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Understanding the impact of mutations on protein–protein binding affinity is a key objective for a wide range of biotechnological applications and for shedding light on disease-causing mutations, which are often located at protein–protein interfaces. Over the past decade, many computational methods using physics-based and/or machine learning approaches have been developed to predict how protein binding affinity changes upon mutations. They all claim to achieve astonishing accuracy on both training and test sets, with performances on standard benchmarks such as SKEMPI 2.0 that seem overly optimistic. Here we benchmarked eight well-known and well-used predictors and identified their biases and dataset dependencies, using not only SKEMPI 2.0 as a test set but also deep mutagenesis data on the severe acute respiratory syndrome coronavirus 2 spike protein in complex with the human angiotensin-converting enzyme 2. We showed that, even though most of the tested methods reach a significant degree of robustness and accuracy, they suffer from limited generalizability properties and struggle to predict unseen mutations. Interestingly, the generalizability problems are more severe for pure machine learning approaches, while physics-based methods are less affected by this issue. Moreover, undesirable prediction biases toward specific mutation properties, the most marked being toward destabilizing mutations, are also observed and should be carefully considered by method developers. We conclude from our analyses that there is room for improvement in the prediction models and suggest ways to check, assess and improve their generalizability and robustness. [ABSTRACT FROM AUTHOR]

The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein (RBDCoV2) has a higher binding affinity to the human receptor angiotensin-converting enzyme 2 (ACE2) than the SARS-CoV RBD (RBDCoV). Here, we performed molecular dynamics (MD) simulations, binding free energy (BFE) calculations, and interface residue contact network (IRCN) analysis to explore the mechanistic origin of different ACE2-binding affinities of the two RBDs. The results demonstrate that, when compared to the RBDCoV2-ACE2 complex, RBDCoV-ACE2 features enhanced dynamicsand inter-protein positional movements and increased conformational entropy and conformational diversity. Although the inter-protein electrostatic attractive interactions are the primary determinant for the high ACE2-binding affinities of both RBDs, the significantly enhanced electrostatic attractive interactions between ACE2 and RBDCoV2 determine the higher ACE2-binding affinity of RBDCoV2 than of RBDCoV. Comprehensive comparative analyses of the residue BFE components and IRCNs between the two complexes reveal that it is the residue changes at the RBD interface that lead to the overall stronger inter-protein electrostatic attractive force in RBDCoV2-ACE2, which not only tightens the interface packing and suppresses the dynamics of RBDCoV2-ACE2, but also enhances the ACE2-binding affinity of RBDCoV2. Since the RBD residue changes involving gain/loss of the positively/negatively charged residues can greatly enhance the binding affinity, special attention should be paid to the SARS-CoV-2 variants carrying such mutations, particularly those near or at the binding interfaces with the potential to form hydrogen bonds and/or salt bridges with ACE2. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state‐of‐the‐art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state‐of‐art methods. [ABSTRACT FROM AUTHOR]

Protein–protein interactions form central elements of almost all cellular processes. Knowledge of the structure of protein–protein complexes but also of the binding affinity is of major importance to understand the biological function of protein–protein interactions. Even weak transient protein–protein interactions can be of functional relevance for the cell during signal transduction or regulation of metabolism. The structure of a growing number of protein–protein complexes has been solved in recent years. Combined with docking approaches or template‐based methods, it is possible to generate structural models of many putative protein–protein complexes or to design new protein–protein interactions. In order to evaluate the functional relevance of putative or predicted protein–protein complexes, realistic binding affinity prediction is of increasing importance. Several computational tools ranging from simple force‐field or knowledge‐based scoring of single protein–protein complexes to ensemble‐based approaches and rigorous binding free energy simulations are available to predict relative and absolute binding affinities of complexes. With a focus on molecular mechanics force‐field approaches the present review aims at presenting an overview on available methods and discussing advantages, approximations, and limitations of the various methods. This article is categorized under:Molecular and Statistical Mechanics > Molecular InteractionsMolecular and Statistical Mechanics > Free Energy MethodsSoftware > Molecular Modeling [ABSTRACT FROM AUTHOR]

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., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Protein-protein binding interaction is the most prevalent biological activity that mediates a great variety of biological processes. The increasing availability of experimental data of protein–protein interaction allows a systematic construction of protein–protein interaction networks, significantly contributing to a better understanding of protein functions and their roles in cellular pathways and human diseases. Compared to well-established classification for protein–protein interactions (PPIs), limited work has been conducted for estimating protein–protein binding free energy, which can provide informative real-value regression models for characterizing the protein–protein binding affinity. In this study, we propose a novel ensemble computational framework, termed ProBAPred (Protein–protein Binding Affinity Predictor), for quantitative estimation of protein–protein binding affinity. A large number of sequence and structural features, including physical–chemical properties, binding energy and conformation annotations, were collected and calculated from currently available protein binding complex datasets and the literature. Feature selection based on the WEKA package was performed to identify and characterize the most informative and contributing feature subsets. Experiments on the independent test showed that our ensemble method achieved the lowest Mean Absolute Error (MAE; 1.657 kcal/mol) and the second highest correlation coefficient (R -value = 0. 4 6 7), compared with the existing methods. The datasets and source codes of ProBAPred, and the supplementary materials in this study can be downloaded at http://lightning.med.monash.edu/probapred/ for academic use. We anticipate that the developed ProBAPred regression models can facilitate computational characterization and experimental studies of protein–protein binding affinity. [ABSTRACT FROM AUTHOR]

Neurons in the developing brain express many different cell adhesion molecules (CAMs) on their surfaces. CAM-binding affinities can vary by more than 200-fold, but the significance of these variations is unknown. Interactions between the immunoglobulin superfamily CAM DIP-α and its binding partners, Dpr10 and Dpr6, control synaptic targeting and survival of Drosophila optic lobe neurons. We design mutations that systematically change interaction affinity and analyze function in vivo. Reducing affinity causes loss-of-function phenotypes whose severity scales with the magnitude of the change. Synaptic targeting is more sensitive to affinity reduction than is cell survival. Increasing affinity rescues neurons that would normally be culled by apoptosis. By manipulating CAM expression together with affinity, we show that the key parameter controlling circuit assembly is surface avidity, which is the strength of adherence between cell surfaces. We conclude that CAM binding affinities and expression levels are finely tuned for function during development. [Display omitted] • DIP-α::Dpr10 binding affinity is important for layer-specific circuit assembly • Cell death and synaptic targeting have different affinity thresholds • Increasing DIP-α::Dpr10 binding affinity blocks neuronal culling through apoptosis • Cell-surface avidity is determined by protein-expression levels and binding affinity Xu et al. demonstrate that altering the affinity of transsynaptic interactions between DIP-α and Dpr10 affects multiple aspects of circuit assembly in the Drosophila visual system. [ABSTRACT FROM AUTHOR]

Protein-protein interaction plays an important role in all biological systems. The binding affinity between two protein binding partners reflects the strength of their association, which is crucial to the elucidation of the biological functions of these proteins and also to the design of protein-based therapeutic agents. In recent years, many studies have been conducted in an effort to improve the ability to predict the binding affinity of a protein-protein complex. Different sequence and structural features have been adopted in the prediction, but the surface or interface areas of the protein-protein complex were often not given adequate consideration. In the present study, different types of interface and surface areas in the protein-protein complex were used to construct or train linear, nonlinear or mixed models using linear regression and artificial neural network to predict the binding affinity of protein-protein interactions. The relative importance of the different types of areas in the selected models for affinity prediction was analyzed using variable-controlling approach. In terms of performance, the best area-based binding affinity predictors appeared to be superior or at least comparable to the widely-used predictors PRODIGY (a contacts-based predictor) and LISA (Local Interaction Signal Analysis). This work highlights the importance of interface and surface areas in protein-protein binding interactions. It also sheds light on the more suitable computational approaches that may aid in solving some of the scientific and technical issues associated with protein-protein binding affinity prediction. Protein-protein interactions are ubiquitous in living systems. Protein-protein binding affinity is a metric that estimates the binding strength between two protein binding partners. Reliable information on their binding affinity is of great value in understanding complex biological processes as well as in designing protein-based therapeutics. In this work, the interface and surface areas in protein-protein interaction are explored with respect to their relative importance in better predicting the protein-protein binding affinity. The results from this study showed that different types of areas contribute importantly to protein-protein interactions and thus should be jointly considered in an explicit manner to improve affinity predictions. In addition, the effective application of interface and surface areas may also facilitate the simulation of the protein folding and binding processes. [Display omitted] • Single class of descriptors, i.e. , area, is adopted to predict protein-protein binding affinity. • Different types of interface and surface areas in protein-protein complex contribute to binding affinity prediction. • Interface and surface areas should be jointly and explicitly considered to improve protein-protein affinity predictions. • This work may provide some insights on the quantitative energy-area relationship in protein-protein interactions. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state-of-the-art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state-of-art methods., (© 2020 The Protein Society.)