A PAK1 Mutational Hotspot Within the Regulatory CRIPaK Domain is Associated With Severe Neurodevelopmental Disorders in Children.

BACKGROUND: P-21-activated kinases (PAKs) are protein serine/threonine kinases, part of the RAS/mitogen-activated protein kinase pathway. PAK1 is highly expressed in the central nervous system and crucially involved in neuronal migration and brain developmental processes. Recently, de novo heterozygous missense variants in PAK1 have been identified as an ultrarare cause of pediatric neurodevelopmental disorders. METHODS: We report a series of children affected with postnatal macrocephaly, neurodevelopmental impairment, and drug-resistant epilepsy. Repeated electroencephalographic (EEG) and video-EEG evaluations were performed over a two- to 10-year period during follow-up to delineate electroclinical histories. Genetic sequencing studies and computational evaluation of the identified variants were performed in our patient cohort. RESULTS: We identified by whole-exome sequencing three novel de novo variants in PAK1 (NM_001128620: c.427A>G, p.Met143Val; c.428T>C, p.Met143Thr; c.428T>A, p.Met143Lys) as the underlying cause of the disease in our families. The three variants affected the same highly conserved Met143 residue within the cysteine-rich inhibitor of PAK1 (CRIPaK) domain, which was identified before as a PAK1 inhibitor target. Computational studies suggested a defective autoinhibition presumably due to impaired PAK1 autoregulation as a result of the recurrent substitution. CONCLUSIONS: We delineated the electroclinical phenotypes of PAK1-related neurological disorders and highlight a novel mutational hotspot that may involve defective autoinhibition of the PAK1 protein. The three novel variants affecting the same hotspot residue within the CRIPaK domain highlight potentially impaired PAK1-CRIPaK interaction as a novel disease mechanism. These findings shed light on possible future treatments targeted at the CRIPaK domain, to modulate PAK1 activity and function.

Description of conformational ensembles of disordered proteins by residue-local probabilities.

The study of proteins with intrinsically disordered regions (IDRs) has emerged as an active field of research due to their intriguing nature. Although IDRs lack a well-defined folded structure, they play important functional roles in cells, following biological mechanisms different from those of the traditional structured proteins. Consequently, it has been necessary to re-design experimental and theoretical methods in order to face the challenges introduced by the dynamic nature of IDRs. In this work, we present an accurate and cost-effective method to study the conformational dynamics of IDRs based on the use of residue-local probabilistic expressions that characterize the conformational ensembles obtained from finite-temperature molecular dynamics (MD) simulations. It is shown that the good performance and the high convergence rates achieved with our method are independent of the IDR lengths, since the method takes advantage of the major influence of the identity and conformation of the nearest residue neighbors on the amino-acid conformational preferences to evaluate the IDR conformational ensembles. This allows us to characterize the conformational space of IDRs using a reduced number of probabilities which can be obtained from comparatively short MD simulations or experimental databases. To exemplify the usefulness of our approach, we present an application to directly detect Molecular Recognition Features (MoRFs) in an IDR domain of the protein p53, and to follow the time evolution of the thermodynamic magnitudes of this system during its exploration of the conformational space.

Replica-exchange optimization of antibody fragments.

In the framework of the rational design of macromolecules capable of binding to a specific target for biosensing applications, we here further develop an evolutionary protocol designed to optimize the binding affinity of protein binders. In particular we focus on the optimization of the binding portion of small antibody fragments known as nanobodies (or VHH) and choose the hen egg white lysozyme (HEWL) as our target. By implementing a replica exchange scheme for this optimization, we show that an initial hit is not needed and similar solutions can be found by either optimizing an already known anti-HEWL VHH or a randomly selected binder (here a VHH selective towards another macromolecule). While we believe that exhaustive searches of the mutation space are most appropriate when only few key residues have to be optimized, in case a lead binder is not available the proposed evolutionary algorithm should be instead the method of choice.

Antibody Affinity Maturation Using Computational Methods: From an Initial Hit to Small-Scale Expression of Optimized Binders.

Nanobodies (VHHs) are engineered fragments of the camelid single-chain immunoglobulins. The VHH domain contains the highly variable segments responsible for antigen recognition. VHHs can be easily produced as recombinant proteins. Their small size is a good advantage for in silico approaches. Computer methods represent a valuable strategy for the optimization and improvement of their binding affinity. They also allow for epitope selection offering the possibility to design new VHHs for regions of a target protein that are not naturally immunogenic. Here we present an in silico mutagenic protocol developed to improve the binding affinity of nanobodies together with the first step of their in vitro production. The method, already proven successful in improving the low Kd of a nanobody hit obtained by panning, can be employed for the ex novo design of antibody fragments against selected protein target epitopes.

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.

Computational Evolution Protocol for Peptide Design.

Computational peptide design is useful for therapeutics, diagnostics, and vaccine development. To select the most promising peptide candidates, the key is describing accurately the peptide-target interactions at the molecular level. We here review a computational peptide design protocol whose key feature is the use of all-atom explicit solvent molecular dynamics for describing the different peptide-target complexes explored during the optimization. We describe the milestones behind the development of this protocol, which is now implemented in an open-source code called PARCE. We provide a basic tutorial to run the code for an antibody fragment design example. Finally, we describe three additional applications of the method to design peptides for different targets, illustrating the broad scope of the proposed approach.

Insights on peptide topology in the computational design of protein ligands: the example of lysozyme binding peptides.

Herein, we compared the ability of linear and cyclic peptides generated in silico to target different protein sites: internal pockets and solvent-exposed sites. We selected human lysozyme (HuL) as a model target protein combined with the computational evolution of linear and cyclic peptides. The sequence evolution of these peptides was based on the PARCE algorithm. The generated peptides were screened based on their aqueous solubility and HuL binding affinity. The latter was evaluated by means of scoring functions and atomistic molecular dynamics (MD) trajectories in water, which allowed prediction of the structural features of the protein-peptide complexes. The computational results demonstrated that cyclic peptides constitute the optimal choice for solvent exposed sites, while both linear and cyclic peptides are capable of targeting the HuL pocket effectively. The most promising binders found in silico were investigated experimentally by surface plasmon resonance (SPR), nuclear magnetic resonance (NMR), and electrospray ionization mass spectrometry (ESI-MS) techniques. All tested peptides displayed dissociation constants in the micromolar range, as assessed by SPR; however, both NMR and ESI-MS suggested multiple binding modes, at least for the pocket binding peptides. A detailed NMR analysis confirmed that both linear and cyclic pocket peptides correctly target the binding site they were designed for.

Enhanced Molecular Dynamics Method to Efficiently Increase the Discrimination Capability of Computational Protein-Protein Docking.

Protein-protein docking typically consists of the generation of putative binding conformations, which are subsequently ranked by fast heuristic scoring functions. The simplicity of these functions allows for computational efficiency but has severe repercussions on their discrimination capabilities. In this work, we show the effectiveness of suitable descriptors calculated along short scaled molecular dynamics runs in recognizing the nearest-native bound conformation among a set of putative structures generated by the HADDOCK tool for eight protein-protein systems.

Effect of Humanizing Mutations on the Stability of the Llama Single-Domain Variable Region.

In vivo clinical applications of nanobodies (VHHs) require molecules that induce minimal immunoresponse and therefore possess sequences as similar as possible to the human VH domain. Although the relative sequence variability in llama nanobodies has been used to identify scaffolds with partially humanized signature, the transformation of the Camelidae hallmarks in the framework2 still represents a major problem. We assessed a set of mutants in silico and experimentally to elucidate what is the contribution of single residues to the VHH stability and how their combinations affect the mutant nanobody stability. We described at molecular level how the interaction among residues belonging to different structural elements enabled a model llama nanobody (C8WT, isolated from a naïve library) to be functional and maintain its stability, despite the analysis of its primary sequence would classify it as aggregation-prone. Five chimeras formed by grafting CDRs isolated from different nanobodies into C8WT scaffold were successfully expressed as soluble proteins and both tested clones preserved their antigen binding specificity. We identified a nanobody with human hallmarks that seems suitable for humanizing selected camelid VHHs by grafting heterologous CDRs in its scaffold and could serve for the preparation of a synthetic library of human-like single domains.

Computational Evolution of Beta-2-Microglobulin Binding Peptides for Nanopatterned Surface Sensors.

The bottom-up design of smart nanodevices largely depends on the accuracy by which each of the inherent nanometric components can be functionally designed with predictive methods. Here, we present a rationally designed, self-assembled nanochip capable of capturing a target protein by means of pre-selected binding sites. The sensing elements comprise computationally evolved peptides, designed to target an arbitrarily selected binding site on the surface of beta-2-Microglobulin (ß2m), a globular protein that lacks well-defined pockets. The nanopatterned surface was generated by an atomic force microscopy (AFM)-based, tip force-driven nanolithography technique termed nanografting to construct laterally confined self-assembled nanopatches of single stranded (ss)DNA. These were subsequently associated with an ssDNA-peptide conjugate by means of DNA-directed immobilization, therefore allowing control of the peptide's spatial orientation. We characterized the sensitivity of such peptide-containing systems against ß2m in solution by means of AFM-based differential topographic imaging and surface plasmon resonance (SPR) spectroscopy. Our results show that the confined peptides are capable of specifically capturing ß2m from the surface-liquid interface with micromolar affinity, hence providing a viable proof-of-concept for our approach to peptide design.

Tuning Local Hydration Enables a Deeper Understanding of Protein-Ligand Binding: The PP1-Src Kinase Case.

Water plays a key role in biomolecular recognition and binding. Despite the development of several computational and experimental approaches, it is still challenging to comprehensively characterize water-mediated effects on the binding process. Here, we investigate how water affects the binding of Src kinase to one of its inhibitors, PP1. Src kinase is a target for treating several diseases, including cancer. We use biased molecular dynamics simulations, where the hydration of predetermined regions is tuned at will. This computational technique efficiently accelerates the SRC-PP1 binding simulation and allows us to identify several key and yet unexplored aspects of the solvent's role. This study provides a further perspective on the binding phenomenon, which may advance the current drug design approaches for the development of new kinase inhibitors.

Multiplexed Discrimination of Single Amino Acid Residues in Polypeptides in a Single SERS Hot Spot.

The SERS-based detection of protein sequences with single-residue sensitivity suffers from signal dominance of aromatic amino acid residues and backbones, impeding detection of non-aromatic amino acid residues. Herein, we trap a gold nanoparticle in a plasmonic nanohole to generate a single SERS hot spot for single-molecule detection of 2 similar polypeptides (vasopressin and oxytocin) and 10 distinct amino acids that constitute the 2 polypeptides. Significantly, both aromatic and non-aromatic amino acids are detected and discriminated at the single-molecule level either at individual amino acid molecules or within the polypeptide chains. Correlated with molecular dynamics simulations, our results suggest that the signal dominance due to large spatial occupancy of aromatic rings of the polypeptide sidechains on gold surfaces can be overcome by the high localization of the single hot spot. The superior spectral and spatial discriminative power of our approach can be applied to single-protein analysis, fingerprinting, and sequencing.

A consensus protocol for the in silico optimisation of antibody fragments.

We present an in silico mutagenetic protocol for improving the binding affinity of single domain antibodies (or nanobodies, VHHs). The method iteratively attempts random mutations in the interacting region of the protein and evaluates the resulting binding affinity towards the target by scoring, with a collection of scoring functions, short explicit solvent molecular dynamics trajectories of the binder-target complexes. The acceptance/rejection of each attempted mutation is carried out by a consensus decision-making algorithm, which considers all individual assessments derived from each scoring function. The method was benchmarked by evolving a single complementary determining region (CDR) of an anti-HER2 VHH hit obtained by direct panning of a phage display library. The optimised VHH mutant showed significantly enhanced experimental affinity with respect to the original VHH it matured from. The protocol can be employed as it is for the optimization of peptides, antibody fragments, and (given enough computational power) larger antibodies.

Assessing the capability of in silico mutation protocols for predicting the finite temperature conformation of amino acids.

Mutation protocols are a key tool in computational biophysics for modelling unknown side chain conformations. In particular, these protocols are used to generate the starting structures for molecular dynamics simulations. The accuracy of the initial side chain and backbone placement is crucial to obtain a stable and quickly converging simulation. In this work, we assessed the performance of several mutation protocols in predicting the most probable conformer observed in finite temperature molecular dynamics simulations for a set of protein-peptide crystals differing only by single-point mutations in the peptide sequence. Our results show that several programs which predict well the crystal conformations fail to predict the most probable finite temperature configuration. Methods relying on backbone-dependent rotamer libraries have, in general, a better performance, but even the best protocol fails in predicting approximately 30% of the mutations.

Binding affinity prediction of nanobody-protein complexes by scoring of molecular dynamics trajectories.

Nanobodies offer a viable alternative to antibodies for engineering high affinity binders. Their small size has an additional advantage: it allows exploiting computational protocols for optimizing their biophysical features, such as the binding affinity. The efficient prediction of this quantity is still considered a daunting task especially for modelled complexes. We show how molecular dynamics can successfully assist in the binding affinity prediction of modelled nanobody-protein complexes. The approximate initial configurations obtained by in silico design must undergo large rearrangements before achieving a stable conformation, in which the binding affinity can be meaningfully estimated. The scoring functions developed for the affinity evaluation of crystal structures will provide accurate estimates for modelled binding complexes if the scores are averaged over long finite temperature molecular dynamics simulations.

Influence of Linker Flexibility on the Binding Affinity of Bidentate Binders.

The design of responsive nanosensors typically relies on the availability of probes capable of capturing their target with high affinity and specificity. This can be achieved by coupling two or more binding units through a linker. In this work, we study the dependence on the binder architecture of the binding affinity between a target molecule and a semirigid bidentate binder. Using two different binder architectures, central-rigid and extreme-rigid, and modifying the length and the flexibility degree of the linker we generated 153 different architectures. We computed their dissociation free energies by means of Monte Carlo simulations and thermodynamic integration. We found that central-rigid bidentate binders are a poor choice, as they dissociate more easily than analogous fully flexible bidentate binders. On the other hand, molecular architectures presenting extreme-rigid units were shown effective for a wide range of set-ups.

Understanding the connection between conformational changes of peptides and equilibrium thermal fluctuations.

Despite the increasing evidence that conformational transitions in peptides and proteins are driven by specific vibrational energy pathways along the molecule, the current experimental techniques of analysis do as yet not allow to study these biophysical processes in terms of anisotropic energy flows. Computational methods offer a complementary approach to obtain a more detailed understanding of the vibrational and conformational dynamics of these systems. Accordingly, in this work we investigate jointly the vibrational energy distribution and the conformational dynamics of trialanine peptide in water solution at room temperature by applying the Instantaneous Normal Mode analysis to the results derived from equilibrium molecular dynamics simulations. It is shown that conformational changes in trialanine are triggered by the vibrational energy accumulated in the low-frequency modes of the molecule, and that excitation is caused exclusively by thermal fluctuations of the solute-solvent system, thus excluding the possibility of an intramolecular vibrational energy redistribution process.

Computational design of cyclic peptides for the customized oriented immobilization of globular proteins.

The oriented immobilization of proteins, key for the development of novel responsive biomaterials, relies on the availability of effective probes. These are generally provided by standard approaches based on in vivo maturation and in vitro selection of antibodies and/or aptamers. These techniques can suffer technical problems when a non-immunogenic epitope needs to be targeted. Here we propose a strategy to circumvent this issue by in silico design. In our method molecular binders, in the form of cyclic peptides, are computationally evolved by stochastically exploring their sequence and structure space to identify high-affinity peptides for a chosen epitope of a target globular protein: here a solvent-exposed site of ß2-microglobulin (ß2m). Designed sequences were screened by explicit solvent molecular dynamics simulations (MD) followed by experimental validation. Five candidates gave dose-response surface plasmon resonance signals with dissociation constants in the micromolar range. One of them was further analyzed by means of isothermal titration calorimetry, nuclear magnetic resonance, and 250 ns of MD. Atomic-force microscopy imaging showed that this peptide is able to immobilize ß2m on a gold surface. In short, we have shown by a variety of experimental techniques that it is possible to capture a protein through an epitope of choice by computational design.

Molecular dynamics simulations and docking enable to explore the biophysical factors controlling the yields of engineered nanobodies.

Nanobodies (VHHs) have proved to be valuable substitutes of conventional antibodies for molecular recognition. Their small size represents a precious advantage for rational mutagenesis based on modelling. Here we address the problem of predicting how Camelidae nanobody sequences can tolerate mutations by developing a simulation protocol based on all-atom molecular dynamics and whole-molecule docking. The method was tested on two sets of nanobodies characterized experimentally for their biophysical features. One set contained point mutations introduced to humanize a wild type sequence, in the second the CDRs were swapped between single-domain frameworks with Camelidae and human hallmarks. The method resulted in accurate scoring approaches to predict experimental yields and enabled to identify the structural modifications induced by mutations. This work is a promising tool for the in silico development of single-domain antibodies and opens the opportunity to customize single functional domains of larger macromolecules.

Steric confinement and enhanced local flexibility assist knotting in simple models of protein folding.

The chaperonin complex GroEL-GroES is able to accelerate the folding process of knotted proteins considerably. However, the folding mechanism inside the chaperonin cage is elusive. Here we use a combination of lattice and off-lattice Monte Carlo simulations of simple Go models to study the effect of physical confinement and local flexibility on the folding process of protein model systems embedding a trefoil knot in their native structure. This study predicts that steric confinement plays a specific role in the folding of knotted proteins by increasing the knotting probability for very high degrees of confinement. This effect is observed for protein MJ0366 even above the melting temperature for confinement sizes compatible with the size of the GroEL/GroES chaperonin cage. An enhanced local flexibility produces the same qualitative effects on the folding process. In particular, we observe that knotting probability increases up to 40% in the transition state of protein MJ0366 when flexibility is enhanced. This is underlined by a structural change in the transition state, which becomes devoid of helical content. No relation between the knotting mechanism and flexibility was found in the context of the off-lattice model adopted in this work.