Principles of flexible protein-protein docking

Biological and molecular approaches of the degradation or decolorization potential of the hypersaline Lake Tuz Bacillus megaterium H2 isolate

The presence of synthetic dyes in water bodies and soil is one of the major issues affecting the global ecology, possibly impacting societal well-being adversely due to the colorants' recalcitrance and toxicity. Herein, the study spectrophotometrically monitored the ability of the Bacillus megaterium H2 azoreductase (AzrBmH2) to degrade four synthetic dyes, reactive blue 4, remazol brilliant red, thymol blue, and methyl red, followed by in-silico assessment using GROMACS. We found that the bacterium degraded as much as 60% of all four synthetic dyes at various tested concentrations. The genome analysis revealed five different azoreductase genes, which were then modeled into the AzrBmH21, AzrBmH22/3, and AzrBmH24/5 templates. The AzrBmH2-substrate complexes showed binding energies with all the dyes of between -10.6 to -6.9 kcal/mol and formed 4-6 hydrogen bonds with the predicted catalytic binding residues (His10, Glu 14, Ser 58, Met 99, Val 107, His 183, Asn184 and Gln 191). In contrast, the lowest binding energies were observed for the AzrBmH21-substrates (-10.6 to -7.9). Molecular dynamic simulations revealed that the AzrBmH21-substrate complexes were more stable (RMSD 0.2-0.25 nm, RMSF 0.05 - 0.3 nm) and implied strong bonding with the dyes. The Molecular Mechanics Poisson-Boltzmann Surface Area results also mirrored this outcome, showing the lowest azoreductase-dye binding energy in the order of AzrBmH21-RB4 (-78.18 ± 8.92 kcal/mol), AzrBmH21-RBR (-67.51 ± 7.74 kcal/mol), AzrBmH21-TB (-46.62 ± 5.23 kcal/mol) and AzrBmH21-MR (-40.78 ± 7.87 kcal/mol). In short, the study demonstrated the ability of the B. megaterium H2 to efficiently decolorize the above-said synthetic dyes, conveying the bacterium's promising use for large-scale dye remediation.Communicated by Ramaswamy H. Sarma.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Recent developments in molecular modeling tools and applications related to pharmaceutical and biomedical research

In modern pharmaceutical and biomedical research, molecular modeling represents a useful tool to explore processes and their mechanistic bases at the molecular level. Integrating experimental and virtual analysis is a fruitful approach to study ligand-receptor interaction in chemical, biochemical and biological environments. In these fields, molecular docking and molecular dynamics are considered privileged techniques for modeling (bio)macromolecules and related complexes. This review aims to present the current landscape of molecular modeling in pharmaceutical and biomedical research by examining selected representative applications published in the last years and highlighting current topics and trends of this field. Thus, a systematic compilation of all published literature has not been attempted herein. After a brief overview of the main theoretical and computational tools used to investigate mechanisms at molecular level, recent applications of molecular modeling in drug discovery, ligand binding and for studying protein conformation and function will be discussed. Furthermore, specific sections will be devoted to the application of molecular modeling for unravelling enantioselective mechanisms underlying the enantioseparation of chiral compounds of pharmaceutical and biomedical interest as well as for studying new forms of noncovalent interactivity identified in biochemical and biological environments. The general aim of this review is to provide the reader with a modern overview of the topic, highlighting advancements and outlooks as well as drawbacks and pitfalls still affecting the applicability of theoretical and computational methods in the field of pharmaceutical and biomedical research.

Approaches to Backbone Flexibility in Protein-Protein Docking

Proteins are the fundamental organic macromolecules in living systems that play a key role in a variety of biological functions including immunological detection, intracellular trafficking, and signal transduction. The docking of proteins has greatly advanced during recent decades and has become a crucial complement to experimental methods. Protein-protein docking is a helpful method for simulating protein complexes whose structures have not yet been solved experimentally. This chapter focuses on major search tactics along with various docking programs used in protein-protein docking algorithms, which include: direct search, exhaustive global search, local shape feature matching, randomized search, and broad category of post-docking approaches. As backbone flexibility predictions and interactions in high-resolution protein-protein docking remain important issues in the overall optimization context, we have put forward several methods and solutions used to handle backbone flexibility. In addition, various docking methods that are utilized for flexible backbone docking, including ATTRACT, FlexDock, FLIPDock, HADDOCK, RosettaDock, FiberDock, etc., along with their scoring functions, algorithms, advantages, and limitations are discussed. Moreover, what progress in search technology is expected, including not only the creation of new search algorithms but also the enhancement of existing ones, has been debated. As conformational flexibility is one of the most crucial factors affecting docking success, more work should be put into evaluating the conformational flexibility upon binding for a particular case in addition to developing new algorithms to replace the rigid body docking and scoring approach.

Assessing Protein-Protein Docking Protocols: Case Studies of G-Protein-Coupled Receptor Interactions

The interactions of G-protein-coupled receptors (GPCRs) with other proteins are critical in several cellular processes but resolving their structural dynamics remains challenging. An increasing number of GPCR complexes have been experimentally resolved but others including receptor variants are yet to be characterized, necessitating computational predictions of their interactions. Although integrative approaches with multi-scale simulations would provide rigorous estimates of their conformational dynamics, protein-protein docking remains a first tool of choice of many researchers due to the availability of open-source programs and easy to use web servers with reasonable predictive power. Protein-protein docking algorithms have limited ability to consider protein flexibility, environment effects, and entropy contributions and are usually a first step towards more integrative approaches. The two critical steps of docking: the sampling and scoring algorithms have improved considerably and their performance has been validated against experimental data. In this chapter, we provide an overview and generalized protocol of a few docking protocols using GPCRs as test cases. In particular, we demonstrate the interactions of GPCRs with extracellular protein ligands and an intracellular protein effectors (G-protein) predicted from docking approaches and test their limitations. The current chapter will help researchers critically assess docking protocols and predict experimentally testable structures of GPCR complexes.

In silico study on antidiabetic and antioxidant activity of bioactive compounds in Ficus carica L

Hyperglycemia is one of the diagnostic issues in diabetes mellitus and is considered as a complex metabolic condition. It has been one of the most prevalent illnesses of the twenty-first century and still rising at an alarming rate across the globe and expected to impact 693 million individuals by 2045. Therefore, it is mandatory to develop more effective and safer treatments to manage diabetes. One of the ways to manage hyperglycemia is through inhibiting carbohydrate digestion and thereby lowering the glucose formation in the human body. The enzyme salivary amylase and pancreatic amylase is responsible for cleaving α-1,4-glucoside bond. Amylase inhibitors can lower blood glucose in diabetics by slowing digestion. Ficus carica is commonly known for its medicinal properties due to its various phytochemicals. In the present study, 10 phytochemicals present in F. carica compounds named, β-carotene, lutein, cyanidin-3-glucoside, gallic acid, luteolin, catechin, kaempferol, vanillic acid, peonidin-3-glucoside, and quercetin hydrate were taken to study their inhibition potential against pancreatic amylase and salivary amylase through molecular docking and molecular dynamics simulations. Further, density functional theory calculations are used to investigate the delocalization of electron density on the molecule as well as study ADME properties of the molecules take. A QSAR model has been developed using the binding energy obtained using molecular docking and thermodynamic parameters from DFT calculations.Communicated by Ramaswamy H. Sarma.

Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.

Identifying the natural compound Catechin from tropical mangrove plants as a potential lead candidate against 3CLpro from SARS-CoV-2: An integrated in silico approach

SARS-CoV-2, a member of beta coronaviruses, is a single-stranded, positive-sense RNA virus responsible for the COVID-19 pandemic. With global fatalities of the pandemic exceeding 4.57 million, it becomes crucial to identify effective therapeutics against the virus. A protease, 3CL^pro, is responsible for the proteolysis of viral polypeptides into functional proteins, which is essential for viral pathogenesis. This indispensable activity of 3CL^pro makes it an attractive target for inhibition studies. The current study aimed to identify potential lead molecules against 3CL^pro of SARS-CoV-2 using a manually curated in-house library of antiviral compounds from mangrove plants. This study employed the structure-based virtual screening technique to evaluate an in-house library of antiviral compounds against 3CL^pro of SARS-CoV-2. The library was comprised of thirty-three experimentally proven antiviral molecules extracted from different species of tropical mangrove plants. The molecules in the library were virtually screened using AutoDock Vina, and subsequently, the top five promising 3CL^pro-ligand complexes along with 3CL^pro-N3 (control molecule) complex were subjected to MD simulations to comprehend their dynamic behaviour and structural stabilities. Finally, the MM/PBSA approach was used to calculate the binding free energies of 3CLpro complexes. Among all the studied compounds, Catechin achieved the most significant binding free energy (-40.3 ± 3.1 kcal/mol), and was closest to the control molecule (-42.8 ± 5.1 kcal/mol), and its complex with 3CL^pro exhibited the highest structural stability. Through extensive computational investigations, we propose Catechin as a potential therapeutic agent against SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Genome-Based Multi-Antigenic Epitopes Vaccine Construct Designing against Staphylococcus hominis Using Reverse Vaccinology and Biophysical Approaches

Staphylococcus hominis is a Gram-positive bacterium from the staphylococcus genus; it is also a member of coagulase-negative staphylococci because of its opportunistic nature and ability to cause life-threatening bloodstream infections in immunocompromised patients. Gram-positive and opportunistic bacteria have become a major concern for the medical community. It has also drawn the attention of scientists due to the evaluation of immune evasion tactics and the development of multidrug-resistant strains. This prompted the need to explore novel therapeutic approaches as an alternative to antibiotics. The current study aimed to develop a broad-spectrum, multi-epitope vaccine to control bacterial infections and reduce the burden on healthcare systems. A computational framework was designed to filter the immunogenic potent vaccine candidate. This framework consists of pan-genomics, subtractive proteomics, and immunoinformatics approaches to prioritize vaccine candidates. A total of 12,285 core proteins were obtained using a pan-genome analysis of all strains. The screening of the core proteins resulted in the selection of only two proteins for the next epitope prediction phase. Eleven B-cell derived T-cell epitopes were selected that met the criteria of different immunoinformatics approaches such as allergenicity, antigenicity, immunogenicity, and toxicity. A vaccine construct was formulated using EAAAK and GPGPG linkers and a cholera toxin B subunit. This formulated vaccine construct was further used for downward analysis. The vaccine was loop refined and improved for structure stability through disulfide engineering. For an efficient expression, the codons were optimized as per the usage pattern of the E coli (K12) expression system. The top three refined docked complexes of the vaccine that docked with the MHC-I, MHC-II, and TLR-4 receptors were selected, which proved the best binding potential of the vaccine with immune receptors; this was followed by molecular dynamic simulations. The results indicate the best intermolecular bonding between immune receptors and vaccine epitopes and that they are exposed to the host’s immune system. Finally, the binding energies were calculated to confirm the binding stability of the docked complexes. This work aimed to provide a manageable list of immunogenic and antigenic epitopes that could be used as potent vaccine candidates for experimental in vivo and in vitro studies.

REMD Simulations of Full-Length Alpha-Synuclein Together with Ligands Reveal Binding Region and Effect on Amyloid Conversion

Alpha-synuclein is a key protein involved in the development and progression of Parkinson’s disease and other synucleinopathies. The intrinsically disordered nature of alpha-synuclein hinders the computational screening of new drug candidates for the treatment of these neurodegenerative diseases. In the present work, replica exchange molecular dynamics simulations of the full-length alpha-synuclein together with low-molecular ligands were utilized to predict the binding site and effect on the amyloid-like conversion of the protein. This approach enabled an accurate prediction of the binding sites for three tested compounds (fasudil, phthalocyanine tetrasulfonate, and spermine), giving good agreement with data from experiments by other groups. Lots of information about the binding and protein conformational ensemble enabled the suggestion of a putative effect of the ligands on the amyloid-like conversion of alpha-synuclein and the mechanism of anti- and pro-amyloid activity of the tested compounds. Therefore, this approach looks promising for testing new drug candidates for binding with alpha-synuclein or other intrinsically disordered proteins and at the same time the estimation of the effect on protein behavior, including amyloid-like aggregation.

In silico analysis of mutations near S1/S2 cleavage site in SARS-CoV-2 spike protein reveals increased propensity of glycosylation in Omicron strain

Cleavage of the severe respiratory syndrome coronavirus-2 (SARS-CoV-2) spike protein has been demonstrated to contribute to viral-cell fusion and syncytia formation. Studies have shown that variants of concern (VOC) and variants of interest (VOI) show differing membrane fusion capacity. Mutations near cleavage motifs, such as the S1/S2 and S2' sites, may alter interactions with host proteases and, thus, the potential for fusion. The biochemical basis for the differences in interactions with host proteases for the VOC/VOI spike proteins has not yet been explored. Using sequence and structure-based bioinformatics, mutations near the VOC/VOI spike protein cleavage sites were inspected for their structural effects. All mutations found at the S1/S2 sites were predicted to increase affinity to the furin protease but not TMPRSS2. Mutations at the spike residue P681 in several strains, such P681R in the Delta strain, resulted in the disruption of a proline-directed kinase phosphorylation motif at the S1/S2 site, which may lessen the impact of phosphorylation for these variants. However, the unique N679K mutation in the Omicron strain was found to increase the propensity for O-linked glycosylation at the S1/S2 cleavage site, which may prevent recognition by proteases. Such glycosylation in the Omicron strain may hinder entry at the cell surface and, thus, decrease syncytia formation and induce cell entry through the endocytic pathway as has been shown in previous studies. Further experimental work is needed to confirm the effect of mutations and posttranslational modifications on SARS-CoV-2 spike protein cleavage sites.

Molecular dynamics simulations reveal the inhibitory mechanism of Withanolide A against α-glucosidase and α-amylase

Diabetes mellitus (DM) is a global chronic disease characterized by hyperglycemia and insulin resistance. The unsavory severe gastrointestinal side-effects of synthetic drugs to regulate hyperglycemia have warranted the search for alternative treatments to inhibit the carbohydrate digestive enzymes (e.g. α-amylase and α-glucosidase). Certain phytochemicals recently captured the scientific community's attention as carbohydrate digestive enzyme inhibitors due to their low toxicity and high efficacy, specifically the Withanolides-loaded extract of Withania somnifera. That said, the present study evaluated in silico the efficacy of Withanolide A in targeting both α-amylase and α-glucosidase in comparison to the synthetic drug Acarbose. Protein-ligand interactions, binding affinity, and stability were characterized using pharmacological profiling, high-end molecular docking, and molecular-dynamic simulation. Withanolide A inhibited the activity of α-glucosidase and α-amylase better, exhibiting good pharmacokinetic properties, absorption, and metabolism. Also, Withanolide A was minimally toxic, with higher bioavailability. Interestingly, Withanolide A bonded well to the active site of α-amylase and α-glucosidase, yielding the lowest binding free energy of -82.144 ± 10.671 kcal/mol and -102.1043 ± 11.231 kcal/mol compared to the Acarbose-enzyme complexes (-63.220 ± 13.283 kcal/mol and -82.148 ± 10.671 kcal/mol). Hence, the findings supported the therapeutic potential of Withanolide A as α-amylase and α-glucosidase inhibitor for DM treatment.Communicated by Ramaswamy H. Sarma.

Scoring of protein–protein docking models utilizing predicted interface residues

Scoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today's best scoring functions can significantly increase the number of top‐ranked models but still fail for most targets. Here, we examine the possibility of utilizing predicted interface residues to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the regions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. This study systematically tests different interface prediction methods for scoring >300.000 low‐resolution rigid‐body template free docking decoys. Overall we find that contact‐based interface prediction by BIPSPI is the best method to score docking solutions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high‐importance metric when estimating interface prediction quality, focusing on docking constraints production. Finally, we discussed several limitations for adopting interface predictions as constraints in a docking protocol.

Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning

Cdc2-like kinase 4 (CLK4) inhibitors are of potential therapeutic value in many diseases particularly cancer. In this study, we combined extensive ligand-based pharmacophore exploration, ligand–receptor contact fingerprints generated by flexible docking, physicochemical descriptors and machine learning-quantitative structure–activity relationship (ML-QSAR) analysis to investigate the pharmacophoric/binding requirements for potent CLK4 antagonists. Several ML methods were attempted to tie these properties with anti-CLK4 bioactivities including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGBoost), probabilistic neural network (PNN), and support vector regression (SVR). A genetic function algorithm (GFA) was combined with each method for feature selection. Eventually, GFA-SVR was found to produce the best self-consistent and predictive model. The model selected three pharmacophores, three ligand–receptor contacts and two physicochemical descriptors. The GFA-SVR model and associated pharmacophore models were used to screen the National Cancer Institute (NCI) structural database for novel CLK4 antagonists. Three potent hits were identified with the best one showing an anti-CLK4 IC₅₀ value of 57 nM.

Ligand-based pharmacophores, ligand–receptor contact fingerprints, physicochemical descriptors and machine learning were combined to probe binding of potent CLK4 antagonists. GFA-SVR gave the best model. Virtual screening identified 3 nanomolar hits.

Identification, Characterization, and Synthesis of Natural Parasitic Cysteine Protease Inhibitors: Pentacitidins Are More Potent Falcitidin Analogues

Protease inhibitors represent a promising therapeutic option for the treatment of parasitic diseases such as malaria and human African trypanosomiasis. Falcitidin was the first member of a new class of inhibitors of falcipain-2, a cysteine protease of the malaria parasite Plasmodium falciparum. Using a metabolomics dataset of 25 Chitinophaga strains for molecular networking enabled identification of over 30 natural analogues of falcitidin. Based on MS/MS spectra, they vary in their amino acid chain length, sequence, acyl residue, and C-terminal functionalization; therefore, they were grouped into the four falcitidin peptide families A-D. The isolation, characterization, and absolute structure elucidation of two falcitidin-related pentapeptide aldehyde analogues by extensive MS/MS spectrometry and NMR spectroscopy in combination with advanced Marfey's analysis was in agreement with the in silico analysis of the corresponding biosynthetic gene cluster. Total synthesis of chosen pentapeptide analogues followed by in vitro testing against a panel of proteases revealed selective parasitic cysteine protease inhibition and, additionally, low-micromolar inhibition of α-chymotrypsin. The pentapeptides investigated here showed superior inhibitory activity compared to falcitidin.

Improved prediction of protein-protein interactions using AlphaFold2

Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR.

Predicting the structure of protein complexes is extremely difficult. Here, authors apply AlphaFold2 with optimized multiple sequence alignments to model complexes of interacting proteins, enabling prediction of both if and how proteins interact with state-of-art accuracy.

A review on the application of bioinformatics tools in food microbiome studies

There is currently a transformed interest toward understanding the impact of fermentation on functional food development due to growing consumer interest on modified health benefits of sustainable foods. In this review, we attempt to summarize recent findings regarding the impact of Next-generation sequencing and other bioinformatics methods in the food microbiome and use prediction software to understand the critical role of microbes in producing fermented foods. Traditionally, fermentation methods and starter culture development were considered conventional methods needing optimization to eliminate errors in technique and were influenced by technical knowledge of fermentation. Recent advances in high-output omics innovations permit the implementation of additional logical tactics for developing fermentation methods. Further, the review describes the multiple functions of the predictions based on docking studies and the correlation of genomic and metabolomic analysis to develop trends to understand the potential food microbiome interactions and associated products to become a part of a healthy diet.

Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures

Proteins are the workhorses of cells to carry out sophisticated and complex cellular processes. Such processes require a coordinated and regulated interactions between proteins that are both time and location specific. The strength, or binding affinity, of protein-protein interactions ranges between the micro- and the nanomolar association constant, often dictating the molecular mechanisms underlying the interaction and the longevity of the complex, i.e., transient or permanent. In consequence, there is a need to quantify the strength of protein-protein interactions for biological, biomedical, and biotechnological applications. While experimental methods are labor intensive and costly, computational ones are useful tools to predict the affinity of protein-protein interactions. In this chapter, we review the methods developed by us to address this question. We briefly present two methods to comprehend the structure of the protein complex derived by either comparative modeling or docking. Then we introduce BADOCK, a method to predict the binding energy without requiring the structure of the protein complex, thus overcoming one of the major limitations of structure-based methods for the prediction of binding affinity. BADOCK utilizes the structure of unbound proteins and the protein docking sampling space to predict protein-protein binding affinities. We present step-by-step protocols to utilize these methods, describing the inputs and potential pitfalls as well as their respective strengths and limitations.

Computational evidence of new putative allosteric sites in the acetylcholinesterase receptor

Acetylcholinesterase (AChE), with a rigid structure and buried active site at the end of a deep narrow gorge, is interesting enough to solve the paradox between high catalytic activity and unavailability of the active site in treatment of Alzheimer's disease (AD). In this way, the blind docking process is performed on an ensemble of AChE structures created with molecular dynamics (MD) simulations to survey the whole space of AChE to find multiple access pathways to the active site and ranking them based on their affinity scores. Our results show that there are other allosteric binding sites in the protein structure whose inhibition, can affect protein function by disrupting the release of the Acetylcholine (AC) degradation products. In this study, inhibitory activities of Hybride14 and two natural compounds (Papaverine and Palmatine) were evaluated for all possible allosteric sites via docking method. The results confirmed the non-competitive inhibition mechanism. The best binding mode for these inhibitors and efficacy of hydrogen bonds and hydrophobic interactions on inhibitory activities of ligands were also disclosed. Furthermore, our studies provide significant molecular insight for AChE inhibition that could aid in the development of new drugs for AD's treatment.

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Baseline Comparisons of Complementary Sampling Methods for Assembly Driven by Short-Ranged Pair Potentials toward Fast and Flexible Hybridization

This work measures baseline sampling characteristics that highlight fundamental differences between sampling methods for assembly driven by short-ranged pair potentials. Such granular comparison is essential for fast, flexible, and accurate hybridization of complementary methods. Besides sampling speed, efficiency, and accuracy of uniform grid coverage, other sampling characteristics measured are (i) accuracy of covering narrow low energy regions that have low effective dimension (ii) ability to localize sampling to specific basins, and (iii) flexibility in sampling distributions. As a proof of concept, we compare a recently developed geometric methodology EASAL (Efficient Atlasing and Search of Assembly Landscapes) and the traditional Monte Carlo (MC) method for sampling the energy landscape of two assembling trans-membrane helices, driven by short-range pair potentials. By measuring the above-mentioned sampling characteristics, we demonstrate that EASAL provides localized and accurate coverage of crucial regions of the energy landscape of low effective dimension, under flexible sampling distributions, with much fewer samples and computational resources than MC sampling. EASAL's empirically validated theoretical guarantees permit credible extrapolation of these measurements and comparisons to arbitrary number and size of assembling units. Promising avenues for hybridizing the complementary advantages of the two methods are discussed.

Decoding Protein-protein Interactions: An Overview

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Structure based peptide design, molecular dynamics and MM-PBSA studies for targeting C terminal dimerization of NFAT5 DNA binding domain

NFAT5 as a transcription factor with an established role in osmotic stress response, has also been revealed to be active under numerous settings, including pathological conditions such as diabetic microvascular complications, chronic arthritis and cancer. Despite these links, current strategies for downregulating NFAT5 activity only relies on indirect modulators, not directly targeting NFAT5, itself. With this study, through using a computational approach, an original peptide was explored to directly target C terminal dimerization of NFAT5 RHR, located in its DNA binding domain. At first, homodimeric NFAT5 RHR bound to its consensus DNA was used for prediction of a preliminary peptide sequence. Possible amino acid replacements for this preliminary peptide were predicted for optimization, which was followed by addition of a cell penetrating peptide sequence. These attempts yielded a small peptide library, which was further investigated for peptide affinities towards C terminal of NFAT5 RHR through molecular docking, 50 ns and 250 ns molecular dynamics simulations, followed by estimation of MM-PBSA based relative binding free energies. Results indicated that after receiving mutations on the preliminary peptide sequence for optimization, a unique peptide could target C terminal dimerization region of NFAT5 RHR through using its cell penetrating peptide sequence. In conclusion, this is the first study presenting computational evidence on identification of a novel peptide capable of directly targeting NFAT5 dimerization. Besides, future implications of these observations were also discussed in terms of methodology and possible applications.

Molecular docking and molecular dynamics simulation of Bacillus thuringiensis dehalogenase against haloacids, haloacetates and chlorpyrifos

The high dependency and surplus use of agrochemical products have liberated enormous quantities of toxic halogenated pollutants into the environment and threaten the well-being of humankind. Herein, this study performed molecular docking, molecular dynamic (MD) simulations, molecular mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculations on the DehH2 from Bacillus thuringiensis, to identify the order of which the enzyme degrades different substrates, haloacids, haloacetate and chlorpyrifos. The study discovered that the DehH2 favored the degradation of haloacids and haloacetates (-3.3 - 4.6 kcal/mol) and formed three hydrogen bonds with Asp125, Arg201 and Lys202. Despite the inconclusive molecular docking result, chlorpyrifos was consistently shown to be the least favored substrate of the DehH2 in MD simulations and MM-PBSA calculations. Results of MD simulations revealed the DehH2-haloacid- (RMSD 0.15 - 0.25 nm) and DehH2-haloacetates (RMSF 0.05 - 0.25 nm) were more stable, with the DehH2-L-2CP complex being the most stable while the least was the DehH2-chlorpyrifos (RMSD 0.295 nm; RMSF 0.05 - 0.59 nm). The Molecular Mechanics Poisson-Boltzmann Surface Area calculations showed the DehH2-L-2CP complex (-24.27 kcal/mol) having the lowest binding energy followed by DehH2-MCA (-22.78 kcal/mol), DehH2-D-2CP (-21.82 kcal/mol), DehH2-3CP (-21.11 kcal/mol), DehH2-2,2-DCP (-18.34 kcal/mol), DehH2-2,3-DCP (-8.34 kcal/mol), DehH2-TCA (-7.62 kcal/mol), while chlorpyrifos was unable to spontaneously bind to DehH2 (+127.16 kcal/mol). In a nutshell, the findings of this study offer valuable insights into the rational tailoring of the DehH2 for expanding its substrate specificity and catalytic activity in the near future.Communicated by Ramaswamy H. Sarma.

Using collections of structural models to predict changes of binding affinity caused by mutations in protein-protein interactions

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.

Plant Antimicrobial Peptides: State of the Art, In Silico Prediction and Perspectives in the Omics Era

Even before the perception or interaction with pathogens, plants rely on constitutively guardian molecules, often specific to tissue or stage, with further expression after contact with the pathogen. These guardians include small molecules as antimicrobial peptides (AMPs), generally cysteine-rich, functioning to prevent pathogen establishment. Some of these AMPs are shared among eukaryotes (eg, defensins and cyclotides), others are plant specific (eg, snakins), while some are specific to certain plant families (such as heveins). When compared with other organisms, plants tend to present a higher amount of AMP isoforms due to gene duplications or polyploidy, an occurrence possibly also associated with the sessile habit of plants, which prevents them from evading biotic and environmental stresses. Therefore, plants arise as a rich resource for new AMPs. As these molecules are difficult to retrieve from databases using simple sequence alignments, a description of their characteristics and in silico (bioinformatics) approaches used to retrieve them is provided, considering resources and databases available. The possibilities and applications based on tools versus database approaches are considerable and have been so far underestimated.

From In Silico to Experimental Validation: Tailoring Peptide Substrates for a Serine Protease

Smart nanocarriers for the transport of drugs to tumor cells are nowadays of great interest for treating cancer. The use of enzymatic stimuli to cleave peptide-based drug nanocapsules for the selective release of nanocapsule cargo in close proximity to tumor cells opens new possibilities in cancer research. In the present work, we demonstrate a methodology for finding and optimizing cleavable substrate sequences by the type II transmembrane serine protease hepsin, which is highly overexpressed in prostate cancer. The design and screening of combinatorial libraries in silico against the binding cavity of hepsin allow the identification of a panel of promising substrates with high-calculated docking scores. In vitro screening verifies the predictions and showed that all substrates are cleaved by hepsin with higher efficiency than the literature known hepsin substrate RQLR↓VVGG. The introduction of d-amino acids on a selected peptide with the highest catalytic efficiency (k_cat/K_m) renders it resistant to cleavage by plasma or serum while maintaining their susceptibility to hepsin.

Identification of a Selective PDE4B Inhibitor From Bryophyllum pinnatum by Target Fishing Study and In Vitro Evaluation of Quercetin 3-O-α-L-Arabinopyranosyl-(1→2)-O-α-L-Rhamnopyranoside

Natural products are considered an important source of bioactive compounds especially in biodiversity-rich countries like Brazil. The identification of potential targets is crucial to the development of drugs from natural sources. In this context, in silico methodologies, such as inverse virtual screening (target fishing), are interesting tools as they are a rational and direct method that reduces costs and experimental time. Among the species of Brazilian biomes, Bryophyllum pinnatum (Lam.) Oken, native to Madagascar, is widely used by the population to treat inflammation conditions. It has a remarkable presence of flavonoids, including quercetin 3-O-α-L-arabinopyranosyl-(1→2)-O-α-L-rhamnopyranoside (1), considered one of its major compounds. However, until now there were no studies addressing its putative mechanism of action and explaining its pharmacological action. The enzyme PDE4B, known as an antiinflammatory protein, was indicated as a promising target by target fishing methods. This activity was confirmed by in vitro enzymatic inhibition, and an expressive selectivity of PDE4B over PDE4A was demonstrated. The interactions were investigated through molecular dynamics simulations. The results were pioneering, representing an advance in the investigation of the antiinflammatory action of B. pinnatum and confirm the potential of the flavonoid as a chemical extract marker. Also, the flavonoid was shown to be a promising lead for the design of other selective PDE4B blockers to treat inflammatory diseases.

Pushing the accuracy limit of shape complementarity for protein-protein docking

Background

Protein-protein docking is a valuable computational approach for investigating protein-protein interactions. Shape complementarity is the most basic component of a scoring function and plays an important role in protein-protein docking. Despite significant progresses, shape representation remains an open question in the development of protein-protein docking algorithms, especially for grid-based docking approaches.

Results

We have proposed a new pairwise shape-based scoring function (LSC) for protein-protein docking which adopts an exponential form to take into account long-range interactions between protein atoms. The LSC scoring function was incorporated into our FFT-based docking program and evaluated for both bound and unbound docking on the protein docking benchmark 4.0. It was shown that our LSC achieved a significantly better performance than four other similar docking methods, ZDOCK 2.1, MolFit/G, GRAMM, and FTDock/G, in both success rate and number of hits. When considering the top 10 predictions, LSC obtained a success rate of 51.71% and 6.82% for bound and unbound docking, respectively, compared to 42.61% and 4.55% for the second-best program ZDOCK 2.1. LSC also yielded an average of 8.38 and 3.94 hits per complex in the top 1000 predictions for bound and unbound docking, respectively, followed by 6.38 and 2.96 hits for the second-best ZDOCK 2.1.

Conclusions

The present LSC method will not only provide an initial-stage docking approach for post-docking processes but also have a general implementation for accurate representation of other energy terms on grids in protein-protein docking. The software has been implemented in our HDOCK web server at http://hdock.phys.hust.edu.cn/.

Fragment-centric topographic mapping method guides the understanding of ABCG2-inhibitor interactions

Our study gains insight into the development of novel specific ABCG2 inhibitors, and develops a comprehensive computational strategy to understand protein ligand interaction with the help of AlphaSpace, a fragment-centric topographic mapping tool.

Protein-protein interaction specificity is captured by contact preferences and interface composition

Motivation

Large-scale computational docking will be increasingly used in future years to discriminate protein–protein interactions at the residue resolution. Complete cross-docking experiments make in silico reconstruction of protein–protein interaction networks a feasible goal. They ask for efficient and accurate screening of the millions structural conformations issued by the calculations.

Results

We propose CIPS (Combined Interface Propensity for decoy Scoring), a new pair potential combining interface composition with residue–residue contact preference. CIPS outperforms several other methods on screening docking solutions obtained either with all-atom or with coarse-grain rigid docking. Further testing on 28 CAPRI targets corroborates CIPS predictive power over existing methods. By combining CIPS with atomic potentials, discrimination of correct conformations in all-atom structures reaches optimal accuracy. The drastic reduction of candidate solutions produced by thousands of proteins docked against each other makes large-scale docking accessible to analysis.

Availability and implementation

CIPS source code is freely available at http://www.lcqb.upmc.fr/CIPS.

Supplementary information

Supplementary data are available at Bioinformatics online.

ProPOSE: Direct Exhaustive Protein-Protein Docking with Side Chain Flexibility

Despite decades of development, protein-protein docking remains a largely unsolved problem. The main difficulties are the immense space spanned by the translational and rotational degrees of freedom and the prediction of the conformational changes of proteins upon binding. FFT is generally the preferred method to exhaustively explore the translation-rotation space at a fine grid resolution, albeit with the trade-off of approximating force fields with correlation functions. This work presents a direct search alternative that samples the states in Cartesian space at the same resolution and computational cost as standard FFT methods. Operating in real space allows the use of standard force field functional forms used in typical non-FFT methods as well as the implementation of strategies for focused exploration of conformational flexibility. Currently, a few misplaced side chains can cause docking programs to fail. This work specifically addresses the problem of side chain rearrangements upon complex formation. Based on the observation that most side chains retain their unbound conformation upon binding, each rigidly docked pose is initially scored ignoring up to a limited number of side chain overlaps which are resolved in subsequent repacking and minimization steps. On test systems where side chains are altered and backbones held in their bound state, this implementation provides significantly better native pose recovery and higher quality (lower RMSD) predictions when compared with five of the most popular docking programs. The method is implemented in the software program ProPOSE (Protein Pose Optimization by Systematic Enumeration).

SnapDock-template-based docking by Geometric Hashing

Motivation

A highly efficient template-based protein–protein docking algorithm, nicknamed SnapDock, is presented. It employs a Geometric Hashing-based structural alignment scheme to align the target proteins to the interfaces of non-redundant protein–protein interface libraries. Docking of a pair of proteins utilizing the 22 600 interface PIFACE library is performed in < 2 min on the average. A flexible version of the algorithm allowing hinge motion in one of the proteins is presented as well.

Results

To evaluate the performance of the algorithm a blind re-modelling of 3547 PDB complexes, which have been uploaded after the PIFACE publication has been performed with success ratio of about 35%. Interestingly, a similar experiment with the template free PatchDock docking algorithm yielded a success rate of about 23% with roughly 1/3 of the solutions different from those of SnapDock. Consequently, the combination of the two methods gave a 42% success ratio.

Availability and implementation

A web server of the application is under development.

Properties that rank protein:protein docking poses with high accuracy

The development of docking algorithms to predict near-native structures of protein:protein complexes from the structure of the isolated monomers is of paramount importance for molecular biology and drug discovery.

Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2

We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall's Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening.

Novel peptide motifs from lysozyme suppress pro-inflammatory cytokines in macrophages by antagonizing toll-like receptor and LPS-scavenging action

Lysozyme is commonly found in spots where bacterial infections are most likely to enter the body. Earlier we found that lysozyme possesses five antimicrobial peptide motifs in its N-terminal region which can be generated by newborn pepsin. In this study, we explore the role of these peptides in the anti-inflammatory activity of lysozyme. The five peptides, helix1 (H1), helix2 (H2), H1 and H2 connected with a loop (HLH), H2 extended with either 2 β-strands (H2-S12) or 3 β-strands (H2-S13), were synthesized and examined for anti-inflammatory action. The five peptides dose-dependently decreased, to different degrees, expression of pro-inflammatory cytokines, TNF-α, IL-6 and IL-1β, in lipopolysaccharide (LPS)- or interferon-gamma (INF-γ)-stimulated mouse macrophage cells (RAW264.7). The HLH peptide and its individual helices (H1 and H2) were markedly the most potent anti-inflammatory. When macrophage cells were stimulated with live bacteria (E. coli), H1 peptide was the most powerful suppressor of TNF-α and IL-6 expression, providing evidence that the peptide is able to antagonize the pathogen-induced inflammatory response. Receptor binding assay and docking simulation provided evidence that H1 peptide bind specifically to the pocket for endotoxin binding of the toll-like receptor 4 (TLR-4) of macrophage. The results demonstrate, for the first time, the molecular basis of anti-inflammatory action of lysozyme that N-terminal helical peptides are the main contributors. This exciting finding offers new classes of therapeutic peptides with potential in the treatment of infection-induced inflammatory diseases.

Modeling complexes of modeled proteins

Structural characterization of proteins is essential for understanding life processes at the molecular level. However, only a fraction of known proteins have experimentally determined structures. This fraction is even smaller for protein-protein complexes. Thus, structural modeling of protein-protein interactions (docking) primarily has to rely on modeled structures of the individual proteins, which typically are less accurate than the experimentally determined ones. Such "double" modeling is the Grand Challenge of structural reconstruction of the interactome. Yet it remains so far largely untested in a systematic way. We present a comprehensive validation of template-based and free docking on a set of 165 complexes, where each protein model has six levels of structural accuracy, from 1 to 6 Å Cα RMSD. Many template-based docking predictions fall into acceptable quality category, according to the CAPRI criteria, even for highly inaccurate proteins (5-6 Å RMSD), although the number of such models (and, consequently, the docking success rate) drops significantly for models with RMSD > 4 Å. The results show that the existing docking methodologies can be successfully applied to protein models with a broad range of structural accuracy, and the template-based docking is much less sensitive to inaccuracies of protein models than the free docking. Proteins 2017; 85:470-478. © 2016 Wiley Periodicals, Inc.

Molecular Simulations of Disulfide-Rich Venom Peptides with Ion Channels and Membranes

Disulfide-rich peptides isolated from the venom of arthropods and marine animals are a rich source of potent and selective modulators of ion channels. This makes these peptides valuable lead molecules for the development of new drugs to treat neurological disorders. Consequently, much effort goes into understanding their mechanism of action. This paper presents an overview of how molecular simulations have been used to study the interactions of disulfide-rich venom peptides with ion channels and membranes. The review is focused on the use of docking, molecular dynamics simulations, and free energy calculations to (i) predict the structure of peptide-channel complexes; (ii) calculate binding free energies including the effect of peptide modifications; and (iii) study the membrane-binding properties of disulfide-rich venom peptides. The review concludes with a summary and outlook.

The ClusPro web server for protein-protein docking

The ClusPro server (https://cluspro.org) is a widely used tool for protein-protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.

Addressing recent docking challenges: A hybrid strategy to integrate template-based and free protein-protein docking

Protein-protein docking is an important computational tool for predicting protein-protein interactions. With the rapid development of proteomics projects, more and more experimental binding information ranging from mutagenesis data to three-dimensional structures of protein complexes are becoming available. Therefore, how to appropriately incorporate the biological information into traditional ab initio docking has been an important issue and challenge in the field of protein-protein docking. To address these challenges, we have developed a Hybrid DOCKing protocol of template-based and template-free approaches, referred to as HDOCK. The basic procedure of HDOCK is to model the structures of individual components based on the template complex by a template-based method if a template is available; otherwise, the component structures will be modeled based on monomer proteins by regular homology modeling. Then, the complex structure of the component models is predicted by traditional protein-protein docking. With the HDOCK protocol, we have participated in the CPARI experiment for rounds 28-35. Out of the 25 CASP-CAPRI targets for oligomer modeling, our HDOCK protocol predicted correct models for 16 targets, ranking one of the top algorithms in this challenge. Our docking method also made correct predictions on other CAPRI challenges such as protein-peptide binding for 6 out of 8 targets and water predictions for 2 out of 2 targets. The advantage of our hybrid docking approach over pure template-based docking was further confirmed by a comparative evaluation on 20 CASP-CAPRI targets. Proteins 2017; 85:497-512. © 2016 Wiley Periodicals, Inc.

The ClusPro web server for protein-protein docking

The ClusPro server (https://cluspro.org) is a widely used tool for protein-protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.

A Protocol for the Design of Protein and Peptide Nanostructure Self-Assemblies Exploiting Synthetic Amino Acids

In recent years there has been increasing interest in nanostructure design based on the self-assembly properties of proteins and polymers. Nanodesign requires the ability to predictably manipulate the properties of the self-assembly of autonomous building blocks, which can fold or aggregate into preferred conformational states. The design includes functional synthetic materials and biological macromolecules. Autonomous biological building blocks with available 3D structures provide an extremely rich and useful resource. Structural databases contain large libraries of protein molecules and their building blocks with a range of sizes, shapes, surfaces, and chemical properties. The introduction of engineered synthetic residues or short peptides into these building blocks can greatly expand the available chemical space and enhance the desired properties. Herein, we summarize a protocol for designing nanostructures consisting of self-assembling building blocks, based on our recent works. We focus on the principles of nanostructure design with naturally occurring proteins and synthetic amino acids, as well as hybrid materials made of amyloids and synthetic polymers.