Locuaz: an in silico platform for protein binders optimization.

MOTIVATION: Engineering high-affinity binders targeting specific antigenic determinants remains a challenging and often daunting task, requiring extensive experimental screening. Computational methods have the potential to accelerate this process, reducing costs and time, but only if they demonstrate broad applicability and efficiency in exploring mutations, evaluating affinity, and pruning unproductive mutation paths. RESULTS: In response to these challenges, we introduce a new computational platform for optimizing protein binders towards their targets. The platform is organized as a series of modules, performing mutation selection and application, molecular dynamics simulations to sample conformations around interaction poses, and mutation prioritization using suitable scoring functions. Notably, the platform supports parallel exploration of different mutation streams, enabling in silico high-throughput screening on High Performance Computing (HPC) systems. Furthermore, the platform is highly customizable, allowing users to implement their own protocols. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/pgbarletta/locuaz and documentation is at https://locuaz.readthedocs.io/. The data underlying this article are available at https://github.com/pgbarletta/suppl_info_locuaz.

Analysis of changes of cavity volumes in predefined directions of protein motions and cavity flexibility.

Dynamics of protein cavities associated with protein fluctuations and conformational plasticity is essential for their biological function. NMR ensembles, molecular dynamics (MD) simulations, and normal mode analysis (NMA) provide appropriate frameworks to explore functionally relevant protein dynamics and cavity changes relationships. Within this context, we have recently developed analysis of null areas (ANA), an efficient method to calculate cavity volumes. ANA is based on a combination of algorithms that guarantees its robustness against numerical differentiations. This is a unique feature with respect to other methods. Herein, we present an updated and improved version that expands it use to quantify changes in cavity features, like volume and flexibility, due to protein structural distortions performed on predefined biologically relevant directions, for example, directions of largest contribution to protein fluctuations (principal component analysis [PCA modes]) obtained by MD simulations or ensembles of NMR structures, collective NMA modes or any other direction of motion associated with specific conformational changes. A web page has been developed where its facilities are explained in detail. First, we show that ANA can be useful to explore gradual changes of cavity volume and flexibility associated with protein ligand binding. Secondly, we perform a comparison study of the extent of variability between protein backbone structural distortions, and changes in cavity volumes and flexibilities evaluated for an ensemble of NMR active and inactive conformers of the epidermal growth factor receptor structures. Finally, we compare changes in size and flexibility between sets of NMR structures for different homologous chains of dynein.

Dynamics fingerprints of active conformers of epidermal growth factor receptor kinase.

Epidermal growth factor receptor (EGFR) is a prototypical cell-surface receptor that plays a key role in the regulation of cellular signaling, proliferation and differentiation. Mutations of its kinase domain have been associated with the development of a variety of cancers and, therefore, it has been the target of drug design. Single amino acid substitutions (SASs) in this domain have been proven to alter the equilibrium of pre-existing conformer populations. Despite the advances in structural descriptions of its so-called active and inactive conformations, the associated dynamics aspects that characterize them have not been thoroughly studied yet. As the dynamic behaviors and molecular motions of proteins are important for a complete understanding of their structure-function relationships we present a novel procedure, using (or based on) normal mode analysis, to identify the collective dynamics shared among different conformers in EGFR kinase. The method allows the comparison of patterns of low-frequency vibrational modes defining representative directions of motions. Our procedure is able to emphasize the main similarities and differences between the collective dynamics of different conformers. In the case of EGFR kinase, two representative directions of motions have been found as dynamics fingerprints of the active conformers. Protein motion along both directions reveals to have a significant impact on the cavity volume of the main pocket of the active site. Otherwise, the inactive conformers exhibit a more heterogeneous distribution of collective motions. © 2018 Wiley Periodicals, Inc.

Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔG Predictions.

The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.

Antibody-Antigen Binding Interface Analysis in the Big Data Era.

Antibodies have become the Swiss Army tool for molecular biology and nanotechnology. Their outstanding ability to specifically recognise molecular antigens allows their use in many different applications from medicine to the industry. Moreover, the improvement of conventional structural biology techniques (e.g., X-ray, NMR) as well as the emergence of new ones (e.g., Cryo-EM), have permitted in the last years a notable increase of resolved antibody-antigen structures. This offers a unique opportunity to perform an exhaustive structural analysis of antibody-antigen interfaces by employing the large amount of data available nowadays. To leverage this factor, different geometric as well as chemical descriptors were evaluated to perform a comprehensive characterization.

Protein Fluctuations and Cavity Changes Relationship.

Protein cavities and tunnels are critical for function. Ligand recognition and binding, transport, and enzyme catalysis require cavities rearrangements. Therefore, the flexibility of cavities should be guaranteed by protein vibrational dynamics. Molecular dynamics simulations provide a framework to explore conformational plasticity of protein cavities. Herein, we present a novel procedure to characterize the dynamics of protein cavities in terms of their volume gradient vector. For this purpose, we make use of algorithms for calculation of the cavity volume that result robust for numerical differentiations. Volume gradient vector is expressed in terms of principal component analysis obtained from equilibrated molecular dynamics simulations. We analyze contributions of principal component modes to the volume gradient vector according to their frequency and degree of delocalization. In all our test cases, we find that low frequency modes play a critical role together with minor contributions of high frequency modes. These modes involve concerted motions of significant fractions of the total residues lining the cavities. We make use of variations of the potential energy of a protein in the direction of the volume gradient vector as a measure of flexibility of the cavity. We show that proteins whose collective low frequency fluctuations contribute the most to changes of cavity volume exhibit more flexible cavities.