Computational prediction of protein–protein binding affinities

Multiscale Computational Protocols for Accurate Residue Interactions at the Flexible Insulin-Receptor Interface

The quantitative characterization of residue contributions to protein-protein binding across extensive flexible interfaces poses a significant challenge for biophysical computations. It is attributable to the inherent imperfections in the experimental structures themselves, as well as to the lack of reliable computational tools for the evaluation of all types of noncovalent interactions. This study leverages recent advancements in semiempirical quantum-mechanical and implicit solvent approaches embodied in the PM6-D3H4S/COSMO2 method for the development of a hierarchical computational protocols encompassing molecular dynamics, fragmentation, and virtual glycine scan techniques for the investigation of flexible protein-protein interactions. As a model, the binding of insulin to its receptor is selected, a complex and dynamic process that has been extensively studied experimentally. The interaction energies calculated at the PM6-D3H4S/COSMO2 level in ten molecular dynamics snapshots did not correlate with molecular mechanics/generalized Born interaction energies because only the former method is able to describe nonadditive effects. This became evident by the examination of the energetics in small-model dimers featuring all the present types of noncovalent interactions with respect to DFT-D3 calculations. The virtual glycine scan has identified 15 hotspot residues on insulin and 15 on the insulin receptor, and their contributions have been quantified using PM6-D3H4S/COSMO2. The accuracy and credibility of the approach are further supported by the fact that all the insulin hotspots have previously been detected by biochemical and structural methods. The modular nature of the protocol has enabled the formulation of several variants, each tailored to specific accuracy and efficiency requirements. The developed computational strategy is firmly rooted in general biophysical chemistry and is thus offered as a general tool for the quantification of interactions across relevant flexible protein-protein interfaces.

The Correlated States Theory of the Hydrophobic Effect

Starting from Frank and Evans' "iceberg" model of hydrophobic hydration of small molecules (the "microscopic" hydrophobic effect, HE) published in 1945, much has been done with respect to understanding the nature of HE and elaborating a quantitative theory able to describe the thermodynamic profile (the "signature" of HE) for the large volume of experimental data accumulated to date. Generally, three sets of approaches addressing this issue were suggested, ranging from the approval of the central role of the water shell to the complete denial of its role, with the focus placed on the solute and its interactions with surrounding water. For this reason, some controversy is still present in understanding the fundamental nature of HE, even at the "microscopic" scale. Nevertheless, the general tendency of the past decade seems to have shifted toward a greater role of the water environment in determining the thermodynamic profile of HE, with a designated place for solute-water interactions as a fine-tuning of thermodynamic observables. In the present work, we developed a novel view on HE at the microscopic scale, appearing as a consequence of solute-water correlated translational and orientational vibration motion, emerging as a new property of hydrophobic hydration/solvation (the Correlated States Theory of HE). We built a fully analytical description of this process, which has enabled us to quantify the "signature" of HE for extended thermodynamic data sets without employing molecular simulations or any numerical functions in the core of the theory. As a consequence, our approach provides a self-consistent view on the known major experimental manifestations of HE across an extended temperature range, addresses some controversial issues existing to date, and creates a new augmentation to current knowledge. Most importantly, the suggested approach offers a paradigm shift from the currently dominating views on HE as a consequence of water-water interactions and the "excluded volume effect" toward the central role of solute-water interactions, and provides the first nonempirical proof of the validity of SASA-based computational models of hydrophobic hydration/solvation, which have been utilized on an empirical basis for more than 40 years.

Structural maturation of the matrix lattice is not required for HIV-1 particle infectivity

During HIV-1 maturation, the matrix (MA) lattice underlying the viral membrane undergoes a structural rearrangement, and the newly released capsid (CA) protein forms a mature CA. While it is well established that CA formation is essential for particle infectivity, the functional role of MA structural maturation remains unclear. Here, we examine maturation of an MA triple mutant, L20K/E73K/A82T, which, despite replicating similarly to wild-type (WT) in some cell lines, exhibits distinct biochemical behaviors that suggest altered MA-MA interactions. Cryo-electron tomography with subtomogram averaging reveals that, although the MA lattice in immature L20K/E73K/A82T virions closely resembles that of the WT, mature L20K/E73K/A82T virions lack a detectable MA lattice. All-atom molecular dynamics simulations suggest that this absence results from destabilized inter-trimer MA interactions in mature L20K/E73K/A82T mutant virions. These findings suggest that an ordered, membrane-associated mature MA lattice is not essential for HIV-1 infectivity, providing insights into the structural requirements for HIV-1 particle maturation and generation of infectious particles.

PackPPI: An integrated framework for protein-protein complex side-chain packing and ΔΔG prediction based on diffusion model

Deep learning methods have played an increasingly pivotal role in advancing side-chain packing and mutation effect prediction (ΔΔG) for protein complexes. Although these two tasks are inherently closely related, they are typically treated separately in practice. Furthermore, the lack of effective post-processing in most approaches results in sub-optimal refinement of generated conformations, limiting the plausibility of the predicted conformations. In this study, we introduce an integrated framework, PackPPI, which employs a diffusion model and a proximal optimization algorithm to improve side-chain prediction for protein complexes while using learned representations to predict ΔΔG. The results demonstrate that PackPPI achieved the lowest atom RMSD (0.9822) on the CASP15 dataset. The proximal optimization algorithm effectively reduces spatial clashes between side-chain atoms while maintaining a low-energy landscape. Furthermore, PackPPI achieves state-of-the-art performance in predicting binding affinity changes induced by multi-point mutations on the SKEMPI v2.0 dataset. These findings underscore the potential of PackPPI as a robust and versatile computational tool for protein design and engineering. The implementation of PackPPI is available at https://github.com/Jackz915/PackPPI.

Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning

The fast evolution of SARS-CoV-2 and other infectious viruses poses a grand challenge to the rapid response in terms of viral tracking, diagnostics, and design and manufacture of monoclonal antibodies (mAbs) and vaccines, which are both time-consuming and costly. This underscores the need for efficient computational approaches. Recent advancements, like topological deep learning (TDL), have introduced powerful tools for forecasting emerging dominant variants, yet they require deep mutational scanning (DMS) of viral surface proteins and associated three-dimensional (3D) protein-protein interaction (PPI) complex structures. We propose an AlphaFold 3 (AF3)-assisted multi-task topological Laplacian (MT-TopLap) strategy to address this need. MT-TopLap combines deep learning with TDA models, such as persistent Laplacians (PL) to extract detailed topological and geometric characteristics of PPIs, thereby enhancing the prediction of DMS and binding free energy (BFE) changes upon virus mutations. Validation with four experimental DMS datasets of SARS-CoV-2 spike receptor-binding domain (RBD) and the human angiotensin-converting enzyme-2 (ACE2) complexes indicates that our AF3-assisted MT-TopLap strategy maintains robust performance, with only an average 1.1% decrease in Pearson correlation coefficients (PCC) and an average 9.3% increase in root mean square errors (RMSE), compared with the use of experimental structures. Additionally, AF3-assisted MT-TopLap achieved a PCC of 0.81 when tested with a SARS-CoV-2 HK.3 variant DMS dataset, confirming its capability to accurately predict BFE changes and adapt to new experimental data, thereby showcasing its potential for rapid and effective response to fast viral evolution.

From Apo to Ligand-Bound: Unraveling PPARγ-LBD Conformational Shifts via Advanced Molecular Dynamics

Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor whose ligand-induced conformational changes, primarily driven by helix 12 (H12) repositioning, regulate transcriptional activity. However, the precise mechanism remains elusive. In this study, we performed classical molecular dynamics (cMD) simulations of the PPARγ ligand binding domain (LBD) in complex with two agonists (BRL, 3EA), a partial agonist (GW0072), and an antagonist (EKP), generating 3 μs trajectories for each system. To gain deeper insights, we integrated machine learning-assisted clustering with MD simulations, revealing a favorable trend in binding free energy (ΔG b), suggesting enhanced complex stability. A case study on EKP demonstrated that, despite fitting within the binding site, it failed to induce rapid LBD or H12 rearrangements in the apo agonist-induced conformation. Additionally, we investigated the apo-state conformations of PPARγ-LBD influenced by agonist and antagonist ligands, utilizing cMD and Gaussian accelerated molecular dynamics (GaMD) over a cumulative 6 μs (3 μs cMD + 3 μs GaMD). Key residues known to modulate PPARγ function upon mutation were analyzed, and simulations confirmed the high stability of both apo and ligand-bound conformations. Notably, in the apo state, specific H12 residues interacted with other PPARγ-LBD regions, preventing disorder and abrupt transitions. These findings guided the selection of collective variables (CVs) for well-tempered metadynamics (WT-MetaD) simulations, which-in the apo-agonist state-captured the H12 shift from agonist- to antagonist-like conformations, consistent with resolved X-ray structures. Overall, this computational framework provides novel insights into PPARγ-LBD conformational dynamics and establishes a valuable approach for rationally assessing the effects of modulators on PPARγ activity.

Structured Random Binding: a minimal model of protein-protein interactions

We describe Structured Random Binding (SRB), a minimal model of protein-protein interactions rooted in the statistical physics of disordered systems. In this model, nonspecific binding is a generic consequence of the interaction between random proteins, exhibiting a phase transition from a high temperature state where nonspecific complexes are transient and lack well-defined interaction interfaces, to a low temperature state where the complex structure is frozen and a definite interaction interface is present. Numerically, weakly-bound nonspecific complexes can evolve into tightly-bound, highly specific complexes, but only if the structural correlation length along the peptide backbone is short; moreover, evolved tightly-bound homodimers favor the same interface structure that is predominant in real protein homodimers.

Assessment of the Topology and Oligomerisation States of Coiled Coils Using Metadynamics with Conformational Restraints

Coiled-coil proteins provide an excellent scaffold for multistate de novo protein design due to their established sequence-to-structure relationships and ability to switch conformations in response to external stimuli, such as changes in pH or temperature. However, the computational design of multistate coiled-coil protein assemblies is challenging, as it requires accurate estimates of the free energy differences between multiple alternative coiled-coil conformations. Here, we demonstrate how this challenge can be tackled using metadynamics simulations with orientational, positional and conformational restraints. We show that, even for subtle sequence variations, our protocol can predict the preferred topology of coiled-coil dimers and trimers, the preferred oligomerization states of coiled-coil dimers, trimers, and tetramers, as well as the switching behavior of a pH-dependent multistate system. Our approach provides a method for predicting the stability of coiled-coil designs and offers a new framework for computing binding free energies in protein-protein and multiprotein complexes.

PCANN Program for Structure-Based Prediction of Protein-Protein Binding Affinity: Comparison With Other Neural-Network Predictors

In this communication, we introduce a new structure-based affinity predictor for protein-protein complexes. This predictor, dubbed PCANN (Protein Complex Affinity by Neural Network), uses the ESM-2 language model to encode the information about protein binding interfaces and graph attention network (GAT) to parlay this information intoK d $$ {K}_{\mathrm{d}} $$ predictions. In the tests employing two previously unused literature-extracted datasets, PCANN performed better than the best of the publicly available predictors, BindPPI, with mean absolute error (MAE) of 1.3 versus 1.4 kcal/mol. Further progress in the development ofK d $$ {K}_{\mathrm{d}} $$ predictors using deep learning models is faced with two problems: (i) the amount of experimental data available to train and test new predictors is limited and (ii) the availableK d $$ {K}_{\mathrm{d}} $$ data are often not very accurate and lack internal consistency with respect to measurement conditions. These issues can be potentially addressed through an AI-leveraged literature search followed by careful human curation and by introducing additional parameters to account for variations in experimental conditions.

Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery

Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, protein structures have been determined through experimental approaches such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy. While these methods are effective and are considered the gold standard, they are very resource-intensive and time-consuming, ultimately limiting their scalability. However, with recent developments in computational biology and artificial intelligence (AI), the field of protein prediction has been revolutionized. Innovations like AlphaFold and RoseTTAFold enable protein structure predictions to be made directly from amino acid sequences with remarkable speed and accuracy. Despite the enormous enthusiasm associated with these newly developed AI-approaches, their true potential in structure-based drug discovery remains uncertain. In fact, although these algorithms generally predict overall protein structures well, essential details for computational ligand docking, such as the exact location of amino acid side chains within the binding pocket, are not predicted with the necessary accuracy. Additionally, docking methodologies are considered more as a hypothesis generator rather than a precise predictor of ligand-target interactions, and thus, usually identify many false-positive hits among only a few correctly predicted interactions. In this paper, we are reviewing the latest development in this cutting-edge field with emphasis on the GPCR target class to assess the potential role of AI approaches in structure-based drug discovery.

Viral entry mechanisms: the role of molecular simulation in unlocking a key step in viral infections

Viral infections are a major global health concern, affecting millions of people each year. Viral entry is one of the crucial stages in the infection process, but its details remain elusive. Enveloped viruses are enclosed by a lipid membrane that protects their genetic material and these viruses are linked to various human illnesses, including influenza, and COVID-19. Due to the advancements made in the field of molecular simulation, significant progress has been made in unraveling the dynamic processes involved in viral entry of enveloped viruses. Simulation studies have provided deep insight into the function of the proteins responsible for attaching to the host receptors and promoting membrane fusion (fusion proteins), deciphering interactions between these proteins and receptors, and shedding light on the functional significance of key regions, such as the fusion peptide. These studies have already significantly contributed to our understanding of this critical aspect of viral infection and assisted the development of effective strategies to combat viral diseases and improve global health. This review focuses on the vital role of fusion proteins in facilitating the entry process of enveloped viruses and highlights the contributions of molecular simulation studies to uncover the molecular details underlying their mechanisms of action.

DeepTGIN: a novel hybrid multimodal approach using transformers and graph isomorphism networks for protein-ligand binding affinity prediction

Predicting protein-ligand binding affinity is essential for understanding protein-ligand interactions and advancing drug discovery. Recent research has demonstrated the advantages of sequence-based models and graph-based models. In this study, we present a novel hybrid multimodal approach, DeepTGIN, which integrates transformers and graph isomorphism networks to predict protein-ligand binding affinity. DeepTGIN is designed to learn sequence and graph features efficiently. The DeepTGIN model comprises three modules: the data representation module, the encoder module, and the prediction module. The transformer encoder learns sequential features from proteins and protein pockets separately, while the graph isomorphism network extracts graph features from the ligands. To evaluate the performance of DeepTGIN, we compared it with state-of-the-art models using the PDBbind 2016 core set and PDBbind 2013 core set. DeepTGIN outperforms these models in terms of R, RMSE, MAE, SD, and CI metrics. Ablation studies further demonstrate the effectiveness of the ligand features and the encoder module. The code is available at: https://github.com/zhc-moushang/DeepTGIN . SCIENTIFIC CONTRIBUTION: DeepTGIN is a novel hybrid multimodal deep learning model for predict protein-ligand binding affinity. The model combines the Transformer encoder to extract sequence features from protein and protein pocket, while integrating graph isomorphism networks to capture features from the ligand. This model addresses the limitations of existing methods in exploring protein pocket and ligand features.

drMD: Molecular Dynamics for Experimentalists

Molecular dynamics (MD) simulations can be used by protein scientists to investigate a wide array of biologically relevant properties such as the effects of mutations on a protein's structure and activity, or probing intermolecular interactions with small molecule substrates or other macromolecules. Within the world of computational structural biology, several programs have become popular for running these simulations, but each of these programs requires a significant time investment from the researcher to run even simple simulations. Even after learning how to run and analyse simulations, many elements remain a "black box." This greatly limits the accessibility of molecular dynamics simulations for non-experts. Here we present drMD, an automated pipeline for running MD simulations using the OpenMM molecular mechanics toolkit. We have created drMD with non-experts in computational biology in mind. The drMD codebase has several functions that automatically handle routine procedures associated with running MD simulations. This greatly reduces the expertise required to run MD simulations. We have also introduced a series of quality-of-life features to make the process of running MD simulations both easier and more pleasant. Finally, drMD explains the steps it is taking interactively and, where useful, provides relevant references so the user can learn more. All these features make drMD an effective tool for learning MD while running publication-quality simulations. drMD is open source and can be found on GitHub: https://github.com/wells-wood-research/drMD.

Optimal Dielectric Boundary for Binding Free Energy Estimates in the Implicit Solvent

Accuracy of binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a multidimensional optimization pipeline is used to find optimal atomic radii, specifically for binding calculations in the implicit solvent. To reduce overfitting, the optimization target includes separate, weighted contributions from both binding and hydration free energies. The resulting five-parameter radii set, OPT_BIND5D, is evaluated against experiment for binding free energies of 20 host-guest (H-G) systems, unrelated to the types of structures used in the training. The resulting accuracy for this H-G test set (root mean square error of 2.03 kcal/mol, mean signed error of -0.13 kcal/mol, mean absolute error of 1.68 kcal/mol, and Pearson's correlation of r = 0.79 with the experimental values) is on par with what can be expected from the fixed charge explicit solvent models. Best agreement with the experiment is achieved when the implicit salt concentration is set equal or close to the experimental conditions.

Structural maturation of the matrix lattice is not required for HIV-1 particle infectivity

HIV-1 assembly is initiated by the binding of Gag polyproteins to the inner leaflet of the plasma membrane, mediated by the myristylated matrix (MA) domain of Gag. Subsequent to membrane binding, Gag oligomerizes and buds as an immature, non-infectious virus particle, which, upon cleavage of the Gag precursor by the viral protease, transforms into a mature, infectious virion. During maturation, the MA lattice underlying the viral membrane undergoes a structural rearrangement and the newly released capsid (CA) protein forms a mature capsid that encloses the viral genome. While it is well established that formation of the mature capsid is essential to particle infectivity, the functional role of MA structural maturation remains unclear. Here, we examine MA maturation of an MA triple mutant, L20K/E73K/A82T, which exhibits distinct biochemical behaviours. The L20K/E73K/A82T mutant is a revertant derived by propagating the L20K mutant, which exhibits reduced infectivity and increased association of the Gag polyprotein with membranes. L20K/E73K/A82T replicates similarly to wild type but retains the increased Gag membrane binding properties of L20K. L20K/E73K/A82T MA also sediments to high-density fractions in sucrose gradients after detergent treatment under conditions that fully solubilize WT MA, suggesting enhanced MA-MA interactions. Cryo-electron tomography with subtomogram averaging reveals that the immature MA lattice of L20K/E73K/A82T closely resembles the wild type. However, mature virions of the triple mutant lack a detectable MA lattice, in stark contrast to both the wild type and L20K mutant. All-atom molecular dynamics simulations suggest that this absence results from destabilized inter-trimer interactions in the mature L20K/E73K/A82T MA. Furthermore, introducing additional mutations designed to disrupt the mature MA lattice does not impair particle infectivity. These findings suggest that an ordered, membrane-associated mature MA lattice is not essential for HIV-1 infectivity, providing new insights into the structural plasticity of the matrix during maturation and its functional role in the viral lifecycle.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Structure and dimerization properties of the plant-specific copper chaperone CCH

Copper chaperones of the ATX1 family are found in a wide range of organisms where these essential soluble carriers strictly control the transport of monovalent copper across the cytoplasm to various targets in diverse cellular compartments thereby preventing detrimental radical formation catalyzed by the free metal ion. Notably, the ATX1 family in plants contains two distinct forms of the cellular copper carrier. In addition to ATX1 having orthologs in other species, they also contain the copper chaperone CCH. The latter features an extra C-terminal extension whose function is still unknown. The secondary structure of this extension was predicted to be disordered in previous studies, although this has not been experimentally confirmed. Solution NMR studies on purified CCH presented in this study disclose that this region is intrinsically disordered regardless of the chaperone's copper loading state. Further biophysical analyses of the purified metallochaperone provide evidence that the C-terminal extension stabilizes chaperone dimerization in the copper-free and copper-bound states. A variant of CCH lacking the C-terminal extension, termed CCHΔ, shows weaker dimerization but similar copper binding. Computational studies further corroborate the stabilizing role of the C-terminal extension in chaperone dimerization and identify key residues that are vital to maintaining dimer stability.

Multilayered Computational Framework for Designing Peptide Inhibitors of HVEM-LIGHT Interaction

The herpesvirus entry mediator (HVEM) and its ligand LIGHT play crucial roles in immune system regulation, including T-cell proliferation, B-cell differentiation, and immunoglobulin secretion. However, excessive T-cell activation can lead to chronic inflammation and autoimmune diseases. Thus, inhibiting the HVEM-LIGHT interaction emerges as a promising therapeutic strategy for these conditions and in preventing adverse reactions in organ transplantation. This study focused on designing peptide inhibitors, targeting the HVEM-LIGHT interaction, using molecular dynamics (MD) simulations of 65 peptides derived from HVEM. These peptides varied in length and disulfide-bond configurations, crucial for their interaction with the LIGHT trimer. By simulating 31 HVEM domain variants, including the full-length protein, we assessed conformational changes upon LIGHT binding to understand the influence of HVEM segments and disulfide bonds on the binding mechanism. Employing multitrajectory microsecond-scale, all-atom MD simulations and molecular mechanics with generalized Born and surface area (MM-GBSA) binding energy estimation, we identified promising CRD2 domain variants with high LIGHT affinity. Notably, point mutations in these variants led to a peptide with a single disulfide bond (C58-C73) and a K54E substitution, exhibiting the highest binding affinity. The importance of the CRD2 domain and Cys58-Cys73 disulfide bond for interrupting HVEM-LIGHT interaction was further supported by analyzing truncated CRD2 variants, demonstrating similar binding strengths and mechanisms. Further investigations into the binding mechanism utilized steered MD simulations at various pulling speeds and umbrella sampling to estimate the energy profile of HVEM-based inhibitors with LIGHT. These comprehensive analyses revealed key interactions and different binding mechanisms, highlighting the increased binding affinity of selected peptide variants. Experimental circular dichroism techniques confirmed the structural properties of these variants. This study not only advances our understanding of the molecular basis of HVEM-LIGHT interactions but also provides a foundation for developing novel therapeutic strategies for immune-related disorders. Furthermore, it sets a gold standard for peptide inhibitor design in drug development due to its systematic approach.

Prebound State Discovered in the Unbinding Pathway of Fluorinated Variants of the Trypsin-BPTI Complex Using Random Acceleration Molecular Dynamics Simulations

The serine protease trypsin forms a tightly bound inhibitor complex with the bovine pancreatic trypsin inhibitor (BPTI). The complex is stabilized by the P1 residue Lys15, which interacts with negatively charged amino acids at the bottom of the S1 pocket. Truncating the P1 residue of wildtype BPTI to α-aminobutyric acid (Abu) leaves a complex with moderate inhibitor strength, which is held in place by additional hydrogen bonds at the protein-protein interface. Fluorination of the Abu residue partially restores the inhibitor strength. The mechanism with which fluorination can restore the inhibitor strength is unknown, and accurate computational investigation requires knowledge of the binding and unbinding pathways. The preferred unbinding pathway is likely to be complex, as encounter states have been described before, and unrestrained umbrella sampling simulations of these complexes suggest additional energetic minima. Here, we use random acceleration molecular dynamics to find a new metastable state in the unbinding pathway of Abu-BPTI variants and wildtype BPTI from trypsin, which we call the prebound state. The prebound state and the fully bound state differ by a substantial shift in the position, a slight shift in the orientation of the BPTI variants, and changes in the interaction pattern. Particularly important is the breaking of three hydrogen bonds around Arg17. Fluorination of the P1 residue lowers the energy barrier of the transition between the fully bound state and prebound state and also lowers the energy minimum of the prebound state. While the effect of fluorination is in general difficult to quantify, here, it is in part caused by favorable stabilization of a hydrogen bond between Gln194 and Cys14. The interaction pattern of the prebound state offers insights into the inhibitory mechanism of BPTI and might add valuable information for the design of serine protease inhibitors.

Exploring the Conformational Ensembles of Protein-Protein Complex with Transformer-Based Generative Model

Protein-protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein-protein interactions is crucial for linking the protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein-protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein-protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism. We demonstrated the use of a deep learning model based on the transformer architecture to explore the conformational ensembles of protein-protein complexes through MD simulations. The results showed that the learned latent space can be used to generate unsampled conformations of protein-protein complexes for obtaining new conformations complementing pre-existing ones, which can be used as an exploratory tool for the analysis and enhancement of molecular simulations of protein-protein complexes.

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery

Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein-ligand complexes with extensive validation of experimental data. Starting from the existing experimental structures, semi-empirical quantum mechanics was used to systematically refine these structures. A large collection of molecular dynamics traces of protein-ligand complexes in explicit water is included, accumulating over 170 μs. We give examples of machine learning (ML) baseline models proving an improvement of accuracy by employing our data. An easy entry point for ML experts is provided to enable the next generation of drug discovery artificial intelligence models.

MPA-Pred: A machine learning approach for predicting the binding affinity of membrane protein-protein complexes

Membrane protein-protein interactions are essential for several functions including cell signaling, ion transport, and enzymatic activity. These interactions are mainly dictated by their binding affinities. Although several methods are available for predicting the binding affinity of protein-protein complexes, there exists no specific method for membrane protein-protein complexes. In this work, we collected the experimental binding affinity data for a set of 114 membrane protein-protein complexes and derived several structure and sequence-based features. Our analysis on the relationship between binding affinity and the features revealed that the important factors mainly depend on the type of membrane protein and the functional class of the protein. Specifically, aromatic and charged residues at the interface, and aromatic-aromatic and electrostatic interactions are found to be important to understand the binding affinity. Further, we developed a method, MPA-Pred, for predicting the binding affinity of membrane protein-protein complexes using a machine learning approach. It showed an average correlation and mean absolute error of 0.83 and 0.91 kcal/mol, respectively, using the jack-knife test on a set of 114 complexes. We have also developed a web server and it is available at https://web.iitm.ac.in/bioinfo2/MPA-Pred/. This method can be used for predicting the affinity of membrane protein-protein complexes at a large scale and aid to improve drug design strategies.

The importance of protein domain mutations in cancer therapy

Cancer is a complex disease that is caused by multiple genetic factors. Researchers have been studying protein domain mutations to understand how they affect the progression and treatment of cancer. These mutations can significantly impact the development and spread of cancer by changing the protein structure, function, and signalling pathways. As a result, there is a growing interest in how these mutations can be used as prognostic indicators for cancer prognosis. Recent studies have shown that protein domain mutations can provide valuable information about the severity of the disease and the patient's response to treatment. They may also be used to predict the response and resistance to targeted therapy in cancer treatment. The clinical implications of protein domain mutations in cancer are significant, and they are regarded as essential biomarkers in oncology. However, additional techniques and approaches are required to characterize changes in protein domains and predict their functional effects. Machine learning and other computational tools offer promising solutions to this challenge, enabling the prediction of the impact of mutations on protein structure and function. Such predictions can aid in the clinical interpretation of genetic information. Furthermore, the development of genome editing tools like CRISPR/Cas9 has made it possible to validate the functional significance of mutants more efficiently and accurately. In conclusion, protein domain mutations hold great promise as prognostic and predictive biomarkers in cancer. Overall, considerable research is still needed to better define genetic and molecular heterogeneity and to resolve the challenges that remain, so that their full potential can be realized.

Predicting Mutational Effects on Ca2+-Activated Chloride Conduction of TMEM16A Based on a Simulation Study

The dysfunction and defects of ion channels are associated with many human diseases, especially for loss-of-function mutations in ion channels such as cystic fibrosis transmembrane conductance regulator mutations in cystic fibrosis. Understanding ion channels is of great current importance for both medical and fundamental purposes. Such an understanding should include the ability to predict mutational effects and describe functional and mechanistic effects. In this work, we introduce an approach to predict mutational effects based on kinetic information (including reaction barriers and transition state locations) obtained by studying the working mechanism of target proteins. Specifically, we take the Ca2+-activated chloride channel TMEM16A as an example and utilize the computational biology model to predict the mutational effects of key residues. Encouragingly, we verified our predictions through electrophysiological experiments, demonstrating a 94% prediction accuracy regarding mutational directions. The mutational strength assessed by Pearson's correlation coefficient is -0.80 between our calculations and the experimental results. These findings suggest that the proposed methodology is reliable and can provide valuable guidance for revealing functional mechanisms and identifying key residues of the TMEM16A channel. The proposed approach can be extended to a broad scope of biophysical systems.

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity

Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.

Molecular Modeling Techniques and In-Silico Drug Discovery

Molecular modeling is the technique to determine the overall structure of an unknown molecule, be it a small one or a macromolecule. The technique encompasses the method of screening ligand libraries for the development of new candidate drug molecules. All these aspects have become an essential topic of research. This field is truly interdisciplinary and finds its applications in almost all fields of life science research. In this chapter, an overview of the protocol associated with molecular modeling techniques will be discussed.

Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants

Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.

NADP+ or CO2 reduction by frhAGB-encoded hydrogenase through interaction with formate dehydrogenase 3 in the hyperthermophilic archaeon Thermococcus onnurineus NA1

IMPORTANCE

The strategy using structural homology with the help of structure prediction by AlphaFold was very successful in finding potential targets for the frhAGB-encoded hydrogenase of Thermococcus onnurineus NA1. The finding that the hydrogenase can interact with FdhB to reduce the cofactor NAD(P)+ is significant in that the enzyme can function to supply reducing equivalents, just as F420-reducing hydrogenases in methanogens use coenzyme F420 as an electron carrier. Additionally, it was identified that T. onnurineus NA1 could produce formate from H2 and CO2 by the concerted action of frhAGB-encoded hydrogenase and formate dehydrogenase Fdh3.

Standard Binding Free-Energy Calculations: How Far Are We from Automation?

Recent success stories suggest that in silico protein-ligand binding free-energy calculations are approaching chemical accuracy. However, their widespread application remains limited by the extensive human intervention required, posing challenges for the neophyte. As such, it is critical to develop automated workflows for estimating protein-ligand binding affinities with minimum personal involvement. Key human efforts include setting up and tuning enhanced-sampling or alchemical-transformation algorithms as a preamble to computational binding free-energy estimations. Additionally, preparing input files, bookkeeping, and postprocessing represent nontrivial tasks. In this Perspective, we discuss recent progress in automating standard binding free-energy calculations, featuring the development of adaptive or parameter-free algorithms, standardization of binding free-energy calculation workflows, and the implementation of user-friendly software. We also assess the current state of automated standard binding free-energy calculations and evaluate the limitations of existing methods. Last, we outline the requirements for future algorithms and workflows to facilitate automated free-energy calculations for diverse protein-ligand complexes.

Alchemical Calculation of Relative Free Energies for Charge-Changing Mutations at Protein-Protein Interfaces Considering Fixed and Variable Protonation States

The calculation of relative free energies (ΔΔG) for charge-changing mutations at protein-protein interfaces through alchemical methods remains challenging due to variations in the system's net charge during charging steps, the possibility of mutated and contacting ionizable residues occurring in various protonation states, and undersampling issues. In this study, we present a set of strategies, collectively termed TIRST/TIRST-H+, to address some of these challenges. Our approaches combine thermodynamic integration (TI) with the prediction of pKa shifts to calculate ΔΔG values. Moreover, special sets of restraints are employed to keep the alchemically transformed molecules separated. The accuracy of the devised approaches was assessed on a large and diverse data set comprising 164 point mutations of charged residues (Asp, Glu, Lys, and Arg) to Ala at the protein-protein interfaces of complexes with known three-dimensional structures. Mean absolute and root-mean-square errors ranging from 1.38 to 1.66 and 1.89 to 2.44 kcal/mol, respectively, and Pearson correlation coefficients of ∼0.6 were obtained when testing the approaches on the selected data set using the GPU-TI module of Amber18 suite and the ff14SB force field. Furthermore, the inclusion of variable protonation states for the mutated acid residues improved the accuracy of the predicted ΔΔG values. Therefore, our results validate the use of TIRST/TIRST-H+ in prospective studies aimed at evaluating the impact of charge-changing mutations to Ala on the stability of protein-protein complexes.

How does the same ligand activate signaling of different receptors in TNFR superfamily: a computational study

TNFα is a highly pleiotropic cytokine inducing inflammatory signaling pathways. It is initially presented on plasma membrane of cells (mTNFα), and also exists in a soluble variant (sTNFα) after cleavage. The ligand is shared by two structurally similar receptors, TNFR1 and TNFR2. Interestingly, while sTNFα preferentially stimulates TNFR1, TNFR2 signaling can only be activated by mTNFα. How can two similar receptors respond to the same ligand in such a different way? We employed computational simulations in multiple scales to address this question. We found that both mTNFα and sTNFα can trigger the clustering of TNFR1. The size of clusters induced by sTNFα is constantly larger than the clusters induced by mTNFα. The systems of TNFR2, on the other hand, show very different behaviors. Only when the interactions between TNFR2 are very weak, mTNFα can trigger the receptors to form very large clusters. Given the same weak binding affinity, only small oligomers were obtained in the system of sTNFα. Considering that TNF-mediated signaling is modulated by the ligand-induced clustering of receptors on cell surface, our study provided the mechanistic foundation to the phenomenon that different isoforms of the ligand can lead to highly distinctive signaling patterns for its receptors.

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations

CONTEXT

Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ('classic type functions' of proteins) or a signal transduction ('non-classic'), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (∆G_binding, K_d) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ∆G_binding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX, etc.) that directly compute ∆G_binding without involving intermediate structural descriptors.

METHODS

Here, we present EnCPdock ( https://www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ∆G_binding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional complementarity plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Pr_fmax) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.

Atomic-level thermodynamics analysis of the binding free energy of SARS-CoV-2 neutralizing antibodies

Understanding how protein-protein binding affinity is determined from molecular interactions at the interface is essential in developing protein therapeutics such as antibodies, but this has not yet been fully achieved. Among the major difficulties are the facts that it is generally difficult to decompose thermodynamic quantities into contributions from individual molecular interactions and that the solvent effect-dehydration penalty-must also be taken into consideration for every contact formation at the binding interface. Here, we present an atomic-level thermodynamics analysis that overcomes these difficulties and illustrate its utility through application to SARS-CoV-2 neutralizing antibodies. Our analysis is based on the direct interaction energy computed from simulated antibody-protein complex structures and on the decomposition of solvation free energy change upon complex formation. We find that the formation of a single contact such as a hydrogen bond at the interface barely contributes to binding free energy due to the dehydration penalty. On the other hand, the simultaneous formation of multiple contacts between two interface residues favorably contributes to binding affinity. This is because the dehydration penalty is significantly alleviated: the total penalty for multiple contacts is smaller than a sum of what would be expected for individual dehydrations of those contacts. Our results thus provide a new perspective for designing protein therapeutics of improved binding affinity.

Semi-Rational Design of L-Isoleucine Dioxygenase Generated Its Activity for Aromatic Amino Acid Hydroxylation

Fe (II)-and 2-ketoglutarate-dependent dioxygenases (Fe (II)/α-KG DOs) have been applied to catalyze hydroxylation of amino acids. However, the Fe (II)/α-KG DOs that have been developed and characterized are not sufficient. L-isoleucine dioxygenase (IDO) is an Fe (II)/α-KG DO that specifically catalyzes the formation of 4-hydroxyisoleucine (4-HIL) from L-isoleucine (L-Ile) and exhibits a substrate specificity toward L-aliphatic amino acids. To expand the substrate spectrum of IDO toward aromatic amino acids, in this study, we analyzed the regularity of the substrate spectrum of IDO using molecular dynamics (MD) simulation and found that the distance between Fe2+, C2 of α-KG and amino acid chain's C4 may be critical for regulating the substrate specificity of the enzyme. The mutation sites (Y143, S153 and R227) were also subjected to single point saturation mutations based on polarity pockets and residue free energy contributions. It was found that Y143D, Y143I and S153A mutants exhibited catalytic L-phenylalanine activity, while Y143I, S153A, S153Q and S153Y exhibited catalytic L-homophenylalanine activity. Consequently, this study extended the substrate spectrum of IDO with aromatic amino acids and enhanced its application property.

NIAS-Server 2.0: A versatile complementary tool for structural biology studies

Increasing the repertoire of available complementary tools to advance the knowledge of protein structures is fundamental for structural biology. The Neighbors Influence of Amino Acids and Secondary Structures (NIAS) is a server that analyzes a protein's conformational preferences of amino acids. NIAS is based on the Angle Probability List, representing the normalized frequency of empirical conformational preferences, such as torsion angles, of different amino acid pairs and their corresponding secondary structure information, as available in the Protein Data Bank. In this work, we announce the updated NIAS server with the data comprising all structures deposited until Sep 2022, 7 years after the initial release. Unlike the original publication, which accounted for only studies conducted with X-ray crystallography, we added data from solid nuclear magnetic resonance (NMR), solution NMR, CullPDB, Electron Microscopy, and Electron Crystallography using multiple filtering parameters. We also provide examples of how NIAS can be applied as a complementary analysis tool for different structural biology works and what are its limitations.

Computational analyses of the interactome between TNF and TNFR superfamilies

Proteins in the tumor necrosis factor (TNF) superfamily (TNFSF) regulate diverse cellular processes by interacting with their receptors in the TNF receptor (TNFR) superfamily (TNFRSF). Ligands and receptors in these two superfamilies form a complicated network of interactions, in which the same ligand can bind to different receptors and the same receptor can be shared by different ligands. In order to study these interactions on a systematic level, a TNFSF-TNFRSF interactome was constructed in this study by searching the database which consists of both experimentally measured and computationally predicted protein-protein interactions (PPIs). The interactome contains a total number of 194 interactions between 18 TNFSF ligands and 29 TNFRSF receptors in human. We modeled the structure for each ligand-receptor interaction in the network. Their binding affinities were further computationally estimated based on modeled structures. Our computational outputs, which are all publicly accessible, serve as a valuable addition to the currently limited experimental resources to study TNF-mediated cell signaling.

Comparative Binding Ability of Human Monoclonal Antibodies against Omicron Variants of SARS-CoV-2: An In Silico Investigation

Mutation(s) in the spike protein is the major characteristic trait of newly emerged SARS-CoV-2 variants such as Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Delta-plus. Omicron (B.1.1.529) is the latest addition and it has been characterized by high transmissibility and the ability to escape host immunity. Recently developed vaccines and repurposed drugs exert limited action on Omicron strains and hence new therapeutics are immediately needed. Herein, we have explored the efficiency of twelve therapeutic monoclonal antibodies (mAbs) targeting the RBD region of the spike glycoprotein against all the Omicron variants bearing a mutation in spike protein through molecular docking and molecular dynamics simulation. Our in silico evidence reveals that adintivimab, beludivimab, and regadanivimab are the most potent mAbs to form strong biophysical interactions and neutralize most of the Omicron variants. Considering the efficacy of mAbs, we incorporated CDRH3 of beludavimab within the framework of adintrevimab, which displayed a more intense binding affinity towards all of the Omicron variants viz. BA.1, BA.2, BA.2.12.1, BA.4, and BA.5. Furthermore, the cDNA of chimeric mAb was cloned in silico within pET30ax for recombinant production. In conclusion, the present study represents the candidature of human mAbs (beludavimab and adintrevimab) and the therapeutic potential of designed chimeric mAb for treating Omicron-infected patients.

Mutation pattern of cancer suppressor p53 gene in factory workers exposed to polypropylene and impact on their reproductive hormones

BACKGROUND

Polypropylene is a thermoplastic polymer playing the role of an endocrine disruptor that interferes with the union, emission, transport or elimination of normal hormones. Epidemiological information indicated the relation of endocrine-disturbing chemicals with prostate cancer, testis tumor and diminished fertility. p53 is a key tumor silencer gene. The present study aimed to evaluate luteinizing hormone (LH) and follicle-stimulating hormone (FSH) levels and the risk of p53 mutations as a result of exposure to polypropylene in non-tumorous adult male factory workers.

METHODS

In total, 150 (controls = 35, workers = 115) subjects were recruited. Groups were maintained according to the tenure of exposure G1 (1-5 years), G2 (6-10 years), G3 (11-15 years) and G4 (16-20 years). Concentrations of LH and FSH were determined through an enzyme-linked immunosorbent assay. Genotyping analysis was performed by polymerase chain reaction based gel electrophoresis followed by DNA sequencing. The structural and functional impact of the mutation on the p53 structure was evaluated using 50-ns molecular dynamics (MD) simulations and protein-DNA docking.

RESULTS

Mean plasma LH levels were significantly decreased in G1 (p > 0.05) as well as the G2, G3 and G4 (p > 0.001) groups. Similarly, FSH levels were significant decrease in G1 (p > 0.05), G2 (p > 0.01), G3 (p > 0.001) and G4 (p > 0.001) compared to the control group. Sequencing results found three variants i.e. g.13450 T>G, g.13430C>T and g.13737G>A. One of them was predicted to be disease-causing others are polymorphisms. MD simulation of missense mutation R273H showed no structural impact on the protein structure in MD simulation, but it resulted in weaker binding of p53 with the DNA that might lower the gene expression of cell cycle regulatory proteins.

CONCLUSIONS

These findings predict decreased fertility and risk of malignancies in the future. The spectrum of p53 mutations as a result of polypropylene exposure in the Pakistani population has not been investigated before. Further studies and meta-analyses are required to elucidate the role of different plasticizers in reproduction and cancer-causing risk factors in a larger population.

Effect of mutation of NS2B cofactor residues on Dengue 2 NS2B-NS3 protease complex - an insight to viral replication

Dengue fever is an endemic virus-borne disease that causes many severe ailments, including dengue hemorrhagic fever and dengue shock syndrome. NS2B-NS3 protease is present in all four strains of the dengue virus. NS2B-NS3 is a non-structural protein that performs three distinct functions: protease activity, helicase activity, and nucleoside triphosphatase activity. NS2B-NS3 pro-complex plays a crucial role in viral replication, and NS2B interacts with NS3 protease at a flat active site with an amino acid of the N-terminal region. NS2B acts as a cofactor for NS3 protease. In the current study, the conserved residues of NS2B were identified. Dengue virus-2 NS2B was mutated at the identified conserved amino acid region to investigate the role of NS2B on activation of NS3 pro. Molecular dynamics simulations were performed to investigate the mutated complex's changes in stability, conformation, and free energy. The EAG mutant complex exhibited more unstable conformation, less hydrogen bond formation, and high binding energy than wild type. This result suggests a vital role of E63, A65, G69 mutation in NS2B for the interruption of activation of the NS3.Communicated by Ramaswamy H. Sarma.

RosettaDDGPrediction for high-throughput mutational scans: From stability to binding

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

A Computational Framework for Determining the Breadth of Antibodies Against Highly Mutable Pathogens

Highly mutable pathogens pose daunting challenges for antibody design. The usual criteria of high potency and specificity are often insufficient to design antibodies that provide long-lasting protection. This is due, in part, to the ability of the pathogen to rapidly acquire mutations that permit them to evade the designed antibodies. To overcome these limitations, design of antibodies with a larger neutralizing breadth can be pursued. Such broadly neutralizing antibodies (bnAbs) should remain targeted to a specific epitope, yet show robustness against pathogen mutability, thereby neutralizing a higher number of antigens. This is particularly important for highly mutable pathogens, like the influenza virus and the human immunodeficiency virus (HIV). The protocol describes a method for computing the "breadth" of a given antibody, an essential aspect of antibody design.

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.

Binding affinity estimation from restrained umbrella sampling simulations

The protein-ligand binding affinity quantifies the binding strength between a protein and its ligand. Computer modeling and simulations can be used to estimate the binding affinity or binding free energy using data- or physics-driven methods or a combination thereof. Here we discuss a purely physics-based sampling approach based on biased molecular dynamics simulations. Our proposed method generalizes and simplifies previously suggested stratification strategies that use umbrella sampling or other enhanced sampling simulations with additional collective-variable-based restraints. The approach presented here uses a flexible scheme that can be easily tailored for any system of interest. We estimate the binding affinity of human fibroblast growth factor 1 to heparin hexasaccharide based on the available crystal structure of the complex as the initial model and four different variations of the proposed method to compare against the experimentally determined binding affinity obtained from isothermal titration calorimetry experiments.

Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures

We employ a recently developed complexity-reduction quantum mechanical (QM-CR) approach, based on complexity reduction of density functional theory calculations, to characterize the interactions of the SARS-CoV-2 spike receptor binding domain (RBD) with ACE2 host receptors and antibodies. QM-CR operates via ab initio identification of individual amino acid residue's contributions to chemical binding and leads to the identification of the impact of point mutations. Here, we especially focus on the E484K mutation of the viral spike protein. We find that spike residue 484 hinders the spike's binding to the human ACE2 receptor (hACE2). In contrast, the same residue is beneficial in binding to the bat receptor Rhinolophus macrotis ACE2 (macACE2). In agreement with empirical evidence, QM-CR shows that the E484K mutation allows the spike to evade categories of neutralizing antibodies like C121 and C144. The simulation also shows how the Delta variant spike binds more strongly to hACE2 compared to the original Wuhan strain, and predicts that a E484K mutation can further improve its binding. Broad agreement between the QM-CR predictions and experimental evidence supports the notion that ab initio modeling has now reached the maturity required to handle large intermolecular interactions central to biological processes.

High-Throughput Molecular Dynamics-Based Alchemical Free Energy Calculations for Predicting the Binding Free Energy Change Associated with the Selected Omicron Mutations in the Spike Receptor-Binding Domain of SARS-CoV-2

The ongoing pandemic caused by SARS-CoV-2 has gone through various phases. Since the initial outbreak, the virus has mutated several times, with some lineages showing even stronger infectivity and faster spread than the original virus. Among all the variants, omicron is currently classified as a variant of concern (VOC) by the World Health Organization, as the previously circulating variants have been replaced by it. In this work, we have focused on the mutations observed in omicron sub lineages BA.1, BA.2, BA.4 and BA.5, particularly at the receptor-binding domain (RBD) of the spike protein that is responsible for the interactions with the host ACE2 receptor and binding of antibodies. Studying such mutations is particularly important for understanding the viral infectivity, spread of the disease and for tracking the escape routes of this virus from antibodies. Molecular dynamics (MD) based alchemical free energy calculations have been shown to be very accurate in predicting the free energy change, due to a mutation that could have a deleterious or a stabilizing effect on either the protein itself or its binding affinity to another protein. Here, we investigated the significance of five spike RBD mutations on the stability of the spike protein binding to ACE2 by free energy calculations using high throughput MD simulations. For comparison, we also used conventional MD simulations combined with a Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) based approach, and compared our results with the available experimental data. Overall, the alchemical free energy calculations performed far better than the MM-GBSA approach in predicting the individual impact of the mutations. When considering the experimental variation, the alchemical free energy method was able to produce a relatively accurate prediction for N501Y, the mutant that has previously been reported to increase the binding affinity to hACE2. On the other hand, the other individual mutations seem not to have a significant effect on the spike RBD binding affinity towards hACE2.

A dynamical view of protein-protein complexes: Studies by molecular dynamics simulations

Protein-protein interactions are at the basis of many protein functions, and the knowledge of 3D structures of protein-protein complexes provides structural, mechanical and dynamical pieces of information essential to understand these functions. Protein-protein interfaces can be seen as stable, organized regions where residues from different partners form non-covalent interactions that are responsible for interaction specificity and strength. They are commonly described as a peripheral region, whose role is to protect the core region that concentrates the most contributing interactions, from the solvent. To get insights into the dynamics of protein-protein complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on eight different protein-protein complexes of different functional class and interface size by taking into account the bound and unbound forms. On the one hand, we characterized structural changes upon binding of the proteins, and on the other hand we extensively analyzed the interfaces and the structural waters involved in the binding. Based on our analysis, in 6 cases out of 8, the interfaces rearranged during the simulation time, in stable and long-lived substates with alternative residue-residue contacts. These rearrangements are not restricted to side-chain fluctuations in the periphery but also affect the core interface. Finally, the analysis of the waters at the interface and involved in the binding pointed out the importance to take into account their role in the estimation of the interaction strength.

Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy

Systematic and quantitative analysis of the reliability of formally exact methods that in silico calculate absolute protein-ligand binding free energies remains lacking. Here, we provide, for the first time, evidence-based information on the reliability of these methods by statistically studying 853 cases from 34 different research groups through meta-analysis. The results show that formally exact methods approach chemical accuracy (error = 1.58 kcal/mol), even if people are challenging difficult tasks such as blind drug screening in recent years. The geometrical-pathway-based methods prove to possess a better convergence ability than the alchemical ones, while the latter have a larger application range. We also reveal the importance of always using the latest force fields to guarantee reliability and discuss the pros and cons of turning to an implicit solvent model in absolute binding free-energy calculations. Moreover, based on the meta-analysis, an evidence-based guideline for in silico binding free-energy calculations is provided.

Repertoire-scale measures of antigen binding

Antibodies and T cell receptors (TCRs) are the fundamental building blocks of adaptive immunity. Repertoire-scale functionality derives from their epitope-binding properties, just as macroscopic properties like temperature derive from microscopic molecular properties. However, most approaches to repertoire-scale measurement, including sequence diversity and entropy, are not based on antibody or TCR function in this way. Thus, they potentially overlook key features of immunological function. Here we present a framework that describes repertoires in terms of the epitope-binding properties of their constituent antibodies and TCRs, based on analysis of thousands of antibody-antigen and TCR-peptide-major-histocompatibility-complex binding interactions and over 400 high-throughput repertoires. We show that repertoires consist of loose overlapping classes of antibodies and TCRs with similar binding properties. We demonstrate the potential of this framework to distinguish specific responses vs. bystander activation in influenza vaccinees, stratify cytomegalovirus (CMV)-infected cohorts, and identify potential immunological "super-agers." Classes add a valuable dimension to the assessment of immune function.

Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses

Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.