scholarly journals Flexibility and binding affinity in protein–ligand, protein–protein and multi-component protein interactions: limitations of current computational approaches

2011 ◽  
Vol 9 (66) ◽  
pp. 20-33 ◽  
Author(s):  
Pierre Tuffery ◽  
Philippe Derreumaux
Keyword(s):  
Binding Affinity ◽  
Protein Interactions ◽  
Large Scale ◽  
De Novo ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Structure Based Drug Design ◽  
Body Protein ◽  
Technical Problems ◽  
Multi Body

The recognition process between a protein and a partner represents a significant theoretical challenge. In silico structure-based drug design carried out with nothing more than the three-dimensional structure of the protein has led to the introduction of many compounds into clinical trials and numerous drug approvals. Central to guiding the discovery process is to recognize active among non-active compounds. While large-scale computer simulations of compounds taken from a library (virtual screening) or designed de novo are highly desirable in the post-genomic area, many technical problems remain to be adequately addressed. This article presents an overview and discusses the limits of current computational methods for predicting the correct binding pose and accurate binding affinity. It also presents the performances of the most popular algorithms for exploring binary and multi-body protein interactions.

scholarly journals Robust de novo design of protein binding proteins from target structural information alone

2021 ◽  
Author(s):  
Longxing Cao ◽  
Brian Coventry ◽  
Inna Goreshnik ◽  
Buwei Huang ◽  
Joon Sung Park ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Binding Proteins ◽  
De Novo ◽  
Structural Information ◽  
Three Dimensional ◽  
Computational Design ◽  
De Novo Design ◽  
Dimensional Structure ◽  
Binding Modes ◽  
Large Space

The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains an outstanding challenge. We describe a general solution to this problem which starts with a broad exploration of the very large space of possible binding modes and interactions, and then intensifies the search in the most promising regions. We demonstrate its very broad applicability by de novo design of binding proteins to 12 diverse protein targets with very different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of four of the binder-target complexes, and all four are very close to the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein-protein interactions, and should guide improvement of both. Our approach now enables targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

A Target-Based Method for Designing Heterochiral Cyclic Peptide Binders: De Novo Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
Cyclic Peptide ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1651-1660
Author(s):  
Anuraj Nayarisseri
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Binding Affinity ◽  
Chemical Biology ◽  
De Novo ◽  
Structure Based Drug Design ◽  
Computational Approaches ◽  
Drug Designing ◽  
Traditional Drug ◽  
Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

scholarly journals Online biophysical predictions for SARS-CoV-2 proteins

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Luciano Kagami ◽  
Joel Roca-Martínez ◽  
Jose Gavaldá-García ◽  
Pathmanaban Ramasamy ◽  
K. Anton Feenstra ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Static Structure ◽  
Homologous Proteins ◽  
Structure Based Drug Design ◽  
Protein Protein Interaction ◽  
Link Type ◽  
Dynamics Simulations ◽  
Β Sheet

Abstract Background The SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into the mode of action of the virus, as well as avenues for structure-based drug design. However, not all of the SARS-CoV-2 proteins, or regions thereof, have a well-defined three-dimensional structure, and as such might exhibit ambiguous, dynamic behaviour that is not evident from static structure representations, nor from molecular dynamics simulations using these structures. Main We present a website (https://bio2byte.be/sars2/) that provides protein sequence-based predictions of the backbone and side-chain dynamics and conformational propensities of these proteins, as well as derived early folding, disorder, β-sheet aggregation, protein-protein interaction and epitope propensities. These predictions attempt to capture the inherent biophysical propensities encoded in the sequence, rather than context-dependent behaviour such as the final folded state. In addition, we provide the biophysical variation that is observed in homologous proteins, which gives an indication of the limits of their functionally relevant biophysical behaviour. Conclusion The https://bio2byte.be/sars2/ website provides a range of protein sequence-based predictions for 27 SARS-CoV-2 proteins, enabling researchers to form hypotheses about their possible functional modes of action.

scholarly journals Biophysical and biochemical investigations of RNA catalysis in the hammerhead ribozyme

1999 ◽  
Vol 32 (3) ◽  
pp. 241-284 ◽  
Author(s):  
William G. Scott
Keyword(s):  
Metal Ions ◽  
Conformational Change ◽  
Large Scale ◽  
Enzyme Inhibitor ◽  
Hammerhead Ribozyme ◽  
Three Dimensional ◽  
Chemical Mechanism ◽  
Dimensional Structure ◽  
Three Dimensional Structure ◽  
Hammerhead Rna

1. How do ribozymes work? 2412. The hammerhead RNA as a prototype ribozyme 2422.1 RNA enzymes 2422.2 Satellite self-cleaving RNAs 2422.3 Hammerhead RNAs and hammerhead ribozymes 2443. The chemical mechanism of hammerhead RNA self-cleavage 2463.1 Phosphodiester isomerization via an SN2(P) reaction 2473.2 The canonical role of divalent metal ions in the hammerhead ribozyme reaction 2513.3 The hammerhead ribozyme does not actually require metal ions for catalysis 2543.4 Hammerhead RNA enzyme kinetics 2574. Sequence requirements for hammerhead RNA self-cleavage 2604.1 The conserved core, mutagenesis and functional group modifications 2604.2 Ground-state vs. transition-state effects 2615. The three-dimensional structure of the hammerhead ribozyme 2625.1 Enzyme–inhibitor complexes 2625.2 Enzyme–substrate complex in the initial state 2645.3 Hammerhead ribozyme self-cleavage in the crystal 2645.4 The requirement for a conformational change 2655.5 Capture of conformational intermediates using crystallographic freeze-trapping 2665.6 The structure of a hammerhead ribozyme ‘early’ conformational intermediate 2675.7 The structure of a hammerhead ribozyme ‘later’ conformational intermediate 2685.8 Is the conformational change pH dependent? 2695.9 Isolating the structure of the cleavage product 2715.10 Evidence for and against additional large-scale conformation changes 2745.11 NMR spectroscopic studies of the hammerhead ribozyme 2786. Concluding remarks 2807. Acknowledgements 2818. References 2811. How do ribozymes work? 241The discovery that RNA can be an enzyme (Guerrier-Takada et al. 1983; Zaug & Cech, 1986) has created the fundamental question of how RNA enzymes work. Before this discovery, it was generally assumed that proteins were the only biopolymers that had sufficient complexity and chemical heterogeneity to catalyze biochemical reactions. Clearly, RNA can adopt sufficiently complex tertiary structures that make catalysis possible. How does the three- dimensional structure of an RNA endow it with catalytic activity? What structural and functional principles are unique to RNA enzymes (or ribozymes), and what principles are so fundamental that they are shared with protein enzymes?

scholarly journals Crystal structure of a tankyrase 1–telomere repeat factor 1 complex

2016 ◽  
Vol 72 (4) ◽  
pp. 320-327 ◽  
Author(s):  
Bo Li ◽  
Ruihong Qiao ◽  
Zhizhi Wang ◽  
Weihong Zhou ◽  
Xin Li ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Large Scale ◽  
Sparse Matrix ◽  
Critical Role ◽  
Three Dimensional ◽  
Mitotic Cell ◽  
Ankyrin Repeat ◽  
Dimensional Structure ◽  
Telomere Repeat ◽  
Tankyrase 1

Telomere repeat factor 1 (TRF1) is a subunit of shelterin (also known as the telosome) and plays a critical role in inhibiting telomere elongation by telomerase. Tankyrase 1 (TNKS1) is a poly(ADP-ribose) polymerase that regulates the activity of TRF1 through poly(ADP-ribosyl)ation (PARylation). PARylation of TRF1 by TNKS1 leads to the release of TRF1 from telomeres and allows telomerase to access telomeres. The interaction between TRF1 and TNKS1 is thus important for telomere stability and the mitotic cell cycle. Here, the crystal structure of a complex between the N-terminal acidic domain of TRF1 (residues 1–55) and a fragment of TNKS1 covering the second and third ankyrin-repeat clusters (ARC2-3) is presented at 2.2 Å resolution. The TNKS1–TRF1 complex crystals were optimized using an `oriented rescreening' strategy, in which the initial crystallization condition was used as a guide for a second round of large-scale sparse-matrix screening. This crystallographic and biochemical analysis provides a better understanding of the TRF1–TNKS1 interaction and the three-dimensional structure of the ankyrin-repeat domain of TNKS.

scholarly journals SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

2019 ◽  
Author(s):  
Sushant Kumar ◽  
Arif Harmanci ◽  
Jagath Vytheeswaran ◽  
Mark B. Gerstein
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Mutations ◽  
Dimensional Structure ◽  
Rapid Decline ◽  
Ras Signaling ◽  
Cancer Genes ◽  
Structural Variations ◽  
Pathogenic Variants

AbstractA rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known to play a crucial role in many diseases by altering the sequence and three-dimensional structure of the genome. Previous studies have suggested a complex interplay of genomic and epigenomic features in the emergence and distribution of SVs. However, the exact mechanism of pathogenesis for SVs in different diseases is not straightforward to decipher. Thus, we built an agnostic machine-learning-based workflow, called SVFX, to assign a “pathogenicity score” to somatic and germline SVs in various diseases. In particular, we generated somatic and germline training models, which included genomic, epigenomic, and conservation-based features for SV call sets in diseased and healthy individuals. We then applied SVFX to SVs in six different cancer cohorts and a cardiovascular disease (CVD) cohort. Overall, SVFX achieved high accuracy in identifying pathogenic SVs. Moreover, we found that predicted pathogenic SVs in cancer cohorts were enriched among known cancer genes and many cancer-related pathways (including Wnt signaling, Ras signaling, DNA repair, and ubiquitin-mediated proteolysis). Finally, we note that SVFX is flexible and can be easily extended to identify pathogenic SVs in additional disease cohorts.

scholarly journals A Novel Missense Variant in the Gene PPP2R5D Causes a Rare Neurodevelopmental Disorder with Increased Phenotype

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Lulu Yan ◽  
Ru Shen ◽  
Zongfu Cao ◽  
Chunxiao Han ◽  
Yuxin Zhang ◽  
...  
Keyword(s):  
De Novo ◽  
Genetic Disorder ◽  
Neurodevelopmental Disorder ◽  
Three Dimensional ◽  
Missense Variant ◽  
Pathogenic Variant ◽  
Dimensional Structure ◽  
Chinese Family ◽  
Speech Impairment ◽  
Pathogenic Variants

PPP2R5D-related neurodevelopmental disorder, which is mainly caused by de novo missense variants in the PPP2R5D gene, is a rare autosomal dominant genetic disorder with about 100 patients and a total of thirteen pathogenic variants known to exist globally so far. Here, we present a 24-month-old Chinese boy with developmental delay and other common clinical characteristics of PPP2R5D-related neurodevelopmental disorder including hypotonia, macrocephaly, intellectual disability, speech impairment, and behavioral abnormality. Trio-whole exome sequencing (WES) and Sanger sequencing were performed to identify the causal gene variant. The pathogenicity of the variant was evaluated using bioinformatics tools. We identified a novel pathogenic variant in the PPP2R5D gene (c.620G>T, p.Trp207Leu). The variant is located in the variant hotspot region of this gene and is predicted to cause PPP2R5D protein dysfunction due to an increase in local hydrophobicity and unstable three-dimensional structure. We report a novel pathogenic variant of PPP2R5D associated with PPP2R5D-related neurodevelopmental disorder from a Chinese family. Our findings expanded the phenotypic and mutational spectrum of PPP2R5D-related neurodevelopmental disorder.

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