Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

The Proteomic Code: a molecular recognition code for proteins

<p>Abstract</p> <p>Background</p> <p>The Proteomic Code is a set of rules by which information in genetic material is transferred into the physico-chemical properties of amino acids. It determines how individual amino acids interact with each other during folding and in specific protein-protein interactions. The Proteomic Code is part of the redundant Genetic Code.</p> <p>Review</p> <p>The 25-year-old history of this concept is reviewed from the first independent suggestions by Biro and Mekler, through the works of Blalock, Root-Bernstein, Siemion, Miller and others, followed by the discovery of a Common Periodic Table of Codons and Nucleic Acids in 2003 and culminating in the recent conceptualization of partial complementary coding of interacting amino acids as well as the theory of the nucleic acid-assisted protein folding.</p> <p>Methods and conclusions</p> <p>A novel cloning method for the design and production of specific, high-affinity-reacting proteins (SHARP) is presented. This method is based on the concept of proteomic codes and is suitable for large-scale, industrial production of specifically interacting peptides.</p

Cavity-based negative images in molecular docking

In drug development, computer-based methods are constantly evolving as a result of increasing computing power and cumulative costs of generating new pharmaceuticals. With virtual screening (VS), it is possible to screen even hundreds of millions of compounds and select the best molecule candidates for in vitro testing instead of investing time and resources in analysing all molecules systematically in laboratories. However, there is a constant need to generate more reliable and effective software for VS. For example, molecular docking, one of the most central methods in structure-based VS, can be a very successful approach for certain targets while failing completely with others. However, it is not necessarily the docking sampling but the scoring of the docking poses that is the bottleneck. In this thesis, a novel rescoring method, negative image-based rescoring (R-NiB), is introduced, which generates a negative image of the ligand binding cavity and compares the shape and electrostatic similarity between the generated model and the docked molecule pose. The performance of the method is tested comprehensively using several different protein targets, benchmarking sets and docking software. Additionally, it is compared to other rescoring methods. R-NiB is shown to be a fast and effective method to rescore the docking poses producing notable improvement in active molecule recognition. Furthermore, the NIB model optimization method based on a greedy algorithm is introduced that uses a set of known active and inactive molecules as a training set. This approach, brute force negative image-based optimization (BR-NiB), is shown to work remarkably well producing impressive in silico results even with very limited active molecule training sets. Importantly, the results suggest that the in silico hit rates of the optimized models in docking rescoring are on a level needed in real-world VS and drug discovery projects.Tietokoneiden laskentatehojen ja lääketutkimuksen tuotekehityskulujen kasvaessa tietokonepohjaiset menetelmät kehittyvät jatkuvasti lääkekehityksessä. Virtuaaliseulonnalla voidaan seuloa jopa satoja miljoonia molekyylejä ja valita vain parhaat molekyyliehdokkaat laboratoriotestaukseen sen sijaan, että tuhlattaisiin aikaa ja resursseja analysoimalla järjestelmällisesti kaikki molekyylit laboratoriossa. Tästä huolimatta on koko ajan jatkuva tarve kehittää luotettavampia ja tehokkaampia menetelmiä virtuaaliseulontaan. Esimerkiksi telakointi, yksi keskeisimmistä työkaluista rakennepohjaisessa lääkeainekehityksessä, saattaa toimia erinomaisesti yhdellä kohteella ja epäonnistua täysin toisella. Ongelma ei välttämättä ole telakoitujen molekyylien luonnissa vaan niiden pisteytyksessä. Tässä väitöskirjassa tähän ongelmaan esitellään ratkaisuksi uudenlainen pisteytysmenetelmä R-NiB, jossa verrataan ligandinsitomisalueen negatiivikuvan muodon ja sähköstaattisen potentiaalin samankaltaisuutta telakoituihin molekyyleihin. Menetelmän suorituskykyä testataan usealla eri molekyylisarjalla, lääkeainekohteella, telakointiohjelmalla ja vertaamalla tuloksia muihin pisteytysmenetelmiin. R-NiB:n näytetään olevan nopea ja tehokas menetelmä telakointiasentojen pisteytykseen tuottaen huomattavan parannuksen aktiivisten molekyylien tunnistukseen. Tämän lisäksi esitellään ns. ahneeseen algoritmiin perustuva negatiivikuvan optimointimenetelmä, joka käyttää sarjaa tunnettuja aktiivisia ja inaktiivisia molekyylejä harjoitusjoukkona. Tämän BR-NiB-menetelmän näytetään toimivan ainakin tietokonemallinnuksessa todella hyvin tuottaen vaikuttavia tuloksia jopa silloin, kun harjoitusjoukko koostuu vain muutamista aktiivisista molekyyleistä. Mikä tärkeintä, in silico -tulokset viittaavat optimointimenetelmän osumaprosentin telakoinnin uudelleenpisteytyksessä olevan riittävän korkea myös oikeisiin virtuaaliseulontaprojekteihin

Exploring the potential of 3D Zernike descriptors and SVM for protein\u2013protein interface prediction

Abstract Background The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Results In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). Conclusions The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class

Computational studies on protein-ligand docking

This thesis describes the development and refinement of a number of techniques for molecular docking and ligand database screening, as well as the application of these techniques to predict the structures of several protein-ligand complexes and to discover novel ligands of an important receptor protein. Global energy optimisation by Monte-Carlo minimisation in internal co-ordinates was used to predict bound conformations of eight protein-ligand complexes. Experimental X-ray crystallography structures became available after the predictions were made. Comparison with the X-ray structures showed that the docking procedure placed 30 to 70% of the ligand molecule correctly within 1.5A from the native structure. The discrimination potential for identification of high-affinity ligands was derived and optimised using a large set of available protein-ligand complex structures. A fast boundary-element solvation electrostatic calculation algorithm was implemented to evaluate the solvation component of the discrimination potential. An accelerated docking procedure utilising pre-calculated grid potentials was developed and tested. For 23 receptors and 63 ligands extracted from X-ray structures, the docking and discrimination protocol was capable of correct identification of the majority of native receptor-ligand couples. 51 complexes with known structures were predicted. 35 predictions were within 3A from the native structure, giving correct overall positioning of the ligand, and 26 were within 2A, reproducing a detailed picture of the receptor-ligand interaction. Docking and ligand discrimination potential evaluation was applied to screen the database of more than 150000 commercially available compounds for binding to the fibroblast growth factor receptor tyrosine kinase, the protein implicated in several pathological cell growth aberrations. As expected, a number of compounds selected by the screening protocol turned out to be known inhibitors of the tyrosine kinases. 49 putative novel ligands identified by the screening protocol were experimentally tested and five compounds have shown inhibition of phosphorylation activity of the kinase. These compounds can be used as leads for further drug development

Examination of Molecular Recognition in Protein-Ligand Interactions

This dissertation is a compilation of two main projects that were investigated during my thesis research. The first project was a prospective study which identified and characterized drug-like inhibitors of a prototype of bacterial two-component signal transduction response regulator using computational and experimental methods. The second project was the development and validation of a scoring function, PHOENIX, derived using high-resolution structures and calorimetry measurements to predict binding affinities of protein-ligand interactions. Collectively, my thesis research aimed to better understand the underlying driving forces and principles which govern molecular recognition and molecular design. A prospective study coupled computational predictions with experimental validation resulted in the discovery of first-in-class inhibitors targeting a signal transduction module important for bacterial virulence. Development and validation of the PHOENIX scoring function for binding affinity prediction derived using high-resolution structures and calorimetry measurements should guide future molecular recognition studies and endeavors in computer-aided molecular design. To request for an electronic copy of this dissertation, please email the author: yattang at gmail dot com)

Plausible blockers of Spike RBD in SARS-CoV2-molecular design and underlying interaction dynamics from high-level structural descriptors

COVID-19 is characterized by an unprecedented abrupt increase in the viral transmission rate (SARS-CoV-2) relative to its pandemic evolutionary ancestor, SARS-CoV (2003). The complex molecular cascade of events related to the viral pathogenicity is triggered by the Spike protein upon interacting with the ACE2 receptor on human lung cells through its receptor binding domain (RBDSpike). One potential therapeutic strategy to combat COVID-19 could thus be limiting the infection by blocking this key interaction. In this current study, we adopt a protein design approach to predict and propose non-virulent structural mimics of the RBDSpike which can potentially serve as its competitive inhibitors in binding to ACE2. The RBDSpike is an independently foldable protein domain, resilient to conformational changes upon mutations and therefore an attractive target for strategic re-design. Interestingly, in spite of displaying an optimal shape fit between their interacting surfaces (attributed to a consequently high mutual affinity), the RBDSpike-ACE2 interaction appears to have a quasi-stable character due to a poor electrostatic match at their interface. Structural analyses of homologous protein complexes reveal that the ACE2 binding site of RBDSpike has an unusually high degree of solvent-exposed hydrophobic residues, attributed to key evolutionary changes, making it inherently "reaction-prone." The designed mimics aimed to block the viral entry by occupying the available binding sites on ACE2, are tested to have signatures of stable high-affinity binding with ACE2 (cross-validated by appropriate free energy estimates), overriding the native quasi-stable feature. The results show the apt of directly adapting natural examples in rational protein design, wherein, homology-based threading coupled with strategic "hydrophobic ↔ polar" mutations serve as a potential breakthrough

Predicting HLA Class I Non-Permissive Amino Acid Residues Substitutions

Prediction of peptide binding to human leukocyte antigen (HLA) molecules is essential to a wide range of clinical entities from vaccine design to stem cell transplant compatibility. Here we present a new structure-based methodology that applies robust computational tools to model peptide-HLA (p-HLA) binding interactions. The method leverages the structural conservation observed in p-HLA complexes to significantly reduce the search space and calculate the system’s binding free energy. This approach is benchmarked against existing p-HLA complexes and the prediction performance is measured against a library of experimentally validated peptides. The effect on binding activity across a large set of high-affinity peptides is used to investigate amino acid mismatches reported as high-risk factors in hematopoietic stem cell transplantation.</p