Ligand binding to protein-binding pockets with wet and dry regions

PLAGCA: Predicting protein-ligand binding affinity with the graph cross-attention mechanism

Accurate prediction of protein-ligand binding affinity plays a crucial role in drug discovery. However, determining the binding affinity of protein-ligands through biological experimental approaches is both time-consuming and expensive. Although some computational methods have been developed to predict protein-ligands binding affinity, most existing methods extract the global features of proteins and ligands through separate encoders, without considering to extract the local pocket interaction features of protein-ligand complexes, resulting in the limited prediction accuracy. In this work, we proposed a novel Protein-Ligand binding Affinity prediction method (named PLAGCA) by introducing Graph Cross-Attention mechanism to learn the local three-dimensional (3D) features of protein-ligand pockets, and integrating the global sequence/string features and local graph interaction features of protein-ligand complexes. PLAGCA uses sequence encoding and self-attention to extract the protein/ligand global features from protein FASTA sequences/ligand SMILES strings, adopts graph neural network and cross-attention to extract the protein-ligand local interaction features from the molecular structures of protein binding pockets and ligands. All these features are concatenated and input into a multi-layer perceptron (MLP) for predicting the protein-ligand binding affinity. The experimental results show that our PLAGCA outperforms other state-of-the-art computational methods, and it can effectively predict protein-ligand binding affinity with superior generalization capability. PLAGCA can capture the critical functional residues that are important contribution to the protein-ligand binding.

Understanding the HIV-CA protein and the ligands that bind at the N-terminal domain (NTD) - C-terminal domain (CTD) interface

Treatment and prevention of HIV/AIDS infections represents a significant global challenge, with this being the cause of a substantial number of deaths each year. HIV-CA, the protein responsible for protecting the viral RNA and facilitating reverse transcription, has emerged as an important target in drug discovery. This review applies various computer drug discovery tools for the analysis and understanding of not only the HIV-CA protein, but also the ligands reported to bind to the site at the NTD-CTD interface between two capsid monomer units. Combining this evaluation with reported experimental data, highlights the effects that changes to the ligands make to the binding affinity. This analysis, including identifying areas of the ligand that have not been adequately explored, allows for the generation of guidelines that can be applied to the design of novel ligands that bind to HIV-CA.

Nonequilibrium Binding Free Energy Simulations: Minimizing Dissipation

While nowadays approaches for equilibrium free energy estimation are well established, nonequilibrium simulations represent both an appealing computational opportunity and a challenge. This kind of simulations allows for a trivially parallel scheme, but at the same time the significant amount of irreversible work often generated during the steering process (either alchemical or physical) can hinder the convergence of free energy estimators. Here, we discuss in depth this issue for the protein-ligand binding free energy estimation carried out via physical paths. We found that the water model and the parametrization of the path collective variables have a remarkable impact on the convergence rate of the estimators (e.g., Crooks). Finally, we provide practical recipes to enhance the convergence speed and minimize dissipation.

Effect of Water Networks On Ligand Binding: Computational Predictions vs Experiments

Rational drug design focuses on the explanation and prediction of complex formation between therapeutic targets and small-molecule ligands. As a third and often overlooked interacting partner, water molecules play a critical role in the thermodynamics of protein-ligand binding, impacting both the entropy and enthalpy components of the binding free energy and by extension, on-target affinity and bioactivity. The community has realized the importance of binding site waters, as evidenced by the number of computational tools to predict the structure and thermodynamics of their networks. However, quantitative experimental characterization of relevant protein-ligand-water systems, and consequently the validation of these modeling methods, remains challenging. Here, we investigated the impact of solvent exchange from light (H2O) to heavy water (D2O) to provide complete thermodynamic profiling of these ternary systems. Utilizing the solvent isotope effects, we gain a deeper understanding of the energetic contributions of various components. Specifically, we conducted isothermal titration calorimetry experiments on trypsin with a series of p-substituted benzamidines, as well as carbonic anhydrase II (CAII) with a series of aromatic sulfonamides. Significant differences in binding enthalpies found between light vs heavy water indicate a substantial role of the binding site water network in protein-ligand binding. Next, we challenged two conceptually distinct modeling methods, the grid-based WaterFLAP and the molecular dynamics-based MobyWat, by predicting and scoring relevant water networks. The predicted water positions accurately reproduce those in available high-resolution X-ray and neutron diffraction structures of the relevant protein-ligand complexes. Estimated energetic contributions of the identified water networks were corroborated by the experimental thermodynamics data. Besides providing a direct validation for the predictive power of these methods, our findings confirmed the importance of considering binding site water networks in computational ligand design.

Role of hydrogen-bond coordination defects in the structural relaxation of supercooled water

In this work, we shall study the role of threefold and fivefold coordination defects in the structure and dynamics of the hydrogen bond network of liquid water, with special emphasis on the glassy regime. A significant defect clusterization propensity will be made evident, with a prevalence of mixed pairs, that is, threefold- and fivefold-coordinated defects being first neighbors of each other. This structural analysis will enable us to determine the existence of defective and nondefective regions compatible with the high local density and low local density molecular states of liquid water, respectively. Hydrogen bond coordination defects will also be shown to promote water's structural relaxation, with the undercoordinated ones playing a main role in driving glassy relaxation dynamics. Moreover, we shall show that the three-foldcoordinated molecules together with their first neighbors present at the initial configuration act as markers of the dynamical heterogeneities that would emerge at later times commensurate with the structural relaxation of the supercooled system.

Ligand modelling of Trachyspermum ammi phytocompounds for Aeromonas hydrophila cell wall synthesis enzyme in Labeo rohita

Aquaculture faces challenges from Aeromonas hydrophila, causing Motile Aeromonas Septicaemia, particularly affecting Labeo rohita (Rohu) in Pakistan. This study explores potential herbal antibacterials targeting A. hydrophila, molecular docking of Trachyspermum ammi (ajwain) phytocompounds against pathogen. The cell wall synthesis ligase, D-alanine-D-alanine ligase (PDB ID 6ll9) was processed in BIOVIA Discovery Studio and docked with 13 antibacterial phytocompounds found after QSAR analysis of T. ammi. Binding energies were calculated using PyRx to assess complex stability. ADME-TOX assessment for selected phytocompounds and parameterisation in CHARMM-GUI were performed. Docking the two best ligands with highest binding energies and ADME-TOX compliance, we found carvacrol and limonene formed most stable protein-ligand complexes, with raw and processed protein. Our findings suggest these herbal compounds can inhibit D-alanine-D-alanine ligase. These in-silico results support the potential of 'ajwain' in managing A. hydrophila, further in-vivo experiments are necessary to validate these inhibitory properties.

Transformation of peptides to small molecules in medicinal chemistry: Challenges and opportunities

Peptides are native binders involved in numerous physiological life procedures, such as cellular signaling, and serve as ready-made regulators of biochemical processes. Meanwhile, small molecules compose many drugs owing to their outstanding advantages of physiochemical properties and synthetic convenience. A novel field of research is converting peptides into small molecules, providing a convenient portable solution for drug design or peptidomic research. Endowing properties of peptides onto small molecules can evolutionarily combine the advantages of both moieties and improve the biological druggability of molecules. Herein, we present eight representative recent cases in this conversion and elaborate on the transformation process of each case. We discuss the innovative technological methods and research approaches involved, and analyze the applicability conditions of the approaches and methods in each case, guiding further modifications of peptides to small molecules. Finally, based on the aforementioned cases, we summarize a general procedure for peptide-to-small molecule modifications, listing the technological methods available for each transformation step and providing our insights on the applicable scenarios for these methods. This review aims to present the progress of peptide-to-small molecule modifications and propose our thoughts and perspectives for future research in this field.

A structural determinant of the behavior of water at hydration and nanoconfinement conditions

The molecular nature of the phases that conform the two-liquid scenario is elucidated in this work in the light of a molecular principle governing water structuring, which unveils the relevance of the contraction and reorientation of the second molecular shell to allow for the existence of coordination defects in water's hydrogen bond network. In turn, such principle is shown to also determine the behavior of hydration and nanoconfined water while enabling to define conditions for wettability (quantifying hydrophobicity and predicting drying transitions), thus opening the possibility to unravel the active role of water in central fields of research.

Dynamics of nucleoplasm in human leukemia cells: A thrust towards designing anti-leukemic agents

The human inosine monophosphate dehydrogenase (hIMPDH) is a metabolic enzyme that possesses a unique ability to self-assemble into higher-order structures, forming cytoophidia. The hIMPDH II isoform is more active in chronic myeloid leukemia (CML) cancer cells, making it a promising target for anti-leukemic therapy. However, the structural details and molecular mechanisms of the dynamics of hIMPDHcytoophidia assembly in vitro need to be better understood, and it is crucial to reconstitute the computational nucleoplasm model with cytophilic-like polymers in vitro to characterize their structure and function. Finally, a computational model and its dynamics of the nucleoplasm for CML cells have been proposed in this short review. This research on nucleoplasm aims to aid the scientific community's understanding of how metabolic enzymes like hIMPDH function in cancer and normal cells. However, validating and justifying the computational results from modeling and simulation with experimental data is essential. The new insights gained from this research could explain the structure/topology, geometrical, and electronic consequences of hIMPDH inhibitors on leukemic and normal cells. They could lead to further advancements in the knowledge of nucleoplasmic chemical reaction dynamics.

Water at the nanoscale: From filling or dewetting hydrophobic pores and carbon nanotubes to "sliding" on graphene

In this work, we study the effect of nanoconfinement on the hydration properties of model hydrophobic pores and carbon nanotubes, determining their wetting propensity and the conditions for geometrically induced dehydration. By employing a recently introduced water structural index, we aim at two main goals: (1) to accurately quantify the local hydrophobicity and predict the drying transitions in such systems, and (2) to provide a molecular rationalization of the wetting process. In this sense, we will further discuss the number and strength of the interactions required by the water molecules to promote wetting. In the case of graphene-like surfaces, an explanation for their unexpectedly significant hydrophilicity will also be provided. On the one hand, the structural index will show that the net attraction to the dense carbon network that a water molecule experiences through several simultaneous weak interactions is sufficient to give rise to hydrophilic behavior. On the other hand, we will show that an additional effect is also at play: the hydrating water molecule is retained on the surface by a smooth exchange of such simultaneous weak interactions, as if "sliding" on graphene.

Capturing the Effects of Single Atom Substitutions on the Inhibition Efficiency of Glycogen Synthase Kinase-3β Inhibitors via Markov State Modeling and Experiments

Small modifications in the chemical structure of ligands are known to dramatically change their ability to inhibit the activity of a protein. Unraveling the mechanisms that govern these dramatic changes requires scrutinizing the dynamics of protein-ligand binding and unbinding at the atomic level. As an exemplary case, we have studied Glycogen Synthase Kinase-3β (GSK-3β), a multifunctional kinase that has been implicated in a host of pathological processes. As such, there is a keen interest in identifying ligands that inhibit GSK-3β activity. One family of compounds that are highly selective and potent inhibitors of GSK-3β is exemplified by a molecule termed COB-187. COB-187 consists of a five-member heterocyclic ring with a thione at C2, a pyridine substituted methyl at N3, and a hydroxyl and phenyl at C4. We have studied the inhibition of GSK-3β by COB-187-related ligands that differ in a single heavy atom from each other (either in the location of nitrogen in their pyridine ring, or with the pyridine ring replaced by a phenyl ring), or in the length of the alkyl group joining the pyridine and the N3. The inhibition experiments show a large range of half-maximal inhibitory concentration (IC50) values from 10 nM to 10 μM, implying that these ligands exhibit vastly different propensities to inhibit GSK-3β. To explain these differences, we perform Markov State Modeling (MSM) using fully atomistic simulations. Our MSM results are in excellent agreement with the experiments in that they accurately capture differences in the binding propensities of the ligands. The simulations show that the binding propensities are related to the ligands' ability to attain a compact conformation where their two aromatic rings are spatially close. We rationalize this result by sampling numerous binding and unbinding events via funnel metadynamics simulations, which show that indeed while approaching the bound state, the ligands prefer to be in their compact conformation. We find that the presence of nitrogen in the aromatic ring increases the probability of attaining the compact conformation. Protein-ligand binding is understood to be dictated by the energetics of interactions and entropic factors, like the release of bound water from the binding pockets. This work shows that changes in the conformational distribution of ligands due to atom-level modifications in the structure play an important role in protein-ligand binding.

Prediction of drug-target binding affinity based on deep learning models

The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery. Computerized virtual screening techniques have been used for DTA prediction, greatly reducing the time and economic costs of drug discovery. However, these techniques have not succeeded in reversing the low success rate of new drug development. In recent years, the continuous development of deep learning (DL) technology has brought new opportunities for drug discovery through the DTA prediction. This shift has moved the prediction of DTA from traditional machine learning methods to DL. The DL frameworks used for DTA prediction include convolutional neural networks (CNN), graph convolutional neural networks (GCN), and recurrent neural networks (RNN), and reinforcement learning (RL), among others. This review article summarizes the available literature on DTA prediction using DL models, including DTA quantification metrics and datasets, and DL algorithms used for DTA prediction (including input representation of models, neural network frameworks, valuation indicators, and model interpretability). In addition, the opportunities, challenges, and prospects of the application of DL frameworks for DTA prediction in the field of drug discovery are discussed.

A water structure indicator suitable for generic contexts: Two-liquid behavior at hydration and nanoconfinement conditions and a molecular approach to hydrophobicity and wetting

In a recent work, we have briefly introduced a new structural index for water that, unlike previous indicators, was devised specifically for generic contexts beyond bulk conditions, making it suitable for hydration and nanoconfinement settings. In this work, we shall study this metric in detail, demonstrating its ability to reveal the existence of a fine-tuned interplay between the local structure and energetics in liquid water. This molecular principle enables the establishment of an extended hydrogen bond network, while simultaneously allowing for the existence of network defects by compensating for uncoordinated sites. By studying different water models and different temperatures encompassing both the normal liquid and the supercooled regime, this molecular mechanism will be shown to underlie the two-state behavior of bulk water. In addition, by studying functionalized self-assembled monolayers and diverse graphene-like surfaces, we shall show that this principle is also operative at hydration and nanoconfinement conditions, thus generalizing the validity of the two-liquid scenario of water to these contexts. This approach will allow us to define conditions for wettability, providing an accurate measure of hydrophobicity and a reliable predictor of filling and drying transitions. Hence, it might open the possibility of elucidating the active role of water in the broad fields of biophysics and materials science. As a preliminary step, we shall study the hydration structure and hydrophilicity of graphene-like systems (parallel graphene sheets and carbon nanotubes) as a function of the confinement dimensionality.

PfgPDI: Pocket feature-enabled graph neural network for protein-drug interaction prediction

Biomolecular interaction recognition between ligands and proteins is an essential task, which largely enhances the safety and efficacy in drug discovery and development stage. Studying the interaction between proteins and ligands can improve the understanding of disease pathogenesis and lead to more effective drug targets. Additionally, it can aid in determining drug parameters, ensuring proper absorption, distribution, and metabolism within the body. Due to incomplete feature representation or the model's inadequate adaptation to protein-ligand complexes, the existing methodologies suffer from suboptimal predictive accuracy. To address these pitfalls, in this study, we designed a new deep learning method based on transformer and GCN. We first utilized the transformer network to grasp crucial information of the original protein sequences within the smile sequences and connected them to prevent falling into a local optimum. Furthermore, a series of dilation convolutions are performed to obtain the pocket features and smile features, subsequently subjected to graphical convolution to optimize the connections. The combined representations are fed into the proposed model for classification prediction. Experiments conducted on various protein-ligand binding prediction methods prove the effectiveness of our proposed method. It is expected that the PfgPDI can contribute to drug prediction and accelerate the development of new drugs, while also serving as a valuable partner for drug testing and Research and Development engineers.

The application of WaterMap-guided structure-based virtual screening in novel drug discovery

INTRODUCTION

Nowadays, it is widely accepted that water molecules play a key role in binding a ligand to a molecular target. Neglecting water molecules in the process of molecular recognition was the result of several failures of the structure-based drug discovery campaigns. The application of WaterMap, in particular WaterMap-guided molecular docking, enables the reasonably accurate and quick description of the location and energetics of water molecules at the ligand-protein interface.

AREAS COVERED

In this review, the authors shortly discuss the importance of water in drug design and discovery and provide a brief overview of the computational approaches used to predict the solvent-related effects for the purposes of presenting WaterMap in the context of other available techniques and tools. A concise description of WaterMap concept is followed by the presentation of WaterMap-assisted virtual screening literature published between 2013 and 2023.

EXPERT OPINION

In recent years, WaterMap software has been extensively used to support structure-based drug design, in particular structure-based virtual screening. Indeed, it is a useful tool to rescore docking results considering water molecules in the binding pocket. Although WaterMap allows for the consideration of the dynamic behavior of water molecules in the binding site, for best accuracy, its application in conjunction with other techniques such as molecular mechanics-generalized Born surface area of FEP (Free Energy Perturbation) is recommended.

Peptide-to-Small Molecule: Discovery of Non-Covalent, Active-Site Inhibitors of β-Herpesvirus Proteases

Viral proteases, the key enzymes that regulate viral replication and assembly, are promising targets for antiviral drug discovery. Herpesvirus proteases are enzymes with no crystallographically confirmed noncovalent active-site binders, owing to their shallow and polar substrate-binding pockets. Here, we applied our previously reported "Peptide-to-Small Molecule" strategy to generate novel inhibitors of β-herpesvirus proteases. Rapid selection with a display technology was used to identify macrocyclic peptide 1 bound to the active site of human cytomegalovirus protease (HCMVPro) with high affinity, and pharmacophore queries were defined based on the results of subsequent intermolecular interaction analyses. Membrane-permeable small molecule 19, designed de novo according to this hypothesis, exhibited enzyme inhibitory activity (IC50 = 10-6 to 10-7 M) against β-herpesvirus proteases, and the design concept was proved by X-ray cocrystal analysis.

The temperature-dependence of host-guest binding thermodynamics: experimental and simulation studies

The thermodynamic parameters of host-guest binding can be used to describe, understand, and predict molecular recognition events in aqueous systems. However, interpreting binding thermodynamics remains challenging, even for these relatively simple molecules, as they are determined by both direct and solvent-mediated host-guest interactions. In this contribution, we focus on the contributions of water to binding by studying binding thermodynamics, both experimentally and computationally, for a series of nearly rigid, electrically neutral host-guest systems and report the temperature-dependent thermodynamic binding contributions ΔGb(T), ΔHb(T), ΔSb(T), and ΔCp,b. Combining isothermal titration calorimetry (ITC) measurements with molecular dynamics (MD) simulations, we provide insight into the binding forces at play for the macrocyclic hosts cucurbit[n]uril (CBn, n = 7-8) and β-cyclodextrin (β-CD) with a range of guest molecules. We find consistently negative changes in heat capacity on binding (ΔCp,b) for all systems studied herein - as well as for literature host-guest systems - indicating increased enthalpic driving forces for binding at higher temperatures. We ascribe these trends to solvation effects, as the solvent properties of water deteriorate as temperature rises. Unlike the entropic and enthalpic contributions to binding, with their differing signs and magnitudes for the classical and non-classical hydrophobic effect, heat capacity changes appear to be a unifying and more general feature of host-guest complex formation in water. This work has implications for understanding protein-ligand interactions and other complex systems in aqueous environments.

Natural photosensitizers potentiate the targeted antimicrobial photodynamic therapy as the Monkeypox virus entry inhibitors: An in silico approach

BACKGROUND

Monkeypox is a viral zoonotic disease that has emerged as a threat to public health. Currently, there is no treatment approved specifically targeting Monkeypox disease. Hence, it is essential to identify and develop therapeutic approaches to the Monkeypox virus. In the current in silico paper, we comprehensively involve using computer simulations and modeling to insights and predict hypotheses on the potential of natural photosensitizers-mediated targeted antimicrobial photodynamic therapy (aPDT) against D8L as a Monkeypox virus protein involved in viral cell entry.

MATERIALS AND METHODS

In the current study, computational techniques such as molecular docking were combined with in silico ADMET predictions to examine how Curcumin (Cur), Quercetin (Qct), and Riboflavin (Rib) as the natural photosensitizers bind to the D8L protein in Monkeypox virus, as well as to determine pharmacokinetic properties of these photosensitizers.

RESULTS

The three-dimensional structure of the D8L protein in the Monkeypox virus was constructed using homology modeling (PDB ID: 4E9O). According to the physicochemical properties and functional characterization, 4E9O was a stable protein with the nature of a hydrophilic structure. The docking studies employing a three-dimensional model of 4E9O with natural photosensitizers exhibited good binding affinity. D8L protein illustrated the best docking score (-7.6 kcal/mol) in relation to the Rib and displayed good docking scores in relation to the Cur (-7.0 kcal/mol) and Qct (-7.5 kcal/mol).

CONCLUSIONS

The findings revealed that all three photosensitizers were found to obey the criteria of Lipinski's rule of five and displayed drug-likeness. Moreover, all the tested photosensitizers were found to be non-hepatotoxic and non-cytotoxic. In summary, our investigation identified Cur, Qct, and Rib could efficiently interact with D8L protein with a strong binding affinity. It can be concluded that aPDT using these natural photosensitizers may be considered an adjuvant treatment against Monkeypox disease.

Investigation of molecular details of a bacterial cationic amino acid transporter (GkApcT) during arginine transportation using molecular dynamics simulation and umbrella sampling techniques

CONTEXT

Cationic amino acid transporters (CATs) facilitate arginine transport across membranes and maintain its levels in various tissues and organs, but their overexpression has been associated with severe cancers. A recent study identified the alternating access mechanism and critical residues involved in arginine transportation in a cationic amino acid transporter from Geobacillus kaustophilus (GkApcT). Here, we used molecular dynamics (MD) simulation methods to investigate the transportation mechanism of arginine (Arg) through GkApcT. The results revealed that arginine strongly interacts with specific binding site residues (Thr43, Asp111, Glu115, Lys191, Phe231, Ile234, and Asp237). Based on the umbrella sampling, the main driving force for arginine transport is the polar interactions of the arginine with channel-lining residues. An in-depth description of the dissociation mechanism and binding energy analysis brings valuable insight into the interactions between arginine and transporter residues, facilitating the design of effective CAT inhibitors in cancer cells.

METHODS

The membrane-protein system was constructed by uploading the prokaryotic CAT (PDB ID: 6F34) to the CHARMM-GUI web server. Molecular dynamics simulations were done using the GROMACS package, version 5.1.4, with the CHARMM36 force field and TIP3P water model. The MM-PBSA approach was performed for determining the arginine binding free energy. Furthermore, the hotspot residues were identified through per-residue decomposition analysis. The characteristics of the channel such as bottleneck radius and channel length were analyzed using the CaverWeb 1.1 web server. The proton wire inside the transporter was investigated based on the classic Grotthuss mechanism. We also investigated the atomistic details of arginine transportation using the path-based free energy umbrella sampling technique (US).

WaterKit: Thermodynamic Profiling of Protein Hydration Sites

Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.

CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism

MOTIVATION

Accurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules.

RESULTS

In this work, a new deep learning approach based on the cross-attention mechanism named CAPLA was developed for improved prediction of protein-ligand binding affinity by learning features from sequence-level information of both protein and ligand. Specifically, CAPLA employs the cross-attention mechanism to capture the mutual effect of protein-binding pocket and ligand. We evaluated the performance of our proposed CAPLA on comprehensive benchmarking experiments on binding affinity prediction, demonstrating the superior performance of CAPLA over state-of-the-art baseline approaches. Moreover, we provided the interpretability for CAPLA to uncover critical functional residues that contribute most to the binding affinity through the analysis of the attention scores generated by the cross-attention mechanism. Consequently, these results indicate that CAPLA is an effective approach for binding affinity prediction and may contribute to useful help for further consequent applications.

AVAILABILITY AND IMPLEMENTATION

The source code of the method along with trained models is freely available at https://github.com/lennylv/CAPLA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Uncovering water effects in protein-ligand recognition: importance in the second hydration shell and binding kinetics

Developing a ligand with high affinity for a specific protein target is essential for drug design, and water molecules are well known to play a key role in protein-drug recognition. However, predicting the role of particularly ordered water molecules in drug binding remains challenging. Furthermore, hydration free energy contributed from the water network, including the second shell of water molecules, is far from being well studied. In this research we focused on these aspects to accurately and efficiently evaluate water effects in protein-ligand binding affinity. We developed a new strategy using a free-energy calculation method, VM2. We successfully predicted the stable ordered water molecules in a number of protein systems: PDE 10a, HSP90, tryptophan synthase (TRPS), CDK2 and Factor Xa. In some of these, the second shell of water molecules appeared to be critical in protein-ligand binding. We also applied the strategy to largely improve binding free energy calculation using the MM/PBSA method. When applying MM/PBSA alone for two systems, CDK2 and Factor Xa, the computed binding free energy resulted in poor to moderate R2 values with experimental data. However, including water free energy correction greatly improved the free energy calculation. Furthermore, our work helped to explain how xk263 is a 1000 times faster binder to HIVp than ritonavir, a potentially useful tool for investigating binding kinetics. Our studies reveal the importance of fully considering water effects in therapeutic developments in pharmaceutical and biotechnology industries and for fundamental research in protein-ligand recognition.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Conserved Water Networks Identification for Drug Design Using Density Clustering Approaches on Positional and Orientational Data

This work describes the development and testing of a method for the identification and classification of conserved water molecules and their networks from molecular dynamics (MD) simulations. The conserved waters in the active sites of proteins influence protein-ligand binding. Recently, several groups have argued that a water network formed from conserved waters can be used to interpret the thermodynamic signature of the binding site. We implemented a novel methodology in which we apply the complex approach to categorize water molecules extracted from the MD simulation trajectories using clustering approaches. The main advantage of our methodology as compared to current state of the art approaches is the inclusion of the information on the orientation of hydrogen atoms to further inform the clustering algorithm and to classify the conserved waters into different subtypes depending on how strongly certain orientations are preferred. This information is vital for assessing the stability of water networks. The newly developed approach is described in detail as well as validated against known results from the scientific literature including comparisons with the experimental data on thermolysin, thrombin, and Haemophilus influenzae virulence protein SiaP as well as with the previous computational results on thermolysin. We observed excellent agreement with the literature and were also able to provide additional insights into the orientations of the conserved water molecules, highlighting the key interactions which stabilize them. The source code of our approach, as well as the utility tools used for visualization, are freely available on GitHub.

Predicting Conserved Water Molecules in Binding Sites of Proteins Using Machine Learning Methods and Combining Features

Water molecules play an important role in many biological processes in terms of stabilizing protein structures, assisting protein folding, and improving binding affinity. It is well known that, due to the impacts of various environmental factors, it is difficult to identify the conserved water molecules (CWMs) from free water molecules (FWMs) directly as CWMs are normally deeply embedded in proteins and form strong hydrogen bonds with surrounding polar groups. To circumvent this difficulty, in this work, the abundance of spatial structure information and physicochemical properties of water molecules in proteins inspires us to adopt machine learning methods for identifying the CWMs. Therefore, in this study, a machine learning framework to identify the CWMs in the binding sites of the proteins was presented. First, by analyzing water molecules' physicochemical properties and spatial structure information, six features (i.e., atom density, hydrophilicity, hydrophobicity, solvent-accessible surface area, temperature B-factors, and mobility) were extracted. Those features were further analyzed and combined to reach a higher CWM identification rate. As a result, an optimal feature combination was determined. Based on this optimal combination, seven different machine learning models (including support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), discriminant analysis (DA), naïve Bayes (NB), and ensemble learning (EL)) were evaluated for their abilities in identifying two categories of water molecules, i.e., CWMs and FWMs. It showed that the EL model was the desired prediction model due to its comprehensive advantages. Furthermore, the presented methodology was validated through a case study of crystal 3skh and extensively compared with Dowser++. The prediction performance showed that the optimal feature combination and the desired EL model in our method could achieve satisfactory prediction accuracy in identifying CWMs from FWMs in the proteins' binding sites.

Peptide-to-Small Molecule: A Pharmacophore-Guided Small Molecule Lead Generation Strategy from High-Affinity Macrocyclic Peptides

Recent technological innovations have led to the development of methods for the rapid identification of high-affinity macrocyclic peptides for a wide range of targets; however, it is still challenging to achieve the desired activity and membrane permeability at the same time. Here, we propose a novel small molecule lead discovery strategy, ″Peptide-to-Small Molecule″, which is a combination of rapid identification of high-affinity macrocyclic peptides via peptide display screening followed by pharmacophore-guided de novo design of small molecules, and demonstrate the applicability using nicotinamide N-methyltransferase (NNMT) as a target. Affinity selection by peptide display technology identified macrocyclic peptide 1 that exhibited good enzymatic inhibitory activity but no cell-based activity. Thereafter, a peptide pharmacophore-guided de novo design and further structure-based optimization resulted in highly potent and cell-active small molecule 14 (cell-free IC₅₀ = 0.0011 μM, cell-based IC₅₀ = 0.40 μM), indicating that this strategy could be a new option for drug discovery.

Ligand-based drug design and molecular docking simulation studies of some novel anticancer compounds on MALME-3M melanoma cell line

Background

Melanoma cancer causes serious health problem worldwide because of its rapid invasion to other organs and lack of satisfactory chemotherapy. The pGI50 anticancer activity values of 70 compounds from the NCI (National Cancer Institute) on MALME-3M cell line was modeled to describe the quantitative structure-activity relationships (QSARs) of the compounds, and some selected compounds were docked.

Results

The generated QSAR model was found to be statistically significant based on the obtained values of the validation keys such as R2 (0.885), $$ {R}_{\mathrm{adjusted}}^2 $$ R adjusted 2 (0.868), Q2cv (0.842), and $$ {R}_{pred}^2 $$ R pred 2 (0.738) required to evaluate the strength and robustness of QSAR model. Compound 39 was selected as a template due to its good pGI50 (9.205) and was modified to design new potent compounds. The predicted pGI50 activity of the designed compounds by the built model was N1 (9.836), N2 (12.876), N3 (10.901), and N4 (11.263) respectively. These proposed compounds were docked with V600E-BRAF receptor and the result shows that, N1, N2, N3, and N4 with free binding energy (FBE) of − 11.7 kcal mol−1, − 12.8 kcal mol−1, − 12.7 kcal mol−1, and − 12.9 kcal mol−1 respectively were better than the parent structure of the template (compound 39, FBE = − 7.0 kcal mol−1) and the standard V600E-BRAF inhibitor (Vemurafenib, FBE = − 11.3 kcal mol−1). Additionally, these compounds passed the drug-likeness criteria successfully to be orally bioavailable.

Conclusion

The proposed compounds were considered optimal as their performances are comparable to vemurafenib and possessed enhanced physicochemical properties. Thus recommends further research such as synthesis, in vivo, and ex-vivo evaluation.

Structure and dynamics of nanoconfined water and aqueous solutions

This review is devoted to discussing recent progress on the structure, thermodynamic, reactivity, and dynamics of water and aqueous systems confined within different types of nanopores, synthetic and biological. Currently, this is a branch of water science that has attracted enormous attention of researchers from different fields interested to extend the understanding of the anomalous properties of bulk water to the nanoscopic domain. From a fundamental perspective, the interactions of water and solutes with a confining surface dramatically modify the liquid's structure and, consequently, both its thermodynamical and dynamical behaviors, breaking the validity of the classical thermodynamic and phenomenological description of the transport properties of aqueous systems. Additionally, man-made nanopores and porous materials have emerged as promising solutions to challenging problems such as water purification, biosensing, nanofluidic logic and gating, and energy storage and conversion, while aquaporin, ion channels, and nuclear pore complex nanopores regulate many biological functions such as the conduction of water, the generation of action potentials, and the storage of genetic material. In this work, the more recent experimental and molecular simulations advances in this exciting and rapidly evolving field will be reported and critically discussed.

Prediction of multiple dry-wet transition pathways with a mesoscale variational approach

Water fluctuates in a hydrophobic confinement, forming multiple dry and wet hydration states through evaporation and condensation. Transitions between such states are critical to both thermodynamics and kinetics of solute molecular processes, such as protein folding and protein-ligand binding and unbinding. To efficiently predict such dry-wet transition paths, we develop a hybrid approach that combines a variational implicit solvation model, a generalized string method for minimum free-energy paths, and the level-set numerical implementation. This approach is applied to three molecular systems: two hydrophobic plates, a carbon nanotube, and a synthetic host molecule Cucurbit[7]uril. Without an explicit description of individual water molecules, our mesoscale approach effectively captures multiple dry and wet hydration states, multiple dry-wet transition paths, such as those geometrically symmetric and asymmetric paths, and transition states, providing activation energy barriers between different states. Further analysis shows that energy barriers depend on mesoscopic lengths, such as the separation distance between the two plates and the cross section diameter of the nanotube, and that the electrostatic interactions strongly influence the dry-wet transitions. With the inclusion of solute atomic motion, general collective variables as reaction coordinates, and the finite-temperature string method, together with an improved treatment of continuum electrostatics, our approach can be further developed to sample an ensemble of transition paths, providing more accurate predictions of the transition kinetics.

Residue interaction networks of K-Ras protein with water molecules identifies the potential role of switch II and P-loop

The mutant K-Ras with aberrant signaling is the primary cause of several cancers. The proposed study investigated the influence of water molecules in K-Ras crystal structure, where they have a significant function by understanding their residue interaction networks (RINs). We analyzed the RINs of K-Ras with and without water molecules and determined their interaction properties. RINs were developed with the help of StructureViz2 and RINspector; further, the changes in K-Ras backbone flexibility were predicted with the DynaMine. We found that the residues K42, I142, and L159 are the hotspots from water, including the K-Ras-GTP complex with the highest residue centrality analysis (RCA) Z-score. The DynaMine prediction calculated the NMR S² value for the frequently mutated positions G12, G13, and Q61 showing a minor shift in flexibility, which make up the P-Loop and switch II of the K-Ras protein. This flexibility shift can account for changes in conformational activity and the protein's GTPase activity, making it difficult to recognize by the effectors and exchange factors. Taken together, our study helps in understanding the functional importance of the water molecules in K-Ras protein and the impact of mutation that modulate the conformational state of the protein.

DeepDTAF: a deep learning method to predict protein-ligand binding affinity

Biomolecular recognition between ligand and protein plays an essential role in drug discovery and development. However, it is extremely time and resource consuming to determine the protein-ligand binding affinity by experiments. At present, many computational methods have been proposed to predict binding affinity, most of which usually require protein 3D structures that are not often available. Therefore, new methods that can fully take advantage of sequence-level features are greatly needed to predict protein-ligand binding affinity and accelerate the drug discovery process. We developed a novel deep learning approach, named DeepDTAF, to predict the protein-ligand binding affinity. DeepDTAF was constructed by integrating local and global contextual features. More specifically, the protein-binding pocket, which possesses some special properties for directly binding the ligand, was firstly used as the local input feature for protein-ligand binding affinity prediction. Furthermore, dilated convolution was used to capture multiscale long-range interactions. We compared DeepDTAF with the recent state-of-art methods and analyzed the effectiveness of different parts of our model, the significant accuracy improvement showed that DeepDTAF was a reliable tool for affinity prediction. The resource codes and data are available at https: //github.com/KailiWang1/DeepDTAF.

Outliers in SAR and QSAR: 3. Importance of considering the role of water molecules in protein-ligand interactions and quantitative structure-activity relationship studies

It is frequently mentioned that QSARs have not generally lived up to expectations, especially in cases where high predictability is expected yet failed to deliver satisfactory results. Even though outliers can provide an increased opportunity in drug discovery research, outliers in SAR and QSAR can contort predictions and affect the accuracy if proper attention is not given. The percentages of outliers in QSARs have not changed appreciably over the last decade. In our previous studies, we suggested two possible sources of outliers in SAR and QSAR. In this paper, we suggest an additional possible source of outliers in QSAR. We presented several literature examples that show one or more water molecules that play a critical role in protein-ligand binding interactions as observed in their crystal structures. These examples illustrate that failing to account for the effects of water molecules in protein-ligand interactions could mislead interpretation and possibly yield outliers in SAR and QSAR. Examples include cases where QSAR, considering the role of water molecules in protein-ligand crystal structures, provided deeper insight into the understanding and interpretation of the developed QSAR.

Molecular Recognition in Water Using Macrocyclic Synthetic Receptors

Molecular recognition in water using macrocyclic synthetic receptors constitutes a vibrant and timely research area of supramolecular chemistry. Pioneering examples on the topic date back to the 1980s. The investigated model systems and the results derived from them are key for furthering our understanding of the remarkable properties exhibited by proteins: high binding affinity, superior binding selectivity, and extreme catalytic performance. Dissecting the different effects contributing to the proteins' properties is severely limited owing to its complex nature. Molecular recognition in water is also involved in other appreciated areas such as self-assembly, drug discovery, and supramolecular catalysis. The development of all these research areas entails a deep understanding of the molecular recognition events occurring in aqueous media. In this review, we cover the past three decades of molecular recognition studies of neutral and charged, polar and nonpolar organic substrates and ions using selected artificial receptors soluble in water. We briefly discuss the intermolecular forces involved in the reversible binding of the substrates, as well as the hydrophobic and Hofmeister effects operating in aqueous solution. We examine, from an interdisciplinary perspective, the design and development of effective water-soluble synthetic receptors based on cyclic, oligo-cyclic, and concave-shaped architectures. We also include selected examples of self-assembled water-soluble synthetic receptors. The catalytic performance of some of the presented receptors is also described. The latter process also deals with molecular recognition and energetic stabilization, but instead of binding ground-state species, the targets become elusive counterparts: transition states and other high-energy intermediates.

Affinity of small-molecule solutes to hydrophobic, hydrophilic, and chemically patterned interfaces in aqueous solution

Performance of membranes for water purification is highly influenced by the interactions of solvated species with membrane surfaces, including surface adsorption of solutes upon fouling. Current efforts toward fouling-resistant membranes often pursue surface hydrophilization, frequently motivated by macroscopic measures of hydrophilicity, because hydrophobicity is thought to increase solute-surface affinity. While this heuristic has driven diverse membrane functionalization strategies, here we build on advances in the theory of hydrophobicity to critically examine the relevance of macroscopic characterizations of solute-surface affinity. Specifically, we use molecular simulations to quantify the affinities to model hydroxyl- and methyl-functionalized surfaces of small, chemically diverse, charge-neutral solutes represented in produced water. We show that surface affinities correlate poorly with two conventional measures of solute hydrophobicity, gas-phase water solubility and oil-water partitioning. Moreover, we find that all solutes show attraction to the hydrophobic surface and most to the hydrophilic one, in contrast to macroscopically based hydrophobicity heuristics. We explain these results by decomposing affinities into direct solute interaction energies (which dominate on hydroxyl surfaces) and water restructuring penalties (which dominate on methyl surfaces). Finally, we use an inverse design algorithm to show how heterogeneous surfaces, with multiple functional groups, can be patterned to manipulate solute affinity and selectivity. These findings, importantly based on a range of solute and surface chemistries, illustrate that conventional macroscopic hydrophobicity metrics can fail to predict solute-surface affinity, and that molecular-scale surface chemical patterning significantly influences affinity-suggesting design opportunities for water purification membranes and other engineered interfaces involving aqueous solute-surface interactions.

The role of water in host-guest interaction

One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system's degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.

Asymmetric dynamics of dimeric SARS-CoV-2 and SARS-CoV main proteases in an apo form: Molecular dynamics study on fluctuations of active site, catalytic dyad, and hydration water

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widely spread around the world. It is necessary to examine the viral proteins that play a notorious role in the invasion of our body. The main protease (3CL^pro) facilitates the maturation of the coronavirus. It is thought that the dimerization of 3CL^pro leads to its catalytic activity; the detailed mechanism has, however, not been suggested. Furthermore, the structural differences between the predecessor SARS-CoV 3CL^pro and SARS-CoV-2 3CL^pro have not been fully understood. Here, we show the structural and dynamical differences between the two main proteases, and demonstrate the relationship between the dimerization and the activity via atomistic molecular dynamics simulations. Simulating monomeric and dimeric 3CL^pro systems for each protease, we show that (i) global dynamics between the two different proteases are not conserved, (ii) the dimerization stabilizes the catalytic dyad and hydration water molecules behind the dyad, and (iii) the substrate-binding site (active site) and hydration water molecules in each protomer fluctuate asymmetrically. We then speculate the roles of hydration water molecules in their catalytic activity.

Binding Thermodynamics and Interaction Patterns of Inhibitor-Major Urinary Protein-I Binding from Extensive Free-Energy Calculations: Benchmarking AMBER Force Fields

Mouse major urinary protein (MUP) plays a key role in the pheromone communication system. The one-end-closed β-barrel of MUP-I forms a small, deep, and hydrophobic central cavity, which could accommodate structurally diverse ligands. Previous computational studies employed old protein force fields and short simulation times to determine the binding thermodynamics or investigated only a small number of structurally similar ligands, which resulted in sampled regions far from the experimental structure, nonconverged sampling outcomes, and limited understanding of the possible interaction patterns that the cavity could produce. In this work, extensive end-point and alchemical free-energy calculations with advanced protein force fields were performed to determine the binding thermodynamics of a series of MUP-inhibitor systems and investigate the inter- and intramolecular interaction patterns. Three series of inhibitors with a total of 14 ligands were simulated. We independently simulated the MUP-inhibitor complexes under two advanced AMBER force fields. Our benchmark test showed that the advanced AMBER force fields including AMBER19SB and AMBER14SB provided better descriptions of the system, and the backbone root-mean-square deviation (RMSD) was significantly lowered compared with previous computational studies with old protein force fields. Surprisingly, although the latest AMBER force field AMBER19SB provided better descriptions of various observables, it neither improved the binding thermodynamics nor lowered the backbone RMSD compared with the previously proposed and widely used AMBER14SB. The older but widely used AMBER14SB actually achieved better performance in the prediction of binding affinities from the alchemical and end-point free-energy calculations. We further analyzed the protein-ligand interaction networks to identify important residues stabilizing the bound structure. Six residues including PHE38, LEU40, PHE90, ALA103, LEU105, and TYR120 were found to contribute the most significant part of protein-ligand interactions, and 10 residues were found to provide favorable interactions stabilizing the bound state. The two AMBER force fields gave extremely similar interaction networks, and the secondary structures also showed similar behavior. Thus, the intra- and intermolecular interaction networks described with the two AMBER force fields are similar. Therefore, AMBER14SB could still be the default option in free-energy calculations to achieve highly accurate binding thermodynamics and interaction patterns.

The AP2/ERF Gene Family in Triticum durum: Genome-Wide Identification and Expression Analysis under Drought and Salinity Stresses

Members of the AP2/ERF transcription factor family play critical roles in plant development, biosynthesis of key metabolites, and stress response. A detailed study was performed to identify TtAP2s/ERFs in the durum wheat (Triticum turgidum ssp. durum) genome, which resulted in the identification of 271 genes distributed on chromosomes 1A-7B. By carrying 27 genes, chromosome 6A had the highest number of TtAP2s/ERFs. Furthermore, a duplication assay of TtAP2s/ERFs demonstrated that 70 duplicated gene pairs had undergone purifying selection. According to RNA-seq analysis, the highest expression levels in all tissues and in response to stimuli were associated with DRF and ERF subfamily genes. In addition, the results revealed that TtAP2/ERF genes have tissue-specific expression patterns, and most TtAP2/ERF genes were significantly induced in the root tissue. Additionally, 13 TtAP2/ERF genes (six ERFs, three DREBs, two DRFs, one AP2, and one RAV) were selected for further analysis via qRT-PCR of their potential in coping with drought and salinity stresses. The TtAP2/ERF genes belonging to the DREB subfamily were markedly induced under both drought-stress and salinity-stress conditions. Furthermore, docking simulations revealed several residues in the pocket sites of the proteins associated with the stress response, which may be useful in future site-directed mutagenesis studies to increase the stress tolerance of durum wheat. This study could provide valuable insights for further evolutionary and functional assays of this important gene family in durum wheat.

Comparing Fragment Binding Poses Prediction Using HSP90 as a Key Study: When Bound Water Makes the Difference

Fragment-Based Drug Discovery (FBDD) approaches have gained popularitynot only in industry but also in academic research institutes. However, the computational prediction of the binding mode adopted by fragment-like molecules within a protein binding site is still a very challenging task. One of the most crucial aspects of fragment binding is related to the large amounts of bound waters in the targeted binding pocket. The binding affinity of fragmentsmay not be sufficientto displace the bound water molecules. In the present work, we confirmed the importance of the bound water molecules in the correct prediction of the fragment binding mode. Moreover, we investigate whether the use of methods based on explicit solvent molecular dynamics simulations can improve the accuracy of fragment posing. The protein chosen for this study is HSP-90.

FEP+ calculations predict a stereochemical SAR switch for first-in-class indoline NIK inhibitors for multiple myeloma

In the search for first-in-class NIK inhibitors for multiple myeloma, we discovered an azaindoline hit class generated from a biochemical NIK autophosphorylation high-throughput screening assay which was optimized to the final cyanoindoline compound class. During the hit-to-lead phase, a prominent stereochemical SAR switch was observed which was accurately predicted by in silico FEP+ calculations. Crystallographic and computational analysis showed that for both stereoisomers comparable contacts, both in nature and number, could be formed by the switching hydroxyl group, making this system particularly interesting from an interaction energy viewpoint. We provide a detailed analysis of our FEP+ and WaterMap calculations and show how this type of computational chemistry methods are useful during hit-to-lead and lead optimization phases.

Mechanism of biomolecular recognition of trimethyllysine by the fluorinated aromatic cage of KDM5A PHD3 finger

The understanding of biomolecular recognition of posttranslationally modified histone proteins is centrally important to the histone code hypothesis. Despite extensive binding and structural studies on the readout of histones, the molecular language by which posttranslational modifications on histone proteins are read remains poorly understood. Here we report physical-organic chemistry studies on the recognition of the positively charged trimethyllysine by the electron-rich aromatic cage containing PHD3 finger of KDM5A. The aromatic character of two tryptophan residues that solely constitute the aromatic cage of KDM5A was fine-tuned by the incorporation of fluorine substituents. Our thermodynamic analyses reveal that the wild-type and fluorinated KDM5A PHD3 fingers associate equally well with trimethyllysine. This work demonstrates that the biomolecular recognition of trimethyllysine by fluorinated aromatic cages is associated with weaker cation-π interactions that are compensated by the energetically more favourable trimethyllysine-mediated release of high-energy water molecules that occupy the aromatic cage.

Improving the Accuracy of Protein Thermostability Predictions for Single Point Mutations

Accurately predicting the protein thermostability changes upon single point mutations in silico is a challenge that has implications for understanding diseases as well as industrial applications of protein engineering. Free energy perturbation (FEP) has been applied to predict the effect of single point mutations on protein stability for over 40 years and emerged as a potentially reliable prediction method with reasonable throughput. However, applications of FEP in protein stability calculations in industrial settings have been hindered by a number of limitations, including the inability to model mutations to and from prolines in which the bonded topology of the backbone is modified and the complexity in modeling charge-changing mutations. In this study, we have extended the FEP+ protocol to enable the accurate modeling of the effects on protein stability from proline mutations and from charge-changing mutations. We also evaluated the influence of the unfolded model in the stability calculations using increasingly longer peptides with native sequence and conformations. With the abovementioned improvements, the accuracy of FEP predictions of protein stability over a data set of 87 mutations on five different proteins has drastically improved compared with previous studies, with a mean unsigned error of 0.86 kcal/mol and root mean square error of 1.11 kcal/mol, comparable with the accuracy of previously published state-of-the-art small-molecule relative binding affinity calculations, which have been shown to be capable of driving discovery projects.

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

Best Practices for Alchemical Free Energy Calculations [Article v1.0]

Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing alchemical intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly. While these methods are highly flexible, care must be taken in avoiding common pitfalls to ensure that computed free energy differences can be robust and reproducible for the chosen force field, and that appropriate corrections are included to permit direct comparison with experimental data. In this paper, we review current best practices for several popular application domains of alchemical free energy calculations performed with equilibrium simulations, in particular relative and absolute small molecule binding free energy calculations to biomolecular targets.

Thermodynamic profiling for fragment-based lead discovery and optimization

Introduction: The enthalpic and entropic components of the ligand-protein binding free energy reflect the type and quality of the interactions and relate to the physicochemical properties of the ligands. These findings have significance in medicinal chemistry optimizations since they suggest that the thermodynamic profiling of the binding may help monitor and control the unfavorable size and hydrophobicity increase typically accompanying affinity improvements and leading to suboptimal pharmacokinetic properties.Areas covered: This review describes the ligand-protein binding event in terms of elementary steps, their associated interactions, and their enthalpic and entropic consequences. The relationships among the breaking and forming interactions, the binding thermodynamic profile, and the physicochemical properties of the ligands are also discussed.Expert opinion: Analysis of the size dependence of available affinity and favorable enthalpy highlights the limitation of the simultaneous optimization of these quantities. Indeed, moderate, rather than very high affinities can be conciliated with favorable physicochemical and pharmacokinetic profiles as it is supported by the affinity range of historical oral drugs. Although thermodynamic quantities are not suitable endpoints for medicinal chemistry optimizations owing to the complexity of the binding thermodynamics, thermodynamic profiling together with structural studies can be advantageously used to understand the details of the binding process and to optimize it.

Prediction of Ligand Transport along Hydrophobic Enzyme Nanochannels

Buried active sites of enzymes are connected to the bulk solvent through a network of hydrophobic channels. We developed a discretized model that can accurately predict ligand transport along hydrophobic channels up to six orders of magnitude faster than any other existing method. The non-dimensional nature of the model makes it applicable to any hydrophobic channel/ligand combination.

Variational implicit-solvent predictions of the dry-wet transition pathways for ligand-receptor binding and unbinding kinetics

Ligand-receptor binding and unbinding are fundamental biomolecular processes and particularly essential to drug efficacy. Environmental water fluctuations, however, impact the corresponding thermodynamics and kinetics and thereby challenge theoretical descriptions. Here, we devise a holistic, implicit-solvent, multimethod approach to predict the (un)binding kinetics for a generic ligand-pocket model. We use the variational implicit-solvent model (VISM) to calculate the solute-solvent interfacial structures and the corresponding free energies, and combine the VISM with the string method to obtain the minimum energy paths and transition states between the various metastable ("dry" and "wet") hydration states. The resulting dry-wet transition rates are then used in a spatially dependent multistate continuous-time Markov chain Brownian dynamics simulation and the related Fokker-Planck equation calculations of the ligand stochastic motion, providing the mean first-passage times for binding and unbinding. We find the hydration transitions to significantly slow down the binding process, in semiquantitative agreement with existing explicit-water simulations, but significantly accelerate the unbinding process. Moreover, our methods allow the characterization of nonequilibrium hydration states of pocket and ligand during the ligand movement, for which we find substantial memory and hysteresis effects for binding vs. unbinding. Our study thus provides a significant step forward toward efficient, physics-based interpretation and predictions of the complex kinetics in realistic ligand-receptor systems.