Efficient Refinement and Free Energy Scoring of Predicted Protein-Protein Complexes Using Replica Exchange with Repulsive Scaling

cPEPmatch Webserver: A comprehensive tool and database to aid rational design of cyclic peptides for drug discovery

Cyclic peptides have emerged as versatile scaffolds in drug discovery due to their stability and specificity. Here, we present the cPEPmatch webserver (accessible at https://t38webservices.nat.tum.de/cpepmatch/), an easy-to-use interface for the rational design of cyclic peptides targeting protein-protein interactions combined with a semi-quantitative evaluation of binding stability. This platform also offers access to a comprehensive database of cyclic peptide crystal structures. We demonstrate the webserver's utility through a series of case studies involving medically relevant protein systems, highlighting its potential to significantly advance drug discovery efforts.

In silico approaches for better understanding cysteine cathepsin-glycosaminoglycan interactions

Cysteine cathepsins constitute the largest cathepsin family, with 11 proteases in human that are present primarily within acidic endosomal and lysosomal compartments. They are involved in the turnover of intracellular and extracellular proteins. They are synthesized as inactive procathepsins that are converted to mature active forms. Cathepsins play important roles in physiological and pathological processes and, therefore, receive increasing attention as potential therapeutic targets. Their maturation and activity can be regulated by glycosaminoglycans (GAGs), long linear negatively charged polysaccharides composed of recurring dimeric units. In this review, we summarize recent computational progress in the field of (pro)cathepsin-GAG complexes analyses.

Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional molecular dynamics (cMD), due to limited simulation time scales. Based on our previously developed ligand Gaussian accelerated molecular dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3″, in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding, and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as the model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 μs simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 were in agreement with the available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligands and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

Ligand Gaussian accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional Molecular Dynamics (cMD), due to limited simulation timescales. Based on our previously developed Ligand Gaussian accelerated Molecular Dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3", in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 microsecond simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 agreed with available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligand and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

Refinement of Docked Protein-Protein Complexes Using Repulsive Scaling Replica Exchange Simulations

Accurate prediction and evaluation of protein-protein complex structures is of major importance to understand the cellular interactome. Predicted complex structures based on deep learning approaches or traditional docking methods require often structural refinement and rescoring for realistic evaluation. Standard molecular dynamics (MD) simulations are time-consuming and often do not structurally improve docking solutions. Better refinement can be achieved with our recently developed replica-exchange-based scheme employing different levels of repulsive biasing between proteins in each replica simulation (RS-REMD). The bias acts specifically on the intermolecular interactions based on an increase in effective pairwise van der Waals radii without changing interactions within each protein or with the solvent. It allows for an improvement of the predicted protein-protein complex structure and simultaneous realistic free energy scoring of protein-protein complexes. The setup of RS-REMD simulations is described in detail including the application on two examples (all necessary scripts and input files can be obtained from https://gitlab.com/TillCyrill/mmib ).

Assembly-dependent Structure Formation Shapes Human Interleukin-23 versus Interleukin-12 Secretion

Interleukin 12 (IL-12) family cytokines connect the innate and adaptive branches of the immune system and regulate immune responses. A unique characteristic of this family is that each member is anα:βheterodimer. For human αsubunits it has been shown that they depend on theirβsubunit for structure formation and secretion from cells. Since subunits are shared within the family and IL-12 as well as IL-23 use the same βsubunit, subunit competition may influence cytokine secretion and thus downstream immunological functions. Here, we rationally design a folding-competent human IL-23α subunit that does not depend on itsβsubunit for structure formation. This engineered variant still forms a functional heterodimeric cytokine but shows less chaperone dependency and stronger affinity in assembly with its βsubunit. It forms IL-23 more efficiently than its natural counterpart, skewing the balance of IL-12 and IL-23 towards more IL-23 formation. Together, our study shows that folding-competent human IL-12 familyαsubunits are obtainable by only few mutations and compatible with assembly and function of the cytokine. These findings might suggest that human α subunits have evolved for assembly-dependent folding to maintain and regulate correct IL-12 family member ratios in the light of subunit competition.

Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket

Ligand binding thermodynamics and kinetics are critical parameters for drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics from molecular simulations due to limited simulation timescales. Protein dynamics, especially in the ligand binding pocket, often plays an important role in ligand binding. Based on our previously developed Ligand Gaussian accelerated molecular dynamics (LiGaMD), here we present LiGaMD2 in which a selective boost potential was applied to both the ligand and protein residues in the binding pocket to improve sampling of ligand binding and dissociation. To validate the performance of LiGaMD2, the T4 lysozyme (T4L) mutants with open and closed pockets bound by different ligands were chosen as model systems. LiGaMD2 could efficiently capture repetitive ligand dissociation and binding within microsecond simulations of all T4L systems. The obtained ligand binding kinetic rates and free energies agreed well with available experimental values and previous modeling results. Therefore, LiGaMD2 provides an improved approach to sample opening of closed protein pockets for ligand dissociation and binding, thereby allowing for efficient calculations of ligand binding thermodynamics and kinetics.

Repulsive Scaling Replica Exchange Molecular Dynamics in Modeling Protein-Glycosaminoglycan Complexes

Glycosaminoglycans are long linear periodic anionic polysaccharides consisting of disaccharide units exhibiting different sulfation patterns forming a highly heterogeneous group of molecules. Due to their flexibility, length, high charge, and periodicity, they are challenging for computational approaches. Despite their biological significance in terms of the important role in various diseases (e.g., Alzheimer, cancer, SARS-CoV-2) and proper cell functioning (e.g., proliferation, maturation), there is a lack of effective molecular docking tools designed specifically for glycosaminoglycans due to their challenging physical-chemical nature. In this chapter we present protocols for the Repulsive Scaling Replica Exchange Molecular Dynamics (RS-REMD) methods to dock glycosaminoglycans with both implicit and explicit solvent models implemented. This novel molecular dynamics-based replica exchange technique should help to elevate our current knowledge on the complexes and interactions between glycosaminoglycans and their protein receptors.

Computer aided drug design in the development of proteolysis targeting chimeras

Proteolysis targeting chimeras represent a class of drug molecules with a number of attractive properties, most notably a potential to work for targets that, so far, have been in-accessible for conventional small molecule inhibitors. Due to their different mechanism of action, and physico-chemical properties, many of the methods that have been designed and applied for computer aided design of traditional small molecule drugs are not applicable for proteolysis targeting chimeras. Here we review recent developments in this field focusing on three aspects: de-novo linker-design, estimation of absorption for beyond-rule-of-5 compounds, and the generation and ranking of ternary complex structures. In spite of this field still being young, we find that a good number of models and algorithms are available, with the potential to assist the design of such compounds in-silico, and accelerate applied pharmaceutical research.

Spontaneous binding of single-stranded RNAs to RRM proteins visualized by unbiased atomistic simulations with a rescaled RNA force field

Recognition of single-stranded RNA (ssRNA) by RNA recognition motif (RRM) domains is an important class of protein-RNA interactions. Many such complexes were characterized using nuclear magnetic resonance (NMR) and/or X-ray crystallography techniques, revealing ensemble-averaged pictures of the bound states. However, it is becoming widely accepted that better understanding of protein-RNA interactions would be obtained from ensemble descriptions. Indeed, earlier molecular dynamics simulations of bound states indicated visible dynamics at the RNA-RRM interfaces. Here, we report the first atomistic simulation study of spontaneous binding of short RNA sequences to RRM domains of HuR and SRSF1 proteins. Using a millisecond-scale aggregate ensemble of unbiased simulations, we were able to observe a few dozen binding events. HuR RRM3 utilizes a pre-binding state to navigate the RNA sequence to its partially disordered bound state and then to dynamically scan its different binding registers. SRSF1 RRM2 binding is more straightforward but still multiple-pathway. The present study necessitated development of a goal-specific force field modification, scaling down the intramolecular van der Waals interactions of the RNA which also improves description of the RNA-RRM bound state. Our study opens up a new avenue for large-scale atomistic investigations of binding landscapes of protein-RNA complexes, and future perspectives of such research are discussed.

Explicit solvent repulsive scaling replica exchange molecular dynamics (RS-REMD) in molecular modeling of protein-glycosaminoglycan complexes

Glycosaminoglcyans (GAGs), linear anionic periodic polysaccharides, are crucial for many biologically relevant functions in the extracellular matrix. By interacting with proteins GAGs mediate processes such as cancer development, cell proliferation and the onset of neurodegenerative diseases. Despite this eminent importance of GAGs, they still represent a limited focus for the computational community in comparison to other classes of biomolecules. Therefore, there is a lack of modeling tools designed specifically for docking GAGs. One has to rely on existing docking software developed mostly for small drug molecules substantially differing from GAGs in their basic physico-chemical properties. In this study, we present an updated protocol for docking GAGs based on the Repulsive Scaling Replica Exchange Molecular Dynamics (RS-REMD) that includes explicit solvent description. The use of this water model improved docking performance both in terms of its accuracy and speed. This method represents a significant computational progress in GAG-related research.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

Protein-Protein Interaction-Gaussian Accelerated Molecular Dynamics (PPI-GaMD): Characterization of Protein Binding Thermodynamics and Kinetics

Protein-protein interactions (PPIs) play key roles in many fundamental biological processes such as cellular signaling and immune responses. However, it has proven challenging to simulate repetitive protein association and dissociation in order to calculate binding free energies and kinetics of PPIs due to long biological timescales and complex protein dynamics. To address this challenge, we have developed a new computational approach to all-atom simulations of PPIs based on a robust Gaussian accelerated molecular dynamics (GaMD) technique. The method, termed "PPI-GaMD", selectively boosts interaction potential energy between protein partners to facilitate their slow dissociation. Meanwhile, another boost potential is applied to the remaining potential energy of the entire system to effectively model the protein's flexibility and rebinding. PPI-GaMD has been demonstrated on a model system of the ribonuclease barnase interactions with its inhibitor barstar. Six independent 2 μs PPI-GaMD simulations have captured repetitive barstar dissociation and rebinding events, which enable calculations of the protein binding thermodynamics and kinetics simultaneously. The calculated binding free energies and kinetic rate constants agree well with the experimental data. Furthermore, PPI-GaMD simulations have provided mechanistic insights into barstar binding to barnase, which involves long-range electrostatic interactions and multiple binding pathways, being consistent with previous experimental and computational findings of this model system. In summary, PPI-GaMD provides a highly efficient and easy-to-use approach for binding free energy and kinetics calculations of PPIs.

Challenges and frontiers of computational modelling of biomolecular recognition

Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterise biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future.

Advanced Molecular Dynamics Approaches to Model a Tertiary Complex APRIL/TACI with Long Glycosaminoglycans

Glycosaminoglycans (GAGs) are linear anionic periodic polysaccharides participating in a number of biologically relevant processes in the extracellular matrix via interactions with their protein targets. Due to their periodicity, conformational flexibility, pseudo-symmetry of the sulfation pattern, and the key role of electrostatics, these molecules are challenging for both experimental and theoretical approaches. In particular, conventional molecular docking applied for GAGs longer than 10-mer experiences severe difficulties. In this work, for the first time, 24- and 48-meric GAGs were docked using all-atomic repulsive-scaling Hamiltonian replica exchange molecular dynamics (RS-REMD), a novel methodology based on replicas with van der Waals radii of interacting molecules being scaled. This approach performed well for proteins complexed with oligomeric GAGs and is independent of their length, which distinguishes it from other molecular docking approaches. We built a model of long GAGs in complex with a proliferation-inducing ligand (APRIL) prebound to its receptors, the B cell maturation antigen and the transmembrane activator and calcium modulator and cyclophilin ligand interactor (TACI). Furthermore, the prediction power of the RS-REMD for this tertiary complex was evaluated. We conclude that the TACI–GAG interaction could be potentially amplified by TACI’s binding to APRIL. RS-REMD outperformed Autodock3, the docking program previously proven the best for short GAGs.

Evaluation of replica exchange with repulsive scaling approach for docking glycosaminoglycans

Glycosaminoglycans (GAGs), long linear periodic anionic polysaccharides, are key molecules in the extracellular matrix (ECM). Therefore, deciphering their role in the biologically relevant context is important for fundamental understanding of the processes ongoing in ECM and for establishing new strategies in the regenerative medicine. Although GAGs represent a number of computational challenges, molecular docking is a powerful tool for analysis of their interactions. Despite the recent development of GAG-specific docking approaches, there is plenty of room for improvement. Here, replica exchange molecular dynamics with repulsive scaling (REMD-RS) recently proved to be a successful approach for protein-protein complexes, was applied to dock GAGs. In this method, effective pairwise radii are increased in different Hamiltonian replicas. REMD-RS is shown to be an attractive alternative to classical docking approaches for GAGs. This work contributes to setting up of GAG-specific computational protocols and provides new insights into the nature of these biological systems.