Black-box optimization methods play an important role in many fields of computational simulation. In particular, such methods are often used in the design and modelling of biological systems, including proteins and their complexes with various ligands. This work is mainly focused on the protein-protein docking that plays a key role in modern drug-design workflows. We develop a black-box approach for such docking problems using a novel technique based on the tensor-train decomposition of high-dimensional interaction functions. Our method shows an advantage in terms of the discovered global minima and has a high potential for further implementation on a wide range of devices, including graphical processing units and quantum processing units.
Here we give a detailed description of MOLS 2.0.The source code and binary of MOLS 2.0 are distributed free (under GNU Lesser General Public License) for the scientific community. They are freely available for download at https://sourceforge.net/projects/mols2-0/files/.
We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard docking procedure and fed into a dual-graph architecture that includes separate sub-networks for the ligand bonded topology and the ligand-protein contact map. This network division allows contributions from ligand identity to be distinguished from effects of protein-ligand interactions on classification. We show, in agreement with recent literatu…
The protein-protein interactions (PPIs) are crucial for understanding the majority of cellular processes. PPIs play important role in gene transcription regulation, cellular signaling, molecular basis of immune response and more. Moreover, a disruption of hese mechanisms is frequently postulated as a possible cause of diseases such as Alzheimer's or cancer. For many of biologically relevant cases the structure of protein-protein complexes remain unknown. Therefore computational techniques, including molecular docking, have become a valuable part of drug discovery pipelines. Unfortunately, none…
Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the "difficult" targets (with significant backbone change or uncertainty). Here, we describe recent developments in protein-protein docking and highlight advances that tackle backbone flexibility. In molecular dynamics and Monte Carlo approaches, enhanced sampling techniques have reduced time-scale limitations. Internal coordina…
Protein-peptide molecular docking is a difficult modeling problem. It is even more challenging when significant conformational changes that may occur during the binding process need to be predicted. In this chapter, we demonstrate the capabilities and features of the CABS-dock server for flexible protein-peptide docking. CABS-dock allows highly efficient modeling of full peptide flexibility and significant flexibility of a protein receptor. During CABS-dock docking, the peptide folding and binding process is explicitly simulated and no information about the peptide binding site or its structur…
A newly developed statistical pair potential based on Distance-scaled Finite Ideal-gas REference (DFIRE) state is applied to unbound protein-protein docking structure selections. The performance of the DFIRE energy function is compared to those of the well-established ZDOCK energy scores and RosettaDock energy function using the comprehensive decoy sets generated by ZDOCK and RosettaDock. Despite significant difference in the functional forms and complexities of the three energy scores, the differences in overall performance for docking structure selections are small between DFIRE and ZDOCK2.3…
Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Prediction
Tsinghua University（清华大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（北京大数据管理与分析方法重点实验室）
We explored the Protein DataBank (PDB) to collect protein-ssDNA structures and create a multiconformational docking benchmark including both bound and unbound protein structures. Due to ssDNA high flexibility when not bound, no ssDNA unbound structure is included in the benchmark. For the 91 sequence-identity groups identified as bound-unbound structures of the same protein, we studied the conformational changes in the protein induced by the ssDNA binding. Moreover, based on several bound or unbound protein structures in some groups, we also assessed the intrinsic conformational variability in…
Protein-peptide interactions play a key role in cell functions. Their structural characterization, though challenging, is important for the discovery of new drugs. The CABS-dock web server provides an interface for modeling protein-peptide interactions using a highly efficient protocol for the flexible docking of peptides to proteins. While other docking algorithms require pre-defined localization of the binding site, CABS-dock doesn't require such knowledge. Given a protein receptor structure and a peptide sequence (and starting from random conformations and positions of the peptide), CABS-do…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Tsinghua University（清华大学）；University of Chinese Academy of Sciences（中国科学院大学）；Peking University（北京大学）；University of Washington（华盛顿大学）；Georgia Institute of Technology（佐治亚理工学院）；Helixon Research（Helixon研究院）
The strength or weakness of an algorithm is ultimately governed by the confidence of its result. When the domain of the problem is large (e.g. traversal of a high-dimensional space), a perfect solution cannot be obtained, so approximations must be made. These approximations often lead to a reported quantity of interest (QOI) which varies between runs, decreasing the confidence of any single run. When the algorithm further computes this final QOI based on uncertain or noisy data, the variability (or lack of confidence) of the final QOI increases. Unbounded, these two sources of uncertainty (alg…
Prediction of protein-ligand complexes for flexible proteins remains still a challenging problem in computational structural biology and drug design. Here we present two novel deep neural network approaches with significant improvement in efficiency and accuracy of binding mode prediction on a large and diverse set of protein systems compared to standard docking. Whereas the first graph convolutional network is used for re-ranking poses the second approach aims to generate and rank poses independent of standard docking approaches. This novel approach relies on the prediction of distance matric…
The Fast Fourier Transform (FFT) correlation approach to protein-protein docking can evaluate the energies of billions of docked conformations on a grid if the energy is described in the form of a correlation function. Here, this restriction is removed, and the approach is efficiently used with pairwise interactions potentials that substantially improve the docking results. The basic idea is approximating the interaction matrix by its eigenvectors corresponding to the few dominant eigenvalues, resulting in an energy expression written as the sum of a few correlation functions, and solving the …
Information on protein-protein interactions (PPIs) not only advances our understanding of molecular biology but also provides important clues for target selection in drug discovery and the design of PPI inhibitors. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. However, a friendly interface for users who are not sufficiently familiar with the command line interface has not been developed so far. In this study, we have developed a graphical user interface, MEGADOCK-GUI, which enables users to…
Simulating drug binding and unbinding is a challenge, as the rugged energy landscapes that separate bound and unbound states require extensive sampling that consumes significant computational resources. Here, we describe the use of interactive molecular dynamics in virtual reality (iMD-VR) as an accurate low-cost strategy for flexible protein-ligand docking. We outline an experimental protocol which enables expert iMD-VR users to guide ligands into and out of the binding pockets of trypsin, neuraminidase, and HIV-1 protease, and recreate their respective crystallographic protein-ligand binding…
Protein-peptide interactions play essential functional roles in living organisms and their structural characterization is a hot subject of current experimental and theoretical research. Computational modeling of the structure of protein-peptide interactions is usually divided into two stages: prediction of the binding site at a protein receptor surface, and then docking (and modeling) the peptide structure into the known binding site. This paper presents a comprehensive CABS-dock method for the simultaneous search of binding sites and flexible protein-peptide docking, available as a users frie…
A molecule of dimension 125nm has caused around 479 Million human infections (80M for the USA) & 6.1 Million human deaths (977,000 for the USA) worldwide and slashed the global economy by US$ 8.5 Trillion over two years. The only other events in recent history that caused comparative human life loss through direct usage (either by (wo)man or nature, respectively) of structure-property relations of 'nano-structures' (either (wo)man-made or nature, respectively) were nuclear bomb attacks of Japanese cities by the USA during World War II and 1918 Flu Pandemic. This molecule is SARS-CoV-2, which c…
Molecular docking, given a ligand molecule and a ligand binding site (called ``pocket'') on a protein, predicting the binding mode of the protein-ligand complex, is a widely used technique in drug design. Many deep learning models have been developed for molecular docking, while most existing deep learning models perform docking on the whole protein, rather than on a given pocket as the traditional molecular docking approaches, which does not match common needs. What's more, they claim to perform better than traditional molecular docking, but the approach of comparison is not fair, since tradi…
Motivation: Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this field, there is no docking method available for rigorous uncertainty quantification (UQ) of its solution quality (e.g. interface RMSD or iRMSD). Results: We introduce a novel algorithm, Bayesian Active Learning (BAL), for optimization and UQ of such black-box functions and flexible protein docking. BAL directly models the posterior distribution of the global optimum (or native structures for protein docking) with ac…
Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative to predict such information. However, ranking near-native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study the two challenging problems in protein d…
Energy evaluation using fast Fourier transforms enables sampling billions of putative complex structures and hence revolutionized rigid protein-protein docking. However, in current methods efficient acceleration is achieved only in either the translational or the rotational subspace. Developing an efficient and accurate docking method that expands FFT based sampling to 5 rotational coordinates is an extensively studied but still unsolved problem. The algorithm presented here retains the accuracy of earlier methods but yields at least tenfold speedup. The improvement is due to two innovations. …
Most widely used ligand docking methods assume a rigid protein structure. This leads to problems when the structure of the target protein deforms upon ligand binding. In particular, the ligand's true binding pose is often scored very unfavorably due to apparent clashes between ligand and protein atoms, which lead to extremely high values of the calculated van der Waals energy term. Traditionally, this problem has been addressed by explicitly searching for receptor conformations to account for the flexibility of the receptor in ligand binding. Here we present a deep learning model trained to ta…
A disease that caused by dengue virus (DENV) has become the major health problem of the world. Nowadays, no effective treatment is available to overcome the disease due to the level of dengue virus pathogeneses. A novel treatment method such as antiviral drug is highly necessary for coping with the dengue disease. Envelope protein is one of the non-structural proteins of DENV, which engaged in the viral fusion process. The fusion process is mediated by the conformational change in the protein structure from dimer to trimer state. The previous research showed the existing cavity on the dimer st…
The present Health Crisis tests the response of modern science and medicine to finding treatment for a new COVID-19 disease. The presentation on the world stage of antivirals such as remdesivir, obeys to the continuous investigation of biologically active molecules with multiple theoretical, computational and experimental tools. Diseases such as COVID:19 remind us that research into active ingredients for therapeutic purposes should cover all available sources, such as plants. In the present work, in silico tools, specifically docking study, were used to evaluate the binding and inhibition cap…
The lack of recommended drugs or vaccines to deal with the COVID-19 is the main concern of this pandemic. The approved drugs for similar health problems, drugs under clinical trials, and molecules from medicinal plants extracts are investigated randomly to deal with the COVID-19 infection. Molecular docking, one of the best approach to search therapeutically potent drugs/molecules in real time with possible hope to apply on COVID-19. In this communication, molecular docking studies of 18 ligands were carried out with the three therapeutic target proteins of SARS-CoV-2, i.e., RNA-dependent RNA …
Despite considerable efforts, structural prediction of protein-peptide complexes is still a very challenging task, mainly due to two reasons: high flexibility of the peptides and transient character of their interactions with proteins. Recently we have developed an automated web server CABS-dock (http://biocomp.chem.uw.edu.pl/CABSdock), which conducts flexible protein-peptide docking without any knowledge about the binding site. Our method allows for full flexibility of the peptide, whereas the flexibility of the receptor is restricted to near native conformations considering the main chain, a…
SARS-CoV-2 (COVID-19), a positive single stranded RNA virus, member of corona virus family, is spreading its tentacles across the world due to lack of drugs at present. Being associated with cough, fever, and respiratory distress, this disease caused more than 15 % mortality worldwide. Due to its vital role in virus replication, Mpro/3CLpro has recently been regarded as a suitable target for drug design. The current study focused on the inhibitory activity of Calotropin, a component from milk of Calotropis gigantean, against Mpro protein from SARS-CoV-2. Till date there is no work is undertake…
Argonne National Laboratory（阿贡国家实验室）；Brookhaven National Laboratory（布鲁克海文国家实验室）
We propose a benchmark to study surrogate model accuracy for protein-ligand docking. We share a dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million "in-stock" molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. Our work shows surrogate docking models have six orders of magnitude more throughput than standard docking protocols on the same supercomputer node types. We demonstrate the power of high-speed surrogate models by running each target against 1 billion molecules in under a day (50k predictions pe…
Virtual screening (VS) is an essential technique for understanding biomolecular interactions, particularly, drug design and discovery. The best-performing VS models depend vitally on three-dimensional (3D) structures, which are not available in general but can be obtained from molecular docking. However, current docking accuracy is relatively low, rendering unreliable VS models. We introduce sequence-based virtual screening (SVS) as a new generation of VS models for modeling biomolecular interactions. The SVS model utilizes advanced natural language processing (NLP) algorithms and optimizes de…
The emergence of COVID-19 has severely compromised the arsenal of antiviral and antibiotic drugs. Drug discovery is a multistep process with a high failure rate, high cost and it takes approximately 10-12 years for the development of new molecules into the clinical candidate. On the other side, drug repurposing also called old drugs for new uses, is an attractive alternative approach for a new application of marketed FDA approved or investigational drugs. In the current pandemic situation raised due to COVID-19, repurposing of existing FDA approved drugs are emerging as the first line of the t…
A Physically-Informed Subgraph Isomorphism Approach to Molecular Docking Using Quantum Annealers
Politecnico di Milano（米兰理工大学）；Department of Physical and Chemical Sciences, University of L’Aquila（拉奎拉大学物理与化学科学系）；Istituto Italiano di Tecnologia（意大利理工学院）；E4 Computer Engineering（E4计算机工程公司）；CINECA；Dompé farmaceutici S.p.A.（Dompé制药股份公司）
Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing faci…
Virtual screening of phytochemicals was performed through molecular docking, simulation, in silico ADMET and drug-likeness prediction to identify the potential hits that can inhibit the effects of SARS-CoV-2. Considering the published literature on medicinal importance, total 154 phytochemicals with analogous structure from limonoids and triterpenoids were selected to search potential inhibitors for the five therapeutic protein targets of SARS-CoV-2, i.e., 3CLpro (main protease), PLpro (papain-like protease), SGp-RBD (spike glycoprotein-receptor binding domain), RdRp (RNA dependent RNA polymer…
Cardinal Stefan Wyszynski University（斯特凡·维申斯基红衣主教大学）；Warsaw University of Technology（华沙理工大学）；IDEAS NCBR（IDEAS NCBR机构）；IMPAN（波兰科学院生物化学与生物物理研究所）；University of Warsaw（华沙大学）
Public cloud computing environments, such as Amazon AWS, Microsoft Azure, and the Google Cloud Platform, have achieved remarkable improvements in computational performance in recent years, and are also expected to be able to perform massively parallel computing. As the cloud enables users to use thousands of CPU cores and GPU accelerators casually, and various software types can be used very easily by cloud images, the cloud is beginning to be used in the field of bioinformatics. In this study, we ported the original protein-protein interaction prediction (protein-protein docking) software, ME…
Proteins play crucial roles in every cellular process by interacting with each other, with 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 3D 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 3D structures of complexes. Docking ca…
GenShin: Guiding Rational Liposome Design by Ranking Liposomal Protein Corona through a Docking-Pose-Free GNN
Key Laboratory of symbolic Computation and Knowledge Engineering of ministry of education（教育部符号计算与知识工程重点实验室）；Jilin University（吉林大学）；State Key Laboratory of Natural and Biomimetic Drugs（国家自然与仿生药物重点实验室）；School of Pharmaceutical Sciences（药学院）；Peking University（北京大学）
Evaluation of Peppermint Leaf Flavonoids as SARS-CoV-2 Spike Receptor-Binding Domain Attachment Inhibitors to the Human ACE2 Receptor: A Molecular Docking Study
Institute of Physics, University of Brasília（巴西利亚大学物理研究所）；Department of Electrical Engineering, University of Brasília（巴西利亚大学电气工程系）
Side chain flexibility is an important factor in ligand binding. In order to determine the extent to which side chain flexibility is involved in ligand binding, a knowledge-based approach was taken. A database composed of examples of protein structures in the presence or absence of a given ligand is used to analyze which side chains undergo side chain conformational changes. Such an analysis has determined that up to 40% of binding site do not present side chain conformational changes. A total of three residues undergoing side chain conformational changes encompass approximately 85% of the bin…
