CABS-dock standalone: a toolbox for flexible protein-peptide docking

Discovery of Highly Bioactive Peptides through Hierarchical Structural Information and Molecular Dynamics Simulations

Peptide drugs play an essential role in modern therapeutics, but the computational design of these molecules is hindered by several challenges. Traditional methods like molecular docking and molecular dynamics (MD) simulation, as well as recent deep learning approaches, often face limitations related to computational resource demands, complex binding affinity assessments, extensive data requirements, and poor model interpretability. Here, we introduce PepHiRe, an innovative methodology that utilizes the hierarchical structural information in peptide sequences and employs a novel strategy called Ladderpath, rooted in algorithmic information theory, to rapidly generate and enhance the efficiency and clarity of novel peptide design. We applied PepHiRe to develop BH3-like peptide inhibitors targeting myeloid cell leukemia-1, a protein associated with various cancers. By analyzing just eight known bioactive BH3 peptide sequences, PepHiRe effectively derived a hierarchy of subsequences used to create new BH3-like peptides. These peptides underwent screening through MD simulations, leading to the selection of five candidates for synthesis and subsequent in vitro testing. Experimental results demonstrated that these five peptides possess high inhibitory activity, with IC50 values ranging from 28.13 ± 7.93 to 167.42 ± 22.15 nM. Our study explores a white-box model driven technique and a structured screening pipeline for identifying and generating novel peptides with potential bioactivity.

Dissecting the neuroprotective interaction between the BH4 domain of BCL-w and the IP3 receptor

BCL-w is a BCL-2 family protein that promotes cell survival in tissue- and disease-specific contexts. The canonical anti-apoptotic functionality of BCL-w is mediated by a surface groove that traps the BCL-2 homology 3 (BH3) α-helices of pro-apoptotic members, blocking cell death. A distinct N-terminal portion of BCL-w, termed the BCL-2 homology 4 (BH4) domain, selectively protects axons from paclitaxel-induced degeneration by modulating IP3 receptors, a noncanonical BCL-2 family target. Given the potential of BCL-w BH4 mimetics to prevent or mitigate chemotherapy-induced peripheral neuropathy, we sought to characterize the interaction between BCL-w BH4 and the IP3 receptor, combining "staple" and alanine scanning approaches with molecular dynamics simulations. We generated and identified stapled BCL-w BH4 peptides with optimized IP3 receptor binding and neuroprotective activities. Point mutagenesis further revealed the sequence determinants for BCL-w BH4 specificity, providing a blueprint for therapeutic targeting of IP3 receptors to achieve neuroprotection.

AbAMPdb: a database of Acinetobacter baumannii specific antimicrobial peptides

Acinetobacter baumannii has emerged as a prominent nosocomial pathogen, exhibiting a progressive rise in resistance to therapeutic interventions. This rise in resistance calls for alternative strategies. Here, we propose an alternative yet specialized resource on antimicrobial peptides (AMPs) against A. baumannii. Database 'AbAMPdb' is the manually curated collection of 300 entries containing the 250 experimental AMP sequences and 50 corresponding synthetic or mutated AMP sequences. The mutated sequences were modified with reported amino acid substitutions intended for decreasing the toxicity and increasing the antimicrobial potency. AbAMPdb also provides 3D models of all 300 AMPs, comprising 250 natural and 50 synthetic or mutated AMPs. Moreover, the database offers docked complexes comprising 5000 AMPs and their corresponding A. baumannii target proteins. These complexes, accessible in Protein Data Bank format, enable the 2D visualization of the interacting amino acid residues. We are confident that this comprehensive resource furnishes vital information concerning AMPs, encompassing their docking interactions with virulence factors and antibiotic resistance proteins of A. baumannii. To enhance clinical relevance, the characterized AMPs could undergo further investigation both in vitro and in vivo. Database URL: https://abampdb.mgbio.tech/.

Novel Methodology for the Design of Personalized Cancer Vaccine Targeting Neoantigens: Application to Pancreatic Ductal Adenocarcinoma

Personalized cancer vaccines have emerged as a promising avenue for cancer treatment or prevention strategies. This approach targets the specific genetic alterations in individual patient's tumors, offering a more personalized and effective treatment option. Previous studies have shown that generalized peptide vaccines targeting a limited scope of gene mutations were ineffective, emphasizing the need for personalized approaches. While studies have explored personalized mRNA vaccines, personalized peptide vaccines have not yet been studied in this context. Pancreatic ductal adenocarcinoma (PDAC) remains challenging in oncology, necessitating innovative therapeutic strategies. In this study, we developed a personalized peptide vaccine design methodology, employing RNA sequencing (RNAseq) to identify prevalent gene mutations underlying PDAC development in a patient solid tumor tissue. We performed RNAseq analysis for trimming adapters, read alignment, and somatic variant calling. We also developed a Python program called SCGeneID, which validates the alignment of the RNAseq analysis. The Python program is freely available to download. Using chromosome number and locus data, SCGeneID identifies the target gene along the UCSC hg38 reference set. Based on the gene mutation data, we developed a personalized PDAC cancer vaccine that targeted 100 highly prevalent gene mutations in two patients. We predicted peptide-MHC binding affinity, immunogenicity, antigenicity, allergenicity, and toxicity for each epitope. Then, we selected the top 50 and 100 epitopes based on our previously published vaccine design methodology. Finally, we generated pMHC-TCR 3D molecular model complex structures, which are freely available to download. The designed personalized cancer vaccine contains epitopes commonly found in PDAC solid tumor tissue. Our personalized vaccine was composed of neoantigens, allowing for a more precise and targeted immune response against cancer cells. Additionally, we identified mutated genes, which were also found in the reference study, where we obtained the sequencing data, thus validating our vaccine design methodology. This is the first study designing a personalized peptide cancer vaccine targeting neoantigens using human patient data to identify gene mutations associated with the specific tumor of interest.

The role and future prospects of artificial intelligence algorithms in peptide drug development

Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.

LiF-MS+, a revised technique for mapping peptide-protein interactions

Short linear motifs are sequences of amino acids present in unstructured polypeptide regions that function as ligands for specific sites on folded protein domains. These interactions, which often occur with low to modest affinity, modulate dynamic biological processes such as signal transduction and membrane trafficking. We recently described Ligand Footprinting-Mass Spectrometry (LiF-MS), a technique that rapidly and precisely maps sites at which short peptide ligands bind their biologically relevant recognition sites on folded protein domains. This approach marks the binding location of a peptide ligand on a structured protein using a cleavable crosslinker appended to the ligand that leaves behind a stable chemical modification following cleavage. This modification serves as a mass tag detectable by mass spectrometry, pinpointing sites of peptide ligand binding. Here we present LiF-MS+, an improved version of the footprinting technique that replaces the butanol mass tag with 1-butylpyrrolidine, which is positively charged at neutral pH and thus aids in ionization of the crosslinked peptide for analysis by mass spectrometry. We show ligand-mediated butylpyrrolidine footprinting effectively maps the well characterized binding interaction of the p38α mitogen-activated protein kinase (MAPK) with a MKK6 D-motif short linear motif peptide ligand, uncovering additional binding site information not observed in our original experiment. LiF-MS+ is thus a straightforward improvement of our previously published methodology for mapping the binding of short linear motifs to folded protein domains.

Prediction of virus-host interactions and identification of hot spot residues of DENV-2 and SH3 domain interactions

Dengue virus, particularly serotype 2 (DENV-2), poses a significant global health threat, and understanding the molecular basis of its interactions with host cell proteins is imperative for developing targeted therapeutic strategies. This study elucidated the interactions between proline-enriched motifs and Src homology 3 (SH3) domain. The SH3 domain is pivotal in mediating protein-protein interactions, particularly by recognizing and binding to proline-rich regions in partner proteins. Through a computational pipeline, we analyzed the interactions and binding modes of proline-enriched motifs with SH3 domains, identified new potential DENV-2 interactions with the SH3 domain, and revealed potential hot spot residues, underscoring their significance in the viral life cycle. This comprehensive analysis provides crucial insights into the molecular basis of DENV-2 infection, highlighting conserved and serotype-specific interactions. The identified hot spot residues offer potential targets for therapeutic intervention, laying the foundation for developing antiviral strategies against Dengue virus infection. These findings contribute to the broader understanding of viral-host interactions and provide a roadmap for future research on Dengue virus pathogenesis and treatment.

Invited review: Camel milk-derived bioactive peptides and diabetes-Molecular view and perspectives

In dairy science, camel milk (CM) constitutes a center of interest for scientists due to its known beneficial effect on diabetes as demonstrated in many in vitro, in vivo, and clinical studies and trials. Overall, CM had positive effects on various parameters related to glucose transport and metabolism as well as the structural and functional properties of the pancreatic β-cells and insulin secretion. Thus, CM consumption may help manage diabetes; however, such a recommendation will become rationale and clinically conceivable only if the exact molecular mechanisms and pathways involved at the cellular levels are well understood. Moreover, the application of CM as an alternative antidiabetic tool may first require the identification of the exact bioactive molecules behind such antidiabetic properties. In this review, we describe the advances in our knowledge of the molecular mechanisms reported to be involved in the beneficial effects of CM in managing diabetes using different in vitro and in vivo models. This mainly includes the effects of CM on the different molecular pathways controlling (1) insulin receptor signaling and glucose uptake, (2) the pancreatic β-cell structure and function, and (3) the activity of key metabolic enzymes in glucose metabolism. Moreover, we described the current status of the identification of CM-derived bioactive peptides and their structure-activity relationship study and characterization in the context of molecular markers related to diabetes. Such an overview will not only enrich our scientific knowledge of the plausible mode of action of CM in diabetes but should ultimately rationalize the claim of the potential application of CM against diabetes. This will pave the way toward new directions and ideas for developing a new generation of antidiabetic products taking benefits from the chemical composition of CM.

Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

Acceleration of Protein Degradation by 20S Proteasome-Binding Peptides Generated by In Vitro Artificial Evolution

Although the 20S core particle (CP) of the proteasome is an important component of the 26S holoenzyme, the stand-alone 20S CP acts directly on intrinsically disordered and oxidized/damaged proteins to degrade them in a ubiquitin-independent manner. It has been postulated that some structural features of substrate proteins are recognized by the 20S CP to promote substrate uptake, but the mechanism of substrate recognition has not been fully elucidated. In this study, we screened peptides that bind to the 20S CP from a random eight-residue pool of amino acid sequences using complementary DNA display an in vitro molecular evolution technique. The identified 20S CP-binding amino acid sequence was chemically synthesized and its effects on the 20S CP were investigated. The 20S CP-binding peptide stimulated the proteolytic activity of the inactive form of 20S CP. The peptide bound directly to one of the α-subunits, opening a gate for substrate entry on the α-ring. Furthermore, the attachment of this peptide sequence to α-synuclein enhanced its degradation by the 20S CP in vitro. In addition to these results, docking simulations indicated that this peptide binds to the top surface of the α-ring. These peptides could function as a key to control the opening of the α-ring gate.

Use of tree-based machine learning methods to screen affinitive peptides based on docking data

Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.

MDM2-Targeting Reassembly Peptide (TRAP) Nanoparticles for p53-Based Cancer Therapy

Gene mutations and functional inhibition are the major obstacles for p53-mediated oncotherapy. For p53-wild-type tumors, the underlying mechanisms of functional inhibition of p53 during oncogenesis are unknown. The results reveal that the expression of the MDM2 inhibitor ARF is inhibited in p53-wild-type tumors, indicating that the restoration of ARF could be a potential oncotherapy strategy for p53-wild-type tumors. Therefore, ARF-mimetic MDM2-targeting reassembly peptide nanoparticles (MtrapNPs) for p53-based tumor therapy is developed. The results elucidated that the MtrapNPs respond to and form a nanofiber structure with MDM2. By trapping MDM2, the MtrapNPs stabilize and activate p53 for the inhibition of p53-wild-type tumors. In most cases, reactivated mutant p53 is inhibited and degraded by MDM2. In the present study, MtrapNPs are used to load and deliver arsenic trioxide, a p53 mutation rescuer, for p53-mutated tumor treatment in both orthotopic and metastatic models, and they exhibit significant therapeutic effects. Therefore, the study provides evidence supporting a link between decreased ARF expression and tumor development in patients with p53-wild-type tumors. Thus, the MDM2-trap strategy, which addresses both the inhibition and mutations of p53, is an efficient strategy for the treatment of p53-mutated tumors.

Design of Cyclic Peptides Targeting Protein-Protein Interactions Using AlphaFold

More than 930,000 protein-protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein-cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein-peptide complexes and one protein-protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.

Open-Source Machine Learning in Computational Chemistry

The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.

Integrative modeling of diverse protein-peptide systems using CABS-dock

The CABS model can be applied to a wide range of protein-protein and protein-peptide molecular modeling tasks, such as simulating folding pathways, predicting structures, docking, and analyzing the structural dynamics of molecular complexes. In this work, we use the CABS-dock tool in two diverse modeling tasks: 1) predicting the structures of amyloid protofilaments and 2) identifying cleavage sites in the peptide substrates of proteolytic enzymes. In the first case, simulations of the simultaneous docking of amyloidogenic peptides indicated that the CABS model can accurately predict the structures of amyloid protofilaments which have an in-register parallel architecture. Scoring based on a combination of symmetry criteria and estimated interaction energy values for bound monomers enables the identification of protofilament models that closely match their experimental structures for 5 out of 6 analyzed systems. For the second task, it has been shown that CABS-dock coarse-grained docking simulations can be used to identify the positions of cleavage sites in the peptide substrates of proteolytic enzymes. The cleavage site position was correctly identified for 12 out of 15 analyzed peptides. When combined with sequence-based methods, these docking simulations may lead to an efficient way of predicting cleavage sites in degraded proteins. The method also provides the atomic structures of enzyme-substrate complexes, which can give insights into enzyme-substrate interactions that are crucial for the design of new potent inhibitors.

The Anti-Dengue Virus Peptide DV2 Inhibits Zika Virus Both In Vitro and In Vivo

The C-terminal portion of the E protein, known as stem, is conserved among flaviviruses and is an important target to peptide-based antiviral strategies. Since the dengue (DENV) and Zika (ZIKV) viruses share sequences in the stem region, in this study we evaluated the cross-inhibition of ZIKV by the stem-based DV2 peptide (419–447), which was previously described to inhibit all DENV serotypes. Thus, the anti-ZIKV effects induced by treatments with the DV2 peptide were tested in both in vitro and in vivo conditions. Molecular modeling approaches have demonstrated that the DV2 peptide interacts with amino acid residues exposed on the surface of pre- and postfusion forms of the ZIKA envelope (E) protein. The peptide did not have any significant cytotoxic effects on eukaryotic cells but efficiently inhibited ZIKV infectivity in cultivated Vero cells. In addition, the DV2 peptide reduced morbidity and mortality in mice subjected to lethal challenges with a ZIKV strain isolated in Brazil. Taken together, the present results support the therapeutic potential of the DV2 peptide against ZIKV infections and open perspectives for the development and clinical testing of anti-flavivirus treatments based on synthetic stem-based peptides.

Computational prediction of disordered binding regions

One of the key features of intrinsically disordered regions (IDRs) is their ability to interact with a broad range of partner molecules. Multiple types of interacting IDRs were identified including molecular recognition fragments (MoRFs), short linear sequence motifs (SLiMs), and protein-, nucleic acids- and lipid-binding regions. Prediction of binding IDRs in protein sequences is gaining momentum in recent years. We survey 38 predictors of binding IDRs that target interactions with a diverse set of partners, such as peptides, proteins, RNA, DNA and lipids. We offer a historical perspective and highlight key events that fueled efforts to develop these methods. These tools rely on a diverse range of predictive architectures that include scoring functions, regular expressions, traditional and deep machine learning and meta-models. Recent efforts focus on the development of deep neural network-based architectures and extending coverage to RNA, DNA and lipid-binding IDRs. We analyze availability of these methods and show that providing implementations and webservers results in much higher rates of citations/use. We also make several recommendations to take advantage of modern deep network architectures, develop tools that bundle predictions of multiple and different types of binding IDRs, and work on algorithms that model structures of the resulting complexes.

Disordered proteins mitigate the temperature dependence of site-specific binding free energies

Biophysical characterization of protein-protein interactions involving disordered proteins is challenging. A common simplification is to measure the thermodynamics and kinetics of disordered site binding using peptides containing only the minimum residues necessary. We should not assume, however, that these few residues tell the whole story. Son of sevenless, a multidomain signaling protein from Drosophila melanogaster, is critical to the mitogen-activated protein kinase pathway, passing an external signal to Ras, which leads to cellular responses. The disordered 55 kDa C-terminal domain of Son of sevenless is an autoinhibitor that blocks guanidine exchange factor activity. Activation requires another protein, Downstream of receptor kinase (Drk), which contains two Src homology 3 domains. Here, we utilized NMR spectroscopy and isothermal titration calorimetry to quantify the thermodynamics and kinetics of the N-terminal Src homology 3 domain binding to the strongest sites incorporated into the flanking disordered sequences. Comparing these results to those for isolated peptides provides information about how the larger domain affects binding. The affinities of sites on the disordered domain are like those of the peptides at low temperatures but less sensitive to temperature. Our results, combined with observations showing that intrinsically disordered proteins become more compact with increasing temperature, suggest a mechanism for this effect.

Human genome-wide analysis and identification of the hyperphosphorylation-elicited interactions between subarachnoid tau protein and phosphoprotein-binding domains

Abnormally hyperphosphorylated tau can be recognized by a variety of phosphoprotein-binding domains (PBDs) to elicit downstream tau signaling in neuropathology, which has been found to have a potential association with subarachnoid hemorrhage. In this study, the genome-wide binding behavior of tau phosphorylation sites (p-sites) to PBDs involved in subarachnoid hyperphosphorylation events was systematically profiled at molecular level by integrating peptide docking, structural minimization, affinity scoring, and binding assay, from which a number of potent PBD-p-site interaction pairs were identified. It was revealed that the PBD domains exhibit distinct binding preferences for phosphotyrosine, phosphoserine, and phosphothreonine p-sites; the PBD-recognition specificity of different tau p-sites is not overlapped with each other, and their phosphorylations would therefore regulate varying biological functions in tau signaling. A number of PBD-p-site pairs were identified to have potent binding potency as compared to others. The KCIP-pS[393-399] pair was found as a strong binder, which was further optimized with a rational peptide design protocol to derive a number of affinity-improved phosphopeptides. Structural analysis revealed diverse noncovalent chemical forces across the complex interface of KCIP domain with a designed high-affinity pS[393-399]-d4, which confers both stability and specificity to the domain-peptide complex system, with affinity improved by 10.9-fold relative to the native pS[393-399].

Computer- and NMR-Aided Design of Small-Molecule Inhibitors of the Hub1 Protein

By binding to the spliceosomal protein Snu66, the human ubiquitin-like protein Hub1 is a modulator of the spliceosome performance and facilitates alternative splicing. Small molecules that bind to Hub1 would be of interest to study the protein-protein interaction of Hub1/Snu66, which is linked to several human pathologies, such as hypercholesterolemia, premature aging, neurodegenerative diseases, and cancer. To identify small molecule ligands for Hub1, we used the interface analysis, peptide modeling of the Hub1/Snu66 interaction and the fragment-based NMR screening. Fragment-based NMR screening has not proven sufficient to unambiguously search for fragments that bind to the Hub1 protein. This was because the Snu66 binding pocket of Hub1 is occupied by pH-sensitive residues, making it difficult to distinguish between pH-induced NMR shifts and actual binding events. The NMR analyses were therefore verified experimentally by microscale thermophoresis and by NMR pH titration experiments. Our study found two small peptides that showed binding to Hub1. These peptides are the first small-molecule ligands reported to interact with the Hub1 protein.

Multi-level analysis of intrinsically disordered protein docking methods

Intrinsically Disordered Proteins (IDPs) are a class of proteins in which at least some region of the protein does not possess any stable structure in solution in the physiological condition but may adopt an ordered structure upon binding to a globular receptor. These IDP-receptor complexes are thus subject to protein complex modeling in which computational techniques are applied to accurately reproduce the IDP ligand-receptor interactions. This often exists in the form of protein docking, in which the 3D structures of both the subunits are known, but the position of the ligand relative to the receptor is not. Here, we evaluate the performance of three IDP-receptor modeling tools with metrics that characterize the IDP-receptor interface at various resolutions. We show that all three methods are able to properly identify the general binding site, as identified by lower resolution metrics, but begin to struggle with higher resolution metrics that capture biophysical interactions.

Multi-modal imaging for uptake of peptide ligand specific for CD44 by hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is rising steadily in incidence, and more effective methods are needed for early cancer detection and image-guided surgery.

METHODS

We used a structural model to optimize the peptide sequence. Specific binding was validated in vitro with knockdown, competition, and co-localization assays. Multi-modal imaging was performed to validate specific binding in vivo in orthotopically-implanted human xenograft tumors.

RESULTS

Binding properties of WKGWSYLWTQQA were characterized by an apparent dissociation constant of k_d = 43 nM, and an apparent association time constant of k = 0.26 min^-1. The target-to-background ratio was significantly higher for the target versus control for both modalities. Ex-vivo evaluation using human HCC specimens supported the ability of the peptide to distinguish HCC from other liver pathologies.

CONCLUSIONS

We have identified a peptide specific for CD44 with properties that are promising for clinical translation to image HCC in vivo.

Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking

We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.

Computational models for studying physical instabilities in high concentration biotherapeutic formulations

Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.

Dipeptidyl Peptidase IV Inhibitory Peptides Generated in Dry-Cured Pork Loin during Aging and Gastrointestinal Digestion

The ability of peptides from an aqueous and salt-soluble protein extract of dry-cured pork loins to inhibit the action of dipeptidyl peptidase IV was determined. This activity was assessed at different times of the production process, i.e., 28, 90, 180, 270 and 360 days. The resistance of the biological property during the simulated digestive process was also assessed. For this, the extracts were hydrolyzed with pepsin and pancreatin as a simulated digestion step of the gastrointestinal tract and fractionated (>7 kDa) as an intestinal absorption step. The results indicate that dried-pork-loin peptides may have potential as functional food ingredients in the prevention and treatment of type 2 diabetes mellitus. In particular, the APPPPAEV, APPPPAEVH, KLPPLPL, RLPLLP, VATPPPPPPK, VPIPVPLPM and VPLPVPVPI sequences show promise as natural food compounds helpful in maintaining good health.

Multiscale Modeling of Amyloid Fibrils Formed by Aggregating Peptides Derived from the Amyloidogenic Fragment of the A-Chain of Insulin

Computational prediction of molecular structures of amyloid fibrils remains an exceedingly challenging task. In this work, we propose a multi-scale modeling procedure for the structure prediction of amyloid fibrils formed by the association of ACC_1-13 aggregation-prone peptides derived from the N-terminal region of insulin’s A-chain. First, a large number of protofilament models composed of five copies of interacting ACC_1-13 peptides were predicted by application of CABS-dock coarse-grained (CG) docking simulations. Next, the models were reconstructed to all-atom (AA) representations and refined during molecular dynamics (MD) simulations in explicit solvent. The top-scored protofilament models, selected using symmetry criteria, were used for the assembly of long fibril structures. Finally, the amyloid fibril models resulting from the AA MD simulations were compared with atomic force microscopy (AFM) imaging experimental data. The obtained results indicate that the proposed multi-scale modeling procedure is capable of predicting protofilaments with high accuracy and may be applied for structure prediction and analysis of other amyloid fibrils.

A conserved Neurite Outgrowth and Guidance motif with biomimetic potential in neuronal Cell Adhesion Molecules

The discovery of conserved protein motifs can, in turn, unveil important regulatory signals, and when properly designed, synthetic peptides derived from such motifs can be used as biomimetics for biotechnological and therapeutic purposes. We report here that specific Ig-like repeats from the extracellular domains of neuronal Cell Adhesion Molecules share a highly conserved Neurite Outgrowth and Guidance (NOG) motif, which mediates homo- and heterophilic interactions crucial in neural development and repair. Synthetic peptides derived from the NOG motif of such proteins can boost neuritogenesis, and this potential is also retained by peptides with recombinant sequences, when fitting the NOG sequence pattern. The NOG motif discovery not only provides one more tile to the complex puzzle of neuritogenesis, but also opens the route to new neural regeneration strategies via a tunable biomimetic toolbox.

Antisense Peptide Technology for Diagnostic Tests and Bioengineering Research

Antisense peptide technology (APT) is based on a useful heuristic algorithm for rational peptide design. It was deduced from empirical observations that peptides consisting of complementary (sense and antisense) amino acids interact with higher probability and affinity than the randomly selected ones. This phenomenon is closely related to the structure of the standard genetic code table, and at the same time, is unrelated to the direction of its codon sequence translation. The concept of complementary peptide interaction is discussed, and its possible applications to diagnostic tests and bioengineering research are summarized. Problems and difficulties that may arise using APT are discussed, and possible solutions are proposed. The methodology was tested on the example of SARS-CoV-2. It is shown that the CABS-dock server accurately predicts the binding of antisense peptides to the SARS-CoV-2 receptor binding domain without requiring predefinition of the binding site. It is concluded that the benefits of APT outweigh the costs of random peptide screening and could lead to considerable savings in time and resources, especially if combined with other computational and immunochemical methods.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Protein-Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.

Information-driven modeling of biomolecular complexes

Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody-antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.

An Immunoinformatics Approach for SARS-CoV-2 in Latam Populations and Multi-Epitope Vaccine Candidate Directed towards the World's Population

The coronavirus pandemic is a major public health crisis affecting global health systems with dire socioeconomic consequences, especially in vulnerable regions such as Latin America (LATAM). There is an urgent need for a vaccine to help control contagion, reduce mortality and alleviate social costs. In this study, we propose a rational multi-epitope candidate vaccine against SARS-CoV-2. Using bioinformatics, we constructed a library of potential vaccine peptides, based on the affinity of the most common major human histocompatibility complex (HLA) I and II molecules in the LATAM population to predict immunological complexes among antigenic, non-toxic and non-allergenic peptides extracted from the conserved regions of 92 proteomes. Although HLA-C, had the greatest antigenic peptide capacity from SARS-CoV-2, HLA-B and HLA-A, could be more relevant based on COVID-19 risk of infection in LATAM countries. We also used three-dimensional structures of SARS-CoV-2 proteins to identify potential regions for antibody production. The best HLA-I and II predictions (with increased coverage in common alleles and regions evoking B lymphocyte responses) were grouped into an optimized final multi-epitope construct containing the adjuvants Beta defensin-3, TpD, and PADRE, which are recognized for invoking a safe and specific immune response. Finally, we used Molecular Dynamics to identify the multi-epitope construct which may be a stable target for TLR-4/MD-2. This would prove to be safe and provide the physicochemical requirements for conducting experimental tests around the world.

Molecular Dynamics Scoring of Protein-Peptide Models Derived from Coarse-Grained Docking

One of the major challenges in the computational prediction of protein-peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein-peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor-ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein-peptide complexes show that the proposed MD-based scoring approach can be used to identify protein-peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.

Design and characterization of high-affinity synthetic peptides as bioreceptors for diagnosis of cutaneous leishmaniasis

Cutaneous leishmaniasis (CL) is one of the illnesses caused by Leishmania parasite infection, which can be asymptomatic or severe according to the infecting Leishmania strain. CL is commonly diagnosed by directly detecting the parasites or their DNA in tissue samples. New diagnostic methodologies target specific proteins (biomarkers) secreted by the parasite during the infection process. However, specific bioreceptors for the in vivo or in vitro detection of these novel biomarkers are rather limited in terms of sensitivity and specificity. For this reason, we here introduce three novel peptides as bioreceptors for the highly sensitive and selective identification of acid phosphatase (sAP) and proteophosphoglycan (PPG), which have a crucial role in leishmaniasis infection. These high-affinity peptides have been designed from the conservative domains of the lectin family, holding the ability to interact with the biological target and produce the same effect than the original protein. The synthetic peptides have been characterized and the affinity and kinetic constants for their interaction with the targets (sAP and PPG) have been determined by a surface plasmon resonance biosensor. Values obtained for K_D are in the nanomolar range, which is comparable to high-affinity antibodies, with the additional advantage of a high biochemical stability and simpler production. Pep2854 exhibited a high affinity for sAP (K_D = 1.48 nM) while Pep2856 had a good affinity for PPG (K_D 1.76 nM). This study evidences that these peptidomimetics represent a novel alternative tool to the use of high molecular weight proteins for biorecognition in the diagnostic test and biosensor devices for CL.

Antioxidant, pancreatic lipase, and α-amylase inhibitory properties of oat bran hydrolyzed proteins and peptides

This work aimed to determine the antioxidant properties of identified hydrolyzed oat proteins and peptides, and their capacity to inhibit lipase and α-amylase. The protein hydrolysates retarded the oxidation of peanut oil by reducing peroxide values (up to 2.5-fold), relative to the control oil. Of the five tested peptides, P1 (YFDEQNEQFR), P3 (SPFWNINAH), and P4 (NINAHSVVY) significantly reduced the oxidation of linoleic acid. In the enzyme assays, P3 was the best lipase inhibitor (IC₅₀ 85.4 ± 3 µM) while P1 was the most potent inhibitor of α-amylase (IC₅₀ 37.5 ± 1.1 µM). The structure-activity relationship assessed using the CABS-dock computational model predicted that interactions between peptides and pancreatic lipase residues of Ser¹⁵³ , His²⁶⁴ , and Asp¹⁷⁷ were important for the inhibition. In the case of α-amylase, interactions with residues of the active sites (Asp¹⁹⁷ , Glu²³³ , and Asp³⁰⁰ ), but not those of calcium- or chloride-binding domains, were important for the inhibition. PRACTICAL APPLICATIONS: In recent years, there have been many studies focussing on isolating multifunctional peptides from food and food waste with antioxidant and bioactivity potential to promote human health. Some of these antioxidant peptides have been found to be effective to prevent diseases and complications such as hypertension, cardiovascular disease, cancer, diabetes, and obesity. The peptides studied in this work showed a great potential to prevent oxidation in a lipid system and demonstrated a significant ability to reduce the enzymatic activity of lipase and α-amylase. These enzymes contribute to the digestion of fat and carbohydrate, and their inhibition can reduce the absorption of these macronutrients and make them a great target for designing antioxidant and anti-obesity compounds. With the multifunctional activity of oat bran-derived peptides, it is proposed that these peptides can be used in food formulations due to their antioxidant and potential anti-obesity properties.

Protocols for Fast Simulations of Protein Structure Flexibility Using CABS-Flex and SURPASS

Conformational flexibility of protein structures can play an important role in protein function. The flexibility is often studied using computational methods since experimental characterization can be difficult. Depending on protein system size, computational tools may require large computational resources or significant simplifications in the modeled systems to speed up calculations. In this work, we present the protocols for efficient simulations of flexibility of folded protein structures that use coarse-grained simulation tools of different resolutions: medium, represented by CABS-flex, and low, represented by SUPRASS. We test the protocols using a set of 140 globular proteins and compare the results with structure fluctuations observed in MD simulations, ENM modeling, and NMR ensembles. As demonstrated, CABS-flex predictions show high correlation to experimental and MD simulation data, while SURPASS is less accurate but promising in terms of future developments.

Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement

The structural description of peptide ligands bound to G protein-coupled receptors (GPCRs) is important for the discovery of new drugs and deeper understanding of the molecular mechanisms of life. Here we describe a three-stage protocol for the molecular docking of peptides to GPCRs using a set of different programs: (1) CABS-dock for docking fully flexible peptides; (2) PD2 method for the reconstruction of atomistic structures from C-alpha traces provided by CABS-dock and (3) Rosetta FlexPepDock for the refinement of protein–peptide complex structures and model scoring. We evaluated the proposed protocol on the set of seven different GPCR–peptide complexes (including one containing a cyclic peptide), for which crystallographic structures are available. We show that CABS-dock produces high resolution models in the sets of top-scored models. These sets of models, after reconstruction to all-atom representation, can be further improved by Rosetta high-resolution refinement and/or minimization, leading in most of the cases to sub-Angstrom accuracy in terms of interface root-mean-square-deviation measure.

Targeting Tumors Using Peptides

To penetrate solid tumors, low molecular weight (Mw < 10 KDa) compounds have an edge over antibodies: their higher penetration because of their small size. Because of the dense stroma and high interstitial fluid pressure of solid tumors, the penetration of higher Mw compounds is unfavored and being small thus becomes an advantage. This review covers a wide range of peptidic ligands—linear, cyclic, macrocyclic and cyclotidic peptides—to target tumors: We describe the main tools to identify peptides experimentally, such as phage display, and the possible chemical modifications to enhance the properties of the identified peptides. We also review in silico identification of peptides and the most salient non-peptidic ligands in clinical stages. We later focus the attention on the current validated ligands available to target different tumor compartments: blood vessels, extracelullar matrix, and tumor associated macrophages. The clinical advances and failures of these ligands and their therapeutic conjugates will be discussed. We aim to present the reader with the state-of-the-art in targeting tumors, by using low Mw molecules, and the tools to identify new ligands.

Identification of the growth factor-binding sequence in the extracellular matrix protein MAGP-1

Microfibril-associated glycoprotein-1 (MAGP-1) is a component of vertebrate extracellular matrix (ECM) microfibrils that, together with the fibrillins, contributes to microfibril function. Many of the phenotypes associated with MAGP-1 gene inactivation are consistent with dysregulation of the transforming growth factor β (TGFβ)/bone morphogenetic protein (BMP) signaling system. We have previously shown that full-length MAGP-1 binds active TGFβ-1 and some BMPs. The work presented here further defines the growth factor-binding domain of MAGP-1. Using recombinant domains and synthetic peptides, along with surface plasmon resonance analysis to measure the kinetics of the MAGP-1-TGFβ-1 interaction, we localized the TGFβ- and BMP-binding site in MAGP-1 to a 19-amino acid-long, highly acidic sequence near the N terminus. This domain was specific for binding active, but not latent, TGFβ-1. Growth factor activity experiments revealed that TGFβ-1 retains signaling activity when complexed with MAGP-1. Furthermore, when bound to fibrillin, MAGP-1 retained the ability to interact with TGFβ-1, and active TGFβ-1 did not bind fibrillin in the absence of MAGP-1. The absence of MAGP was sufficient to raise the amount of total TGFβ stored in the ECM of cultured cells, suggesting that the MAGPs compete with the TGFβ large latent complex for binding to microfibrils. Together, these results indicate that MAGP-1 plays an active role in TGFβ signaling in the ECM.

Docking interactions determine early cleavage events in insulin proteolysis by pepsin: Experiment and simulation

In silico modelling of cascade enzymatic proteolysis is an exceedingly complex and challenging task. Here, we study partial proteolysis of insulin by pepsin: a process leading to the release of a highly amyloidogenic two chain 'H-fragment'. The H-fragment retains several cleavage sites for pepsin. However, under favorable conditions H-monomers rapidly self-assemble into proteolysis-resistant amyloid fibrils whose composition provides snapshots of early and intermediate stages of the proteolysis. In this work, we report a remarkable agreement of experimentally determined and simulation-predicted cleavage sites on different stages of the proteolysis. Prediction of cleavage sites was based on the comprehensive analysis of the docking interactions from direct simulation of coupled folding and binding of insulin (or its cleaved derivatives) to pepsin. The most frequent interactions were found to be between the pepsin's active site, or its direct vicinity, and the experimentally determined insulin cleavage sites, which suggest that the docking interactions govern the proteolytic process.

Flexible docking of peptides to proteins using CABS-dock

Molecular docking of peptides to proteins can be a useful tool in the exploration of the possible peptide binding sites and poses. CABS-dock is a method for protein-peptide docking that features significant conformational flexibility of both the peptide and the protein molecules during the peptide search for a binding site. The CABS-dock has been made available as a web server and a standalone package. The web server is an easy to use tool with a simple web interface. The standalone package is a command-line program dedicated to professional users. It offers a number of advanced features, analysis tools and support for large-sized systems. In this article, we outline the current status of the CABS-dock method, its recent developments, applications, and challenges ahead.

Protocols for All-Atom Reconstruction and High-Resolution Refinement of Protein-Peptide Complex Structures

Structural characterizations of protein-peptide complexes may require further improvements. These may include reconstruction of missing atoms and/or structure optimization leading to higher accuracy models. In this work, we describe a workflow that generates accurate structural models of peptide-protein complexes starting from protein-peptide models in C-alpha representation generated using CABS-dock molecular docking. First, protein-peptide models are reconstructed from their C-alpha traces to all-atom representation using MODELLER. Next, they are refined using Rosetta FlexPepDock. The described workflow allows for reliable all-atom reconstruction of CABS-dock models and their further improvement to high-resolution models.

Computational reconstruction of atomistic protein structures from coarse-grained models

Three-dimensional protein structures, whether determined experimentally or theoretically, are often too low resolution. In this mini-review, we outline the computational methods for protein structure reconstruction from incomplete coarse-grained to all atomistic models. Typical reconstruction schemes can be divided into four major steps. Usually, the first step is reconstruction of the protein backbone chain starting from the C-alpha trace. This is followed by side-chains rebuilding based on protein backbone geometry. Subsequently, hydrogen atoms can be reconstructed. Finally, the resulting all-atom models may require structure optimization. Many methods are available to perform each of these tasks. We discuss the available tools and their potential applications in integrative modeling pipelines that can transfer coarse-grained information from computational predictions, or experiment, to all atomistic structures.

Structure prediction of biological assemblies using GALAXY in CAPRI rounds 38-45

We participated in CARPI rounds 38-45 both as a server predictor and a human predictor. These CAPRI rounds provided excellent opportunities for testing prediction methods for three classes of protein interactions, that is, protein-protein, protein-peptide, and protein-oligosaccharide interactions. Both template-based methods (GalaxyTBM for monomer protein, GalaxyHomomer for homo-oligomer protein, GalaxyPepDock for protein-peptide complex) and ab initio docking methods (GalaxyTongDock and GalaxyPPDock for protein oligomer, GalaxyPepDock-ab-initio for protein-peptide complex, GalaxyDock2 and Galaxy7TM for protein-oligosaccharide complex) have been tested. Template-based methods depend heavily on the availability of proper templates and template-target similarity, and template-target difference is responsible for inaccuracy of template-based models. Inaccurate template-based models could be improved by our structure refinement and loop modeling methods based on physics-based energy optimization (GalaxyRefineComplex and GalaxyLoop) for several CAPRI targets. Current ab initio docking methods require accurate protein structures as input. Small conformational changes from input structure could be accounted for by our docking methods, producing one of the best models for several CAPRI targets. However, predicting large conformational changes involving protein backbone is still challenging, and full exploration of physics-based methods for such problems is still to come.

Modeling of Disordered Protein Structures Using Monte Carlo Simulations and Knowledge-Based Statistical Force Fields

The description of protein disordered states is important for understanding protein folding mechanisms and their functions. In this short review, we briefly describe a simulation approach to modeling protein interactions, which involve disordered peptide partners or intrinsically disordered protein regions, and unfolded states of globular proteins. It is based on the CABS coarse-grained protein model that uses a Monte Carlo (MC) sampling scheme and a knowledge-based statistical force field. We review several case studies showing that description of protein disordered states resulting from CABS simulations is consistent with experimental data. The case studies comprise investigations of protein⁻peptide binding and protein folding processes. The CABS model has been recently made available as the simulation engine of multiscale modeling tools enabling studies of protein⁻peptide docking and protein flexibility. Those tools offer customization of the modeling process, driving the conformational search using distance restraints, reconstruction of selected models to all-atom resolution, and simulation of large protein systems in a reasonable computational time. Therefore, CABS can be combined in integrative modeling pipelines incorporating experimental data and other modeling tools of various resolution.