Binding Free Energy Change Research Articles

Phytochemicals of Swertia chirayita Roxb. ex Fleming against malarial dihydroorotate dehydrogenase: an in silico study

The global economic burden of malaria is rising and lack of effective drugs against it have imposed an insurmountable challenge in designing it that is both affordable and resistanceless. The plants with ethnobotanical values are relatively safer and its use is a viable option as therapeutics for various demographic locations. In this regard, the chemical components of a plant, Swertia chirayita Roxb. ex Fleming (traditionally used as an antimalarial agent) were explored by multiple computational tools against dihydroorotate dehydrogenase (DHODH) protein of Plasmodium falciparum (PDB ID: 6GJG), a causative agent of the disease. The docking scores were calculated to be − 10.220, − 10.131, − 10.071, − 10.024, and − 9.905 kcal/mol for the top five molecules (decussatin, swerchirin, 1-hydroxy-3,5,8-trimethoxyxanthen-9-one, swertinin, and chiratol), respectively with the native ligand possessing a higher value of − 9.873 kcal/mol. The adducts were geometrically and thermodynamically stable as revealed by several parameters extracted from 200 ns molecular dynamics simulations (MDS) and verified by a single run of 600 ns. The smooth RMSD profiles from a cumulative MDS of 2.0 μs, pointed towards a sturdy nature of the receptor geometry with limited translational or rotational motion of the ligands. The involvement of LEU11, CYS14, LEU15, CYS23, PHE27, ILE102, ARG104, TYR337, LEU340, and VAL341 amino acid residues with mainly hydrophobic interactions at the orthosteric site were observed for most of the adducts. The binding free energy changes (up to − 30.18 ± 4.38 kcal/mol) hinted at the feasibility and sustained thermodynamical spontaneity of the forward reaction of the complex formation. The hit molecules could possibly disrupt or inhibit the normal functioning of the DHODH receptor leading to the likely prevention and cure of the disease and are recommended for experimental trials.

Preventing future zoonosis: SARS-CoV-2 mutations enhance human–animal cross-transmission

The COVID-19 pandemic has driven substantial evolution of the SARS-CoV-2 virus, yielding subvariants that exhibit enhanced infectiousness in humans. However, this adaptive advantage may not universally extend to zoonotic transmission. In this work, we hypothesize that viral adaptations favoring animal hosts do not necessarily correlate with increased human infectivity. In addition, we consider the potential for gain-of-function mutations that could facilitate the virus’s rapid evolution in humans following adaptation in animal hosts. Specifically, we identify the SARS-CoV-2 receptor-binding domain (RBD) mutations that enhance human–animal cross-transmission. To this end, we construct a multitask deep learning model, MT-TopLap trained on multiple deep mutational scanning datasets, to accurately predict the binding free energy changes upon mutation for the RBD to ACE2 of various species, including humans, cats, bats, deer, and hamsters. By analyzing these changes, we identified key RBD mutations such as Q498H in SARS-CoV-2 and R493K in the BA.2 variant that are likely to increase the potential for human–animal cross-transmission.

SAAMBE-MEM: a sequence-based method for predicting binding free energy change upon mutation in membrane protein-protein complexes.

Mutations in protein-protein interactions can affect the corresponding complexes, impacting function and potentially leading to disease. Given the abundance of membrane proteins, it is crucial to assess the impact of mutations on the binding affinity of these proteins. Although several methods exist to predict the binding free energy change due to mutations in protein-protein complexes, most require structural information of the protein complex and are primarily trained on the SKEMPI database, which is composed mainly of soluble proteins. A novel sequence-based method (SAAMBE-MEM) for predicting binding free energy changes (ΔΔG) in membrane protein-protein complexes due to mutations has been developed. This method utilized the MPAD database, which contains binding affinities for wild-type and mutant membrane protein complexes. A machine learning model was developed to predict ΔΔG by leveraging features such as amino acid indices and position-specific scoring matrices (PSSM). Through extensive dataset curation and feature extraction, SAAMBE-MEM was trained and validated using the XGBoost regression algorithm. The optimal feature set, including PSSM-related features, achieved a Pearson correlation coefficient of 0.64, outperforming existing methods trained on the SKEMPI database. Furthermore, it was demonstrated that SAAMBE-MEM performs much better when utilizing evolution-based features in contrast to physicochemical features. The method is accessible via a web server and standalone code at http://compbio.clemson.edu/SAAMBE-MEM/. The cleaned MPAD database is available at the website.

Evaluation of AlphaFold 3's Protein-Protein Complexes for Predicting Binding Free Energy Changes upon Mutation.

AlphaFold 3 (AF3), the latest version of protein structure prediction software, goes beyond its predecessors by predicting protein-protein complexes. It could revolutionize drug discovery and protein engineering, marking a major step toward comprehensive, automated protein structure prediction. However, independent validation of AF3's predictions is necessary. In this work, we evaluate AF3 complex structures using the SKEMPI 2.0 database which involves 317 protein-protein complexes and 8338 mutations. AF3 complex structures when applied to the most advanced TDL model, MT-TopLap (MultiTask-Topological Laplacian), give rise to a very good Pearson correlation coefficient of 0.86 for predicting protein-protein binding free energy changes upon mutation, which is slightly less than the 0.88 achieved earlier with the Protein Data Bank (PDB) structures. Nonetheless, AF3 complex structures led to a 8.6% increase in the prediction RMSE compared to original PDB complex structures. Additionally, some of AF3's complex structures have large errors, which were not captured in its ipTM performance metric. Finally, it is found that AF3's complex structures are not reliable for intrinsically flexible regions or domains.

A Computational Approach of Anti-diabetic Potential Evaluation of Flower and Seed of Nyctanthes arbor tristis Linn

Exploring the medicinal significance of bioactive compounds through computational methods is an increasingly practiced approach in contemporary medicinal research. This study aims to assess the antidiabetic potential of compounds extracted from the plant Nyctanthes arbor tristis by evaluating their ability to inhibit the carbohydrate metabolic enzyme α-glucosidase. The research work was conducted through molecular docking calculation, molecular dynamics simulation (MDS), and ADMET prediction techniques. Among the compounds, arbortistoside-C (NAS03), and arbortristoside-D (NAS04) found in the seed of the plant were identified as hit inhibitors of the target protein with docking scores, -9.9 and -9.4 kcal/mol, respectively. The compounds showed a comparable docking score with the drug of diabetes acarbose (-8.6 kcal/mol). Geometrical parameters like radius of gyration, solvent accessibility surface, root mean square deviation, and root mean square fluctuation from MDS supported the stability of the protein-ligand complex. MMPBSA calculations demonstrated the stability and feasibility of the complex with binding free energy changes of -29.06±6.06 and -23.58±8.80 kcal/mol for compounds NAS03 and NAS04, respectively. The ADMET prediction suggested the drug-likeness of the compounds compared with that of the standard drugs. The results could be used in proposing the antidiabetic potential of the two compounds from the plant as a potential inhibitors of α-glucosidase enzyme. Further, in vitro and in vivo experiments on such compounds could be a more reliable path to validate the output of this computational research.

Discovery of highly potent phosphodiesterase-1 inhibitors by a combined-structure free energy perturbation approach

Accurate receptor/ligand binding free energy calculations can greatly accelerate drug discovery by identifying highly potent ligands. By simulating the change from one compound structure to another, the relative binding free energy (RBFE) change can be calculated based on the theoretically rigorous free energy perturbation (FEP) method. However, existing FEP-RBFE approaches may face convergence challenges due to difficulties in simulating non-physical intermediate states, which can lead to increased computational costs to obtain the converged results. To fundamentally overcome these issues and accelerate drug discovery, a new combined-structure RBFE (CS-FEP) calculation strategy was proposed, which solved the existing issues by constructing a new alchemical pathway, smoothed the alchemical transformation, increased the phase-space overlap between adjacent states, and thus significantly increased the convergence and accelerated the relative binding free energy calculations. This method was extensively tested in a practical drug discovery effort by targeting phosphodiesterase-1 (PDE1). Starting from a PDE1 inhibitor (compound 9, IC50 = 16.8 μmol/L), the CS-FEP guided hit-to-lead optimizations resulted in a promising lead (11b and its mesylate salt formulation 11b-Mesylate, IC50 = 7.0 nmol/L), with ∼2400-fold improved inhibitory activity. Further experimental studies revealed that the lead showed reasonable metabolic stability and significant anti-fibrotic effects in vivo.

DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.

Protein-protein interactions (PPIs) play a vital role in cellular functions and are essential for therapeutic development and understanding diseases. However, current predictive tools often struggle to balance efficiency and precision in predicting the effects of mutations on these complex interactions. To address this, we present DDMut-PPI, a deep learning model that efficiently and accurately predicts changes in PPI binding free energy upon single and multiple point mutations. Building on the robust Siamese network architecture with graph-based signatures from our prior work, DDMut, the DDMut-PPI model was enhanced with a graph convolutional network operated on the protein interaction interface. We used residue-specific embeddings from ProtT5 protein language model as node features, and a variety of molecular interactions as edge features. By integrating evolutionary context with spatial information, this framework enables DDMut-PPI to achieve a robust Pearson correlation of up to 0.75 (root mean squared error: 1.33 kcal/mol) in our evaluations, outperforming most existing methods. Importantly, the model demonstrated consistent performance across mutations that increase or decrease binding affinity. DDMut-PPI offers a significant advancement in the field and will serve as a valuable tool for researchers probing the complexities of protein interactions. DDMut-PPI is freely available as a web server and an application programming interface at https://biosig.lab.uq.edu.au/ddmut_ppi.

ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots.

Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providing essential guidance for protein engineering. Aiming at protein-DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively.

DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences.

Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting KD and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface: http://14.139.174.41:8080/.

Generating mutants of monotone affinity towards stronger protein complexes through adversarial learning

Despite breakthroughs achieved in protein sequence-to-structure and function-to-sequence predictions, the affinity-to-mutation prediction problem remains unsolved. Such a problem is of exponential complexity deemed to find a mutated protein or protein complex having a guaranteed binding-affinity change. Here we introduce an adversarial learning-based mutation method that creates optimal amino acid substitutions and changes the mutant’s affinity change significantly in a preset direction. The key aspect in our method is the adversarial training process that dynamically labels the real side of the protein data and generates fake pseudo-data accordingly to construct a deep learning architecture for guiding the mutation. The method is sufficiently flexible to generate both single- and multipointed mutations at the adversarial learning step to mimic the natural circumstances of protein evolution. Compared with random mutants, our mutated sequences have in silico exhibited more than one order of change in magnitude of binding free energy change towards stronger complexes in the case study of Novavax–angiotensin-converting enzyme-related carboxypeptidase vaccine construct optimization. We also applied the method iteratively each time, using the output as the input sequence of the next iteration, to generate paths and a landscape of mutants with affinity-increasing monotonicity to understand SARS-CoV-2 Omicron’s spike evolution. With these steps taken for effective generation of protein mutants of monotone affinity, our method will provide potential benefits to many other applications including protein bioengineering, drug design, antibody reformulation and therapeutic protein medication.

Assessment of iridoids and their similar structures as antineoplastic drugs by in silico approach

Iridoids commonly found in plants as secondary metabolites have been reported to possess significant biological activities such as anticancer, antioxidant, hypoglycemic, antimicrobial etc. The strong interactions of iridoids with cyclic-dependent kinase 8 (CDK8) protein could show inhibitory effects that could modulate tumour growth. From the molecular docking calculations, some iridoids interacted effectively with the target CDK8 protein (PDB ID: 5ICP) with better binding affinities of −9.1, −9.0, −9.0, −8.9 kcal/mol, than that observed for the native ligand with −8.7 kcal/mol and for the reference compound gemcitabine with −6.9 kcal/mol. The GI50 values (<5 μM) obtained from graph-based signatures showed activity in breast, colon, leukaemia, and renal cancer cell lines. The IC50 predictions as CDK2 inhibitors were greater than 10 µM with type I non-allosteric binding mode. The stability analysis of protein-ligand complex from 125 ns long molecular dynamics simulations showed moderately smooth trajectories and RSMD value around 5 Å for the docked ligands. The binding free energy changes up to −47.65 ± 5.97 kcal/mol from MMGBSA method and −30.33 ± 5.40 kcal/mol from MMPBSA method hinted at the spontaneous nature of the complex formation. Furthermore, geometrical evaluators like RMSF, Rg, SASA, and hydrogen bond count also corroborated with the structural stability of the complexes and the capacity of hit molecules to inhibit the target, indicating its therapeutic potential against cancer. The toxicity and drug-likeness from ADMET predictions suggested experimental verification and that the proposed candidates could be employed for further trials in the development of safer and more effective anticancer drugs. Communicated by Ramaswamy H. Sarma

Exploration of Anti-Diabetic Potential of Rubus ellipticus smith through Molecular Docking, Molecular Dynamics Simulation, and MMPBSA Calculation

Diabetes is a chronic metabolic disorder affecting a majority of the population worldwide. Hyperglycemia leading to diabetes mellitus could be managed through the inhibition of human pancreatic α-amylase enzyme. Phytochemicals are frequently reported to possess anti-diabetic activity through inhibition of normal functioning of α-amylase. This study aims to find potential α-amylase inhibitors from Rubus ellipticus Smith with molecular-level understanding using different computational tools. From the molecular docking calculations, rubuside F and rubuside D possessed good binding affinity of -10.0 kcal/mol and -9.9 kcal/mol, respectively better than the reference drugs (acarbose, miglitol, voglibose and metformin). Both the compounds showed good geometrical stability from molecular dynamics simulation accessed in terms of RMSD, hydrogen bond count, SASA, Rg and RMSF. Binding free energy changes of -27.92±4.15 kcal/mol and -28.75±4.15 kcal/mol, respectively for the two ligands indicated sustained thermodynamic spontaneity present in the adducts. The two phytochemicals could be proposed as potential inhibitors of human pancreatic α-amylase for the treatment of diabetes. Further, in vitro and in vivo experiments are recommended for the verification of computational insights in the course of drug design and discovery process.

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation.

Accurate prediction of binding free energy changes upon mutations is vital for optimizing drugs, designing proteins, understanding genetic diseases, and cost-effective virtual screening. While machine learning methods show promise in this domain, achieving accuracy and generalization across diverse data sets remains a challenge. This study introduces Geometric Graph Learning for Protein-Protein Interactions (GGL-PPI), a novel approach integrating geometric graph representation and machine learning to forecast mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multiscale weighted colored geometric subgraphs to capture structural features of biomolecules, demonstrating superior performance on three standard data sets, namely, AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 data sets. The model's efficacy extends to predicting protein thermodynamic stability in a blind test set, providing unbiased predictions for both direct and reverse mutations and showcasing notable generalization. GGL-PPI's precision in predicting changes in binding free energy and stability due to mutations enhances our comprehension of protein complexes, offering valuable insights for drug design endeavors.

Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation

COVID-19 has resulted in millions of deaths and severe impact on economies worldwide. Moreover, the emergence of SARS-CoV-2 variants presented significant challenges in controlling the pandemic, particularly their potential to avoid the immune system and evade vaccine immunity. This has led to a growing need for research to predict how mutations in SARS-CoV-2 reduces the ability of antibodies to neutralize the virus. In this study, we assembled a set of 1813 mutations from the interface of SARS-CoV-2 spike protein's receptor binding domain (RBD) and neutralizing antibody complexes and developed a machine learning model to classify high or low escape mutations using interaction energy, inter-residue contacts and predicted binding free energy change. Our approach achieved an Area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.91 using the Random Forest classifier on the test dataset with 217 mutations. The model was further utilized to predict the escape mutations on a dataset of 29,165 mutations located at the interface of 83 RBD-neutralizing antibody complexes. A small subset of this dataset was also validated based on available experimental data. We found that top 10 % high escape mutations were dominated by charged to nonpolar mutations whereas low escape mutations were dominated by polar to nonpolar mutations. We believe that the present method will allow prioritization of high/low escape mutations in the context of neutralizing antibodies targeting SARS-CoV-2 RBD region and assist antibody design for current and emerging variants.

Eco-friendly production of palladium-modified γ-cyclodextrin and its methyl cinnamate inclusion complex: Catalyst for reduction and antibacterial properties

Addressing the conversion of toxic substances into non-toxic was a significant challenge in our environmentally compromised world. This manuscript focuses on elucidating the synthesis, chemical composition, and structure of a cinnamic acid (CA) derivative named methyl cinnamate (MC) using analytical techniques such as 1H NMR and FT-IR spectroscopy. Furthermore, we implemented an adapted synthetic procedure to effectively produce a modified version of γ-cyclodextrin known as palladium (Pd)-modified γ-CD (Pd-γ-CD). The Pd metal particles were uniformly distributed throughout the structure. The resulting product underwent characterization through a range of techniques, encompassing XRD, SEM, FT-IR, EDAX, TGA, and UV–visible spectroscopy. The UV–visible spectroscopy results confirmed the formation of a 1:1 inclusion complex between MC and both native γ-CD and Pd-γ-CD. Using the Benesi-Hildebrand equation and the Gibbs free energy relation, we calculated the binding constant and free energy change for the complex formation, respectively. The molecular interaction between Pd-γ-CD and MC was elucidated by 1H NMR and 2D NMR (ROESY) spectroscopy. Additionally, we investigated the catalytic reduction properties of our novel complexes, as well as native γ-CD and Pd-γ-CD, against 2,4-dinitrophenol. Notably, the Pd-γ-CD complex exhibited exceptional catalytic reduction performance. Moreover, we also evaluated the antibacterial effects of γ-CD, Pd-γ-CD, and their complexes against S. aureus and E. coli. The results indicated that the Pd-γ-CD complex showed superior efficacy against S. aureus bacteria compared to the other samples.

Structure, dynamics and free energy studies on the effect of point mutations on SARS-CoV-2 spike protein binding with ACE2 receptor.

The ongoing COVID-19 pandemic continues to infect people worldwide, and the virus continues to evolve in significant ways which can pose challenges to the efficiency of available vaccines and therapeutic drugs and cause future pandemic. Therefore, it is important to investigate the binding and interaction of ACE2 with different RBD variants. A comparative study using all-atom MD simulations was conducted on ACE2 binding with 8 different RBD variants, including N501Y, E484K, P479S, T478I, S477N, N439K, K417N and N501Y-E484K-K417N on RBD. Based on the RMSD, RMSF, and DSSP results, overall the binding of RBD variants with ACE2 is stable, and the secondary structure of RBD and ACE2 are consistent after the point mutation. Besides that, a similar buried surface area, a consistent binding interface and a similar amount of hydrogen bonds formed between RBD and ACE2 although the exact residue pairs on the binding interface were modified. The change of binding free energy from point mutation was predicted using the free energy perturbation (FEP) method. It is found that N501Y, N439K, and K417N can strengthen the binding of RBD with ACE2, while E484K and P479S weaken the binding, and S477N and T478I have negligible effect on the binding. Point mutations modified the dynamic correlation of residues in RBD based on the dihedral angle covariance matrix calculation. Doing dynamic network analysis, a common intrinsic network community extending from the tail of RBD to central, then to the binding interface region was found, which could communicate the dynamics in the binding interface region to the tail thus to the other sections of S protein. The result can supply unique methodology and molecular insight on studying the molecular structure and dynamics of possible future pandemics and design novel drugs.

Can Free Energy Perturbation Simulations Coupled with Replica-Exchange Molecular Dynamics Study Ligands with Distributed Binding Sites?

Free energy perturbation coupled with replica exchange with solute tempering (FEP/REST) offers a rigorous approach to compute relative free energy changes for ligands. To determine the applicability of FEP/REST for the ligands with distributed binding poses, we considered two alchemical transformations involving three putative inhibitors I0, I1, and I2 of the Venezuelan equine encephalitis virus nuclear localization signal sequence binding to the importin-α (impα) transporter protein. I0 → I1 and I0 → I2 transformations, respectively, increase or decrease the polarity of the parent molecule. Our objective was three-fold─(i) to verify FEP/REST technical performance and convergence, (ii) to estimate changes in binding free energy ΔΔG, and (iii) to determine the utility of FEP/REST simulations for conformational binding analysis. Our results are as follows. First, our FEP/REST implementation properly follows FEP/REST formalism and produces converged ΔΔG estimates. Due to ligand inherent unbinding, the better FEP/REST strategy lies in performing multiple independent trajectories rather than extending their length. Second, I0 → I1 and I0 → I2 transformations result in overall minor changes in inhibitor binding free energy, slightly strengthening the affinity of I1 and weakening that of I2. Electrostatic interactions dominate binding interactions, determining the enthalpic changes. The two transformations cause opposite entropic changes, which ultimately govern binding affinities. Importantly, we confirm the validity of FEP/REST free energy estimates by comparing them with our previous REST simulations, directly probing binding of three ligands to impα. Third, we established that FEP/REST simulations can sample binding ensembles of ligands. Thus, FEP/REST can be applied (i) to study the energetics of the ligand binding without defined poses and showing minor differences in affinities |ΔΔG| ≲ 0.5 kcal/mol and (ii) to collect ligand binding conformational ensembles.

Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations.

The development of methods and algorithms to predict the effect of mutations on protein stability, protein-protein interaction, and protein-DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods require a database of experimentally measured folding and binding free energy changes for training. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residues on the above-mentioned thermodynamic characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variants (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases, and the distribution of the free energy changes is not the same. It is shown that the Pearson correlation coefficients (PCCs) of folding and binding free energy changes obtained in cases involving SNVs are smaller than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. Furthermore, it is demonstrated that some methods are sensitive to the chemical nature of the mutations, resulting in PCCs that differ by a factor of four across chemically different mutations. All methods are found to underestimate the energy changes by roughly a factor of 2.

Investigating the novel-binding site of RPA2 on Menin and predicting the effect of point mutation of Menin through protein–protein interactions

Protein–protein interactions (PPIs) play a critical role in all biological processes. Menin is tumor suppressor protein, mutated in multiple endocrine neoplasia type 1 syndrome and has been shown to interact with multiple transcription factors including (RPA2) subunit of replication protein A (RPA). RPA2, heterotrimeric protein required for DNA repair, recombination and replication. However, it’s still remains unclear the specific amino acid residues that have been involved in Menin-RPA2 interaction. Thus, accurately predicting the specific amino acid involved in interaction and effects of MEN1 mutations on biological systems is of great interests. The experimental approaches for identifying amino acids in menin-RPA2 interactions are expensive, time-consuming, and challenging. This study leverages computational tools, free energy decomposition and configurational entropy scheme to annotate the menin-RPA2 interaction and effect on menin point mutation, thereby proposing a viable model of menin-RPA2 interaction. The menin–RPA2 interaction pattern was calculated on the basis of different 3D structures of menin and RPA2 complexes, constructed using homology modeling and docking strategy, generating three best-fit models: Model 8 (− 74.89 kJ/mol), Model 28 (− 92.04 kJ/mol) and Model 9 (− 100.4 kJ/mol). The molecular dynamic (MD) was performed for 200 ns and binding free energies and energy decomposition analysis were calculated using Molecular Mechanics Poisson–Boltzmann Surface Area (MM/PBSA) in GROMACS. From binding free energy change, model 8 of Menin-RPA2 exhibited most negative binding energy of − 205.624 kJ/mol, followed by model 28 of Menin-RPA2 with − 177.382 kJ/mol. After S606F point mutation in Menin, increase of BFE (ΔGbind) by − 34.09 kJ/mol in Model 8 of mutant Menin-RPA2 occurs. Interestingly, we found a significant reduction of BFE (ΔGbind) and configurational entropy by − 97.54 kJ/mol and − 2618 kJ/mol in mutant model 28 as compared the o wild type. Collectively, this is the first study to highlight the configurational entropy of protein–protein interactions thereby strengthening the prediction of two significant important interaction sites in menin for the binding of RPA2. These predicted sites could be vulnerable for structural alternation in terms of binding free energy and configurational entropy after missense mutation in menin.

An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design.

Accurate identification of beneficial mutations is central to antibody design. Many knowledge-based (KB) computational approaches have been developed to predict beneficial mutations, but their accuracy leaves room for improvement. Thermodynamic integration (TI) is an alchemical free energy algorithm that offers an alternative technique for identifying beneficial mutations, but its performance has not been evaluated. In this study, we developed an efficient TI protocol with high accuracy for predicting binding free energy changes of antibody mutations. The improved TI method outperforms KB methods at identifying both beneficial and deleterious mutations. We observed that KB methods have higher accuracies in predicting deleterious mutations than beneficial mutations. A pipeline using KB methods to efficiently exclude deleterious mutations and TI to accurately identify beneficial mutations was developed for high-throughput mutation scanning. The pipeline was applied to optimize the binding affinity of a broadly sarbecovirus neutralizing antibody 10-40 against the circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant. Three identified beneficial mutations show strong synergy and improve both binding affinity and neutralization potency of antibody 10-40. Molecular dynamics simulation revealed that the three mutations improve the binding affinity of antibody 10-40 through the stabilization of an altered binding mode with increased polar and hydrophobic interactions. Above all, this study presents an accurate and efficient TI-based approach for optimizing antibodies and other biomolecules.