxic action of anticancer drugs can be potentiated by inhibiting DNA repair mechanisms. RAD51 is a crucial protein for genomic stability due to its critical role in the homologous recombination (HR) pathway. BRCA2 assists RAD51 fibrillation and defibrillation in the cytoplasm and nucleus and assists its nuclear transport. BRC4 is a peptide derived from the fourth BRC repeat of BRCA2, and it lacks the nuclear localization sequence. Here, we used BRC4 to (i) reverse RAD51 fibrillation; (ii) avoid the nuclear transport of RAD51; and (iii) inhibit HR and enhance the efficacy of chemotherapeutic treatments. Specifically, using static and dynamic light scattering, transmission electron microscopy, and microscale thermophoresis, we show that BRC4 eroded RAD51 fibrils from their termini through a "domino" mechanism and yielded monomeric RAD51 with a cumulative nanomolar affinity. Using cellular assays (BxPC-3, pancreatic cancer), we show that a myristoylated BRC4 (designed for a more efficient cell entry) abolished the formation of nuclear RAD51 foci. The present study provides a molecular description of RAD51 defibrillation, an essential step in BRCA2-mediated homologous recombination and DNA repair.

Charged dielectric spheres interacting in electrolytic solution: A linearized Poisson-Boltzmann equation model.

We present an analytical theory of electrostatic interactions of two spherical dielectric particles of arbitrary radii and dielectric constants, immersed into a polarizable ionic solvent (assuming that the linearized Poisson-Boltzmann framework holds) and bearing arbitrary charge distributions expanded in multipolar terms. The presented development entails a novel two-center re-expansion analytical theory that expands upon and improves the existing ones, bypassing the conventional expansions in modified Bessel functions. On this basis, we develop a specific matrix formalism that facilitates the construction of asymptotic expansions in ascending order of Debye screening terms of potential coefficients, which are then employed to find exact closed-form expressions for the total electrostatic energy. In particular, this work allows us to explicitly and precisely quantify the k-screened terms of the potential coefficients and mutual interaction energy. Specific cases of monopolar and dipolar distributions are described in particular detail. Comprehensive numerical examples and tests of series convergence and the relative balance of leading and higher-order terms of the mutual interaction energy are presented depending on the inter-particle distance and particles' radii. The results of this work find application in soft matter modeling and, in particular, in computational biophysics and colloid science, where the availability of increasingly larger experimental structures at the atomic-level resolution makes numerical treatment challenging and calls for more efficient expressions and an increased range of validity.

NanoShaper-VMD interface: computing and visualizing surfaces, pockets and channels in molecular systems.

SUMMARY: NanoShaper is a program specifically aiming the construction and analysis of the molecular surface of nanoscopic systems. It uses ray-casting for parallelism and it performs analytical computations whenever possible to maximize robustness and accuracy of the approach. Among the other features, NanoShaper provides volume, surface area, including that of internal cavities, for any considered molecular system. It identifies pockets via a very intuitive definition based on the concept of probe radius, intrinsic to the definition of the solvent excluded surface. We show here that, with a suitable choice of the parameters, the same approach can also permit the visualisation of molecular channels. NanoShaper has now been interfaced with the widely used molecular visualization software VMD, further enriching its already well furnished toolset. AVAILABILITY AND IMPLEMENTATION: VMD is available at http://www.ks.uiuc.edu/Research/vmd/. NanoShaper, its documentation, tutorials and supporting programs are available at http://concept.iit.it/downloads. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Kinetics of Drug Binding and Residence Time.

The 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/koff). 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.

PypKa: A Flexible Python Module for Poisson-Boltzmann-Based pKa Calculations.

The protonation of titratable residues has a significant impact on the structure and function of biomolecules, influencing many physicochemical and ADME properties. Thus, the importance of the estimation of protonation free energies (pKa values) is paramount in different scientific communities, including bioinformatics, structural biology, or medicinal chemistry. Here, we introduce PypKa, a flexible tool to predict Poisson-Boltzmann/Monte Carlo-based pKa values of titratable sites in proteins. This application was benchmarked using a large data set of experimental values to show that our single structure-based method is fast and has a competitive performance. This is a free and open-source tool that provides a simple, reusable, and extensible Python API and CLI for pKa calculations with a valuable trade-off between fast and accurate predictions. PypKa allows pKa calculations in existing protocols with the addition of a few extra lines of code. PypKa supports CPU parallel computing on solvated proteins obtained from the PDB repository but also from MD simulations using three common naming schemes: GROMOS, AMBER, and CHARMM. The code and documentation to this open-source project is publicly available at https://github.com/mms-fcul/PypKa.

Multiplexed Discrimination of Single Amino Acid Residues in Polypeptides in a Single SERS Hot Spot.

The SERS-based detection of protein sequences with single-residue sensitivity suffers from signal dominance of aromatic amino acid residues and backbones, impeding detection of non-aromatic amino acid residues. Herein, we trap a gold nanoparticle in a plasmonic nanohole to generate a single SERS hot spot for single-molecule detection of 2 similar polypeptides (vasopressin and oxytocin) and 10 distinct amino acids that constitute the 2 polypeptides. Significantly, both aromatic and non-aromatic amino acids are detected and discriminated at the single-molecule level either at individual amino acid molecules or within the polypeptide chains. Correlated with molecular dynamics simulations, our results suggest that the signal dominance due to large spatial occupancy of aromatic rings of the polypeptide sidechains on gold surfaces can be overcome by the high localization of the single hot spot. The superior spectral and spatial discriminative power of our approach can be applied to single-protein analysis, fingerprinting, and sequencing.

BiKi Life Sciences: A New Suite for Molecular Dynamics and Related Methods in Drug Discovery.

In this paper, we introduce the BiKi Life Sciences suite. This software makes it easy for computational medicinal chemists to run ad hoc molecular dynamics protocols in a novel and task-oriented environment; as a notebook, BiKi (acronym of Binding Kinetics) keeps memory of any activity together with dependencies among them. It offers unique accelerated protein-ligand binding/unbinding methods and other useful tools to gain actionable knowledge from molecular dynamics simulations and to simplify the drug discovery process.

Including diverging electrostatic potential in 3D-RISM theory: The charged wall case.

Although three-dimensional site-site molecular integral equations of liquids are a powerful tool of the modern theoretical chemistry, their applications to the problem of characterizing the electrical double layer originating at the solid-liquid interface with a macroscopic substrate are severely limited by the fact that an infinitely extended charged plane generates a divergent electrostatic potential. Such potentials cannot be treated within the standard 3D-Reference Interaction Site Model equation solution framework since it leads to functions that are not Fourier transformable. In this paper, we apply a renormalization procedure to overcome this obstacle. We then check the validity and numerical accuracy of the proposed computational scheme on the prototypical gold (111) surface in contact with water/alkali chloride solution. We observe that despite the proposed method requires, to achieve converged charge densities, a higher spatial resolution than that suited to the estimation of biomolecular solvation with either 3D-RISM or continuum electrostatics approaches, it still is computationally efficient. Introducing the electrostatic potential of an infinite wall, which is periodic in 2 dimensions, we avoid edge effects, permit a robust integration of Poisson's equation, and obtain the 3D electrostatic potential profile for the first time in such calculations. We show that the potential within the electrical double layer presents oscillations which are not grasped by the Debye-Hückel and Gouy-Chapman theories. This electrostatic potential deviates from its average of up to 1-2 V at small distances from the substrate along the lateral directions. Applications of this theoretical development are relevant, for example, for liquid scanning tunneling microscopy imaging.

A simple and accurate protocol for absolute polar metabolite quantification in cell cultures using quantitative nuclear magnetic resonance.

Absolute analyte quantification by nuclear magnetic resonance (NMR) spectroscopy is rarely pursued in metabolomics, even though this would allow researchers to compare results obtained using different techniques. Here we report on a new protocol that permits, after pH-controlled serum protein removal, the sensitive quantification (limit of detection [LOD] = 5-25 µM) of hydrophilic nutrients and metabolites in the extracellular medium of cells in cultures. The method does not require the use of databases and uses PULCON (pulse length-based concentration determination) quantitative NMR to obtain results that are significantly more accurate and reproducible than those obtained by CPMG (Carr-Purcell-Meiboom-Gill) sequence or post-processing filtering approaches. Three practical applications of the method highlight its flexibility under different cell culture conditions. We identified and quantified (i) metabolic differences between genetically engineered human cell lines, (ii) alterations in cellular metabolism induced by differentiation of mouse myoblasts into myotubes, and (iii) metabolic changes caused by activation of neurotransmitter receptors in mouse myoblasts. Thus, the new protocol offers an easily implementable, efficient, and versatile tool for the investigation of cellular metabolism and signal transduction.

GPU linear and non-linear Poisson-Boltzmann solver module for DelPhi.

SUMMARY: In this work, we present a CUDA-based GPU implementation of a Poisson-Boltzmann equation solver, in both the linear and non-linear versions, using double precision. A finite difference scheme is adopted and made suitable for the GPU architecture. The resulting code was interfaced with the electrostatics software for biomolecules DelPhi, which is widely used in the computational biology community. The algorithm has been implemented using CUDA and tested over a few representative cases of biological interest. Details of the implementation and performance test results are illustrated. A speedup of ~10 times was achieved both in the linear and non-linear cases. AVAILABILITY AND IMPLEMENTATION: The module is open-source and available at http://www.electrostaticszone.eu/index.php/downloads.

A Pipeline To Enhance Ligand Virtual Screening: Integrating Molecular Dynamics and Fingerprints for Ligand and Proteins.

The importance of taking into account protein flexibility in drug design and virtual ligand screening (VS) has been widely debated in the literature, and molecular dynamics (MD) has been recognized as one of the most powerful tools for investigating intrinsic protein dynamics. Nevertheless, deciphering the amount of information hidden in MD simulations and recognizing a significant minimal set of states to be used in virtual screening experiments can be quite complicated. Here we present an integrated MD-FLAP (molecular dynamics-fingerprints for ligand and proteins) approach, comprising a pipeline of molecular dynamics, clustering and linear discriminant analysis, for enhancing accuracy and efficacy in VS campaigns. We first extracted a limited number of representative structures from tens of nanoseconds of MD trajectories by means of the k-medoids clustering algorithm as implemented in the BiKi Life Science Suite ( http://www.bikitech.com [accessed July 21, 2015]). Then, instead of applying arbitrary selection criteria, that is, RMSD, pharmacophore properties, or enrichment performances, we allowed the linear discriminant analysis algorithm implemented in FLAP ( http://www.moldiscovery.com [accessed July 21, 2015]) to automatically choose the best performing conformational states among medoids and X-ray structures. Retrospective virtual screenings confirmed that ensemble receptor protocols outperform single rigid receptor approaches, proved that computationally generated conformations comprise the same quantity/quality of information included in X-ray structures, and pointed to the MD-FLAP approach as a valuable tool for improving VS performances.

Steered molecular dynamics simulations for studying protein-ligand interaction in cyclin-dependent kinase 5.

In this study, we applied steered molecular dynamics (SMD) simulations to investigate the unbinding mechanism of nine inhibitors of the enzyme cyclin-dependent kinase 5 (CDK5). The study had two major objectives: (i) to create a correlation between the unbinding force profiles and the inhibition activities of these compounds expressed as IC50 values; (ii) to investigate the unbinding mechanism and to reveal atomistic insights, which could help identify accessory binding sites and transient interactions. Overall, we carried out 1.35 µs of cumulative SMD simulations. We showed that SMD could qualitatively discriminate binders from nonbinders, while it failed to properly rank series of inhibitors, particularly when IC50 values were too similar. From a mechanistic standpoint, SMD provided useful insights related to transient and dynamical interactions, which could complement static description obtained by X-ray crystallography experiments. In conclusion, the present study represents a further step toward a systematic exploitation of SMD and other dynamical approaches in structure-based drug design and computational medicinal chemistry.

Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔG Predictions.

The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.

SiteFerret: Beyond Simple Pocket Identification in Proteins.

We present a novel method for the automatic detection of pockets on protein molecular surfaces. The algorithm is based on an ad hoc hierarchical clustering of virtual probe spheres obtained from the geometrical primitives used by the NanoShaper software to build the solvent-excluded molecular surface. The final ranking of putative pockets is based on the Isolation Forest method, an unsupervised learning approach originally developed for anomaly detection. A detailed importance analysis of pocket features provides insight into which geometrical (clustering) and chemical (amino acidic composition) properties characterize a good binding site. The method also provides a segmentation of pockets into smaller subpockets. We prove that subpockets are a convenient representation to pinpoint the binding site with great precision. SiteFerret is outstanding in its versatility, accurately predicting a wide range of binding sites, from those binding small molecules to those binding peptides, including difficult shallow sites.

Replica-exchange optimization of antibody fragments.

In 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.

Theoretical analysis of divalent cation effects on aptamer recognition of neurotransmitter targets.

Aptamer-based sensing of small molecules such as dopamine and serotonin in the brain, requires characterization of the specific aptamer sequences in solutions mimicking the in vivo environment with physiological ionic concentrations. In particular, divalent cations (Mg2+ and Ca2+) present in brain fluid, have been shown to affect the conformational dynamics of aptamers upon target recognition. Thus, for biosensors that transduce aptamer structure switching as the signal response, it is critical to interrogate the influence of divalent cations on each unique aptamer sequence. Herein, we demonstrate the potential of molecular dynamics (MD) simulations to predict the behaviour of dopamine and serotonin aptamers on sensor surfaces. The simulations enable molecular-level visualization of aptamer conformational changes that, in some cases, are significantly influenced by divalent cations. The correlations of theoretical simulations with experimental findings validate the potential for MD simulations to predict aptamer-specific behaviors on biosensors.

Arbitrary-Shape Dielectric Particles Interacting in the Linearized Poisson-Boltzmann Framework: An Analytical Treatment.

This work considers the interaction of two dielectric particles of arbitrary shape immersed into a solvent containing a dissociated salt and assuming that the linearized Poisson-Boltzmann equation holds. We establish a new general spherical re-expansion result which relies neither on the conventional condition that particle radii are small with respect to the characteristic separating distance between particles nor on any symmetry assumption. This is instrumental in calculating suitable expansion coefficients for the electrostatic potential inside and outside the objects and in constructing small-parameter asymptotic expansions for the potential, the total electrostatic energy, and forces in ascending order of Debye screening. This generalizes a recent result for the case of dielectric spheres to particles of arbitrary shape and builds for the first time a rigorous (exact at the Debye-Hückel level) analytical theory of electrostatic interactions of such particles at arbitrary distances. Numerical tests confirm that the proposed theory may also become especially useful in developing a new class of grid-free, fast, highly scalable solvers.